esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Multi-hop Key Establishment Protocols Using the Sink  
We start by explaining protocol \(MKE_{S}\) and its optimization \(MKE_{S}-light\). They both use the sink to establish a new key between two nodes. Protocol \(MKE_{S}\), depicted in Figure 3, uses the shared symmetric keys \(K_{DH}(I,S)\) and \(K_{DH}(R,S)\) computed before the deployment between any node in the network and the sink \(S\). These shared keys are used to communicate the public keys of \(I\) and \(R\). We consider that the sink knows all the public keys of all nodes and a node only knows its public key and the public of the sink. The initiator node \(I\) builds a request containing the identity of node \(R\) and a nonce \(n_{I}\). This request is encrypted with \(K_{DH}(I,S)\) and sent to \(S\). The sink \(S\) sends  
* to \(R\), the identity of \(I\), a nonce \(n_{S}\), the nonce \(n_{I}\) received from \(I\) and the public key of \(I\) encrypted with the shared symmetric key \(K_{DH}(S,R)\).
* to \(I\), the identity of \(R\), the same nonce \(n_{S}\), the public key of \(R\) encrypted with the shared symmetric key \(K_{DH}(I,S)\),  
Once these two messages are received by \(I\) and \(R\), the two nodes are able to compute \(K_{DH}(I,R)\) as follows:  
* Node \(I\) computes \(sk(I)\times pk(R)=sk(I)\times sk(R)\times G=K_{DH}(I,R)\).
* Node \(R\) computes \(sk(R)\times pk(I)=sk(R)\times sk(I)\times G=K_{DH}(I,R)\).  
Figure 2: \(IJS\): \(I\) joins the network through \(N\). Intermediate nodes between \(N\) and \(S\) forward messages without any encryption or decryption.  
The mutual authentication of \(R\) and \(I\) is ensured using the sink \(S\) as the trusted third party. Indeed, only \(S\) in this protocol is able to deliver public keys to nodes. The authentication between nodes is done by the verification of nonces. Node \(R\) verifies that the received nonces \(n_{I}\) and \(n_{S}\) from \(S\) are the same as the ones sent by \(I\). Then, node \(R\) confirms the establishment of \(K_{DH}(I,R)\), by sending \(n_{I}\) and \(n_{S}\) to the initiator \(I\) encrypted with \(K_{DH}(I,R)\). Upon reception, node \(I\) verifies first its own nonce and if \(n_{S}\) received from \(R\) is the same as the one sent by the sink.  
Notice that the computation of the new key \(K_{DH}(I,R)\) can be done by the sink in order to save some computations on nodes \(R\) and \(I\). Instead of sending the respective public keys, the sink generates the new shared key and sends it to the two nodes. Indeed, the sink is the trusted entity that generated and distributed the public/private keys to all nodes in the first place during the pre-deployment phase. Another possibility would be to generate symmetric keys at the sink using any standard random key generation function. In this version, we chose to use the \(DH\) method at the sink. This version, called \(MKE_{S}-light\), is depicted in Figure 4.  
Figure 4: \(MKE_{S}-light\): Multi-hop Key Establishment using the sink \(S\) to compute and deliver \(K_{DH}(I,R)\) to the initiator and the responder.  
Figure 3: \(MKE_{S}\): Multi-hop Key Establishment using the sink \(S\) to deliver public keys.
## 1 Introduction
### Design Space for QoS Multicast  
QoS multicast in ad hoc networks represents specializations of regular data communication. All of these specializations require specific elements in order to address the added requirements. Our main focus is on the QoS aspects, and therefore the elements that are needed to provide QoS in multicast ad hoc environments.  
Providing QoS in ad hoc networks has additional challenges due to the wireless medium and the mobility of nodes. The wireless medium itself induces errors and packets are lost as part of the normal operating context. Often the MAC layer offers a shared common channel for nodes in the vicinity of each other. In addition, the interference range of a transmission may be larger than the transmission range. The available resources and the probability of a successful transmission therefore may depend on the details of the topology and the traffic load at neighboring nodes.
## 2 Results and Discussion
### Analytical Performances
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Fiber**} & \multirow{2}{*}{**RSD\%**} & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**LDR**} & \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Ref.**} \\ \cline{3-4} \cline{6-7}  & & & & & & & \\ \hline HS-SPME–GC–FID & \multicolumn{2}{c}{IL–mediated} & \multirow{2}{*}{6.5} & \multirow{2}{*}{0.007 ng\(\cdot\)mL\({}^{-1}\)} & \multirow{2}{*}{0.03–200 ng\(\cdot\)mL\({}^{-1}\)} & \multirow{2}{*}{Water} & \multirow{2}{*}{[25]} \\  & PDMS-MWCNTs & & & & & \\ \hline HS-SPME–GC–MS & \multicolumn{2}{c}{DVB/CAR/PDMS} & \multirow{2}{*}{\textless{10}} & \multirow{2}{*}{0.03 \(\mu\)g\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{0.10–40 \(\mu\)g\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Blood serum} & \multirow{2}{*}{[26]} \\ \hline HS-SPME–GC-FID & \multicolumn{2}{c}{DVB/CAR/PDMS} & \multirow{2}{*}{6–8} & \multirow{2}{*}{0.02 \(\mu\)g\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{0.1–400 \(\mu\)g\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Water} & \multirow{2}{*}{[27]} \\ \hline HS-SPME-IMS & \multicolumn{2}{c}{PDMS/CAR} & \multirow{2}{*}{8.3} & \multirow{2}{*}{5 mg\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{10–1390 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Water} & \multirow{2}{*}{[28]} \\ \hline HS-SPME–GC–MS & \multicolumn{2}{c}{Carboxen-PDMS} & \multirow{2}{*}{10–11} & \multirow{2}{*}{10 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{5–250 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Water} & \multirow{2}{*}{[29]} \\ \hline HS-SPME–GC–MS & \multicolumn{2}{c}{SWCNTs} & \multirow{2}{*}{\textless{15}} & \multirow{2}{*}{10 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{100–5000 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Urine} & \multirow{2}{*}{[30]} \\ \hline HS-SPME–GC–MS & \multicolumn{2}{c}{Carboxen-PDMS} & \multirow{2}{*}{5} & \multirow{2}{*}{1.5 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{0.3–2.4 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Whole blood} & \multirow{2}{*}{[31]} \\ \hline HS-SPME–GC–FID & \multicolumn{2}{c}{PDMS/DVB} & \multirow{2}{*}{6.3} & \multirow{2}{*}{0.45 ng\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{5–500 \(\mu\)g\(\cdot\)L\({}^{-1}\)} & \multirow{2}{*}{Water} & \multirow{2}{*}{[32]} \\ \hline HS-SPME–GC–FID & \multicolumn{2}{c}{PPy@MIL-101(Cr)} & \multirow{2}{*}{8.4} & \multirow{2}{*}{0.01 ng\(\cdot\)g\({}^{-1}\)} & \multirow{2}{*}{5–40,000 ng\(\cdot\)g\({}^{-1}\)} & \multirow{2}{*}{Soil} & \multirow{2}{*}{This work} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the developed HS-SPME–GC-FID method with some of the important reported procedures, for the extraction and determination of MTBE. Figure 6: Comparison of the PPy@MIL-101(Cr)-coated SPME fiber with commercial fibers and PPy handmade fiber for sampling of MTBE in solid samples.
## 5 Simulation Experiments and Results Analysis  
This section conducts simulation experiments for the proposed architecture. Section 5.1 describes the design of the simulation experiment. Section 5.2 analyzes the performance of this paper in terms of the scheduling success rate, delay reduction, and load balance of the computing cluster. It also proves the superiority of this solution by comparing it with the CFN architecture and the solution using the DNS.
## 3 Detection of Damage Occurrence
### Time Domain Methods
#### 3.1.4 Methods for Changing Environmental Conditions  
In order to distinguish the influences on the signal driven by damage itself from those driven by environmental changes, the AR model is also used for damage detection under changing environmental and operational conditions [46; 47; 48]. The effect of environmental conditions has been investigated [49] and various methods are adopted to remove this effect, such as principal component analysis (PCA) [50; 51], factor analysis [52], nonlinear PCA based on unsupervised support vector machine [53], the Cointegration concept for non-stationary time series [54], PCA method in time-varying systems [55], etc. Moreover, the novelty detection and outlier analysis are used to perform the lowest level of damage identification in operational conditions [56; 57], and the multivariate statistical analysis method becomes popular for automatically revealing the damage existence using vibration data under changing environmental and operational conditions [58; 59; 60].
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 2 MIMO-QSM Transmission System
### QSM Modulation  
In order to generate the QSM signals \(\mathbf{x}\), the input sequence of bits \(\mathbf{a}\) was split into three flows. One flow was utilized to modulate an \(M\)-QAM signal, and the other two flows (spatial bits) were used to modulate the position in the output vector \(\mathbf{x}\). For an input bit sequence of length \(m_{QSM}\) bits, the first \(\log_{2}(M)\) bits modulated an \(M\)-QAM symbol. The remaining \(2\log_{2}(n_{T})\) bits were split into two flows of \(\log_{2}(n_{T})\) spatial bits. These spatial bits modulated the position in the output vector \(\mathbf{x}\) as follows: the real part of the QAM symbol was assigned to one specific position in the output vector of length \(n_{T}\). The imaginary part of the QAM symbol was assigned to another one or even the same position antenna in the output vector \(\mathbf{x}\). Finally, these two SM signals were combined to obtain the QSM output vector [9].  
Table 1 shows a mapping rule example for QSM using four-QAM and \(n_{T}=2\). The first column shows the input bit sequence of length \(m_{QSM}\), where the first two bits modulate a four-QAM symbol and the remaining two bits modulate the position of the non-zero entries in the output vector \(\mathbf{x}\) as follows: the real part of the QAM symbol was assigned to one Tx antenna out of \(n_{T}\) available in order to modulate \(\log_{2}(n_{T})=1\) bit, whereas the imaginary part of the QAM symbol was assigned to another or even the same position to modulate \(\log_{2}(n_{T})=1\) bit to define the \(\ell_{\Re}\) and \(\ell_{\Im}\) index transmitter antenna, respectively, as shown in the third column of Table 1.  
Figure 1: The MIMO-QSM system model.
## 1 Introduction
investigated the aroma compounds in raw and dried porcini from China, using SAFE (solvent-assisted flavor evaporation)-GC-MS, a technique which employs a mild treatment for extracting volatile fractions [10]. Of the total quantified aroma compounds, the authors found that raw porcini contained high amounts of 1-octen-3-ol, (E)-2-octen-1-ol, (E)-2-octenal, and 1-octen-3-one, while dried porcini contained 3-(methylthio)propanal and pyrazines as major aroma components, but the applied drying conditions have not been described. There are not many studies reporting the health benefits of biogenic volatile compounds, despite the fact that the so-called mushroom alcohol 1-octen-3-ol is broadly used in perfumes or as a food and flavoring agent; papers also describe some negative health effects [13]. In addition to conventional drying methods, a different method of drying _B. edulis_ based on a centrifugal vacuum process at 60 \({}^{\circ}\)C has been reported by our group [14]. Because our previous results showed that drying a _B. edulis_ paste is efficient in preserving the phenolic content and antioxidant activity, the aim of the present work was to explore the effects of two different types of drying on the following: (1) the physical and powder properties of mushrooms (loose and tapped density, Hausner ratio, solubility, rehydration, and emulsifying parameters); (2) the profile of fatty acids using the GC-MS approach, and (3) the volatile aroma compounds using headspace in-tube extraction coupled with gas chromatography-mass spectrometry (HS-ITEX/GC-MS), properties which additionally affect the industrial technologies of mushroom powders processing.
## 2 Materials and Methods
### Measurement of LDL Oxidation  
To measure the effect of TMA and TMAO on LDL oxidation, conjugated dienes (a measure of lipid oxidation) were monitored in the LDL samples incubated in the presence or absence of TMA, TMAO or TMA+TMAO in a 96-well microplate reader. LDL, at a concentration of 0.01 mg/mL, were incubated with or without different concentrations of TMA (0.6 uM, 0.82, 2.46, 5.74, 24.6 mM) and/or TMAO (5.37 uM, 2.86, 9.54, 28.6 mM) starting from the plasma mean value reported by [PERSON] et al., [15]. As a positive control to induce LDL oxidation, Cu\({}^{++}\) (5 uM) was used as reported by [PERSON] et al. [16]. Five samples were prepared for each experiment: the first with LDL (control), the second with LDL and 5 \(\mu\)M of Cu\({}^{++}\) (positive control), the third with LDL and TMA, the fourth with LDL and TMAO, the fifth with LDL, TMA and TMAO. The samples were incubated at 25 \({}^{\circ}\)C; the absorbance measurements at 234 nm were carried out at different incubation times (0, 30, 60, 80, 90, 100, 120, 140, 150, 160 and 180 min). All experiments were repeated at least twice under the same conditions.
## 5 Numerical Evidence and Noise Model
### Reconciliation Characteristics
At \(d=16\), improved the reconciliation efficiency to \(\beta=0.97\), which resulted in significantly higher transmission distances and secret key rates. The scalar reconciliation applied on the two-way CVQKD protocol resulted in approximately 160 km of achievable transmission distance (for the computations of the secret key rate, and the  
Figure 5:0 The performance of scalar reconciliation in two-way PM-RR continuous-variable quantum key distribution (CVQKD) at \(d=16\) (homodyne measurement at both sides). Excess noise: \(\mathfrak{N}=0.015\), transmittance: \(T=0.8\), Eve’s variance \(\sigma_{\omega_{t}}^{2}=1.06\), channel correlation: \(n_{C}=0.5\), signal variance \(\sigma_{\phi}^{2}=20\). Figure 5:1 The SNRs of the logical channel at variance \(\sigma_{X}^{2}=1.06\). As the dimension increases the variance of the logical channel reaches the variance of the physical quantum channel. At \(d=16\) the variances perfectly coincidence. detection parameters, see the derivations of [1; 7; 8]). The results indicate that the range of the current two-way CVQKD without our post-processing technique can be significantly extended, and the maximal 80.5 km range of the current one-way CVQKD systems [12] can be doubled, and almost tripled compared with existing two-way CVQKD systems [7; 8]. The reason behind the phenomenon is the possibility of the conversion of the Gaussian quantum channel to a logical binary Gaussian channel, similar to the multidimensional reconciliation approaches developed for one-way CVQKD. The favorable properties of the multidimensional solutions are preserved here, however the proposed scalar reconciliation does not require any multidimensional spherical calculations [9; 10; 11] and can be extended to arbitrary high dimensions thanks to the fact that it completely eliminates the spherical operations. From the use of higher dimensions, a more precise approximation of the logical binary Gaussian channel has also become available which resulted in significantly higher reconciliation efficiency in comparison to current two-way CVQKD reconciliation methods. The proposed scalar reconciliation is available at low SNRs, and the transmission ranges of experimental long-distance CVQKD can significantly be improved because at low SNRs the capacity of the logical binary Gaussian channel coincidences with the capacity of the Gaussian quantum channel, and the logical channel resulted from the conversion can approximate it with arbitrary-high precision.
###### Abstract  
As a key technology for intelligent vehicles, automatic parking is becoming increasingly popular in the area of research. Automatic parking technology is available for safe and quick parking operations without a driver, and improving the driving comfort while greatly reducing the probability of parking accidents. An automatic parking path planning and tracking control method is proposed in this paper to resolve the following issues presented in the existing automatic parking systems, that is, low degree of automation in vehicle control; lack of conformity between segmented path planning and real vehicle motion models; and low success rates of parking due to poor path tracking. To this end, this paper innovatively proposes preview correction which can be applied to parking path planning, and detects the curvature outliers in the parking path through the preview algorithm. In addition, it is also available for correction in advance to optimize the reasonable parking path. Meanwhile, the dual sliding mode variable structure control algorithm is used to formulate path tracking control strategies to improve the path tracking control effect and the vehicle control automation. Based on the above algorithm, an automatic parking system was developed and the real vehicle test was completed, thus exploring a highly intelligent automatic parking technology roadmap. This paper provides two key aspects of system solutions for an automatic parking system, i.e., parking path planning and path tracking control.  
intelligent vehicle; automatic parking system; preview correction; path planning; sliding mode control; path tracking
## 2 Methods
### Background Theory
Since flow resistance \(r\) is defined as \(r=\frac{\Delta p}{u}\), and in the PVPM model, \(\frac{\Delta p}{u}=\frac{\mu}{\alpha}I_{\text{eff}}\), the parameter that needs to be defined in ANSYS Fluent 17.2 to accommodate the extra flow resistance as described in Section 2.2.1 is essentially a parameter called inverse permeability \(\frac{1}{\alpha}\)[33], which can be obtained by combining the above two equations \(\frac{1}{\alpha}=\frac{r^{2}}{|\alpha|t}\). On the other hand, the PVPM model represents a perforated liner as a homogenous material without the necessity of resolving small-scale details of the perforation. The model was successfully validated by [PERSON] et al. [32] against a number of highly cited papers reporting experiments in impedance tubes, where the acoustic signal was incident normal to the perforated liner. [PERSON] et al. [32] demonstrated that the PVMP model was able to represent the acoustic attenuation of perforated liner absorbers in both the linear and non-linear damping regimes and also the inertial damping effect in the presence of a mean bias flow. In the PVPM model proposed by [PERSON] et al. [32], the pressure loss due to viscous effects is directly related to the geometry of perforations, and this is inevitably lost by the homogenous geometry assumption. In cases without a mean bias flow, the viscous resistance is important and is included as a viscous resistance term in the pressure loss source term in \(S_{M}\) in Equation (7). The so-called acoustic end correction effect also cannot be directly represented by the homogenous porous model. In this case, the thickness of the porous media region is defined to be the effective acoustic thickness of a perforated liner according to Equation (8) [32]:  
\[l_{\text{eff}}=l_{\text{t}}+0.85d\ \Psi\bigg{(}1+\frac{\text{M}}{\sigma}\bigg{)}^{-1} \tag{8}\]  
where \(l_{\text{t}}\) is the real liner thickness, \(d\) is the diameter of perforations,  
\[\Psi=1-1.4092\sqrt{\sigma}+0.33818\sigma^{1.5}+0.06793\sigma^{2.5} \tag{9}\]  
represents the hole-hole interaction effect according to [PERSON]'s function \(\Psi\)[34] and M is the neck particle Mach number which accounts for both the bias mean flow and the acoustic pressure-induced acoustic flow:  
\[\text{M}=\frac{\left[\text{U}_{\text{b}}+\sqrt{2}\text{RMS}(\text{U}_{\text{ ac}})\right]}{c} \tag{10}\]  
where U\({}_{\text{b}}\) is the bias flow velocity and U\({}_{\text{ac}}\) represents the acoustic velocity inside the perforations [32]. The original PVPM model was validated for cases where the noise signals and bias flow are normal to a perforated wall [32]. However, in many industrial applications, such as gas turbine combustors or automotive noise mufflers, noise signals and flows are often coincident and parallel to the perforated walls. In this section, the PVPM model is further developed for situations where perforated liners are exposed to grazing flows and also a simultaneous grazing/bias flow.
# Comparative Technical-Economical Analysis of Transient Stability Improvements in a Power System
## 3 IEEE 9 Bus System Description
The decision to show only the results obtained with the STATCOM is due to the fact that the difference between these results and those obtained with SVC is very small (as shown in the following sections). These results show that the most critical fault is in lines 7-8. For this fault location, the best bus location for the improving solution is bus 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **(a)** & & \\ \hline \multirow{2}{*}{**Line Fault Location**} & \multirow{2}{*}{**w/o Device**} & \multicolumn{3}{c}{**Device Location (STATCOM)**} & \multirow{2}{*}{**Gen.**} \\ \cline{3-3} \cline{5-6}  & & **Bus 5** & **Bus 6** & **Bus 8** & \\ \hline
4–5 & 71.38 & 71.57 & 70.75 & 67.06 & 2 \\
4–6 & 57.39 & 57.56 & 56.67 & 53.58 & 2 \\
5–7 & 102.65 & 102.67 & 102.39 & 98.89 & 2 \\
6–9 & 67.22 & 67.51 & 66.86 & 64.37 & 3 \\
7–8 & 139.02 & 138.95 & 140.28 & 137.5 & 2 \\
8–9 & 83.1 & 81.31 & 83.17 & 81.98 & 2 \\ \hline \multicolumn{5}{c}{**(b)**} \\ \hline \multirow{2}{*}{**Line Fault Location**} & \multirow{2}{*}{**w/o Parallel**} & \multicolumn{3}{c}{**Parallel Line**} & \multirow{2}{*}{**Gen.**} \\ \cline{3-3} \cline{5-6}  & & **Line** & **5–7** & **6–9** & \\ \hline
7–8 & 139.02 & 116.31 & 127.28 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: (a) Sensitivity analyses on max rotor angle (deg) for short circuit and device location; (b) Sensitivity analyses for line doubling impact on rotor angle (deg) for short circuit. To understand which line needs to be doubled to achieve the most effective improvement, the two longest lines (with higher reactance), lines 5-7 and 6-9, are considered. These sensitivity analyses are shown in Table 2b. It can be seen that the smallest value of maximum rotor angle is obtained by doubling lines 5-7. The short-circuit fault in the transmission line leads to oscillations of the rotor angle of the generators. Figure 2 shows the rotor angle curves of the three generators before and after the fault. The angles are calculated about the load angle of generator 1. generator 2 is the most stressed generator. Its rotor angle reaches the maximum value of 139.02\({}^{\circ}\), so the focus in the following sections is on generator 2.