esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Propagation Characteristics of Unsaturated Porous Media Containing CO\({}_{2}\) and Oil
## 5 Propagation Characteristics under the Modified Model  
Following the previous section, we obtained the modified models for live oil's density and velocity, which are the functions of temperatures, pressures, and CO\({}_{2}\) solubility. It provides a basis to accurately describe the changes of the pore fluid physical parameters in CGUS. Figure 9 shows the phase velocities and attenuations of P1 and S waves at different CO\({}_{2}\) saturation and frequencies, combining [PERSON]'s model and the modified models of fluid parameters.  
Comparing Figures 1, 2 and 9, it can be seen that the trend of wave velocities and attenuations with frequency and CO\({}_{2}\) saturation in Figure 9 is essentially unchanged, except for the S velocity. In fact, the velocity of S is almost constant before the critical saturation because of little change in the live oil's density due to CO\({}_{2}\) dissolution. After the critical saturation, the velocity of S linearly increases with increasing free CO\({}_{2}\). To show the effect of fluid parameters models more obviously, we calculated the P1 and S phase velocities and attenuations at the typical seismic frequency (\(\textit{f}=100\) Hz), as shown in Figure 10. The fluid parameters of [PERSON]'s model overestimate the attenuations of P1 and S. There is a considerable variation in the prediction of S velocity, with a lower prediction for P velocity at low CO\({}_{2}\) saturation and a higher prediction at high CO\({}_{2}\) saturation. The above results indicate that the fluid parameters of [PERSON]'s model are inaccurate for characterizing the physical parameters of the CO\({}_{2}\)-oil mixture.  
Figure 8: Comparison of model calculation results and experimental data (Ratnakar [35]): (**a**) before correction; (**b**) after correction.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Range of Experimental Data** & **R\({}^{2}\) of Correction** & **R\({}^{2}\) of Correction** & **R\({}^{2}\) of Correction** \\
**(46 Data Points in Total)** & **for (\(M_{g}\), \(N_{g}\))** & **for (\(M_{s1}\), \(M_{s2}\))** & **for (\(N_{s1}\), \(N_{s2}\))** \\ \hline P \(\leq\) 40 MPa (22 data points) & 0.9927 & 0.9634 & 0.9633 \\ T \(\leq\) 40 °C (23 data points) & 0.9931 & 0.9629 & 0.9630 \\ X\({}_{\text{CO2}}\geq\) 40\% (15 data points) & 0.9644 & 0.9637 & 0.9637 \\ X\({}_{\text{CO2}}\geq\) 8\% (31 data points) & 0.9943 & 0.9637 & 0.9636 \\ Random (20 data points) & 0.9940 & 0.9636 & 0.9633 \\ Random (10 data points) & 0.9896 & 0.9637 & 0.9608 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of correlation coefficients (R\({}^{2}\)) of different correction parameters.  
Figure 10: Effects of fluid parameter model on the phase velocities and attenuations of waves: (**a**) P1 velocity; (**b**) S velocity; (**c**) P1 attenuation; (**d**) S attenuation.  
Figure 9: Effects of frequency and CO\({}_{2}\) saturation on the phase velocities and attenuations of waves: (**a**) P1 velocity; (**b**) S velocity; (**c**) P1 attenuation; (**d**) S attenuation.
# Reconstructing Nerve Structures from Unorganized Points
## 3 Motivation of This Study  
The method described in this paper was developed while working on a framework called ProprioStim [29], which focuses on planning biomimetic stimulation strategies to restore natural proprioceptive sensation, primarily for individuals with peripheral nerve damage, such as amputees. One of the framework's key contributions is its departure from the traditional methods of generating in silico nerve models. The mentioned study demonstrated that the innovative approach of 3D modeling, which faithfully reconstructs peripheral nerves with consideration for their intricate complexity, branching, and internal nerve structures, yields significantly different results compared to the commonly used extruded models [8; 9; 30]. This approach substantially enhances the accuracy of the simulation.  
During the development of ProprioStim, previously collected experimental data were utilized, including histological images of transverse sections of the sciatic nerves from two transfemoral amputees (as the one shown in Figure 2A) [4]. They were utilized for CAD modeling of the detailed fascicular nerve geometry (Figure 2B,C), which was then used to create an FEM model in the Comsol software (version 5.5). The spatial distribution of electrical potentials, obtained during the process, along with the nerve geometry were imported into Matlab for the purpose of calculating the recruitment of individual nerve fibers within the fascicle. Both nerves had similar cross-sectional dimensions (approximately 20 mm \(\times\) 7.5 mm). The acquired histologies allowed for the modeling of nerve segments with a length of 20 mm along the longitudinal (\(z\)) axis. The nerve segments were discretized into 19 equidistant cross-sections for manipulation in Matlab, with a layer-to-layer spacing of 100 \(\mu\)m.  
Figure 2: (**A**) Histological image captured from a layer of the sciatic nerve from a transfemoral amputee. (**B**) The associated nerve segmentation derived from this cross-section. (**C**) The reconstruction of the geometry of a peripheral human nerve, illustrating the branching of fascicles.  
The motivation behind the method presented in this paper emerged as a result of the challenges encountered during the data transfer of nerve geometry from Comsol to Matlab. The vertices of the contours, which correspond to cross-sectional slices of nerve fascicles, initially became randomized upon import into Matlab, requiring a sorting process. Figure 3 depicts an example of a problem this paper focused on. The contour should be (re)constructed as precisely as possible from the nonuniformly distributed sample points (Figure 3A). Figure 3B illustrates its expected shape. If the points were connected according to the initial randomized order, the resulting contour would look as depicted in Figure 3C. Figure 3D shows an enlarged view of the reconstruction presented in Figure 3C, while four randomly chosen points are shown as references in both Figure 3C,D.
## 4 Numerical Simulation of Roof Cutting Parameters
### Numerical Simulation of Cutting Angle
#### 4.4.2.  
Road Displacement Distribution Law of Different Roof Cutting Angles  
The roof displacement cloud map after the mining of the working face is intercepted, and the roof displacement data are extracted to draw the curve as shown in Figure 11(a),b. The different drilling angles will affect the friction between the roof rock blocks. Therefore, it is necessary to compare the vertical displacement of the roof on the side of the gob. It can be seen that when the roof cutting angle is 10\({}^{\circ}\), the cantilever length of the roadway roof becomes shorter. When the roof of the working face rotates and sinks, it is hinged with the roof of the roadway. This part of the rock block is difficult to collapse along the cutting seam, resulting in a suspended roof. At this time, the vertical subsidence of the roof is 0.6 m and the forming effect of the roadway side is poor. When the roof cutting angle increases to 15\({}^{\circ}\) and 20\({}^{\circ}\), the roof subsidence on the goaf side is 2.13 m and 2.19 m, respectively, indicating that the increase of the roof cutting angle can effectively reduce the friction between the roof rock blocks, which is conducive to cutting off the roof.  
Figure 11. The stress distribution law of roadway surrounding rock under different roof cutting angles.  
Figure 12. Roof displacement distribution law of roadway with different roof cutting angles.  
The roof subsidence of the roadway under different roof cutting angles is compared. When the roof cutting angle is 10\({}^{\circ}\), the maximum roof subsidence is 0.09 m. When the roof cutting angle is 15\({}^{\circ}\), the roof subsidence is 0.10 m; when the roof cutting angle is 20\({}^{\circ}\), the cantilever length of the roof increases, and the roof rotation of the working face has an impact on the roof of the roadway, and the roof subsidence increases to 0.13 m. The roof displacement shows the regional characteristics of 'the largest on the top cutting side, the second in the middle of the roadway, and the smallest on the side of the solid coal pillar'.  
Based on the above analysis, when the roof cutting angle is small, the friction between the roof rock blocks is large, and the rock blocks are hinged and difficult to collapse during the rotary sinking process. When the roof cutting angle is large, the increase of the lateral cantilever length of the roof affects the pressure relief effect of the surrounding rock of the roadway, and the subsidence of the roadway roof increases. Therefore, the optimal angle of roof cutting is 15\({}^{\circ}\).
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 2 System Model  
### IEEE 802.11p  
IEEE 802.11p is based on orthogonal frequency-division multiplexing (OFDM). OFDM improves the spectral efficiency and mitigates the severity of multipath fading by modulating the encoded data to \(N\) parallel orthogonal subcarriers for transmission. The modulation and demodulation of OFDM are performed through inverse fast Fourier transform and fast Fourier transform (FFT), respectively. OFDM inserts a guard interval of length \(G\), called a cyclic prefix (CP), in the time domain, which avoids intersymbol interference and intercarrier interference between OFDM symbols. An OFDM symbol occupies a bandwidth \(B\), and the sampling time is \(T_{s}=1/B\) at the receiver. Thus, the duration of an OFDM symbol is defined as \(T_{OFDM}=(N_{tot}+G)T_{s}\). The specific system parameters are listed in Table 1. The transmission is performed by framing \(M\) OFDM symbols, which can vary depending on the desired payload size. The transmitter utilizes a convolutional encoder, and the receivers adopt the Viterbi decoder.  
An IEEE 802.11p packet consists of a preamble, signal field, and data field. In particular, two long training symbols in the preamble, which are located at the beginning of the packet, are used for channel estimation. The data field is composed of a number of data symbols, and each OFDM symbol consists of 64 subcarriers with indexes ranging from \(-32\) to \(31\). Of the 64 subcarriers, those with indexes \(-21\), \(-7\), \(7\), and \(21\) are allocated as pilot symbols, and those with indexes from \(-26\) to \(26\) are allocated to \(48\) data subcarriers, excluding those with indexes \(-21\), \(-7\), \(0\), \(7\), and \(21\). The remaining \(12\) indexes are set to null subcarriers.  
The 802.11p standard uses a block-comb-type pilot pattern for channel estimation, as shown in Figure 1. The two symbols at the beginning of the IEEE 802.11p packet are the block pilot, and each OFDM symbol then uses the pilot as the comb type. However, the pilot subcarriers specified in the standard are not sufficient to track frequency-selective fading channels, resulting in a performance degradation of the system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency: \(f_{c}\) & 5.9 GHz \\ Bandwidth: \(B\) & 10 MHz \\ The number of data subcarriers: \(N_{d}\) & 48 \\ The number of pilot subcarriers: \(N_{p}\) & 4 \\ The number of total subcarriers: \(N_{tot}\) & 64 \\ The length of CP: \(G\) & 16 \\ Sampling time: \(T_{s}\) & 0.1 μs \\ Symbol duration: \(T_{OFDM}\) & 8.0 μs \\ \hline \hline \end{tabular}
\end{table}
Table 1: OFDM system parameters in IEEE 802.11p.
# Parallel Reservoir Simulation with OpenACC and Domain Decomposition
## 4 Results and Discussion
### Experimental Setup  
All experiments have been executed on work bench which is a single-node workstation, the configurations are presented in Table 1. As shown in Table 1, the main hardware platform is Intel i7 5820K CPU and Maxwell architecture GPU (GTX TITAN X), the host memory and device memory are respectively 32 GB DDR4 and 12 GB DDR5, in software respect, the OS is Windows 10 (64-bit), the compiler is PGI Visual Fortran 16.9 where the latest OpenACC 2.5 is supported. In this paper the experimental datasets used are actual production data, which is composed of multiple data blocks organized by keywords and forms as a text file. For comparison, a CPU-based reservoir simulation project with Fortran is used in our following experiments. The reservoir simulation is based on a black-oil model, uses conjugate gradient as the linear solver, and employs the block triangular preconditioner. We used the Newton iteration method for simulations and the number of total time steps is up to 3000.  
The overall experimental plan is mentioned here. We evaluated performance of the parallel reservoir work by following three experiments: In the first experiment, we compare the parallel method with the serial in terms of simulation time, after that, we count the workload by computing the line number of OpenACC directive clauses added during the project modification, which is the evaluation of OpenACC's availability. In last experiment, we compare two kinds of parallel reservoir simulation methods which differentiate from the other depending on whether the domain decomposition is used. The runtime is the performance merit here. The number of overlapping layers as another variable to influence reservoir time is also in consideration.
# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 4 Experiments
### Radar Plot Recognition
The experimental results shown in Table 4 indicate that in low-clutter datasets with fewer radar plots, the recognition accuracy of all algorithms decreases, except for PSO-SVM and the RPREC. The reason is that PSO-SVM is an optimization algorithm of SVM, which is very suitable for small samples and can achieve a recognition accuracy of 0.854. In this case, its recognition performance is similar to the RNN. Comparing Tables 3 and 4, it can be found that the recognition accuracy of PSO-MLP, FNN, CNN, and RNN has decreased by approximately 2 to 6 percentage points. The reason why the RPREC is not also significantly affected is that its classifier can iteratively learn the inherent characteristics of radar plots and maintain optimization, but the cost is that the CPU time is several times that of other algorithms. Overall, PSO-MLP, FNN, CNN, and RNN perform well in high-clutter density radar point datasets, while PSO-SVM can demonstrate its advantages in low-clutter density radar point datasets. The RPREC maintains good performance in both datasets but has the highest CPU time. Therefore, in terms of being able to adapt to various different scenarios, the RPREC is the best. But if we pursue computational timeliness, the RPREC is not the best choice.
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
## 3 Analysis of Grating Lobes in DSC Distributed Antennas
### Grating Lobe Effect
For arbitrary beam pointing, the condition for side lobe suppression is the following [15]:  
\[\left|u\right|_{\max}=\left|kd(\cos\theta-\cos\theta_{m})\right|_{\max}<2\pi \tag{13}\]  
Since \(\theta=0\sim\pi_{s}\left|\cos\theta-\cos\theta_{m}\right|_{\max}=1+\left|\cos \theta_{m}\right|\), therefore  
\[d<\lambda/(1+\left|\cos\theta_{m}\right|) \tag{14}\]  
This equation can also serve as a condition for the absence of grating lobes in non-uniform linear arrays (such as Taylor arrays, Chebyshev arrays, etc. ).
# New Trends in 4D Printing: A Critical Review
## 4 Shape Memory Materials
### Hydrogels and Hydrophilic Polymers  
Hydrogels are water-swollen visco-elastic materials characterized by a 3D microstructure transformation ability based on wettability and solubility [3]. They can respond to water or moisture [22] by expanding their original volume up to 200% [15]. These hydrophilic crosslinked polymers are widely employed to fabricate 4 DP components for soft robotics, microgrippers, micro-actuators, and biomedical applications [105] with a biocompatibility obtainable by employing Direct Ink Writing (DIW). Unfortunately, they exhibit a very slow reverse response, which requires hours for drying and shrinking. A solution is a particular programming method able to provide anisotropy to the swelling. Moreover, 3D components can be composed of a series of hydrogel-based inks sensitive to hydration and temperature. By adding UV-curable monomers, interpenetrating polymer networks are obtained after the polymerization.  
Typical stimuli for 4 DP Shape Memory Hydrogels are pH, temperature, and ions. These materials possess the ability to swell by absorbing water, allowing the fabrication of water-sensitive micro-actuators [92, 106] and reversible origami architectures [107]. Unfortunately, the absorption of water is provided until saturation is reached, making it difficult to control it at intermediate steps. A way to overcome this problem is to control the temperature of the aqueous medium [15]. The slow response speed mainly depends on the diffusivity of water (\(10^{-10}\) to \(10^{-9}\) m\({}^{2}\) s\({}^{-1}\)) and its small modulus (in the range of a few hundred kPa), thus requiring a large swelling for shape change.  
Conversely to the Shape Memory Hydrogels, the SMPs show a very fast response, but they are one-way. A good solution is the combination of both materials: hydrogels are combined with a non-swelling polymer or filament in the hydrogel-based 4 DP environment [108]. The hydrogel expands when the printed structure is submerged in a solvent, causing mismatch stresses between two materials that result in a general form shift. With this method, activation programming requirements can be completely disregarded. Unfortunately, hydrogels' rigidity and strength both fall short of expectations. Effective reduction of this flaw is possible using a composite method that combines rigid SMPs and soft gel [27].  
Lastly, a new material category able to provide a fast and reversible shape change is Liquid Crystal Elastomers. The principle is based on the transition between the liquid crystal (nematic) state and the isotropic state. Stimuli can be light, heat, and electrical or magnetic fields [109].
## 5 Stability Analysis  
The stability proof is based on the general state derivative form in Equation (6) and control input in Equation (15). For stability proof, the following assumptions are defined:  
**Assumption 1**.: _The error between the state derivative estimate using a low-pass filter in Equation (17) and the true state derivative is bounded such as \(\forall\kappa_{x_{0}},\ \ e_{\dot{x}_{0}}=\dot{x}_{0}-\dot{\epsilon}_{0},\ \ \Big{\|} \epsilon_{\dot{x}_{0}}\Big{\|}\leq\kappa_{x_{0}}<\infty.\)_  
**Assumption 2**.: _The nonlinear disturbance observer estimate error \(\epsilon_{e}=\varepsilon-\hat{\epsilon}\) designed using Equations (10)-(13) converges exponentially as in Equation (14)._  
Let us define an error dynamics as follows:  
Figure 2: Block diagram of incremental dynamic based on the rotational flight controller with nonlinear disturbance observer.  
\[e=x-x_{ref},\ \ \dot{x}_{ref}=f_{ref}\Big{(}x_{ref},r_{cmd}\Big{)}, \tag{25}\]  
\[\dot{e}=\
u+e_{\dot{x}_{0}}+e_{\varepsilon}-f_{ref}, \tag{26}\]  
\[\
u=f_{ref}-Ke. \tag{27}\]  
Then  
\[\dot{e}=Ae+\Delta_{e} \tag{28}\]  
where \(A=-K\) which is chosen as the Hurwitz matrix and \(\Delta_{e}=e_{\dot{x}_{0}}+e_{\varepsilon}\). Selecting the Lyapunov function as follows:  
\[V(e)=e^{T}Pe \tag{29}\]  
where \(P=P^{T}\) is a positive definite matrix obtained from solving the Lyapunov equation \(PA+A^{T}P=-I\)[27]. The time derivative of the above Lyapunov equation becomes  
\[\begin{array}{l}\dot{V}(e)=e^{T}\big{(}PA+A^{T}P\big{)}e+2e^{T}P\Delta_{e}\\ \leq-\|e\|^{2}+2\|e\|\|P\|\overline{\Delta}_{e}\\ \leq-\overline{\theta}\|e\|^{2},\ \ \forall\|e\|\geq\frac{2\|P\|\overline{ \Delta}_{e}}{1-\overline{\theta}},\ \ \overline{\theta}\in(0,1).\end{array} \tag{30}\]  
Then, from [27] (Chapter 4, Theorem 4.18), the solution of the above equation satisfies  
\[\begin{array}{l}\|e\|\leq\Gamma(\|e_{0}\|,t-t_{0}),\ \ \forall t_{0}\leq t \leq t_{0}+T,\\ \|e\|\leq\alpha_{1}^{-1}\big{(}\alpha_{2}\big{(}k_{1}\overline{\Delta}_{e} \big{)}\big{)},\ \ \forall t\geq t_{0}+T.\end{array} \tag{31}\]  
where \(\Gamma(r,s)\) is a class KL function, \(t_{0}\) is an initial time, \(T\) is a time constant, and \(\alpha_{1}(s)\) and \(\alpha_{2}(s)\) are the class K functions. The definition of the class KL and K functions can be found in [27], Chapter 4. This completes the stability proof.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
## 8 Conclusions  
This work investigates the adaptive packet coding for reliable UWA communications. This paper proposes two schemes based on two different ARQs. The first scheme is based on the conventional S&W ARQ scheme. The sender chooses the optimal number of packets in each transmission according to the feedback transmission result about the last transmission. We also set maximum retransmission times to avoid infinite retransmission considering the error-prone characteristic of UWA channels. This problem is formulated as a finite horizon optimization and solved with the DP algorithm. To overcome the impact of long propagation delay, we propose the modified juggling-like ARQ scheme for RLPC. Compared with the standard J-ARQ scheme, the transmission duration of the proposed scheme is variant and adapts to the rateless characteristics of the random linear packet coding. A two-step DP algorithm is proposed to find out the optimal solution. Simulation results demonstrate the performance gain of the proposed schemes as well as the impact of various practical factors such as data length, group size, channel steady-state distribution and transmission distance.  
Conceptualization, L.J. and Y.T.; formal analysis, L.J.; methodology, L.J. and H.Y.; software, L.J. and Y.T.; validation, C.H. and H.Y.; writing--original draft, L.J.; writing-- review and editing, H.Y. and C.H.; visualization, L.J. and Y.T.; supervision, H.Y.; project administration, L.J. and H.Y.; funding acquisition, L.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61801079, 62071383, 61871418), the open research fund of State Key Laboratory of Integrated Services Networks (ISN22-15), and the Science and Technology on Underwater Information and Control Laboratory (6142218200408).  
Not applicable.  
Figure 6: Throughput efficiency as a function of the steady-state probability \(\pi_{0}\).  
Data Availability Statement: The data presented in this paper are available after contacting the corresponding author.  
The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.