esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Complete Soft Galloping (SG) Responses and Complete Hard Galloping (HG) Responses
### Soft Galloping (SG)
#### 3.1.5 Summary  
In addition to the four typical response paths mentioned above, there are lots of response paths in SG response. In all the response paths, an interesting \(U_{r}\) region, which is from 7.25 to 11, is observed. In this region, the prism can be self-excited from VIV to galloping in any cases. On the contrary, for \(U_{r}<7\) or \(U_{r}>11\), the prism cannot be self-excited from VIV to galloping. Especially for \(U_{r}>11\), the prism can go into the galloping branch by applying a threshold initial displacement. Therefore, the \(U_{r}\) region from 7.25 to 11 is the self-excited region where the prism can be self-initiated from VIV to galloping. Moreover, the flow velocities of \(U_{r}=7.25\) and \(U_{r}=11\) are the upper and lower limits of the self-excited region, respectively. It is noteworthy that the self-excited region has first been found in the present experiment and its upper and lower limits are the key characteristics to distinguish the SG and HG.  
In conclusion, the complete response characteristics of SG can be summarized as follows: (a) The VIV lower branch can be observed in SG response; (b) The VIV upper branch cannot be observed due to the existence of the self-excited region (VIV-galloping transition branch); (c) When \(U_{r}\) is in the self-excited region, the prism has the ability to be self-initiated from VIV to galloping; and (d) When \(U_{r}\) exceeds the upper limit of the self-excited region, the prism loses its self-initiated ability.
## 3 Results
### Bioactivity of SE-NPs  
#### 3.2.1 Stress Markers  
(a) MDA  
Free radical production is the primary step in the sequence of events leading to membrane lipid peroxidation, apoptosis as well as necrosis. MDA is an indicative parameter of the lipid peroxidation. Here, the MDA was dramatically increased in the CCl\({}_{4}\) group rats in comparison to the control GSHs. CCl\({}_{4}\) group rats exhibited a 176.92% increase in MDA levels over controls, and CCl\({}_{4}\) + SE-NP group animals showed MDA levels 63.71% lower than CCl\({}_{4}\) group rats (Figure 3). Thus, SE-NPs were found to restore the MDA concentration CCl\({}_{4}\) group in comparison to the controls.  
(b) GSHs  
Antioxidants provide substantial protection against CCl\({}_{4}\)-induced hepatic toxicity. The GSHs declined in the CCl\({}_{4}\) group rats in comparison to the control GSHs. CCl\({}_{4}\) rats showed a 63.55% reduction in GSHs compared to controls. SE-NPs was found to significantly restore the GSHs in CCl\({}_{4}\) animals in comparison to the control GSHs. The GSHs showed a 73.84% increase in CCl\({}_{4}\)-SE-NPs over levels in the CCl\({}_{4}\) group (Figure 3).  
Figure 1: SEM of stabilized nano-selenium with magnifications of 6000 (**A**) and 10,000 (**B**).  
Figure 2: (**A**) EDS elemental analysis of stabilized nano-selenium. (**B**) The TEM image of nano-selenium.
## 2 Schemes of SR-ODBR-DCSK
### Transmitter  
Figure 4 shows the block diagram of SR-ODBR-DCSK system. In the modulator, each information slot transmits two bits at the same time. Taking the k-frame transmission signal as an example, the chaotic signal generated by the chaotic signal generator is used as the reference signal of the symbol period. The reference signal is repeated \(P\) times, and then the delay \(R\) is \(x_{i,k-R}\). In order to transmit the \(K\)th pair of information bits \(b_{2k}\) and \(b_{2k+1}\), the delay signal is divided into two channels, one for transmitting data information bits \(b_{2k}\), and the other for generating another chaotic signal orthogonal to the delay signal through the Walsh orthogonal generator, which is used as a new chaotic carrier to transmit data information bits (in order to ensure the average power of the transmitted signal remains unchanged, the amplitudes of the two modulated chaotic signals generated here are multiplied by the coefficients) \(b_{2k+1}\). Finally, the two paths of chaotic modulated  
Figure 1: Frame structure of the SR-ODBR-DCSK system.  
Figure 3: Even number of information bits demodulation structure for the SR-OBDR-DCSK.  
Figure 2: Frame structure of the DCSK system.  
information are added, and the whole system will be transmitted as a sending signal. Where, \(x_{0,k},x_{1,k},\cdots x_{k-2,k},x_{k-1,k}\) denote the chaotic sequence with the length of \(R\) in the \(k\)th frame, and \(x^{\prime}_{0,k},x^{\prime}_{1,k},\cdots x^{\prime}_{R-2,k},x^{\prime}_{R-1,k}\) denote the chaotic sequence with the length of \(\beta\) orthogonal transformation function. A new chaotic signal orthogonal to \(x_{0,k},x_{1,k},\cdots x_{k-2,k},x_{R-1,k}\) is generated as a new chaotic carrier to transmit data information \(b_{2k+1}\).  
So the SR-ODBR-DCSK signal expression transmitted in frame \(i\) is  
\[s_{i}=\left\{\begin{array}{cc}x_{i,k}&0\leq i<R\\ \frac{1}{\sqrt{2}}(b_{2k}x_{i-R,k}+b_{2k+1}x^{\prime}_{i-R,k})&R\leq i\leq(1+ P)R\\ x_{i-R,k}\equiv x_{0,k}&\mod(R)\end{array}\right. \tag{1}\]  
The flow of the SR-ODBR-DCSK system signal generation can be summarized in the following steps.  
Step 1: Chaotic signal generator generates chaotic reference signals of length \(R\).  
Step 2: The chaotic reference signal generated by step 1 is copied \(P\) times and then delayed \(R\).  
Step 3: The chaotic signal after delaying \(R\) is then divided into two branches. One branch is modulated directly with the even information data, and the chaotic signal of the other branch is modulated with the odd information data by producing an orthogonal chaotic signal through the orthogonal functions. The results of the modulation of the information data of these two branches are summed up correspondingly.  
Step 4: Finally, the output signal is combined with the reference signal of length R and information data of length \(PR\).
## 1 Introduction
The Oasis mixed-mode WAX sorbent is a derivative of Oasis HLB copolymer, poly(N-vinylpyrrolidone-co-divinylbenzene), with piperazine ligands. SOLA WAX (Thermo Scientific, Waltham, MA, USA) polymeric SPE utilizes a hydrophobic backbone functionalized with a primary amine with a pKa of approximately 6. [PERSON] et al. developed mixed-mode WAX sorbents based on poly(vinylbenzylchloride-co-divinylbenzene) microspheres modified with 1,2-ethylenediamine and piperazine moieties [18]. These WAX sorbents all need an additional modification step for introducing the WAX functionalities. Direct copolymerization of functional monomers undoubtedly provides the simplest approach for preparation of functionalized sorbents. Using this technique, a synthesized copolymer based on poly(N-vinylimidazole-co-divinylbenzene) was evaluated as a mixed mode WAX sorbent for SPE, since it contained an imidazole group with a pKa of approximately 7 [19]. Moreover, most existing strategies for the synthesis of such mixed-mode polymeric microspheres are based on classical emulsions stabilized by surfactants. In contrast, Pickering emulsions utilize solid microparticles or nanoparticles that localize at the interface between liquids as stabilizers to enhance the droplet lifetime, which afford higher stability, lower toxicity, and stimuli-responsiveness. Therefore, one-step Pickering emulsion polymerization can be considered attractive due to its easy preparation, enhanced stability, and various possibilities for preparing new materials [20; 21; 22]. In current work, we used a facile one-step Pickering emulsion polymerization for preparing poly(DEAEMA-co-DVB) microspheres that could simultaneously and efficiently extract strongly (niflumic acid) and weakly (diflunisal, naproxen, ketoprofen, ofenamic acid, and diclofenac) acidic NSAIDs. The developed poly(DEAEMA-co-DVB) sorbent contains a tertiary amine with a pKa value of around 10.7, which allows for control of the ionization of stationary phase by modifying the pH. Tertiary amine functionality can easily exchange strongly acidic NSAIDs anion. Furthermore, tertiary amine is more basic than primary amine (pKa \(\approx\) 6), imidazole and pyridine, and therefore provides better anion-exchange (acid-base) interactions with those weak acidic NSAIDs. Therefore, our poly(DEAEMA-co-DVB) WAX can simultaneously enrich, purify, and analyze strongly and weakly acidic NSAIDs.
# Helicopter Safe Landing Trajectory after Main Rotor Actuator Failures
###### Abstract  
Main rotor actuator failure leads to catastrophic accidents for single main rotor helicopters. This paper focuses on safe landing trajectories after an actuator is locked in place by the remaining actuators, without introducing other control inputs. A general swashplate geometry is described, and new reconfiguration solutions for the control mixer are presented. The safe landing trajectories are obtained by formulating a nonlinear optimal control problem based on a nonlinear helicopter dynamic model and geometry constraints due to actuator failure. Safe landing trajectory results are shown with various initial forward velocities of all actuator failure cases. The safe initial speed boundaries are also explored by employing speed sweeps.  
helicopter actuator failure; optimal control; safe landing trajectory; control mixer reconfiguration
# Phonon Scattering and Thermal Conductivity of Actinide Oxides with Defects
## 3 Results and Discussion
### The Effect of Fission Gases on Thermal Transport in Actinide Oxides
Although the atomic mass of oxygen is much smaller than the atomic mass of uranium, the oxygen vacancies have a detrimental effect on the thermal conductivity of UO\({}_{2}\), comparable to uranium vacancies. In Figure 9b, the local PDOS near the location of a fission gas bubble and the local PDOS far from the fission gas bubble are compared to the PDOS of stoichiometric UO\({}_{2}\). For the case of the local PDOS near the location of the fission gas bubble, a large amount of phonon scattering is observed. The degeneracy of LA and TA phonons is reduced significantly, and their peaks are shifted drastically; in fact, the peaks of LA and TA phonons become almost unidentifiable. In the case of the local PDOS far from the location of the fission gas bubble, however, the peak for LA phonon mode (\(\approx\) 20 meV) is not shifted. This indicates that LA phonon modes are quickly recovered after being scattered by the fission gas bubble. Since LA phonon mode is a dominant thermal energy carrier in actinide oxides, thermal transport property in actinide oxides is expected to be recovered promptly once phonons pass through the location of fission gas bubbles [38]. However, the peak for TA phonon mode (near 17 meV) shows a slight shift and reduced degeneracy, and optical phonons are altered significantly. Figure 8: (a) Temperature profile during RNEMD for UO\({}_{2}\) with 15 Kr/15 Xe fission gases. (b) Alteration of the lattice structure of UO\({}_{2}\) caused by the presence of fission gas bubble. [PERSON] et al. [38] calculated the contribution of TA phonon mode to the thermal conductivity of UO\({}_{2}\) to be approximately 4% and the contribution of optical phonon modes to the thermal conductivity to be about 6%. Therefore, thermal transport is expected to become more diffusive, even at the region far from the location of fission gas bubbles, when compared to the thermal transport in stoichiometric UO\({}_{2}\). This is supported by the temperature profile in Figure 8a where no temperature slips are identified near the hot bath and the cold baths. For the case of UO\({}_{2}\) with a fission gas bubble of 15 Kr/15 Xe, the thermal conductivity is estimated to be still very low (3.8 W/m-K), even when only the linear portion of the temperature profile is considered for the thermal conductivity calculation, (completely ignoring the temperature jump near the location of the fission gas bubble); for the same size of stoichiometric UO\({}_{2}\), the thermal conductivity is estimated to be higher than 12 W/m-K [2]. This again demonstrates a diffusive nature of thermal transport in UO\({}_{2}\) with a fission gas bubble.
## 3 Analysis of Fluid-Solid Coupled Heat Transfer on Different Radial Ventilation Ducts Structure
### Simulation Analysis of Fluid Field in the Internal Cooling System of a Generator
#### 3.2.2 Numerical Study of the Fluid Field in Radial Ventilation Ducts with Different Widths  
To investigate the impact of the radial ventilation ducts width, CFD finite element modeling is employed to perform numerical calculations on the wind turbine generator cooling system within the ventilation width range of [1; 2; 3; 4; 5; 6; 7; 8; 9; 10 mm]. The results show the velocity and flow rate within 19 radial ventilation ducts of different widths, as illustrated in Figures 7 and 8.  
According to CFD fluid theory, convective heat transfer is closely related to fluid velocity. Therefore, analyzing the fluid flow within the radial ventilation ducts of the stator and rotor is crucial for optimizing the ventilation structure.  
Based on the analysis of Figures 7 and 8, the following conclusions are drawn:  
1. The velocity of the airflow within the radial ventilation ducts increases as the width of the ducts decreases, with more significant increases observed for narrower ducts.
2. When the ventilation ducts width is larger, the velocity and flow rate distribution along the axial direction of the 19 ducts generally show lower values on the sides and higher values in the middle.  
Figure 6: Velocity field of internal cooling structure.  
(3) Although the cross-sectional area of the radial ventilation ducts decreases with the reduction in ducts width, the overall flow rate does not decrease. Instead, the distribution of the flow rate becomes more uniform.  
According to theoretical Formulas (5)-(9), as the velocity increases, the wind friction loss also increases accordingly. The reduction of the ventilation ducts width increases the inlet velocity of the ducts, thereby increasing the overall wind friction loss. To investigate the impact of different ventilation ducts widths on wind friction loss, CFD simulation is used to obtain the distribution of wind friction loss, as shown in Figure 9.  
As shown in Figure 9, when the ventilation duct width is 1 mm, the wind friction loss is 24 kW. As the width increases, the wind friction loss shows an exponential downward trend. When the width reaches 5 mm, the friction loss stabilizes at around 2 kW. When the width ranges from 5 mm to 10 mm, the friction loss remains almost unchanged. The average interpolation method is used to fit the FEM results, from which the relation between wind friction loss and ventilation duct width is derived:  
\[\overline{W}_{\text{fr}}=13.39L^{4}-408.25L^{3}+4556.51L^{2}-22057.05L+40931.05 \tag{10}\]  
where \(\overline{W}_{\text{fr}}\) represent the wind friction loss of the generator (W) and \(L\) is the width of the generator core ventilation duct (mm); the value range of \(L\) is 1 to 10 mm.  
Figure 8: Comparison of circumferential cross-sectional flow rate in radial ventilation ducts with different widths.  
Figure 7: Comparison of airflow velocity in 19 radial ventilation ducts with different widths.
###### Abstract  
In the past decade, graphene has been widely researched to improve or overcome the performance of conventional radio-frequency (RF) nanodevices and circuits. In recent years, novel RF bio and gas sensors based on graphene and its derivatives, graphene oxide (GO) and reduced graphene oxide (rGO), have emerged as new RF sensing platforms using a wireless remote system. Although the sensing schemes are still immature, this review focuses on  
electronic sensor. It can provide an inexpensive sensing scheme to detect in real-time the biomedical substance of interest.  
Thus, this review focuses on the recent progress in RF bio and gas sensing platforms with graphene/graphene oxide (GO) in a wireless health care system. This paper is organized as follows: first, among the excellent graphene properties, the vital physical and chemical properties of graphene/GO as sensing materials reported in the literature are described, and then the developed graphene/GO-based RF bio and gas sensing platforms are introduced. Next, the pros and cons of these sensing schemes are analyzed, and finally, the challenges and prospects for robust RF sensing platforms in a real-time and continuous health care monitoring system are discussed and suggested. In this paper, the term RF means all science and techniques that are related to transmitting and receiving information or power in the free space (atmosphere) or transmission lines utilizing electromagnetic waves--so-called radio waves and microwaves--and the equipment, e.g., devices and circuits, needed therein for wireless communication.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis
### EPFD Calculation Engine  
Based on the above EPFD calculation and the EIRP characteristics of the satellite emission, corresponding amendments needed to be made with respect to the ITU Recommendations. For a time step \(t\), it was necessary to count the total EPFD generated by all interfering satellites at the RAS receiver. The instantaneous EPFD calculation at the receiver could be summarized as follows:  
* Determine the number of interfering satellites visible to the RAS receiver \(N_{ras}\), calculate the elevation and azimuth of the RAS station relative to each satellite, where we denote the sets of interfering satellites, elevation and azimuth angles of RAS receiver, and interfering satellites as \(\{I_{1}(t),I_{2}(t),\cdots,I_{N_{ras}}(t)\}\), \(\{(EI_{1}^{ras}(t),Az_{1}^{ras}(t)),(EI_{2}^{ras}(t),Az_{2}^{ras}(t)),\cdots,(EI_{N _{ras}}^{ras}(t),Az_{N_{ras}}^{ras}(t))\}\), and \(\{(EI_{1}^{lat}(t),Az_{1}^{lat}(t)),(EI_{2}^{lat}(t),Az_{2}^{lat}(t)),\cdots,(EI _{N_{ras}}^{lat}(t),\)\(Az_{N_{ras}}^{sat}(t))\}\), respectively. The distance between each satellite and the RAS station also needs to be counted, whose set is denoted as \(\{d_{1}(t),d_{2}(t),\cdots,d_{N_{ras}}(t)\}\).
* For the satellite \(I_{n}(t)\), \(n\in\{1,2,\cdots,N_{ras}\}\), we need to determine in which cell in the satellite's field of view the RAS station is located, and the expression for the expected EPFD for satellite \(I_{n}(t)\) at the cell \(G_{c}^{ras}\) of the RAS receiver is expressed as Equation (34), for \(c\in\{1,2,\cdots,2334\}\): \[EPFD_{I_{n}(t)}^{c}=\frac{\sum_{i,j}I((EI_{n}^{sat},Az_{n}^{sat})\in G_{ij}^{ sat})p(\gamma_{I_{n}(t)})\int_{-\infty}^{\infty}ep_{ij,j}(e)deG_{r}(\phi_{I_{n} (t)}^{c})}{4\pi d_{n}^{2}(t)}\] (34) where \(\gamma_{I_{n}(t)}\) is the latitude the subsatellite of satellite \(I_{n}(t)\) and \(G_{r}(\phi_{I_{n}(t)}^{c})\) is the off-axis angle between the satellite \(I_{n}(t)\) and the RAS observation within cell \(c\). If \(EI_{n}^{sat}\in[i\times\Delta EI-El_{max},(i+1)\times\Delta EI-El_{max})\) and \(Az_{n}^{sat}\in[j\times\Delta AZ-El_{max},(j+1)\times\Delta Az-El_{max})\), \(I((EI_{n}^{sat},Az_{n}^{sat})\in G_{ij}^{sat})\) equals to 1, otherwise it equals to 0.
* Based on the above steps, the total EPFD is denoted as below, \[EPFD^{c}(t)=\sum_{n\in\{1,2,\cdots,N_{ras}\}}\frac{\sum_{i,j}I((EI_{n}^{sat}, Az_{n}^{sat})\in G_{ij}^{ras})p(\gamma_{I_{n}(t)})\int_{-\infty}^{\infty}ep_{ij,j}(e)deG_{r}(\phi_{I_{n}(t)}^{c})}{4\pi d_{n}^{2}(t)}.\] (35) Then, the average EPFD at cell \(c\) in a time interval \(\Delta t\) with a start time \(t_{s}\) can be expressed as the following: \[\overline{EPFD}^{c}(t)=\frac{1}{\Delta t}\int_{t_{s}}^{t_{s}+\Delta t}EPFD^{ c}(t)dt\] (36) Repeating the above process multiple times, the CDF of the EPFD in every cell of the RAS telescope could be obtained according to Equation (5).
# Risk-Sensitive Policy with Distributional Reinforcement Learning
## 3 Materials and Methods
### Methodology
#### 3.2.2 Practical Modelling of the Risk  
As previously hinted, this research work assumes that the risk associated with an action is related to the worst achievable returns when executing that particular action and then following a certain decision-making policy \(\pi\). In such a context, the distributional RL approach becomes particularly interesting, by providing access to the full probability distribution of the random return \(Z^{\pi}\). Thus, the risk can be efficiently assessed by examining the so-called tail of the learnt probability distribution. Moreover, the new constraint in Equation (11) can be approximated through popular risk measures such as the _Value at Risk_ and _Conditional Value at Risk_. Illustrated in Figure 2, these two risk measures are formally expressed as follows:  
\[\text{VaR}_{\rho}(Z^{\pi})=\inf\{z\in\mathbb{R}:F_{Z^{\pi}}(z)\geq\rho\}, \tag{12}\]  
\[\text{CVaR}_{\rho}(Z^{\pi})=\mathbb{E}\big{[}z\mid z\leq\text{VaR}_{\rho}(Z^{ \pi})\big{]}, \tag{13}\]  
where \(F_{Z^{\pi}}\) represents the CDF of the random return \(Z^{\pi}\).  
More generally, the present research work introduces the _state-action risk function_\(R^{\pi}:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}\) of a decision-making policy \(\pi\), which is the equivalent of the \(Q\) function for the risk. More precisely, that function \(R^{\pi}(s,a)\) quantifies the riskiness of the discounted sum of rewards obtained by executing an action \(a\in\mathcal{A}\) in a state \(s\in\mathcal{S}\) and then following policy \(\pi\):  
\[R^{\pi}(s,a)=\mathcal{R}_{\rho}[Z^{\pi}(s,a)], \tag{14}\]  
where:  
* \(\mathcal{R}_{\rho}:\mathcal{Z}\rightarrow\mathbb{R}\) is a function extracting risk features from the random return probability distribution \(Z^{\pi}\), such as \(\text{VaR}_{\rho}\) or \(\text{CVaR}_{\rho}\),
* \(\rho\in[0,1]\) is a parameter corresponding to the cumulative probability associated with the worst returns, generally between 0% and 10%. In other words, this parameter controls the size of the random return distribution tail from which the risk is estimated.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 5 Numerical Simulation
### Simulation Results
Finally, we reviewed the five offloading strategies, including access satellite offloading, neighbor satellite offloading, random offloading, first fit offloading and joint offloading, as shown in Figure 16. Compared with other offloading strategies, the joint optimization strategy of the whole LEO constellation has the best performance, and the joint optimization strategy not only considers the energy consumption generated by the computation, but also considers the transmission path energy loss and the load balancing between the satellites. Furthermore, we can tune \(\beta\) to adjust the trade-off between energy cost and load balancing cost. Figure 8: The maximum utilization of satellites computing resources in different LEO constellation sizes. Figure 9: Satellite computing resource utilization varies with number of tasks in constellation 2 with 72 satellites. Figure 11: Satellite computing resource utilization of the three offloading strategies varies with the number of tasks in the whole constellation offloading scenario. Figure 12: Task response rate of the three offloading strategies varies with the number of tasks in the whole constellation offloading scenario. Figure 10: Task response rate varies with number of tasks in constellation 3 with 144 satellites. Figure 14: Load balance cost of the three offloading strategies varies with the number of tasks in the whole constellation offloading scenario. Figure 13: Energy cost of the three offloading strategies varies with the number of tasks in the whole constellation offloading scenario. Figure 15: Joint optimization cost of the three offloading strategies varies with the number of tasks in the whole constellation offloading scenario.