esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion  
We conducted extensive numerical experiments in MATLAB according to simulation assumptions and parameters used in 3 GPP [38]. Table 2 shows a few summarized parameters. For simplicity, we have considered the widely used full buffer traffic model. It is characterized by the fact that the UE always has data to transmit or receive in the full buffer traffic model. The simulated interference scenario is shown in Figure 1. We assumed the dense urban deployed HeNB located in \(25\times 25\) house at the boundary of the centered eNB. The radius of eNB and HeNB is set to \(500\) m and \(25\) m, respectively. The eNB has a maximum transmitting power of \(43\) dBm. Similarly, the HeNB has \(21\) dBm and \(0\) dBm maximum and minimum transmitting power respectively. In this simulation, the close access mode is considered, which is the most favorite mode of indoor users, in which the CSG users enjoy higher data rate and capacity and non-CSG users are not allowed to use the femtocell services. The interference is severe in closed access mode as compared to other access modes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Input** & **Interference Messages and FUE feedback report.** \\
**Output** & **Downlink Transmission Power tuning.** \\ \hline step 1 & Using (10), the macro user decides to send IM including HeNB information to eNB. \\ step 2 & HeNB instruct its users to send a feedback report. Based on the (16), which indicates the minimum required QoS, the FUE send the information to HeNB. \\ step 3 & Based on the IM from the MUE and QoS indication report from the FUE, HeNB actively tune its downlink transmission power using (17) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Active Power Control Approach.  
In this paper, we conducted the simulation with \"No Power Control or Fixed Power Control Technique (FPCT)\" as baseline, where there is no power control technique activated in the femtocell, therefore, the femtocell transmits maximal power. It is a fact that high transmission power of HeNB provides better signal strength, good coverage to the femto users; conversely, the cell edge macro users receive interference from it. Therefore, the interference technique operating in the femtocell should provide a balanced trade-off between the throughput of macrocell and femtocell users.  
In the following subsections, we evaluated the performance of proposed and existing power control techniques in-terms of downlink throughput distribution, average throughput distribution, FBS power consumption and green impact and CO\({}_{2}\) emission by conducting numerical experiments.
## 2 Model and Nominal NDI Control
### Nominal NDI Controller Design  
During the HFV flight, in order to make the system state variable \(x=[\alpha,q,\theta]^{T}\) track the given command \(x_{c}=[\alpha_{c},q_{c},\theta_{c}]^{T}\), a nominal NDI controller is designed according to Equation (1). When HFV has no inertia uncertainty, the nominal NDI controller is designed as follows:  
\[u=B^{-1}(-Ax+v) \tag{15}\]  
where \(v\) is the virtual controller, the design method is as follows:  
\[v=\dot{x}_{c}-Ke \tag{16}\]  
where  
\[e=x-x_{c} \tag{17}\]  
Since the desired state quantity is usually constant, \(\dot{x}_{c}\) is usually 0. \(K\) is the feedback gain matrix to be designed, the form is as follows:  
\[K=\begin{bmatrix}w_{\alpha}&0&0\\ 0&w_{q}&0\\ 0&0&w_{\theta}\end{bmatrix} \tag{18}\]  
where \(w_{\alpha}\), \(w_{q}\), \(w_{\theta}\) represent the control loop bandwidth, and the values are 40 rad/s, 0.6 rad/s, and 40 rad/s, respectively. The test shows that the larger \(w_{\alpha}\), \(w_{\theta}\), the more severe the response curve oscillation; the smaller \(w_{\theta}\), the more severe the response curve oscillation. The block diagram of the nominal NDI controller is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter Meaning** & **Unit** \\ \hline \(C_{m,\dot{x}}\) & Pitching moment coefficient due to attack angle rate & n.d. \\ \(C_{m,\dot{q}}\) & Pitch moment coefficient due to pitch rate & n.d. \\ \(\mathrm{Y}^{\alpha}\) & Derivative of lift coefficient with respect to attack angle & n.d. \\ \(\mathrm{Y}^{\dot{\alpha}_{c}}\) & Derivative of lift coefficient with respect to elevator deflection angle & n.d. \\ \(M_{\alpha}^{\dot{x}_{a}}\) & Longitudinal static stability derivative & n.d. \\ \(M_{\alpha}^{\dot{x}_{c}}\) & Derivative of pitch moment coefficient to elevator deflection angle & n.d. \\ \(M_{\alpha}^{\dot{x}_{c}}\) & Derivative of pitch moment coefficient with respect to attack angle rate & n.d. \\ \(M_{\alpha}^{\dot{x}_{d}}\) & Derivative of pitch moment coefficient with respect to pitch rate & n.d. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Longitudinal HFV nominal NDI control scheme.
# Slot Waveguide Enhanced Bloch Surface Waves
## 3 Results  
### Multilayer  
The optimization of the dielectric multilayer can be separated from the design of the slot waveguide although one will influence the other. The multilayer being the most sensitive, it is easier to adapt the slot waveguide than the BSW sustaining platform.  
The multilayer is an alternating of titanium dioxide and silicon dioxide on a polymer substrate. The termination of the multilayer is of utmost importance to generate and tune the BSW. In the present case, a different thickness for the last TiO\({}_{2}\) layer (thickness \(t_{\rm last}\)) is considered. In addition to the different thicknesses involved in the structure, the angle of incidence has to be taken into account and is used to adjust the wavelength of the BSW mode at \(\lambda\) = 488 nm.  
It is important to note at this early stage in the design that the different layers can be deposited by atomic layer deposition, which implies a control of the thickness at the nanometer scale. The two termination layers are used to precisely tune the resonance at the desired wavelength, and this can be done as a post-process step, as demonstrated by [PERSON] et al. [18,19] recently.  
Figure 2 shows the simulation results obtained by the use of the transfer matrix method on the multilayer. The incident medium is the polymer--intended to be further fed by the waveguide mode--and the output medium is air, i.e., the slot region of the waveguide. The target is a BSW mode centered at \(\lambda\) = 488 nm. We consider a 5-period multilayer on top of which a last termination layer (high refractive index) is designed. The multilayer is illuminated under TM polarization, i.e., the electric field is in the plane of the wafer (see Figure 1). This polarization corresponds to the TE polarization for the slot waveguide. This is a crucial parameter since slot waveguides confine the electric component of a quasi-TE polarized mode in the low index region gap (here, air). However, one can also excite a TE BSW, leading to only a weak confinement of the field inside the slot waveguide.  
The reflectance spectrum and its dependence with the angle of incidence is presented in Figure 2a. A zoom-in on the BSW mode is shown in Figure 2b as a function of the wavelength and in Figure 2c as a function of the incidence angle. For \(\lambda\) = 488 nm, the incidence angle is \(\theta\) = 74\({}^{\circ}\). The layer thicknesses are \(t_{\rm{H}}\) = 90 nm and \(t_{\rm{L}}\) = 120 nm. The multilayer is terminated by a last high refractive index layer of thickness \(t_{\rm{last}}\) = 80 nm. The extinction ratio is about 75%.  
Figure 2: (**a**) Spectral and angular reflection spectrum of the multilayer calculated by transfer matrix method. The green dashed lines represent the target wavelength (\(\lambda\) = 488 nm) and the corresponding angle of incidence (\(\theta\) = 74\({}^{\circ}\)); (**b**) Wavelength dependence of the reflectivity on the multilayer; (**c**) Angle dependence of the reflectivity. All calculations are performed under TM illumination (TE in the waveguide referential) according to notations Figure 1.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 2 Related Works
### 5G Security Research  
As 5G expands its presence worldwide, the scientific literature evaluating the security aspects of 5G has grown, especially regarding surveys into specific areas of 5G's imple  
Figure 2: 3 GPP security domains defined in TS 33.501. The interfaces are relative to the list 1–6 above.  
mentation. For example, in [4], the current research and challenges of security for different aspects of cloud services, RAN, MEC, and network slicing are investigated. The insights presented in this paper are focused on the need to deploy future 5G networks while taking great care of the vast security aspects. Understanding that these networks will serve applications to a wide range of consumers, they need to provide adaptability, resilience, and flexibility, embodied and provided by Software-Defined Networking (SDN) and Network Function Virtualization (NFV), in order to be able to provide and enforce comprehensive security measures. The authors of [5] focus their security research on access, handover, IoT, D2D, V2X, and 5G network slicing, whereas in [6], the focus is on architecture and design, attacks on 5G, physical security, and V2X. Both of these publications showcase potential vulnerabilities to the security mechanisms within architecture, specifically for authentication and network slicing, and thereby emphasize the need for more fine-grained security control in the network. A comparative analysis of 5G security mechanisms is discussed in [7; 8], which includes a wide survey into the overall 5G security framework, Core Network, Radio Access Network, cloud infrastructure, and the Internet of Things. IoT protocols for 5G are reviewed in [9]. An overview of the threats to, and vulnerabilities of, both 5G and 6G, is presented in [10] with a specific focus on SDN, NFV, and MEC in 5G networks. The security issues of network coexistence are investigated in [11] with a focus on 5G/6G coexistence with WiFi 6. Application-specific attacks are outlined in [12] with a focus on cyber-physical systems, fog computing, and SDNs. The contributions from these primary surveys showcase the need for continued research and a strong focus on improving the adoption and implementation of security mechanisms. For confidential communication and heightened network monitoring and security, a Zero Trust Architecture (ZTA) is recommended, particularly in a 5G setting. ZTA opens the door for operations in which the assumption is that the attacker is always one step ahead of the fix or patch to a network issue.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 1 Introduction  
Side channel attacks (SCAs) recover a secret key from a cryptographic device by collecting leakage information, such as the power consumption traces or the electro-magnetic traces, and by analyzing them statistically. Since the proposal by [PERSON] et al. [1], differential power analysis (DPA) has been one of the serious threats in the real world.
# Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar
## 2 Methods  
### FMCW Radar Signal Model  
The FMCW radar transmits a radio wave that has linearly-increasing frequency. The transmitted signal is mixed with the received signal, then subjected to low-pass filtering, to obtain the intermediate frequency (IF) signal, \(x(t,n)\)[26; 27]:  
\[x(t,n)=\sum_{r}M(t,r)\cdot\cos(2\pi\cdot f_{r}\cdot n+P(t,r)), \tag{1}\]  
where \(t\) is the scan time, \(n\) is the sampling time in a chirp, \(r\) is the range, \(f_{r}\) is the beat frequency, \(M(t,r)\) is the magnitude, and \(P(t,r)\) is the phase.  
\(x(t,n)\) contains the range information in \(f_{r}\). DFT is applied to \(x(t,n)\) at each \(f_{r}\) to calculate \(M(t,r)\) and \(P(t,r)\), which contain displacement information of the target at \(r\). When a target exists in range-bin \(r\), the signal model in [37] for \(M(t,r)\) and \(P(t,r)\) are  
\[\begin{split}& M(t,r)=\frac{M_{0}}{4\pi\cdot r^{2}},\\ & P(t,r)=\frac{4\pi\cdot f_{c}}{c}\cdot r,\end{split} \tag{2}\]  
where \(M_{0}\) is the baseline magnitude, \(f_{c}\) is the center frequency, and \(c\) is the lightspeed.  
Moreover, with regard to the small displacement \(\Delta(t)\) in the target, the displacement affects \(M(t,r)\) and \(P(t,r)\), as  
\[\begin{split}& M(t,r)=\frac{M_{0}}{4\pi\cdot(r+\Delta(t))^{2}} \approx\frac{M_{0}}{4\pi\cdot r^{2}}\cdot\bigg{(}1-2\frac{\Delta(t)}{r}\bigg{)},\\ & P(t,r)=\frac{4\pi\cdot f_{c}}{c}\cdot(r+\Delta(t)).\end{split} \tag{3}\]  
However, \(M(t,r)\) in (2) is valid for an isotropic antenna model. In general, radar uses directional antennas and \(M(t,r)\) should be modeled considering antenna gain \(G\), radar cross section \(\sigma\), and effective area \(A_{c}\)[44; 45], as  
\[M(t,r)=\frac{M_{0}\cdot\sqrt{G\cdot\sigma\cdot A_{c}}}{4\pi\cdot r^{2}}. \tag{4}\]According to the conventional studies [46; 47], \(\sigma\) changes by respiratory activity. Considering the results, \(G\), \(\sigma\), and \(A_{e}\) in (4) could change by VS displacement, \(\Delta(t)\). Therefore, \(M(t,r)\) in (4) is difficult to describe for \(\Delta(t)\) unlike in (3). Considering the model in (3) and (4), the relationship between \(M(t,r)\) and \(\Delta(t)\) is not clear, whereas \(P(t,r)\) is linearly proportional to \(\Delta(t)\). With regard to VS measurements, \(\Delta(t)\) is the displacement of the body surface caused by respiration and heartbeat, and \(P(t,r)\) measures displacement more precisely than \(M(t,r)\) does.
# Effect of Local Gas Flow in Full Penetration Laser Beam Welding with High Welding Speeds
## 3 Results
### 3.3. _Spatter Reduction by Use of Local Gas Flow_
#### 3.3.4 Effect on Keyhole Dimensions  
In order to quantify the effect of the exerted compressive force of the gas flow on the keyhole dimensions and to evaluate the outgassing conditions of the metal vapor, the width and the length of the keyhole was measured as function of flow rate and welding speed (Figure 8). Based on 30 analyzed sequential frames, the dimensions were calculated by using the arithmetic mean.  
Figure 7: Top-sided particle speed depending on flow rate for welding speeds of: (**a**) 8 min; (**b**) 10 m/min; (**c**) 12 m/min and (**d**) 16 m/min.  
By analyzing the keyhole width (Figure 8a), no significant effect of flow rate neither of welding speed could be determined, while the keyhole was characterized by a mean width of \(\approx\)0.38 mm. In contrast, the length of the keyhole showed varying dimensions (Figure 8b). Based on the increase in keyhole inclination at high welding speeds (see Section 1), the length of the keyhole of the reference process was raised by up to factor of 1.9 by varying welding speeds from 8 m/min to 16 m/min. Additionally, a change in flow rate showed an effect. While flow rates up to 8 L/min resulted in constant dimensions, the keyhole length was noticeably increased by a change in flow rate from 8 L/min to 16 L/min. Consequently, it can be stated that the gas flow induced compressive force inside the keyhole had to exceed a critical threshold to have an influence on keyhole dimensions by melt displacement. This threshold seems to correlate to a resulting dynamic pressure of about 6.7-26.8 kPa in this investigation. Below this critical compressive force, the changes in keyhole length can be attributed to the rising keyhole inclination at high welding speeds. However, to quantify this threshold, further investigations are required, to consider the effects of flow orientation, gas type, thermophysical and rheological material properties as well as the regime of welding process.
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 3 Kinematic and Dynamic Analysis
### Working Range  
The Cost of Transport (COT or specific resistance) is defined as the power consumption divided by the weight multiplied by the speed of the robot or as the energy required to travel a distance divided by the weight times the distance.  
\[COT=\frac{Power}{m\cdot g\cdot v}=\frac{E}{m\cdot g\cdot d} \tag{12}\]  
Figure 12: The maximum height of obstacles the robot can overcome. (**A**) BSTAR. (**B**) RSTAR. (**C**) RSTAR getting a boost up from BSTAR.  
Figure 13: Flowchart of the obstacles that the collaborating parent and child robots can overcome.  
where \(m\) is the mass of the robot, \(g\) is the gravity, \(E\) is the consumed energy for the travelled distance \(d\) and \(v\) is the speed. Given that RSTAR consumes 3.33 Watts at 0.8 m/s [12] and that the power consumption of BSTAR at 1.43 m/s is 50.62 Watts (see Section 4.3.1), the COT of RSTAR and BSTAR are respectively 1.12 and 0.37.
# Antioxidant and Antimicrobial Activity of Porcine Liver Hydrolyates Using Flavourzyme
## 3 Results and Discussion
### Antioxidant Activity of Extracts from Flavourzyme Hydrolysates
Regarding hydrolysis time of the enzymes such as alcalase, trypsin and papain, it has been demonstrated that the antioxidant capacity of liver hydrolysates increased with the time [43]. Although, our data did not follow the same trend, maybe due to the difference in antioxidant capacity was not too high. Moreover, the oxygen radical absorbance capacity (ORAC) assay was tested (Table 2). There were significant differences among the hydrolysis times except for hydrolysates filtered through 10 kDa. Nevertheless, the highest antioxidant capacity was not exhibit at longest hydrolysis time of Flavourzyme studied. Regarding the molecular size of peptides, the lowest values were achieved for 10 kDa hydrolysates (21.72, 9.80, and 18.06 mg Trolox/g at 6, 8, and 10 h). Pork liver hydrolysates filtered through 30 kDa displayed the highest value at 6 h (33.71 mg Trolox/g). However, most cases the 30 kDa hydrolysates did not show the highest antioxidant value. This finding was unexpected because of the higher amount of peptide types (whole fraction) should exhibit the highest antioxidant capacity. A possible explanation for these results may be the presence of prooxidant molecules in the larger fraction or compounds could interact with the ORAC method. The ferric reducing antioxidant power (FRAP) assay was also carried out on porcine liver hydrolysates (Table 2). FRAP values increased with the pore size of membrane, 30 kDa filtrates displayed the highest values (41.81, 48.43, 42.54, and 36. 77 umol Fe\({}^{+2}\)/100 g at 4, 6, 8, and 10 h). This finding followed a similar trend as the values of ABTS and DPPH as discussed above. For proteins of plasma, it was found that the FRAP activity of <3 kDa hydrolysate fraction reached more than twice with respect to >10 kDa fraction (1206.4 vs. 557.3 umol) ([PERSON] et al., 2010). The peptides of each size were evaluated separately in contrast to the results of the present study. For long hydrolysis times, the antioxidant activity was significantly lower (33.19, 36.03, and 36.77 umol Fe\({}^{+2}\)/100 g for 5, 10, and 30 kDa, respectively). Although, the behavior was not the same in all the samples, since the highest values were not found for the same hydrolysis time. In 30 kDa filtrates, 6 h of hydrolysis were enough to obtain the best results, while only 4 h were necessary for 10 kDa filtrates. Finally, the filtrated obtained with the lower pore size were those than need a higher hydrolysis time.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 4 Experiments and Results
Notably, as the satellite's operational period increases, the maximum error remains nearly uniform, indicating the robustness and reliability of the proposed technique over time. Therefore, the proposed technique can reflect a battery's aging state and provide reliable information regarding its actual performance. This ensures more precise mission planning and enhances the operational reliability of a satellite throughout its lifespan.
## 2 Materials and Methodology
### Hyperspectral Image Processing  
Preprocessing is a crucial step for HS image analysis, since HSI data suffers from high dimensionality, redundancy within image bands and instrumentation noise. First, the raw reflectance data are normalized in order to correct for the dark current noise by applying the following computation:  
\[I_{ref}=\frac{I_{raw}-I_{dark}}{I_{white}-I_{dark}}\, \tag{1}\]  
where \(I_{ref}\) denotes the normalized reflectance value, \(I_{raw}\) is the diffuse reflectance value, \(I_{white}\) the intensity value for a white reference plate and \(I_{dark}\) represents the dark reference, acquired by keeping the camera shutter closed. HS images are typically degraded due to noise and low contrast [16]. To effectively enhance the IR image, contrast methods are therefore required to enhance the global contrast and the perception of details without highlighting the noise and introducing undesirable artifacts [27]. Conventional adaptive histogram equalization is based on separate histograms for different image regions, which are used to redistribute the local intensity level values. This approach solves the main limitation of having a global histogram equalization, which tends to stretch out the intensity range of an image, resulting in the over-enhancement of the background (introduction of noise) and a contrast deterioration of small targets [27]. As an alternative, adaptive histogram equalization has been proposed to improve contrast enhancement. However, this can also visually amplify noise due to the over-enhancement effect in the homogeneous regions. Contrast-limited adaptive histogram equalization (CLAHE [28]) prevents this by limiting the amplification, and it has been successfully applied in infrared imagery and surveillance imagery. A dedicated Gaussian filter and contrast-limited adaptive filtering have been designed for the VIS and the NIR wavelength range, respectively. The Gaussian filter size is \(2\,\sigma+1\), where \(\sigma\) represents the standard deviation, set at \(\sigma=0.05\). To enhance the contrast, we employ CLAHE, setting a normalized intensity limit of 0.05 for the VIS range and 0.1 for the NIR. The clip limit for the NIR range is higher, since the contrast within this range is lower compared to the VIS images.  
For each subject, a region of interest (ROI) is manually delineated for further analysis in order to discard the optical markers positioned in the image edges. The selected ROI is chosen as the closest area to the optical markers to ensure the same displacement during breathing (Figure 4b).
# Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control
###### Abstract  
In order to solve the problem that the traditional Proportional Integral (PI) controller cannot take into account both response speed and input current overshoot and cannot adapt to complex working conditions, an energy-feedback DC load simulation control algorithm based on fuzzy control was proposed. By adding a fuzzy adaptive PI setting to the traditional load simulation control algorithm, the dynamic response performance of the system and the accuracy of load simulation were improved. A small-signal model of a load analog boost circuit was established based on the state average method. Based on the phase margin, the PI parameter setting of series correction was completed, and the corresponding fuzzy control rules were formulated to identify the response state. Simulation results show that the regulation time of constant current load simulation based on a fuzzy control strategy is shortened by 75.2%, the steady-state error is reduced by 60%, and the overshoot is reduced from 28.3% to 7.47%. The prototype test results show that the maximum relative error of input current load simulation is only 4.50%, and the system response speed can meet the requirements of variable load simulation. The results show that the load simulation control algorithm based on fuzzy control has the characteristics of high precision and excellent dynamic characteristics.  
energy feedback; load simulation; fuzzy control; small-signal model +
Footnote †: journal: Applied Sciences