esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 2 Background
### Gm-PHD Filter
The PHD of the newborn targets can also be expressed in a Gaussian mixed form as:  
\[\gamma_{k}(\mathbf{x})=\sum\limits_{i=1}^{I_{\gamma,k}}\omega_{\gamma,k}^{i}N \Big{(}\mathbf{x};\mathbf{m}_{\gamma,k}^{i},\mathbf{P}_{\gamma,k}^{i}\Big{)}, \tag{12}\]  
where \(\omega_{\gamma,k}^{i}\), \(\mathbf{m}_{\gamma,k}^{i}\), \(\mathbf{P}_{\gamma,k}^{i}\), \(i=1,\cdots,J_{\gamma,k}\) are newborn targets parameters. If the measurement equation can also satisfy the linear Gaussian condition, the updated PHD at time \(k\) can also be expressed in the form of a Gaussian mixture:  
\[ \begin{array}{l}D_{k|k}(\mathbf{x})=(1-P_{D,k})D_{k|k-1}(\mathbf{x})\\ \quad\quad\quad\quad+\sum\limits_{i=1}^{I_{k-1}}\omega_{k|k}^{i}(\mathbf{z})N\Big{(} \mathbf{x};\mathbf{m}_{k|k}^{i}(\mathbf{z}),\mathbf{P}_{k|k}^{i}\Big{)},\end{array}  \tag{13}\]  
\[\omega_{k|k}^{i}(\mathbf{z})=\frac{P_{D,k}\omega_{k|k-1}^{i}N\Big{(}\mathbf{z};\mathbf{H}_ {k}\mathbf{m}_{k|k-1}^{i},\mathbf{S}_{k}^{i}\Big{)}}{\lambda_{k}(\mathbf{z})+P_{D,k}\sum \limits_{j=1}^{I_{k-1}}\omega_{k|k-1}^{j}N\Big{(}\mathbf{z};\mathbf{H}_{k}\mathbf{m}_{k|k-1 }^{i},\mathbf{S}_{k}^{i}\Big{)}}, \tag{14}\]  
\[\mathbf{m}_{k|k}^{i}(\mathbf{z})=\mathbf{m}_{k|k-1}^{i}(\mathbf{z})+\mathbf{K}_{k}^{i}\Big{(}\mathbf{z }-\mathbf{H}_{k}\mathbf{m}_{k|k-1}^{i}\Big{)}, \tag{15}\]  
\[\mathbf{P}_{k|k}^{i}=\Big{[}\mathbf{I}-\mathbf{K}_{k}^{i}\mathbf{H}_{k}\Big{]}\mathbf{P}_{k|k-1}^{i}, \tag{16}\]  
\[\mathbf{S}_{k}^{i}=\mathbf{R}_{k}+\mathbf{H}_{k}\mathbf{P}_{k|k-1}^{i}\mathbf{H}_{k}^{i}, \tag{17}\]  
\[\mathbf{K}_{k}^{i}=\mathbf{P}_{k|k-1}^{i}\mathbf{H}_{k}^{i}\Big{(}\mathbf{H}_{k}\mathbf{P}_{k|k-1}^{ i}\mathbf{H}_{k}^{i}+\mathbf{R}_{k}\Big{)}^{-1}, \tag{18}\]  
where \(\lambda_{k}\) is the clutter or false measurements received by the radar and \(P_{D,k}\) is the detection probability. In order to reduce the computational effort, the increasing Gaussian components must be pruned and merged [19]. Then the posterior intensity, \(D_{k|k}\), is expressed as:  
\[D_{k|k}(\mathbf{x})=\sum\limits_{i=1}^{I_{k}}\omega_{k}^{i}(\mathbf{z})N\Big{(}\mathbf{x}; \mathbf{m}_{k}^{i},\mathbf{P}_{k}^{i}\Big{)}, \tag{19}\]  
where \(J_{k}\) is the number of components of \(D_{k|k}\).
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 2 MCSQD Protocol
### Preliminary Preparations
#### 2.1.2 Huffman Compression Coding  
Huffman compression coding [29] is a classical lossless data compression algorithm. Its core mechanism constructs an optimal prefix binary tree that assigns shorter codewords to high-frequency characters and longer codewords to low-frequency characters, thereby reducing overall data storage requirements. As a fundamental and pivotal tree data structure, a binary tree is characterized by its defining property where each node possesses at most two child nodes (conventionally termed the left child node and right child node). Arithmetic coding [30] might offer higher compression ratios but comes with increased complexity, whereas Huffman coding is relatively simple to implement and suitable for many practical systems. To ensure efficient data transmission between the classical party and the quantum party while balancing cost constraints and classical-side equipment limitations, the protocols can employ Huffman compression coding to reduce data volume. Notably, Huffman compression coding introduces no additional security vulnerabilities, while its prefix-free code property synergizes effectively with quantum error detection mechanisms. The specific steps are outlined below:1. Frequency Calculation: First, calculate the frequency of each character in the data to be compressed. For example, in the string \"\(xyzxyzxpq\)\" that [PERSON] intends to transmit, the frequencies of the characters are as follows: \(P(m_{i}=x)=0.4\), \(P(m_{i}=y)=P(m_{i}=z)=0.2\), and \(P(m_{i}=p)=P(m_{i}=q)=0.1\).
2. Building the Huffman Tree: Treat each character and its frequency as a leaf node and arrange them in ascending order of frequency. Repeat the following steps until only one root node remains: 1. Extract the two nodes with the smallest frequencies, merge them into a new node, and set the frequency of the new node as the sum of the two frequencies. 2. Reinsert the new node back into the queue.  
The final tree formed through this process is the Huffman tree. The path from the root node to each leaf node determines the binary encoding of the corresponding character.
3. Assigning Codes: Starting from the root node, assign a '0' for moving to the left subtree and a '1' for moving to the right subtree. The binary sequence along the path from the root node to a specific character represents the encoding of that character. Figure 1 illustrates the Huffman tree constructed using the string \"\(xyzxyzxpq\)\" as an example.  
Based on the Huffman tree, we can obtain the corresponding binary codes for each character in the string \"\(xyzxyzxpq\)\", as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Character Values** & **Binary Encoding** \\ \hline \(x\) & 0 \\ \(y\) & 110 \\ \(z\) & 111 \\ \(p\) & 100 \\ \(q\) & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Binary codes corresponding to each character.  
Figure 1: Example of a Huffman Tree Diagram.
# Experimental Study on the Damage of Optical Materials by out of Band Composite Laser
## 2 Laser Absorption in Optical Elements
### Temperature Field of Optical Film  
The direct factor leading to the destruction of the optical film under laser irradiation is the temperature rise caused by the thermal effect. To study the laser damage of optical film, the time and space distribution of the temperature field of optical film should be considered, and these can be determined by solving the heat conduction equation with the laser source term. The heat conduction equation of a multilayer dielectric film and its initial and boundary conditions can be expressed as:  
\[c_{i}\frac{\partial}{\partial t}T_{i}(r,z,t)-k_{i}\
abla^{2}T_{i}(r,z,t)=g_{i} (r,z,t) \tag{5}\]  
Boundary conditions: \(r\)\(\rightarrow\)\(\infty\) or \(z\)\(\rightarrow\)\(\infty\):\(T\)\(\rightarrow\)\(0\)  
Initial conditions: \(T_{1}(r\),\(2\)) = \(T_{0}(r\),\(z)\)  
Among them, \(c_{i}\) and \(k_{i}\) are the specific heat capacity and thermal conductivity of the \(i\)-th layer film, respectively; H is the surface heat exchange constant, which is related to the surface thermal radiation and thermal convection; and \(g_{i}\) is the laser energy deposited in the \(i\)-th film:  
\[g_{i}(r,z,t)=\frac{\partial^{3}p_{i}(r,z,t)}{\partial r\partial z\partial t} \tag{6}\]  
where \(P_{i}(r\),\(t\),\(z)\) is the Poynting vector of the \(i\)-th film. The energy transmission between the membrane layers meets the principles of temperature continuity and heat flow balance (\(i\) = 1, 2,... ) leads to:  
\[T_{i}(r,z_{i},t)=T_{i+1}(r,z_{i},t), \tag{7}\] \[\left(\frac{T_{i}-T_{i+1}}{R_{i}}\right)_{z=z_{i}}=\left(\frac{T_{ i+1}-T_{i+2}}{R_{i+1}}\right)_{z=z_{i+1}}\]  
where \(R_{i}\) is the thermal resistance at the interface between the \(i\) and \(i\) + 1 films, \(z_{i}\) and \(z_{i+1}\) are the positions of the interface, and \(T_{i}\) and \(T_{i+1}\) are the temperatures of the two films at the corresponding interfaces. Solving the equations provides the temperature distribution of the optical film [17; 18; 19; 20].
## 3 Experimental Results
As a result of this consideration, the Von Mises stresses at the surface centerline were recalculated with a compression stress due to the contact effect and tension as a result of the bending moment. Table 3 shows the results of the predicted cycles to failure using the vibration regression equation best fit line, and those actually obtained from three-point bending (TPB). By correcting the effect of contract stress in the calculation of the stress ratios determined in three-point bending, a compatible fatigue prediction technique can be seen. In the original S-N curve generated, the effect of contact stress was not considered in the three-point bending stress ratio calculation. The separation of the two best fit lines for vibrations and three-point bending, shown in Figure 16, was reduced by including the effect of contact stress in the three-point bending stress ratios. The new S-N three-point bending curve, along with the vibration S-N curve showing this improvement, is given in Figure 17.  
Figure 17: Fatigue lifetime S-N curves for Vibrations and Three-Point Bending. The TPB stress ratio calculation takes into account the effect of contact stress. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Corrected Contact Stress Ratio**} & \multicolumn{2}{c}{**Average Cycles to Failure**} & \multirow{2}{*}{**Predicted Cycles to Failure**} & \multirow{2}{*}{**Error Difference**} \\  & **Using Three-Point Bending** & & **Using Vibrations** & **(Using TPB as Base)** \\ \hline
0.78 & 4507 & 4742 & 5.22\% \\ \hline
0.71 & 14,726 & 15,332 & 4.12\% \\ \hline
0.63 & 66,741 & 58,586 & 12.22\% \\ \hline
0.56 & 166,742 & 189,365 & 13.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results from Three-Point Bending and Vibration tests, considering the contact stress.
## 3 System Model
### Propagation Model for Underwater Sensor Networks  
The submarine channel undergoes with many characteristics including different kinds of noise, multipath fading, a variable propagation speed (in function of salinity, temperature... ), and transmission and -absorption loss due to distances. Considering these characteristics, the absorption loss can be expressed as [PERSON]'s equation [15],  
\[\alpha(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100f^{2}}+2.75\;*10^{-4}f^ {2}+0.0033\;\left(\frac{\text{dB}}{\text{km}}\right) \tag{1}\]  
and it is a function of frequency \(f\). Equation (1) is an empirical equation that provides a good approximation at frequencies from 100 Hz to 1 MHz. Figure 2 shows the value of (1) up to 100 kHz.  
The Transmission Loss (\(TL\)) is calculated as a function of distance \(r\) (m) and absorption coefficient \(\alpha\) (dB/km). It can be expressed in two ways, in cylindrical (\(TL_{CS}\)) for shallow waters (depth less than 100 m) and spherical spreading (\(TL_{SS}\)) for oceanic waters [16], respectively, as  
\[TL_{cs} =10\log(r)+\alpha(f)*r*10^{-3} \tag{2}\] \[TL_{ss} =20\log(r)+\alpha(f)*r*10^{-3} \tag{3}\]  
Figure 1: Random deployment of nodes of a 3D underwater wireless sensor network (100 nodes).  
Figure 2: Absorption coefficient vs. frequency.  
where \(r\) indicates the hop distance (m) and \(f\) is frequency. The speed of sound in underwater is given by \(c\) (m/s) [17]  
\[c\ =1449.2+4.6\ T-0.055\ T^{2}+0.00029\ T^{3}+(1.34-0.01\ T)(S-35)+0.016\ d\, \tag{4}\]  
where \(T\) is temperature in Celsius scale, \(S\) is salinity in parts per thousand, and \(d\) is depth in meters. Ambient noise is a contribution of at least four factors: turbulence noise (\(N_{t}\)), shipping noise (\(N_{s}\)), wave and other surface noise (\(N_{m}\)), and thermal noise (\(N_{th}\)). The frequency dependence of every ambient noise component, is given by [17]  
\[N(f)=N_{t}\ (f)+N_{s}\ (f)+N_{w}\ (f)+N_{th}\ (f), \tag{5}\]  
\[10\ \text{log}\ N_{t}\ (f)=17-30\ \text{log}\ (f), \tag{6}\]  
\[10\ \text{log}\ N_{s}(f)=40+20\ (s-0.5)+26\ \text{log}f-60\ \text{log}\ (f+0.03), \tag{7}\]  
\[10\ \text{log}\ N_{w}(f)=50+7.5\ \sqrt{w}+20\ \text{log}\ f-40\ \text{log}\ f\ (f+0.4), \tag{8}\]  
\[10\ \text{log}\ N_{th}\ (f)=-15+20\ \text{log}f. \tag{9}\]  
In underwater environment, signal-to-noise ratio (SNR) is based on source level, directivity index, ambient noise, and transmission loss (Equations (2) and (3)). The SNR at the receiver input, can be calculated, in logarithmic scale as [18]  
\[SNR=SL-TL-NL+DL \tag{10}\]  
where \(SL\) stands for the source level expressed in (dB uPa), which is directly related to the transmitting power; \(TL\) is the transmission loss (dB); \(NL\) is the ambient noise; and finally, \(DL\) is the directivity index (dB) of the transducers.
# Gauge-Invariant Formulation of Time-Dependent Configuration Interaction Singles Method
## 1 Introduction
[1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16], and the velocity gauge (VG) in Ref. [17; 18; 19; 20; 21; 22; 23; 24]. Although gauge dependence of the TDCIS method using fixed orbitals has been noted already in Ref. [2], comparative assessment of the LG and VG treatments (within the grid-based TDCIS) has not been reported to the best of our knowledge, except for being briefly mentioned in Ref. [42]. In particular, the channel orbital-based approach [2] has been applied only in the LG [1; 2; 14; 15], and as shown below in this paper, the VG treatment with fixed orbitals is not very appropriate for applications to high-field phenomena. This is a serious drawback, since for an efficient simulation of molecules, it is highly appreciated to take advantage of the velocity-gauge treatment, e.g., the feasibility of exterior complex scaling [43; 44] as an absorbing boundary, to reduce the computational cost related to the number of grid points. In the present work, we propose a gauge-invariant reformulation of the channel orbital-based TDCIS method. To this end, instead of applying the fixed-orbital TDCIS ansatz to the velocity-gauge time-dependent Schrodinger equation (TDSE), we adopt the formulation using unitary-rotated orbital \(|\phi_{p}^{\prime}(t)\rangle=\Omega(t)|\phi_{p}\rangle\), where \(\Omega(t)\) is the gauge transformation operator connecting the (exact) solution of TDSE in the LG and VG. The resulting EOMs in the reformulated VG is equivalent to the LG ones with fixed orbitals by construction, and at the same time allows to exploit advantages of the velocity-gauge simulations as mentioned above. This paper proceeds as follows. In Section 2, after defining the target Hamiltonian and the gauge transformation in Section 2.1 and reviewing the TDCIS method using fixed orbitals both in the CI coefficient-based (Section 2.2) and channel orbital-based (Section 2.3) approaches, we present the gauge-invariant reformulation in Section 2.4, and a consistent method for evaluating the time derivative of one-electron observables in Section 2.5. Then in Section 3 we apply the channel orbital-basedTDCIS method, using LG with fixed orbitals, VG with fixed orbitals, and the reformulated VG, to the model one-dimensional (1D) Hamiltonian to compare the results of various TDCIS approaches with numerically exact TDSE results, and demonstrate the importance of non-Ehrenfest method to compute dipole acceleration. Finally, concluding remarks are given in Section 4. The Hartree atomic units are used throughout unless otherwise noted.
## 3 Results
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Stage** & **Design A** & **Design B** \\ \hline
2 & [10,0,9,6,0,9,9,0,0,10,6,9,9,0,9] & [9,0,10,4,0,10,5,0,0,6,4,10,0,10,0,5] \\  & [10,0,3,10,0,4,10,0,0,10,10,2,0,10,0,5, [10,10,4,2,0,0,0,0,0,10,10,10,4,9,5,3,3, \\ 3 & 10,0,10,10,0,10,8,0,0,10,2,10,0,10,0,10,0,10, \\ 10,0,9,10,8,7,0,6,9,8,0,2,0,10,0,10, & 0,0,0,0,9,7,10,10,4,6,4,10,6,10,10,10, \\ 9,0,3,2,10,3,0,3,0,0,10,8,10,10,10,4] & 0,0,0,0,10,10,10,10,10,0,0,0,10,0,0,10,0,10,2] \\  & [10,0,2,0,2,2,0,0,9,10,10,0,10,0,2, [10,2,2,10,10,2,2,2,10,10,2,10,2,10,2,10,2,10,2, \\ 10,0,10,10,10,0,2,0,10,10,2,0,10,0,2,10,0,2,0,10,2, & 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, \\ 10,0,10,0,10,10,0,0,10,0,10,2,2,10,0,10,0,10, & 10,10,3,2,10,3,2,10,10,9,10,2,2,10,10,2,10,5,2, \\ 2,0,2,2,0,10,2,0,10,2,0,10,2,2,10,0,5, & 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,10,10, \\ 10,0,4,2,0,10,2,0,0,5,10,10,10,0,10,10, & 10,10,2,2,2,3,10,10,10,10,10,10,10,2,2,2, \\ 10,0,10,5,10,2,0,2,0,2,2,0,4,0,9, & 10,5,10,2,0,2,3,10,3,6,3,9,0,0,0,0,0, \\ 4 & 10,0,2,2,0,10,10,0,0,10,2,10,0,10,0,10, & 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, \\ 10,0,10,0,0,9,2,0,10,2,0,10,2,0,10,0,10, & 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, \\ 10,0,2,10,0,9,2,0,10,4,2,0,10,4,0,0,10,0,3, & 10,2,2,10,10,2,2,10,2,10,2,3,10,2,10,2,2, \\ 10,0,2,10,4,2,0,0,2,6,2,6,0,10, & 2,2,3,10,10,10,2,10,6,2,2,10,10,3,6,10, \\ 2,0,4,10,10,0,10,0,10,0,10,2,4,0,10,2, & 3,10,10,2,10,2,10,2,10,2,0,0,0,0,0,0,0,0, \\ 10,0,6,5,0,10,2,0,10,10,10,0,10,0,10, & 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, \\ 10,0,2,2,0,10,10,0,0,0,10,0,10,2,2,0,10,0,10, & 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, \\ 10,0,2,10,10,10,0,0,10,10,2,0,8,0,4] & 10,210,9,2,2,2,2,2,2,2,2] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimized amplitude excitation weights for each fractal stage in Design A and Design B. Zero values denote inactive elements. Figure 11: Optimized array layout and relative amplitude weights for Design B: (**a**) growth stage 2 and 10 active elements, (**b**) growth stage 3 and 40 active elements, (**c**) growth stage 4 and 160 active elements, (**d**) growth stage 5 and 640 active elements. A comparison between the original [PERSON] array comprising the complete set of antenna elements and the optimized designs with reduced element counts and adjusted relative amplitude values reveals improved performance in SLL suppression across multiple operational frequencies. The optimized configurations either reduce or maintain a comparable HPBW relative to the original full-element structure. These performance enhancements are summarized in Table 5, which provides detailed quantitative data supporting the effectiveness of the proposed design strategies.
## 2 Nonlinear Model Predictive Control
### Mathematical Model of Nonlinear System
#### 2.2.2 Vehicle Kinematic Model  
The research goal of this study is to ensure the accuracy and real-time performance of the vehicle while tracking its desired trajectory. To reduce lateral errors and improve control accuracy, it is necessary to establish a suitably simplified kinematic model. The research focuses on the longitudinal, lateral and yaw movement of the vehicle.  
Therefore, the modeling is appropriately simplified shown in Figure 2, and the following assumptions were made as follows: Ignoring the effects of the steering system, using the steering angle of the front wheels as the steering input, ignoring the role of the suspension, ignoring the air resistance, and ignoring the impact of the ground tangential force on the tire. For convenience, the center of the vehicle's rear axis was used as the origin of the vehicle coordinate system.  
The simplified model of an intelligent vehicle is as follows:  
\[\left\{\begin{array}{l}\dot{x}=vcos\theta\\ \dot{y}=vsin\theta\\ \dot{\theta}=vtan\delta/L\end{array}\right. \tag{3}\]  
Figure 1: Design architecture of the trajectory tracking controller.  
where \(x\) is the abscissa of the center of the rear axle of the vehicle, \(y\) is the ordinate of the center of the rear axle of the vehicle, \(\theta\) is the yaw angle of the vehicle, \(\delta\) is the steering wheel angle, \(L\) is the wheelbase of the vehicle, and \(v\) is the vehicle speed.
# Anti-Inflammatory Mechanisms of Novel Synthetic
## 4 Ruthenium Compounds Recover Inflammatory Mediators  
Alanine transaminase (ALT) and aspartate transaminase (AST) are the major liver marker enzymes increased in serum could specify the loss of hepatocyte structural integrity and liver injury [36]. One study has found that among the TZP (1,2,4)triazole[4,3-a]pyridine derivatives, derivative 8d expressively reserved concanavalin A-induced ALT and AST activities in mice liver [37]. L6H2I, a derivative of chalcone, had been found to reduce ethanol and LPS induced inflammation by reducing ALT and AST [38]. Consistent with these findings, our group have discovered that among the tested ruthenium (TQ) compounds, TQ 4 strikingly reduces the serum levels of ALT and AST in the LPS-injected mice, whereas TQ-1 and 2 did not alter these enzymes [39].  
Lipopolysaccharide induce inflammatory events via increasing proinflammatory cytokines TNF-\(\alpha\), IL-6 and IL-1\(\beta\), thus reduction of these cytokines could decrease inflammatory responses. The increased expression of iNOS induces inflammation and liver injury via the production of NO, an additional activator involves in oxidative stress mediated inflammation [40]. Furthermore, in the LPS induced inflammation, a considerable amount of NO is found to increase by the elevated synthesis of iNOS. In this regard, it can be considered as NO activation might be an initial marker in inducing liver damage, and hence inhibition of NO could be a target for controlling inflammation. NO are produced by macrophages. Microglia respond strongly to an LPS exposure and induce NO along with several inflammatory cytokines. NO is regarded as a toxic mediator in microglia-mediated brain inflammation [41]. Also, LPS induce macrophage activation through NO production. Our studies have found TQ compounds TQ-4 inhibits NO and iNOS [39], and TQ-6 inhibits  
Figure 1: Scheme of synthesis of ligands (**A**) and complexes (**B**) of ruthenium compounds.  
NO, iNOS and COX-2 in LPS induced macrophages and microglia [42], respectively, as shown in Figures 2 and 3.  
Figure 3: TQ-6 effectively reduced inflammatory mediators NO, iNOS, COX-2 and NF-kB p65 phosphorylation and its nuclear translocation in LPS-stimulated microglia. TQ-6 increased the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and hemeoxygenase-1(HO-1).  
Figure 2: In vitro anti-inflammatory mechanism of TQ-4 compound: TQ-4 inhibits NO production and iNOS expression, JNK phosphorylation, \(\mathrm{lxB}\alpha\) degradation, NF-kBp65 phosphorylation and its nuclear translocation in LPS-induced RAW 264.7 cells.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
###### Abstract  
The Internet Protocol (IP) uses numerical address values carried in IP packets at the network layer to allow correct forwarding of packets between source and destination. Those address values must be kept visible in all parts of the network. By definition, those addresses must carry enough information to identify the source and destination for the communication. This means that successive flows of IP packets can be _correlated_--it is possible for an observer of the flows to easily link them to an individual source and so, potentially, to an individual user. To alleviate this privacy concern, it is desirable to have _ephemeral_ address values--values that have a limited lifespan and so make flow correlation more difficult for an attacker. However, the IP address is also used in the end-to-end communication state for transport layer flows so must remain consistent to allow correct operation at the transport layer. We present a solution to this tension in requirements by the use of _ephemeral Node Identifier (eNID)_ values in IP packets as part of the address value. We have implemented our approach as an extension to IPv6 in the FreeBSD14 operating system kernel. We have evaluated the implementation with existing applications over both a testbed network in a controlled environment, as well as with global IPv6 network connectivity. Our results show that eNIDs work with existing applications and over existing IPv6 networks. Our analyses shows that using eNIDs creates a disruption to the correlation of flows and so effectively perturbs linkability. As our approach is a network layer (layer 3) mechanism, it is usable by any transport layer (layer 4) protocol, improving privacy for all applications and all users.  
Internet privacy; Internet Protocol (IP) networks; Identifier Locator Network Protocol (ILNP); transport protocols; computer security +
Footnote †: journal:  
_Article_