esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Method and Calculation Procedure
### Particle Interaction Models  
The kernel function is used to associate the target particle \(i\) with its neighboring particles \(j\). Both the Gaussian kernel function (Equation (4)) and hyperbolic kernel function (Equation (5)) [49] are adopted in the current MPS-MAFL method.  
\[G(r)=\frac{9}{\pi r_{e}^{2}}\text{exp}\left(-\frac{9r^{2}}{r_{e}^{2}}\right) \tag{4}\]  
\[H(r)=\begin{cases}\frac{r_{e}}{r}-1&(r<r_{e})\\ 0&(r\geq r_{e})\end{cases} \tag{5}\]  
where \(G\) is the Gaussian kernel function, \(H\) is the hyperbolic kernel function, \(r_{e}\) is the radius of the interaction zone, and \(r\) is the distance between particle \(j\) and \(i\).  
The Laplacian model shown in Equation (6) [49] is adopted to discrete the Laplacian operators in Equations (2) and (3).  
\[\langle\
abla^{2}\varphi\rangle_{i}=\frac{18}{r_{e}^{2}}\sum_{j}\frac{\left(n _{i}^{2}+n_{j}^{2}\right)}{n_{i}n_{j}^{2}}\times\varphi_{ij}G_{ij} \tag{6}\]  
where \(\varphi\) stands for the arbitrary physical quantities, n is the Particle Number Density (PND), \(G_{ij}\) is short for \(G_{j}\)-\(G_{i}\), and subscripts i and j represent the particle i and j, respectively. Moreover, re is the radius of the interaction zone, which was set to be 3.1 times of the particle size. Equation (6) is deduced based on the Shepherd function [50], which considers the influence of particle i itself; thus, Equation (3) is needed as the kernel function. More details can be referred to in the author's previous work [49].  
The gradient calculation represents the transfer of physical quantitiesbetween particles, and the gradient model shown in Equation (7) is adopted to discrete the gradient operator [51].  
\[\langle\
abla\varphi\rangle_{i}=\frac{D}{n_{0}}\sum_{j\
eq i}\frac{\varphi_{ ij}\overset{\rightarrow}{r}_{ij}}{\left|\overset{\rightarrow}{r}_{j}- \overset{\rightarrow}{r}_{i}\right|^{2}}H_{ij} \tag{7}\]  
where \(D\) is the number of dimensions, and \(n_{0}\) is the initial PND. Equation (7) is always used to calculate the pressure gradient. In the fully Lagrangian method, the Particle Stabilizing Term (PST) is always needed for particles near free surfaces [52]. However, owing to the hybrid scheme, the PST is not necessary for the MPS-MAFL method.  
The divergence model shown in Equation (8) is utilized to calculate the divergence of particle temporal velocity, which is another way to present the particle density; thus, the PND is not precisely required to be a constant to satisfy the incompressibility model in the MPS-MAFL method.  
\[\langle\
abla\overset{\rightarrow}{u}\rangle_{i}=\frac{D}{n_{0}}\sum_{j\
eq i }\frac{\overset{\rightarrow}{u}_{ij}\overset{\rightarrow}{r}_{ij}}{\left| \overset{\rightarrow}{r}_{j}-\overset{\rightarrow}{r}_{i}\right|^{2}}H_{ij} \tag{8}\]
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 2 Experiments
### Fiber Laser Design  
The laser source of the LIVE lidar is a pulsed 1.5 \(\upmu\)m all-fiber laser based on master oscillator power amplifier architecture. To achieve the required accuracies for wind velocity measurement, the laser pulses should have an energy higher than 300 \(\upmu\)J and a duration of 700 ns. The coherent Doppler lidar application has additional requirements such as polarized laser emission, near-diffraction limited spatial beam quality and Fourier-limited spectral linewidth for coherent detection.  
In fiber amplifiers, this last specification on linewidth limits the achievable laser energy, or more precisely, the achievable peak power. Indeed, to avoid nonlinear effects (particularly stimulated Brillouin scattering (SBS)), peak power must remain below the SBS threshold. Many methods have been experimented with to overcome this limitation, i.e., special fibers [11] or the use of a temperature or strain gradient along the amplifying fiber [12]. The simplest one from the lidar system point of view is the use of large mode area (LMA) fibers. In this case, the technical challenge is the fabrication of the amplifying fiber. The core diameter must be increased while reducing the numerical aperture to minimize the number of guided modes and maintain a quasi-single mode operation, since high beam quality is essential for lidar efficiency. In commercial pulsed power amplifiers at 1.5 \(\upmu\)m, amplifying fibers are double-clad polarization maintaining Erbium-Ytterbium co-doped fibers. Two years ago, commercially available fibers of this type limited the peak power of a 700 ns pulse to 250-300 W when no SBS mitigation technic was implemented. A new glass matrix was proposed and after several tests, a new Erbium-Ytterbium co-doped fiber has been fabricated. This fiber enables us to reach a higher peak power, around 600 W, with all the necessary characteristics in terms of polarization maintenance, beam quality and amplification efficiency [13].  
For the LIVE lidar, we developed the last power amplifier stage of the laser source with this new fiber and evaluated the laser performance. A picture of the laser is presented in Figure 1. The emission is linearly polarized, and we measured a polarization extinction ratio of 17 dB. An excellent beam quality is also observed with a measured beam quality factor M\({}^{2}\) = 1.1. Pulses were temporally shaped thanks to the acousto-optic modulator to compensate for distortions in the different amplification stages in order to obtain a duration of 715 ns and the pulse shape is presented in Figure 1. The pulse repetition frequency is 14 kHz and the pulse energy is limited by the SBS to 410 \(\upmu\)J. A double-pass architecture in the preamplifier enabled us to minimize ASE (Amplified Spontaneous Emission) generation and amplification in the power amplifier; a low level of ASE (<2%) is measured [14].
## 5 Numerical Examples
### Sliding Three-Dimensional Beam with Eccentricity  
Two initial straight flexible beams connected through a sliding joint are involved in this example, as shown in Figure 4. Two spherical joints locate at the two ends of the carrier beam AB. A concentrated mass M is fixed at the free end of sliding beam AM. All the geometrical and material parameters are displayed in Table 3. This is a typical test case of sliding joint, which has been proposed by [PERSON] et al. [19] and applied by other researchers [20; 23]. The ANCF and GEBT are employed in [19; 20] and [23], respectively. In reference [19; 23], the carrier beam and sliding beam are both discretized into one element thereby obtaining an un-converged solution. The convergence of solution was investigated in [23].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & & **Update** & **Frozen** \\ \hline \multirow{2}{*}{\(h\) = 0.0005 s} & Update times & 1434 & 0 \\  & Total iterations & 1435 & 1473 \\ \hline \multirow{2}{*}{\(h\) = 0.0001 s} & Update times & 5714 & 0 \\  & Total iterations & 5715 & 5715 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of times that the iteration matrix is updated (update times) and the total iterations in the simulation for different time discretizations and update conditions of the truss structure unit. Update: the iteration matrix is updated all the time. Frozen: the iteration matrix in the reference configuration is maintained for the whole simulation.  
Figure 3: The deformation rate of Rod II.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Scenarios with Homogeneous Nodes
#### 5.1.6 Jitter  
A jitter-based analysis is shown in Figure 6. To create a baseline for comparison, all protocols show comparatively minimal jitter at 20 nodes. Due to their continuous route maintenance, proactive protocols exhibit moderate jitter, which makes them more effective for smaller networks. For very large networks, however, the overhead of maintaining routing tables reduces their performance. Reactive protocols noticeably increase jitter as the network capacity reaches 80 nodes because of the overhead of route discovery in moderate traffic situations. When a network expands from 20 to 200 nodes, jitter increases due to congestion and frequent route changes, that alter packet arrival times. At 500 nodes, however, denser connections offer more dependable and stable pathways, which lower jitter by guaranteeing more constant delivery times.  
Higher node density presents scalability and adaptation problems, as evidenced by the most noticeable increase in jitter at 200 nodes. Due to their balanced performance, hybrid protocols--which blend proactive and reactive tactics--are especially well-suited for very high-density networks (500 nodes), where they can effectively handle the increased jitter. For most protocols, jitter tends to stabilize or decrease at 500 nodes, suggesting that efficiency has returned at the highest network densities. This analysis highlights each protocol category's effectiveness and scalability while offering important insights into their operational performance and possible limitations in a range of network scenarios.  
Figure 6: Jitter Across the Four Scenarios.  
With an emphasis on jitter, Table 10 shows how well proactive, reactive, and hybrid routing systems perform across a range of network sizes. Due to their regular route updates, proactive protocols are effective in small networks (20 nodes). However, because of the complexity of maintaining routing tables, they have trouble scaling in bigger networks (200 nodes), where jitter increases dramatically. Reactive protocols have increased jitter because of on-demand route discovery, which peaks at medium and large network sizes (80 and 200 nodes), even if they are more flexible for smaller and dynamic networks. They do, however, stabilize in networks with 500 nodes or more, suggesting that efficiency improves with increased connectedness. Hybrid protocols efficiently manage jitter even as the network expands by continuously balancing performance across all network sizes.
## Data, Methodology and Results
### Data and Model
#### 2.1.2 EGM2008 Model  
The spatial resolution of the EGM2008 model is about 5\({}^{\prime}\times 5^{\prime}\), and the geoid undulation propagated standard deviation is 10.925 cm [41]. This study used the highest spatial resolution model which is interpolated to a 1\({}^{\prime}\times 1^{\prime}\) grid and the interpolation error does not exceed \(\pm\)1 mm [42].
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 9 Conclusions  
In this study, we conducted a comprehensive analysis of the R-KEM Mechanism. Our findings demonstrate the protocol's ability to securely encapsulate ephemeral keys in significant environmental noise or deliberate noise injections, ultimately facilitating the generation of error-free cryptographic keys for decryption on endpoint devices. This protocol outperforms other CRP-based error detection mechanisms, such as RBC, rendering it particularly useful for devices constrained by computational resources.  
Conceptualization, B.C. and D.G.M.; methodology, B.C. and D.G.M.; software, D.G.M.; validation, D.G.M.; formal analysis, D.G.M.; investigation, D.G.M. and B.C.; resources, D.G.M.; data curation, D.G.M.; writing--original draft preparation, D.G.M.; writing--review and editing, D.G.M., M.L.G., S.J., M.A. and S.A.; visualization, D.G.M.; supervision, B.C.; project administration, B.C.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911 NF-23-2-0014. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
## 2 Description of the Mathematical Model  
### Mathematical Model of the PM  
Figure 1 shows the design of the PM. It is made of natural rubber with fibers wrapped inside and (see Figure 1a) metal fittings attached at each end. The PM changes shape when the pressure of the air that flows into the interior of the rubber tube is changed. Injecting compressed air into the rubber tube expands the PM and produces a force that drags the sprung mass downward (see Figure 1b). Otherwise, the sprung mass returns to its original shape if the PM releases air. For simplicity, the reversible physical deformation during contraction and expansion produces a linear motion. According to the standard static physical model [22; 23], (see Figure 2), the length and the diameter of the PM are formulated as:  
\[L=l\cos\theta \tag{1}\]  
and:  
\[D=\frac{l\sin\theta}{n\pi} \tag{2}\]  
where \(L\) represents the actual length of PM, \(L_{0}\) represents the original length of PM, \(l\) represents the length of the thread, \(n\) represents the number of turns in the thread, \(D\) represents the diameter, and \(\theta\) represents the angle that the threads make with a horizontal axis.  
Figure 1: Design of the pneumatic muscle (PM) (**a**) Components of the PM; (**b**) Actions of the PM.  
Considering the PM as a cylindrical shape, its volume is defined as:  
\[V=\frac{\pi D^{2}L}{4} \tag{3}\]  
Substituting Equations (1) and (2) into (3) yields:  
\[V=\frac{l^{3}(1-\cos^{2}\theta)\cos\theta}{4\pi n^{2}} \tag{4}\]  
Because the active area of the cylinder changes over time, the volume of the PM changes with time. The active area is expressed as \(\overline{A}=dV/dL\), which is:  
\[\overline{A}=\frac{3L^{2}-l^{2}}{4\pi n^{2}} \tag{5}\]  
According to the Principle of the Conservation of Energy [24], the geometric force [22] is the product of the pressure and the change in the volume with respect to the length, which is:  
\[F_{a}=-(P-P_{e})\frac{dV}{dL} \tag{6}\]  
where \(P\) represents the pressure inside the PM and \(P_{e}\) represents atmospheric pressure. Substituting Equation (5) into (6) yields:  
\[F_{a}=-(P-P_{e})\frac{3L^{2}-l^{2}}{4\pi n^{2}} \tag{7}\]  
A proportional pressure regulator (PPR) is used to regulate the pressure \(P\) inside the PM. This is modeled by a first order transfer function, which is:  
\[P=\frac{K_{v}}{T_{s}s+1}u \tag{8}\]  
where \(K_{v}\) and \(T_{s}\) respectively represent the constant gain and the time constant for the PM, and \(u\) represents the input voltage. Substituting Equation (8) into (7) yields:  
Figure 2: Model of the PM.  
\[F_{a}=-(\frac{K_{\
u}}{T_{s}s+1}u-P_{e})\frac{3L^{2}-l^{2}}{4\pi n^{2}} \tag{9}\]  
**Remark 1**.: _In general, a PPR exhibits complex dynamic behavior and must be modeled by a high order nonlinear differential equation. Fortunately, for the PM-ASS, the bandwidth for a PPR is much wider than that for a PM; thus, the dynamics of a PPR can be modeled as a first order linear differential equation, as shown in Equation (8)._
## 4 Discussion
### Verification of the IOP Model  
We have verified the ocean RT and IOP model by comparing the calculated and measured diffuse attenuation coefficients in [PERSON] et al. (2019). Here, we briefly describe the results of the verification. In situ data used in the comparisons are from measurements taken during the Aerosol Characterization Experiment. The (ACE)-Asia experiment was carried out in 2001 in the Pacific Ocean. The dataset includes profile measurements of downward irradiance \(E_{d}\) and upward irradiance \(E_{u}\) at 17 wavelengths ranging from 313 to 710 nm. In the field measurements of \(K_{d}\), the surface-wave introduced fluctuations in the subsurface light field make it hard to accurately determine \(K_{d}(z)\). To compare with the calculated \(K_{d}\), we averaged the measurements over a 10 m layer using linear regression analysis between \(\ln(E_{d}(z))\) and \(z\). Comparisons of the calculated and measured \(K_{d}\), which were completed for chlorophyll concentrations less than 0.7 mg/m\({}^{3}\) to ensure the validity of the Case I water model, showed that the correlation coefficient varied from 0.89 at 313, 320 and 340 nm to 0.87 at 380 nm. The relative RMS error for all the cruise stations was less than 20%. There was no obvious bias between the measured and calculated \(K_{d}\), suggesting that the IOP model reasonably reflects the observed absorption and scattering in the ocean. Comparisons between the calculated and measured quantities were also carried out for the diffuse reflectance, \(R=E_{u}/E_{d}\) in [PERSON] et al. (2018). The comparisons showed a good agreement between the data: the correlation coefficient varied from 0.85 to 0.90 depending on wavelength.
## 3 Experiment
### Experimental Considerations Detail  
The experimental implementation employed Python version 3.9, the PyTorch 2.3.0 framework, and CUDA version 12.2.19. The graphics card used was the NVIDIA GeForce RTX 3060 8G. During the training process, the model's learning rate was set to 0.0001, and the batch size was set to 64. The optimizer used was Adam, with \(\varepsilon=10^{-7}\), \(\beta 1=0.5\), and \(\beta 2=0.9\). To ensure sufficient training of the generator, discriminator, and encoder, and to avoid the discriminator suppressing the learning of the generator and encoder due to a too-fast learning rate, the training iterations for the generator and encoder were increased in the experiment to enhance their learning capabilities.  
The revised TGAN objective combines adversarial loss with layout energy terms:  
\[\mathcal{L}_{total}=\underbrace{\mathbb{E}[\log D(x|y)]+\mathbb{E}[\log(1-D(G (z|y)))]}_{\text{Adversarial loss}}+\lambda\sum_{i=1}^{6}\omega_{i}E_{i}(X) \tag{12}\]  
where \(\lambda\) controls the weight of layout quality metrics (alignment, balance, whitespace, etc.) derived from our energy model.
## 4 Sensitivity Analysis
### Effect of Span Length  
Several researchers have investigated the importance of span length on the distribution of loads on the skewed superstructures [4]. For instance, several North American codes [13; 14] have proposed _LDFs_ to be functions of bridge span length.  
Figure 7a shows the effect of span length on the _LDFs_ for shear force and bending moment of a non-skewed bridge with a girder spacing of 2.5 m and a total width of 9.5 m. The results show that the _LDFs_ decreased for both the external and internal bridge girders as the span length increased. The _LDFs_ for the bending moment of the internal and external girders decreased by up to 27% and 46%, respectively, as the span length dropped from 30 m to 90 m. The same trend was visible for the _LDFs_ for shear forces, which were reduced by up to 32% for both the internal and external girders as the span length increased. Although the current AASHTO LRFD [14] specifications have adopted [PERSON]'s formulas for the _LDFs_ of the shear and bending moment, the data presented in Figure 7 reveal that the AASHTO LRFD formulas produce highly conservative values for both the bending moment and shear force, which are up to 55% and 25% higher, respectively, than the results obtained by the FE analysis.
## Nomenclature  
### Abbreviations  
BDC  
Bottom Dead Center  
DAP  
Double-Arc Profile  
DC  
Diaphragm Compressor  
DEP  
Double Exponential Profile  
DSDC  
Dual-Stage Diaphragm Compressor  
FEM  
Finite Element Method  
FSI  
Fluid-Structure Interaction  
SEP  
Single Exponential Profile  
TDC  
Top Dead Center  
**Symbols**  
\(b\)  
Diaphragm thickness (m)  
\(D\)  
Diameter (m)  
\(E\)  
Elastic modulus (Pa)  
\(h\)  
Cavity deflection curve (m)  
\(H\)  
Maximum diaphragm deflection (m)  
\(k_{m},k_{n}\)  
Expansion and compression indexes (-)  
\(l_{c}\)  
Connecting rod length (m)  
\(L\)  
Length (m)  
\(M_{b},N_{b}\)  
Design parameters of the DEP profile (-)  
\(n\)  
Rotational speed (rpm)  
\(N_{MG}\)  
Mesh grid number (-)  
\(p\)  
Gas pressure (Pa)  
\(Q_{mg}\)  
Experimental gas mass flowrate (kg\(\cdot\)h\({}^{-1}\))  
\(Q\)_th_  
Theoretical gas mass flowrate (kg\(\cdot\)h\({}^{-1}\))  
\(r\)  
Radius (m)  
\(r_{c}\)  
Crank radius (m)  
\(R\)  
Diaphragm cavity radius (m)  
\(R_{Na},R_{Nb}\)  
Segment radii of the DAP profile (m)  
\(s\)  
Characteristic diaphragm length (m)  
\(V\)  
Volume (m\({}^{3}\))  
\(V_{c}\)  
Residual volume (m\({}^{3}\))  
\(V_{in}\)  
Inlet gas volume (m\({}^{3}\))  
\(V_{loss}\)  
Suction volume loss (m\({}^{3}\))  
\(V_{mo}\)  
Diaphragm swept volume (m\({}^{3}\))  
\(V_{ac}\)  
Total oil cavity volume (m\({}^{3}\))  
\(Z_{a}\)  
Design parameter of the SEP profile (-)  
\(a\)  
Crank angle (rad)  
\(\beta\)  
Oil bulk modulus (Pa)  
\(\Delta p\)  
Diaphragm pressure difference (Pa)  
\(\delta\)  
Diaphragm stress (MPa)  
\(\theta\)  
Deflection angle (rad)  
\(\lambda_{d}\)  
Volumetric efficiency (-)  
\(\lambda_{N}\)  
Design parameter of the DAP profile (-)  
\(\mu\)  
Poisson's ratio (-)  
\(\rho_{r}\)  
Specific radius (-)  
\(\varphi\)  
Radial angle (rad)
## 3 System Configuration
### Infrared Detector
#### 3.2.1 Quantum Heterostructure Detector  
Quantum heterostructure-based infrared detectors, including quantum cascade (QC), quantum well (QW), quantum dot, and combinations of both QDs and QWs in a dot-in-a-well (DWELL) strategy detectors, have high detectivity and low dark currents [103; 104; 31]. QC detectors have low noise due to their low interference of background radiation. QW detectors are characterized by a narrow spectral band and are easily fabricated. However, the disadvantages of QW detectors include low operating temperatures and the requirement of a scattering filter to adjust the incident light angle [105]. QD detectors have higher operating temperatures and are capable of absorbing normally incident photons. However, they have much lower quantum efficiency due to the lower absorption and capture probabilities of incident light [104; 106]. DWELL devices have shown promise in terms of detectivity and spectral range, though their operating temperatures remained fairly low.  
Most quantum heterostructure detectors are still at the experimental stage, with different fabrication technologies and materials being used. Few commercial products have been reported, which leads to the restriction of such detectors' application in MIR detection. However, recent advances in this field such as new device-chip hybridization [107], antimonide-based membrane synthesis integration, and strain engineering [108] may be scalable methods for the fabrication of commercially available heterostructure detectors.
# High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6
## 2 Decoding Process and Parameters Calculation of the Decoder
### Co-Processing Parameter Calculation
#### 2.2.3 Calculate the SBs, PBs, and RBs  
From \(N_{\_}{CW}\), \(N_{\_}{subits}\), \(L_{\_}{LDPC}\), and R, the SBs \(N_{\_}{shtr}\), PBs \(N_{\_}{punc}\), and RBs \(N_{\_}{rep}\) are obtained according to Equations (5)-(7). The \(N_{\_}{punc}\) may need to recompute with increment \(N_{\_}{subits}\)[1].  
\[N_{\_}{shtr}=\max(0,(N_{\_}{CW}\times L_{\_}{LDPC}\times R)-N_{\_}{pld}) \tag{5}\]  
\[N_{\_}{punc}=\max(0,(N_{\_}{CW}\times L_{\_}{LDPC})-N_{\_}{subits}-N_{\_}{pld}) \tag{6}\]  
\[N_{\_}{rep}=\max(0,N_{\_}{subits}-N_{\_}{CW}\times L_{\_}{LDPC}\times(1-R)-N_ {\_}{pld}) \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Range of \(N_{\_}{subits}\) (bits)** & \(N_{\_}{CW}\) & \(L_{\_}{LDPC}\) (bits) \\ \hline \(N_{\_}{subits}\leq 648\). & 1 & 1296, if \(N_{\_}{subits}\geq N_{\_}{pld}+912\times(1-R)648\), otherwise \\ \(648<N_{\_}{subits}\leq 1296\) & 1 & 1944, if \(N_{\_}{subits}\geq N_{\_}{pld}+1464\times(1-R)1296\), otherwise \\ \(1296<N_{\_}{subits}\leq 1944\) & 1 & 1944 \\ \(1944<N_{\_}{subits}\leq 2592\) & 2 & 1944, if \(N_{\_}{subits}\geq N_{\_}{pld}+2916\times(1-R)\) 1296, otherwise \\ \(2592<N_{\_}{subits}\) & \(\left\lceil\frac{N_{\_}{pld}}{944\times R}\right\rceil\) & 1944 \\ \hline \end{tabular}
\end{table}
Table 1: The number and length of codewords.
## 1 Introduction
However, conducting long-term measurements can be very costly, both in terms of time and equipment. Therefore, modeling RF-EMF changes in a location with the least time and cost is crucial.