esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Electro-Optic Properties of Polymers
### Steady-State Properties of the Induced Electro-Optic Coefficient  
In this paragraph, we show a theoretical model describing the nonlinear optical response of a medium constituted by nonlinear units (chromophores) dispersed in a linear matrix and oriented along a preferential axis [33,34]. We suppose that each molecule is identified by an intrinsic reference system with the axes named 1, 2, 3, and this system can be rotated in respect to the system that describes the whole macroscopic structure with the axes named x, y, z (laboratory axis). Let us consider that the orientation of the nonlinear molecules is described by a statistical angular distribution function \(f(\Omega)=f(\theta,\varphi)\) of the molecular axes, where \(\theta\) and \(\varphi\) are shown in Figure 4. \(f(\Omega)\) must fulfill the normalization condition \(\int_{\Omega}\,f(\Omega)d\Omega=1\). In the following we consider that \(z\) is the direction of the poling field and 3 is the direction of the long axis of the molecule.  
The second order nonlinear dipole moment results in:  
\[p_{i}^{\omega}=\varepsilon_{0}\beta_{ijk}\left(-\omega,\omega,0\right)f^{ \omega}f^{\omega}E_{j}^{\omega}\cdot E_{k}^{\omega} \tag{9}\]  
Considering the cylindrical and elongated shape of the chromophores, we can assume that the only nonvanishing component of \(\beta_{ijk}\) is \(\beta_{333}\).  
The macroscopic electro-optic polarization induced by an external dc field \(\vec{E}^{\,0}=E_{z}^{\,0}\,\hat{z}\) and by an optical field \(\vec{E}^{\,\omega}=E_{z}^{\,\omega}\,\hat{z}\), both of them along the \(z\) direction, is:  
\[\begin{split} P_{z}^{\omega}&=N\int p_{3}^{\omega} \cos(3,z)f\big{(}\Omega\big{)}d\Omega=\\ &=\varepsilon_{0}\left[N\,f^{\,0}\,f^{\,\omega}\int\beta_{33} \left(-\omega,\omega,0\right)\cos^{3}(3,z)f\big{(}\Omega\big{)}d\Omega\right] E_{z}^{\,\omega}E_{z}^{\,0}\end{split} \tag{10}\]  
where  
\[f(\Omega)=\frac{\exp\left(-\frac{U}{kT}\right)}{\int\limits_{\Omega}\exp \left(-\frac{U}{kT}\right)d\Omega}\propto\exp\left(-\frac{U}{kT}\right) \tag{11}\]  
Figure 4: Relationship between the molecular axes 1,2,3 and laboratory axes x,y,z.  
minimizing their energy and forming an anti-ferroelectric state, where neighbouring chromophores are preferentially oriented in opposite directions, leading to a decrease of the effective electro-optic coefficient [27]. In order to avoid the negative interaction between chromophores, non-polar second order nonlinear molecule were proposed [35]. Unfortunately, in these molecules, since they have no \(\mu_{0}\), it is not possible to align them using the standard poling procedure shown previously. A more complicated all-optical poling was proposed [35].
## 3 Results and Discussion
The GO nanosheets are indexed from 1 to 12, where the labels GO1 up to GO4 correspond to the nanosheets of the outermost layer, the GO5 to GO8 nanosheets lie at the middle layer, and the GO9 to GO12 nanosheets are attached to the polymeric film. We will now discuss the initial and final configurations of the specified nanosheets. The differences of the center-of-mass displacement of the nanosheets between the layers are marginal. We may note that the outermost nanosheets are displaced more than those in direct contact with the support. This is especially true for the PVDF-supported membrane, in which the polymeric film is hydrophobic. In Figure 7, we present the tilt angle of the individual nanosheets in respect to the plane of the polymeric film. The tilt angle decreases for the layers closer to the support. This confirms the partial stabilization effect of the polymeric substrate. The nanosheets of the PVDF supported membrane obtain the greatest rotation. Both PAI-GO membranes are shown to be efficiently stable where we observe small GO rotations. In the case of the pristine PAI-GO, the rotation angle of the nanosheets is more uniform throughout the layers. Figure 6: GO sheet three-dimensional displacement: GO in water (red triangles), GO-PAI (purple squares), GO-PVDF (green circles), and GO-PAI-cr (yellow rhombuses).
## 4 Advanced Persistent Threat Life Cycle Analysis
### Six Stage Attack Model  
The authors in [70; 71] proposed a six-stage life cycle model to describe an APT attack. This model emphasises that attackers must trick a person into running malware and exploit any zero-day vulnerability. Attackers then access the corporate network from the compromised computer and execute a cycle of hard-to-reach manoeuvres to achieve their ultimate goals. The six stages of this life cycle are as follows:  
1. Information Gathering: The objective of this stage is to gather information on the structure of the organisation through public social network profiles.
2. Point of entry: Social engineering, spear-phishing and zero-day exploit are the most used techniques for the victim to allow the attacker to gain access to the computer.
3. Command and control server: The attacker establishes a connection from the compromised host to the C&C server to maintain the connection. Secure Sockets Layer (SSL) encryption is the method usually used to send traffic to the C&C server.
4. Lateral movement: The attacker can move through the network to find a vulnerable host when access has been gained.
5. Data of interest: Critical information on hosts or servers is identified.
6. External server: the data of interest is transmitted to the C&C servers of the attackers.  
The authors in [2] have adopted a six-stage life cycle based on the intrusion kill chain model. This model organises the stages as follows:  
1. Reconnaissance and weaponization is a preparation stage to study and collect technical information from the target organisation. Some techniques used are social engineering and open-source intelligence (OSINT).
2. Delivery: The attackers send the exploits to the targets directly or indirectly, for example, a direct technique can be through spear-phishing and in an indirect way through watering-hole attack.
3. Initial intrusion: The information obtained in the previous stage (such as credentials), allows attackers to gain access to the target, execute malicious code and exploit vulnerabilities.
4. Command and control: The attackers establish a mechanism to take control of the compromised hosts; for this, the attackers create social networking sites, TOR anonymity networks or use remote access tools.
5. Lateral movement: When attackers have established a connection to their C&C servers, they move around the network of the organisation looking for useful information to gain access to other systems.
6. Data exfiltration: Attackers send critical encrypted information to servers.
## CFD Modeling
### Turbulence Modeling  
The turbulence effects become significant under supersonic flow conditions, playing an important role in the breakup of the smallest sub-droplets [32]. In this CFD study, the unsteady mean turbulent flow around the droplet is numerically predicted by solving the URANS equations with the scale-adaptive simulation (SAS) method [20]. The SAS-URANS approach is based on the shear-stress transport (SST) \(k\)-\(\omega\) turbulence model [34], where two additional transport equations are solved for the turbulent kinetic energy and the specific turbulent dissipation rate. Among the different versions of the \(k\)-\(\omega\) model, the SST formulation represents the most commonly used in engineering research given its high accuracy-to-expense ratio. This model provides a better prediction of separated flows, having the ability to account for the transport of the principal shear stress in adverse pressure gradient boundary layers. In the SAS framework, an extra production term is added in the \(\omega\) equation, which is sensitive to resolved unsteady fluctuations. Contrary to the standard SST \(k\)-\(\omega\) model adopted in [19], the present SAS formulation provides a turbulent length scale that is depending on the local flow structure. Practically, the method allows for resolving unsteady turbulence structures with LES-like accuracy, where it is appropriate, and is particularly suitable for flows with strong instabilities [35]. In addition, the \(\gamma\) transition model is used in conjunction with the SST model, where an additional transport equation is solved for the so-called turbulence intermittency [36].  
Due to these features, the SAS-URANS method, which is increasingly used by industrial researchers, results in being particularly suitable for simulating the complex unsteady two-phase flow under investigation. For the sake of brevity, the mean-flow-governing equations, including the model equations, are not reported here and can be found in the referenced works.
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 5 Conclusions  
We introduce the development status of the BDSBAS system, including the system architecture and the message user algorithms. The accuracies of the BDSBAS SF service and the DFMC service are discussed with system monitoring receiver observations. The preliminary performance of the BDSBAS SF service integrity and availability are evaluated according to ICAO standards. The results show that the position accuracies of the BDSBAS SF service are about 1.0 m and 2.0 m in the horizontal and vertical directions, respectively, which are improved by about 39% and 33% compared with the GPS SF service. The position accuracies of the BDSBAS DFMC service are about 0.6 m and 1.2 m in the horizontal and vertical directions, respectively, which are improved by about 25% and 20% compared with the BDS dual-frequency service. For the BDSBAS SF service, no integrity risk event was detected during the test period, and the average service availability is about 98%. By increasing the effective monitoring areas of grid ionospheric corrections, the availability of the BDSBAS SF service could be further improved, which is an important research field for BDSBAS system upgrading.  
Conceptualization, Y.C., J.C., X.H. and S.Z.; methodology, Y.C., J.X., L.Z. and S.Z.; software, Y.L. and Q.T.; validation, X.H. and B.W.; formal analysis, L.L.; investigation, Y.C. and Q.T.; resources, Y.L.; data curation, Y.L. and J.X.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., J.C., L.L., X.H. and B.W.; visualization, Y.C.; supervision, J.C. and L.L.; project administration, J.C., L.L., X.H. and B.W.; funding acquisition, Y.C. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research Program of China as the \"Collaborative Precision Positioning Project\", grant number [2016 YFB0501900], National Natural Science Foundation of China, grant number [41674041, 12173072 and 11203059] and the Shanghai Key Laboratory of Space Navigation and Position Techniques, grant number [12 DZ273300].  
The authors are grateful for the comments and remarks of the reviewers, which helped to improve the manuscript.  
The authors declare no conflict of interest.
## 2 Theoretical Background  
The ion transport path through the electrolyte in the electrode can be quantified by the tortuosity (\(\tau\)) [38].  
\[\tau=\frac{I_{\text{real}}}{I_{\text{direct}}} \tag{1}\]  
Here, \(I_{\text{real}}\) is the actual transport distance of ions in the electrolyte, and \(I_{\text{direct}}\) is the vertical straight transport distance of ions in the electrolyte. The degree of tortuosity is a relation showing how much the actual transport path is distorted compared to a straight transport path of ions in the electrolyte, with a higher value indicating a more complicated path. Because the ease of ion transport can be represented by the ionic conductivity, Equation (1) is identical to the equation below [39].  
\[\tau=\frac{\sigma_{\text{electrolyte}}}{\sigma_{\text{eff}}}\cdot\varepsilon \tag{2}\]  
where \(\sigma_{\text{electrolyte}}\) is the intrinsic conductivity of the electrolyte, \(\sigma_{\text{eff}}\) is the effective conductivity, and \(\varepsilon\) is the volume fraction of the electrolyte. The Bruggeman relation is a typical empirical formula that links the tortuosity with the ease of ion transport [40; 41].  
\[\tau=\varepsilon^{-\alpha} \tag{3}\]  
Here, \(\varepsilon\) is the porosity, and \(\alpha\) is the Bruggeman coefficient. In the above formula, \(\varepsilon\) is the ratio of the electrolyte that can move ions, and \(\alpha\) represents the connectivity of the ion transport pathway [42].
# A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks
## 3 Hybrid Handover Scheme  
In this section, the hybrid handover decision-making problem is formulated as a continuous-time MDP. The key components of the reinforcement learning are first defined, which include the state space, the action space, the transition probability, the cost function, and the policy [25]. Then, we reformulate the continuous-time MDP as a discrete problem  
Figure 1: The vehicular VLC/RF communication networks.  
through uniformization [26]. Lastly, a value iteration algorithm is applied to solve for the optimal policy, and the hybrid handover decision is made according to this policy.
## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM  
Table 7 shows the evaluation of the base classifiers and MFELCM on the testing set, in which class 0 to class 3 represent passenger ships, tankers, fishing boats, and cargo ships, respectively.  
\[\left\{\begin{array}{c}F1-score=(1/4)\sum\limits_{i=1}^{4}w_{i}\times F1- score_{i}\\ F1-score_{i}=2P_{i}R_{i}/(P_{i}+R_{i})\\ P_{i}=TP_{i}/(TP_{i}+FP_{i})\\ R_{i}=TP_{i}/(TP_{i}+FN_{i})\end{array}\right. \tag{17}\]  
\(F1\) score is calculated by Equation (17). \(TP_{i}\), \(FP_{i}\) and \(FN_{i}\) are the number of true positive, false positive, and false-negative samples of type \(i\) ships, respectively. \(P_{i}\) and \(R_{i}\) are the accuracy and the recall of the model in classifying class \(i\) samples, respectively. The F1 score of a model is the weighted average of F1 scores for each type of ship, and the weight \(w_{i}\) is the proportion of class \(i\) samples in the total number of samples. In addition, to reduce the effect of sample imbalance, the loss of samples is weighted during the training process, and the weight of the class \(i\) samples is set as \(w_{i}{}^{-1}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Number of Messages** & **Number of Ships** \\ \hline Passenger Ships & 1285 & 1008 \\ Cargo Ships & 61,251 & 15,866 \\ Tankers & 21,094 & 6984 \\ Fishing & 5978 & 4565 \\ Total & 89,608 & 28,423 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The number of static data.  
Figure 16: Dynamic data used in experiments.
## 9 Estimation of Mechanical Parameters Based on RQD Value
### Estimation of the Modulus Ratio as a Function of RQD  
[PERSON] [62] introduced the modulus ratio (MR) of the intact rock, which is the ratio of the deformation modulus (E\({}_{r}\)) and uniaxial compressive strength (\(\sigma_{\rm c}\)) of the intact rock:  
\[\rm MR=\frac{E_{r}}{\sigma_{c}} \tag{47}\]  
Using Equations (39) and (45) the modulus ratio of the rock mass (MR\({}_{\rm rm}\)) can be calculated as a function of the RQD value:  
\[\rm MR_{rm}=0.269\;MR\;e^{0.0129 RQD} \tag{48}\]
## 2 Materials and Methods
### Inoculation and Treatment of Clean and Protein-Soiled Stainless Steel  
Stainless-steel (SS) 304 samples were used as food contact surfaces (1 \(\times\) 1 cm), as this SS type is very commonly used in the food industry due to its physicochemical stability and resistance to corrosion. Before inoculation, the coupons were thoroughly cleaned following the procedure described by [PERSON] et al. (2012). The study included two surfaces of different cleanliness (clean and protein-soiled) to determine the inactivation efficiency of cold plasma before and after food factory cleaning. Bovine serum albumin (BSA) was used as a natural organic food contaminant to prepare the protein-soiled food contact surface. A stock BSA solution (6 mg/mL) was prepared with sterile deionized water and sterilized by filtration (0.2 \(\upmu\)m pore size). The choice to use BSA was based on the EN 13697:2015 protocol for the evaluation of chemical disinfectants, which adopts BSA as a way to simulate realistic conditions typically found in a food-processing facility [23].  
For each pathogen, two different solutions were prepared to inoculate the SS surfaces. For the protein-soiled solution, 1 mL of BSA solution was transferred into a sterile container, and 1 mL of the microbial suspension was subsequently added (3 mg/mL final BSA concentration). For the clean surface solution, 1.0 mL of PBS was transferred into a container, and 1.0 mL of the microbial suspension was added. The solutions were 10-fold serially diluted and spread plated on TSA plates and incubated at 37 \({}^{\circ}\)C for 24 h to determine the levels of each pathogen before SS surface inoculation. A volume of 100 \(\upmu\)L of either solution was placed on the SS surface and spread to cover the whole surface area. Subsequently, all SS surfaces were air-dried for 60 min under aseptic conditions before treatment. _Salmonella_ and _L. monocytogenes_ were inoculated onto the SS surfaces separately.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## Advanced RF PA Architectures
### Doherty RF PA (DPA)
Large overall area. Architecture utilizes a power splitter at the input and a power combiner at the output, both of which have a form factor dependence on the operating frequency;
4. Narrow operating bandwidth due to the nature of the output \(\lambda/4\) microstrip impedance inverter. Methods of increasing the bandwidth are reported, sacrificing the overall area and stressing the overall manufacturability and current handling capability of the solutions;
5. Low potential of full ASIC integration. Impossible to implement wideband integrated solutions for up to 2 GHz due to large impedance inverter quarter wavelength values.
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Flooding Time Synchronization Protocol (FTSP)  
The flooding time synchronization protocol (FTSP) was proposed by [PERSON] et al. and aims to achieve synchronization in the whole network by sending broadcast synchronization messages [30]. A broadcast message starts with the preamble bytes, followed by the SYNC bytes, then with the actual message data which describes the message, and ends with the CRC bytes. The dotted lines in Figure 9 are actual bytes and the solid lines are bytes in the buffer. When the preamble bytes are in transmission by the sender, the receiver adjusts its carrier frequency of the incoming signals. As soon as the SYNC bytes are received, the receiver can calculate the byte offset needed to reassemble the message with correct byte alignment. The data field contains the target, length of the data, and other fields of the message which need to be notified on the receiver side. The message is verified as not being corrupted by using CRC bytes.  
Figure 8: Multi-hop synchronization.  
In the FTSP, a mesh network can be formed by the nodes where each node sends a synchronization message to all the other nodes, or nodes can form a star network where one of the nodes acts as the master node and all other nodes are slave nodes [46]. The node with the lowest Node ID is elected as the leader which periodically floods the network with synchronization messages and is used as a source of reference time [30]. If current leader nodes fail, the next node with lowest Node ID is elected as the new leader. Timestamps and time of arrival are collected by each node, and linear regression is used on these data points to obtain the offset and rate difference estimation from the leader node. The FTSP provides global synchronization while using low bandwidth and it uses timestamps in the MAC layer which helps it to eliminate many sources of errors [32]. However, it always needs access to the hardware and it is not a pure software-based protocol. The major drawback is that any node can elect itself as the leader after a certain period of time if it has not received any new timestamps, which makes it vulnerable to attacks. A corrupt node can claim itself to be a leader and can misguide the others to break the synchronization of nodes with the actual leader [47].
## 3 Results and Discussion
### Catalytic Tests Results
#### 3.2.2 Catalytic Tests in Heterogeneous Reactions
Our results show higher activity in heterogeneous media than is reported in the literature [7]. The next step was to test the catalytic properties of the NiIL-MCM-41 (entries 17, 18 and 19, Table 6) and NiIL-Al-MCM-41 (entry 20) catalysts under the best conditions obtained for the \(\beta\)-zeolite-supported \(\beta\)-diimine nickel complex: temperature (10 \({}^{\circ}\)C), moles of catalyst (5 \(\upmu\)mol), pressure (10 atm) and reaction time (30 min). As shown in Table 5, these catalysts are less active than the \(\beta\)-zeolite-supported system; however, these catalysts are 100% selective for butenes. When the Al/Ni ratio was increased from 25 to 100, it was found that, at an Al/Ni ratio of 50, the selectivity of the system did not vary significantly with respect to the activity; the catalytic system was more active, which indicates an increase in the active species present in the reaction medium. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Entry**} & \multirow{2}{*}{**Al/Ni**} & \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Pressure (atm)**} & \multirow{2}{*}{**TOF (s\({}^{-1}\))**} & \multicolumn{2}{c}{**Selectivity (\%)**} \\ \cline{5-6}  & & & & & **C\({}_{\textbf{4}}\) (1-C\({}_{\textbf{4}}\))** & **C\({}_{\textbf{6}}\)** & **C\({}_{\geq\textbf{8}}\)** \\ \hline
8 & 25 & 10 & 5 & 1.1 \(\pm\) 0.1 & 96 (94) & 2 & 2 \\
9 & 50 & 10 & 5 & 3.3 \(\pm\) 0.2 & 96 (86) & 4 & - \\
10 & 100 & 10 & 5 & 3.8 \(\pm\) 0.1 & 86 (74) & 6 & 8 \\
11 & 100 & 23 & 5 & 23.5 \(\pm\)1.0 & 100 (43) & - & - \\
12 & 50 & 10 & 2.5 & 3.0 \(\pm\) 0.2 & 78 (89) & 4 & 18.5 \\
13 & 50 & 10 & 10 & 9.1 \(\pm\) 0.2 & 94 (82) & 4 & 2 \\
14 & 50 & 10 & 5 & 5.0 \(\pm\) 0.1 & 100 (91) & - & - \\
15 & 50 & 10 & 5 & 0.12 \(\pm\) 0.01 & 100 (98) & - & - \\
16 & 50 & 10 & 5 & 0.06 \(\pm\) 0.01 & 100 (98) & - & - \\ \hline \end{tabular} Reaction conditions: solvent = toluene, cocatalyst = EASC, n catalyst = 5 \(\upmu\)mol.  
\end{table} 
Table 5: Results of catalytic tests of the complex supported on \(\beta\)-zeolite modified with ionic liquid. In our analysis of the results of the catalytic test with NiIL-Al-MCM-41, we observed that the system was highly active (approximately 2.3 s\({}^{-1}\)) and reached 90% selectivity for 1-butene, which makes its activity superior to that of the NiIL-MCM-41 system. Comparing the selectivities of the three heterogeneous and homogeneous systems, we observed that the effect of the confinement of the catalyst inside the MCM-41 pores was a determining factor in the high selectivity of the heterogeneous system, in this case. However, when comparing the supports used, we observed that the difference in the pore size of these materials was not a determining factor for the activity of the reactions, but rather the presence of acid sites in the structure of the \(\beta\)-zeolite and Al-MCM-41 was a determining factor. [PERSON] et al. (2017) reported that the content of both nickel and acid sites contribute to the activation of the oligomerization reaction.