esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 5 Covert Channel Performance Evaluation
### Scenario 1--Periodic Transmission with No Retransmission
However, as the transmission window expanded due to consecutive frame transmissions adding to the congestion, the delay increased to values exceeding 4 ms.  
We also conducted an analysis to assess the impact of a covert channel on network throughput. The primary objective was to ensure that the covert channel does not consume network resources intended for regular stations and operates without negatively affecting the network. To conduct this experiment, we utilized the same network infrastructure and introduced traffic generation from multiple STAs, causing the saturation of the network throughput. We assumed a worst-case scenario, where PR frames are generated by covert station every 10 ms. Our network configurations included 1, 10, 20, 30, 40, and 50 STAs. The increasing number of STAs in saturation conditions resulted in a significant increase in the number of collisions and a decrease in the overall efficiency of the network. In each  
Figure 8: Covert channel delay as a function of transmission interval in scenario with retransmission disabled. Figure 9: Covert channel jitter as a function of transmission interval in scenario with retransmission disabled. scenario, we compared the saturation throughput without the covert STA and subsequently introduced the covert STA to observe the resulting changes in saturation throughput (see Figure 10). The impact on saturation throughput is most pronounced when there is only one regular STA and one covert STA, leading to a significant drop of nearly 6 Mbps. This effect is clearly evident. This underscores the heightened competition for channel resources in such a scenario. Nevertheless, as the number of STAs increases to 10, the impact gradually diminishes, resulting in a drop of approximately 3 Mbps. In scenarios involving 20, 30, 40, and 50 STAs, the inclusion of the covert STA has a minimal observable impact, with the throughput drop not exceeding 1 Mbps. These findings lead us to conclude that in scenarios with a limited number of STAs, the presence of a covert channel adversely affects saturation throughput (a decrease of about 8%). However, as the number of STAs grows, this effect becomes less pronounced (in the last case, it is a decrease of only about 2.5%). As a result, covert channels are better suited for networks that resemble typical Wi-Fi networks encountered in everyday situations with a large number of STAs.
## 3 Results and Discussion
### Screening of DES for Extraction Efficiency
Furthermore, examinations on grape stem [27] and grape seed [28] polyphenol extraction showed that in most cases higher yields in total polyphenols, total flavanols and proanthocyanidins were favored at pH \(<\) 3.5. For other plant tissues, such as _Solanum melongena_, effective extraction was accomplished with 70% ethanol adjusted at pH 3 [29]. Such a phenomenon was ascribed to the protective effect of low pH on polyphenol against polyphenol oxidation because polyphenol oxidizability is higher at neutral or alkaline environment, due to phenolic hydroxyl dissociation. Likewise, it could be argued that acidic DES might act protectively with regard to polyphenol oxidation, and this would be likely to contribute to achieving higher Y\({}_{\text{TP}}\).
# A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks
## 3 Results and Discussions  
In order to measure the scattering parameters of the fabricated prototype, the port of the fabricated design is connected with a vector network analyzer (VNA). The picture of the prototype is visible in Figure 7a. The S\({}_{11}\) (dB) of the projected antenna is accessible in Figure 7b. Due to intolerances in the fabrication process and surrounding noises, there are some variations in the measured results. The simulated and measured S\({}_{11}\) (dB) are in good agreement, as both are covering the whole UWB band for wireless communications.  
The simulated and measured (E & H) plane of the UWB antenna at 3.5 GHz, 4.1 GHz, 8 GHz, and 10.5 GHz are given in Figure 8. It can be seen that there is an omnidirectional pattern at the frequencies of 3.5 GHz and 4.1 GHz along the E-plane while elliptical along the H-plane, and the antenna has a broadside radiation pattern in both planes at the frequencies of 8 GHz and 10.5 GHz. The simulated and measured gain graph is presented in Figure 9. It can be noticed that the antenna has attained the average peak gain ranges from 2-4.1 dB and the antenna's efficiency is attained for more than 85% over the entire band. A comparison with the previous research is given in Table 1.  
Figure 7: (**a**) Printed UWB prototype; (**b**) comparison of simulated and measured reflection coefficients (S\({}_{11}\)).  
Figure 8: (a) Simulated and measured 2D radiation pattern setup inside chamber (**b**) at 3.5 GHz, (**c**) at 4.1 GHz, (**d**) at 8 GHz, (**e**) at 10.5 GHz.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 3 Main Error Sources and Suppression Methods of Dynamic Measurement
### Vibration Noise  
As shown in Figure 6, because two beams of Raman light propagating in the opposite direction are needed to increase the effective wave vector and improve the sensitivity, one beam of light needs to be reflected by the mirror. However, the ground vibration will lead to the displacement of the position of the mirror \(\delta z\) and the phase shift of the reflected Raman light, which is coupled to the gravity phase and results in an inaccurate gravity measurement. Because of the high sensitivity of the atom gravimeter, even low-level vibration may exceed its measuring range of gravity, resulting in blurred interference fringes. Under dynamic conditions, the level of vibration noise is higher, which is the main source of error and limits the accuracy of atom gravimeters.  
Figure 5: Dynamic measurement device of atom gravimeter of the authors’ team.  
There are mainly three methods for suppressing vibration noise: passive vibration isolation based on super springs [51], active vibration isolation with feedback control [52; 53; 54], and using a motion sensor such as a seismometer or accelerometer to measure vibration noise and compensate [46; 48]. In practical application, in the case of obvious external vibration and large interference, the vibration compensation method has a better effect than the active and passive vibration isolation methods. Moreover, the vibration isolation device is often complex and not easy to carry, and it is not suitable for the field dynamic measurement scene. Therefore, the vibration compensation method is generally used to suppress the vibration noise in the dynamic measurement scene. This method was first proposed in 2009 by [PERSON] et al., who compared it with fringe fitting and nonlinear phase-locking and achieved a sensitivity of 55 \(\upmu\)Gal/Hz\({}^{1/2}\) in the absence of vibration isolation in the center of Paris [48]. In 2019, Hanover University compared the performance of various types of accelerometers and seismometers using post-correction, pointing out that seismometers that are more sensitive to low-frequency motion are suitable for low inertial noise environments, while accelerometers with high dynamic range perform better in high inertial motion environments [55]. The method of using accelerometers or other vibration sensors to suppress the influence of vibration noise under dynamic measurement conditions has been provided in the previous Principle and Device section and will not be repeated.
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 2 Numerical Method  
### Turbulence Model  
LES is a turbulence model developed rapidly in recent years. After averaging the Navier-Stokes equations (N-S equation) in a small space domain, it filters out the small-scale vortices and derives the equation satisfied by the large-scale vortices, then simulates the influence of small vortices through the sub grid scale model.  
The N-S equation of instantaneous quantity in each direction after filtering can be expressed as follows [22]  
\[\frac{1}{\rho}\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}( \overline{u_{i}})=0 \tag{1}\]  
\[\frac{\partial\overline{u_{i}}}{\partial t}+\frac{\partial}{\partial x_{j}}( \overline{u_{i}}\overline{u_{j}})=-\frac{1}{\rho}\frac{\partial\overline{p}} {\partial x_{i}}+\
u\frac{\partial}{\partial x_{j}}(\frac{\partial\overline{u _{i}}}{\partial x_{j}})-\frac{\partial}{\partial x_{j}}(\overline{u_{i}u_{j}} -\overline{u_{i}}\overline{u_{j}}) \tag{2}\]  
where \(\overline{u}\) is the filtered velocity; \(\overline{p}\) is the pressure; \(\rho\) is the density of air; \(t\) and \(x_{i}\) are time and Cartesian coordinates, respectively; \((\overline{u_{i}u_{j}}-\overline{u_{i}}\overline{u_{j}})\) is the subgrid scale stress.  
The above equation is an open equation. In order to solve it, the Smagorinsky eddy viscosity model is adopted in this paper. By assuming that the generation and consumption of energy are balanced, the relationship between subgrid stress and eddy viscosity is  
\[\tau_{ij}-\frac{1}{3}\tau_{ik}\delta_{ij}=-2\
u_{t}\overline{S}_{ij} \tag{3}\]  
where \(\tau_{ij}=(\overline{u_{i}u_{j}}-\overline{u_{i}}\overline{u_{j}})\); \(\frac{1}{3}\tau_{ik}\delta_{ij}\) is the equilibrium component; \(\overline{S}_{ij}=\frac{1}{2}\left(\frac{\partial\overline{u_{i}}}{\partial \overline{u_{j}}}+\frac{\partial\overline{u_{j}}}{\partial\overline{u_{i}}}\right)\); \(\
u_{t}\) is subgrid eddy viscosity coefficient and can be expressed as  
\[\
u_{t}=(c_{s}\overline{\Delta})^{2}|\overline{S}| \tag{4}\]  
where \(c_{s}\) is the Smagorinsky constant; \(\overline{\Delta}\) is the filtering scale; \(|\overline{S}|=\sqrt{2\overline{S}_{ij}\overline{S}_{ij}}\).
## 2 Materials and Methods
### Fasting Blood Glucose Levels  
At week 0, there was no difference in blood glucose levels between all groups (Figure 1). However, 8 weeks later, db/db mice had higher blood glucose levels than db/+ mice; additionally, there were differences between each group of db/db mice. At week 3, blood glucose levels in the RG_30E and RG_50E groups were significantly lower (29.68% and 25.71%, respectively) compared to the db/db control (_p_ < 0.05). However, there was no significant difference between groups except the db/+ control group at 5 weeks. At 8 weeks, only RG_50E mice had a significantly lower blood glucose level compared to the db/db control, with a decrease of 12.37% (_p_ < 0.05).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Group (1)**} \\ \hline  & **db/+** & **db/db** & **FA** & **RG\_30E** & **RG\_50E** \\ \hline Body weight (g) & & & & & \\ Initial weight (g) & 22.49 \(\pm\) 0.76 b  & 37.30 \(\pm\) 0.33 a  & 37.20 \(\pm\) 0.70 a  & 38.28 \(\pm\) 1.15 a  & 36.34 \(\pm\) 1.77 a  \\ Final weight (g) & 27.66 \(\pm\) 0.49 c  & 50.78 \(\pm\) 1.04 ab  & 48.34 \(\pm\) 3.82 b  & 52.60 \(\pm\) 1.53 a  & 51.98 \(\pm\) 1.15 a  \\ Food intake (g/day) & 2.87 \(\pm\) 0.10 d  & 18.14 \(\pm\) 0.58 a  & 15.56 \(\pm\) 0.09 c  & 16.83 \(\pm\) 0.12 b  & 15.92 \(\pm\) 0.02 c  \\ Organ weight (g) & & & & & & \\ Liver (g/100 g BW) & 0.93 \(\pm\) 0.03 c  & 2.89 \(\pm\) 0.13 a  & 2.58 \(\pm\) 0.11 ab  & 2.69 \(\pm\) 0.16 ab  & 2.40 \(\pm\) 0.12 b  \\ Epididymal adipose tissue (g/100 g BW) & 0.54 \(\pm\) 0.05 b  & 2.81 \(\pm\) 0.09 a  & 2.70 \(\pm\) 0.11 a  & 2.44 \(\pm\) 0.07 a  & 2.61 \(\pm\) 0.23 a  \\ Kidney (g/100 g BW) & 0.30 \(\pm\) 0.00 a  & 0.35 \(\pm\) 0.02 a  & 0.35 \(\pm\) 0.02 a  & 0.33 \(\pm\) 0.03 a  & 0.30 \(\pm\) 0.00 a  \\ \hline \hline \end{tabular} (1) db/+, C57 BL/6 KsJ-db/+ mice and normal diet; db/db, C57 BL/6 KsJ-db/+ mice and normal diet; RG_30E, C57 BL/6 KsJ-db/+ mice and modified diet with RG_50E; FA, C57 BL/6 KsJ-db/+ mice and modified diet with RG_50E; FA, C57 BL/6 KsJ-db/+ mice and modified diet with ferulic acid. Data are presented as mean \(\pm\) standard error (SE) (n = 8). Data with different letters determined by [PERSON]’s multiple range test were significantly different at \(p\) < 0.05.  
\end{table}
Table 2: Effect of fermented rice germ extracts on body weight, food intake, and organ weights.  
Figure 1: The effects of fermented rice germ extracts on blood glucose levels in db/+ mice and db/db mice over 8 weeks. db/+, C57 BL/6 KsJ-db/+ mice and normal diet; db/db, C57 BL/6 KsJ-db/+ mice and normal diet; RG_30E, C57 BL/6 KsJ-db/+ mice and modified diet with RG_30E; RG_50E, C57 BL/6 KsJ-db/+ mice and modified diet with RG_50E; FA, C57 BL/6 KsJ-db/+ mice and modified diet with ferulic acid. Data with letters are significantly different (_p_ < 0.05) according to [PERSON]’s multiple range test.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### Topology-Based Routing Protocols  
Topology-based routing protocols use Internet Protocol (IP) addresses to define the nodes and use existing link information to forward packets through appropriate paths. Topology-based routing protocols can be divided into static routing protocols, proactive routing protocols, reactive routing protocols and hybrid routing protocols.
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
## 2 Experimental Procedure
### Evaluation of the Fundamental Properties of the Steel Slag  
As previously mentioned, steel slag is solid waste generated from the iron and steel industry [4]. Consequently, to use steel slag as aggregate in concrete, its fundamental properties must be investigated. In particular, we must ensure that it satisfies the required quality standards before using it in concrete. To evaluate the fundamental properties of steel slag, experiments were performed to determine its density unit weight volume, and absorption ratio, while its chemical composition was determined using X-ray fluorescence spectrometer (Philips, PW 2404, Korea Basic Science Institute, Daejeon, Korea).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & & **Binder** & **OPC** & **FA** & **BFS** \\ \hline Physical & Specific gravity & 3.15 & 2.19 & 2.92 \\  & Blaine (cm\({}^{2}\)/g) & 3200 & 3400 & 6300 \\  & SiO\({}_{2}\) & 21.36 & 53.0 & 27.2 \\  & Al\({}_{2}\)O\({}_{3}\) & 5.03 & 20.3 & 9.87 \\  & Fe\({}_{2}\)O\({}_{3}\) & 3.31 & 10.4 & 0.51 \\ Chemical & CaO & 63.18 & 6.57 & 53.5 \\ (\%) & MgO & 2.89 & 0.98 & 3.29 \\  & SO\({}_{3}\) & 2.30 & 1.08 & 3.17 \\  & Equivalent alkalis (Na\({}_{2}\)O + 0.658K\({}_{2}\)O) & 0.85 & 2.58 & 0.58 \\  & Loss on ignition (LOI) & 1.40 & 2.56 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties and chemical compositions of the binders.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Density (g/cm\({}^{3}\))** & **Absorption (\%)** & **Fineness Modulus (F.M.)** \\ \hline Natural fine aggregate & 2.55 & 1.07 & 2.79 \\ Natural coarse aggregate & 2.68 & 1.35 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of the natural aggregates.  
The steel slag for XRF analysis is prepared by the briquette method in which the slag is well-dried and ground into powder, which is press-formed.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 4 xPON Activation Processes
### Xg-PON Activation Process
Once the network is in operation and all ONUs are working with their assigned equalization delay, all upstream bursts are synchronized with all ONUs. State O6, IntermittentLODS state: The ONU will move to this state from the Operation state when it does not synchronize with the downstream signal. Upon entering this state, the ONU will start the TO2 timer (the recommended value for this timer is 100 ms). After the timer expires, the ONU returns to the Initial state. State O7, Emergency Stop State: The ONU moves to this state if it receives the Disable Serial Number message with the \"Disable\" option. In this state, it switches off the laser and rejects all TC settings (ONU-ID, equalization delay, burst profiles, etc.). The ONU keeps the downstream  
Figure 1: Synchronization state machine for XG-PON downstream [27]. synchronization machine running and analyzes the XGTC frames in the downstream direction (at this point, however, it is forbidden to pass any downstream data or send any upstream data). If the problem is resolved, the OLT can re-enable the ONU and bring it back to normal operation by sending the Disable Serial Number with the \"enable\" option. As a result, the ONU returns to the Initial state.
## 2 Numerical Modeling
### Physical Model Details
#### 2.1.2 Two Actively Cooled Options for the Simplified All-Movable Rudder  
Two different active convection cooling schemes are proposed for this simplified all-movable rudder. Figure 4 shows the structural layout of the first scheme, denoted as A1, where \"1\" represents the standard configuration. The rudder mainly consists of five parts: the rudder shaft, the inlet liquid-collecting cavity, the skin (containing the cooling channels), the outlet liquid-collecting cavity, and the lattice core layer. (i) The rudder shaft comprises two layers: the inner layer serves as the coolant inflow channel, while the outer layer functions as the coolant outflow channel. (ii) The inlet liquid-collecting cavity is used to revive the coolant flowing from the inner layer of the rudder shaft and towards the cooling channels of the skins. (iii) The skin functions as a carrier for the cooling channels (the forward skin cooling channel and backward skin cooling channel) in the active cooling system. These channels encircle the rudder within the skin, with the inlet connected to the inlet liquid-collecting cavity and the outlet connected to the outlet liquid-collecting cavity. The internal coolant flows along the cooling channels and convectively exchanges heat with the skin, removing most of the heat energy. (iv) The outlet liquid-collecting cavity gathers the heated coolant from the cooling channel and directs it into the outer layer of the rudder shaft. (v) The lattice core layer is the main bearing structure of the air rudder and has superior strength, stiffness, and lightweight characteristics over conventional beam-rib structures. It also enhances structural heat transfer and reduces thermal stress.  
The structural layout of the second scheme, denoted as B1, is illustrated in Figure 5. In comparison to the previous scheme, this scheme incorporates forward and backward core cooling channels while modifying the structure of the inlet liquid- and outlet liquid-collecting cavities. These changes can all be more vividly illustrated by the flow of the coolant: Firstly, the coolant is directed through the inner layer of the rudder shaft to reach the inlet liquid-collecting cavity and is subsequently guided towards the inlet of the forward core cooling channel. From this point, it traverses along the forward core cooling channel until it reaches the leading edge, where it bifurcates. Subsequently, the coolant sequentially flows through both the forward and backward skin cooling channels before converging at the trailing edge. Finally, it follows along the backward core cooling channel to reach the outlet liquid-collecting cavity and exits via the outer layer of the rudder shaft.  
Figure 4: The structural layout of actively cooled Scheme A1.
## 4 Plasmon Dispersion
### Graphene on A Metal Substrate  
Consider a structure shown in Figure 3c where a monolayer graphene is at a distance \(d_{2}\) above a metal substrate of a thickness of \(d_{\text{sub}}\) deposited on a semi-infinite dielectric material. The permittivity of the metal substrate is modeled as  
\[\varepsilon_{\text{sub}}=\varepsilon_{0}\left(1-\frac{\omega_{\text{p}}^{2}}{ \omega\left(\omega+\text{i}\gamma\right)}\right) \tag{17}\]  
where \(\omega_{\text{p}}\) is the bulk plasma frequency and \(\gamma\) is the scattering rate of the metal. Using Equations (7), (15) and (17), the plasmon mode of the system due to the coupling between the graphene layer and the metal slab can be numerically solved, as shown in Figure 5a, where the metal is assumed to be gold with \(\omega_{\text{p}}=8.89\,\text{eV}\) and \(\gamma=17\,\text{THz}\)[48]. As can be seen, by coupling graphene with a metal substrate, the wave number \(q\) of the plasmon mode increases. This coupling effect gradually vanishes as \(d_{2}\) increases. A larger \(\varepsilon_{2}\) (larger \(\varepsilon_{\text{avg}}\)) also gives a larger \(q\) according to Equation (13). However,  
Figure 5: Plasmon dispersion of (**a**) graphene on a gold substrate and (**b**,**c**) double-layer graphene. The related structures are shown in Figure 3c,d. In all figures, \(\mu=100\,\text{meV}\) and \(\tau^{-1}=10\,\text{THz}\) are used for graphene. Thin solid, thick solid, and dashed curves are calculated for \(d_{2}=30\,\text{nm}\), \(d_{2}=50\,\text{nm}\), and \(d_{2}\rightarrow\infty\), respectively. Black and red curves are calculated for \(\varepsilon_{2}=2\varepsilon_{0}\) and \(\varepsilon_{2}=\varepsilon_{0}\), respectively. The dotted curves are obtained using the RPA-RT approach with parameters \(d_{2}=30\,\text{nm}\) and \(\varepsilon_{2}=2\varepsilon_{0}\). In (**a**), \(d_{\text{sub}}=50\,\text{nm}\), \(\varepsilon_{1}=3.9\varepsilon_{0}\), \(\omega_{\text{p}}=8.89\,\text{eV}\), and \(\gamma=17\,\text{THz}\) are used. In (**b**,**c**), \(\varepsilon_{1}=\varepsilon_{0}\) is assumed. Curves for the same plasmon mode are grouped by an ellipse.  
and \(\varepsilon_{\mathrm{l}}\) have nearly no effect on shaping the plasmon dispersion because \(d_{\mathrm{sub}}\) is always much larger than the skin depth of gold, which is about \(c\,/\,\omega_{\mathrm{p}}=0.22\,\mathrm{nm}\) in the THz frequency region. We also calculated the plasmon dispersion for graphene on different metal substrates; the dispersion curves overlapped and can hardly be distinguished. Therefore, the choice of the metal substrate is not important regarding the plasmon dispersion within the THz frequency region. The plasmon dispersion of the system is also calculated using \(\sigma_{\mathrm{PRA}}\) and plotted as the dotted curve in Figure 5a. At high \(q_{\mathrm{l}}\) region, the RPA-RT results start to deviate from the curves obtained using the Drude conductivity. Nevertheless, in the low-\(q_{\mathrm{l}}\) region, the Drude model is sufficiently accurate to describe the plasmon dispersion of the system.
## 3 OLF Transmission System Case Study
The effect of limited acceptance cone can be written as:  
\[\mathbf{y}_{r}=\mathbf{s}(\mathbf{t}-\mathbf{t}_{0})\!\!\sum_{k=1}^{N/I}\!\sum_{j=1}^{M}\bigg{(} \sum_{i=1}^{I}\mathbf{h}_{ji}\sqrt{\eta_{n}\big{(}P_{in}^{n}-L_{bj}^{n}\big{)}}+z_ {j}\bigg{)}. \tag{40}\]  
Here, \(\mathbf{N}/\mathbf{I}\) represents the total number of subgroups each with \(\mathbf{I}\) slots. A simulation for the system is conducted using MATLAB (R2020b) with OOK modulation scheme, \(\mathbf{N}=100\) transmitters and \(\mathbf{M}=50\) receivers. We obtained the BER results shown in Figure 7. The effects of different types of noises and the photo-detection is assumed to contribute to the overall noise as an AWGN. The simulation assumes a uniform slot distance of 0.5 m, distance between Tx OLF and 5 m for the Rx OLF. An acceptance cone with solid angle of 14.13\({}^{\circ}\) is assumed. This investigated configuration results of five transmitting slots that are within the acceptance cone.