esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Simulation Setup  
We utilize the open-source simulation platform with great realism for testing IEEE 802.11 ad/ay standards by extending the multipath congestion control and scheduling design at the TRA layer. We have utilized and built upon the NS-3 modules developed in [11; 33], and our simulation setup consists of four main software tools as follows:  
* In NS-3, we have created an MPTCP module [11] that adheres to RFC-6824 and closely resembles the MPTCP Linux kernel architecture. See [11], where we can obtain a quick overview of our module. Several path management approaches (e.g., FullMesh and NdiffPorts), MPTCP congestion control and scheduling protocols (e.g., LIA, OLIA, BALIA, DALIA) are supported by the module.
* IEEE 802.11 ad/ay NS-3 implementation is another main foundation of our performance evaluation setup [33]. We utilize the IEEE 802.11_ay_ MAC layer design with a very accurately abstracted PHY layer. In addition, we implement a realistic mmWave channel model based on channel profiles obtained by the ray-tracing software at 60 GHz. Our NS-3 MPTCP module is coupled tightly with the 802.11 ad/ay implementation.
* We adopt a MATLAB-based code/beam generator for creating codebook instances that describe the beam patterns of an IEEE 802.11 ad/ay enabled device's phased antenna array(s) ([[https://github.com/wigig-tools/codebook-generator](https://github.com/wigig-tools/codebook-generator)]([https://github.com/wigig-tools/codebook-generator](https://github.com/wigig-tools/codebook-generator)), accessed on 22 November 2022);
* We implement a MATLAB-based mmWave channel modeling as a ray-tracing software for channel realization building upon quasi-deterministic channel realization software publicly available in GihHub ([[https://github.com/wigig-tools/qd-realization](https://github.com/wigig-tools/qd-realization)]([https://github.com/wigig-tools/qd-realization](https://github.com/wigig-tools/qd-realization)), accessed on 22 November 2022);
* The _training and ablation analysis_ in this paper follows the lines of that of [34; 35; 36; 37], and therefore we include the essential steps only.  
In our network setup, ten devices are moving in an elliptical path and are associated with multiple APs as shown in Figure 6, with the following settings (unless stated otherwise): (i) between the server and devices, we used three MPTCP connections (each with three subflows selected from seven links EDMG-SC 802.11_ay_ networks with spatial streaming up to eight); (ii) a setup of five APs where devices are always moving in an elliptical orbit at a speed of 15 m per sec. (iii) Various size files from an HTTP server are downloaded, which is creating the emulated packet traffic, ranging in size from 1 MB to 20 MB. Other parameters and protocols used in the simulation are summarized in Table 2.
# Novel Instrument for Clinical Evaluations of Active Extraocular Muscle Tension
## 3 Results  
Three male subjects aged 26.3 \(\pm\) 2.3 years (mean \(\pm\) SD; range: 25-29 years) had heights of 169.3 \(\pm\) 2.1 cm (range: 167-171 cm) and body masses of 72 \(\pm\) 6.6 kg (range: 65-78 kg). None of the subjects had any limitations in eye movement, and all were confirmed as physical status classification I on the American Society of Anesthesiologists score. The exclusion criteria were the presence of other ocular diseases (e.g., strabismus, nystagmus, ptosis, blepharospasm, or glucoma), thyroid disorder, muscular or neurological diseases (e.g., Parkinson's disease or myasthenia gravis), history of ocular trauma, previous strabismus or ocular surgery, or a history of receiving medications known to affect muscle tension (e.g., muscle relaxants).  
After topical anesthetic instillation, the cotton-tipped swab of the ATMD was placed on the limbus of the eyeball that was looking in the direction of the muscle that would be measured. The subject then tried to move the eyeball toward the direction of the cotton swab. The ATMD could measure active EOM tension while restricting eyeball movement, as shown in Figure 8b. The maximum tension in each rectus muscle was collected.  
The active EOM tensions for the four rectus muscles were 101.7 \(\pm\) 15.0 g, 88.0 \(\pm\) 15.4 g, 61.3 \(\pm\) 6.8 g, and 121.3 \(\pm\) 38.5 g for the LR, medial rectus, inferior rectus, and superior rectus, respectively, as shown in Figure 8a. The substantial variation observed in SR values may be attributed to a unique anatomical feature distinguishing it from other muscles. Unlike other muscles, the SR forms a complex with the levator muscle and shares neural innervation. It is impractical to exclude the possibility that the force of the levator muscle has a concurrent influence when measuring the active force of the SR. This could potentially  
Figure 7: Calibration of ATMD between mass and calculated output (**a**) and schematic of calibration process (**b**).  
be a significant contributing factor to the observed variation. In future research endeavors, we plan to conduct prospective analyses using large datasets to address this issue. In addition, EOM tensions in Figure 8a were higher than those measured in previous studies of 45 g to 60 g [16; 31]. Those previous studies only measured the EOM tension directly in detached EOMs during saccadic eye movements. The EOM was detached from the globe and attached to a strain gauge system using a silk suture. However, in the current study, we did not detach the EOM and measured the active EOM force under a typical clinical condition. Our measurements, therefore, contained passive elastic tensions that arose from ocular connective tissue, which represents the counteracting elasticity of the orbit that attempts to move the eye back to its central position. Our method is non-invasive and similar to the traditional method of conventional FGT using a cotton-tipped swab, making it easy to perform, thus lowering the barrier of application for clinicians.
## 6 Hydration of 1-Haloalkynes
### Metal-Catalysed Hydrations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Route** & **Catalyst (mol\%)** & **Reaction Conditions** & **Scope (Yield)** \\ \hline He (2016) [194] & Cu(OAc)\({}_{2}\cdot\)H\({}_{2}\)O (10) & H\({}_{2}\)O (1 equiv.) in TFA & 15 \(\alpha\)-bromoketones (77–96\%) \\  & & at 70 \({}^{\circ}\)C for 6 h & 3 \(\alpha\)-chloroketones (91–92\%) \\  & & at 4 \(\alpha\)-iodoketones (90–96\%) \\ Liu (2014) [195] & AgF (5) & H\({}_{2}\)O (1 equiv.) in TFA & 15 \(\alpha\)-bromoketones (61–95\%) \\  & & at 40 \({}^{\circ}\)C for 6–16 h & 3 \(\alpha\)-chloroketones (81–90\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cu\({}^{\text{II}}\)- and Ag\({}^{\text{I}}\)-catalysed hydrations. [PERSON] and [PERSON] independently described an FeCl\({}_{x}\) yH\({}_{2}\)O-catalysed hydration [196; 197]. Their systems required the presence of methanesulfonic acid (MsOH) in order to transform 1-haloalkynes in sufficiently high yields (Scheme 74). The substrate scope was rather limited, as [PERSON]'s procedure generated one \(\alpha\)-bromoketone and [PERSON]'s reported only three aromatic \(\alpha\)-chloroketones. The acidic nature of these protocols further narrowed the hydration scope, as acid-sensitive functional groups (e.g., OH, NH\({}_{x}\)) did not tolerate these conditions. [PERSON] and co-workers designed an In(OTf)\({}_{3}\) (10 mol%)-catalysed hydration optimised for 1-bromoalkynes (Scheme 75) [198]. The hydration reaction was only effective if carried out in an acidic solvent, i.e., HOAc. Together with the high reaction temperature, this route requires harsh hydration conditions, which limits its applicability. Nevertheless, 16 \(\alpha\)-bromo, 3 \(\alpha\)-chloro and 3 \(\alpha\)-iodoketones were obtained in good to excellent yields (78-93%). Despite the acidic solvent, the system mediated the hydration of an OH-substituted 1-bromoalkyne, affording the corresponding ketone in 82% yield. Recently, [PERSON] and co-workers designed a highly versatile Ce(SO\({}_{4}\))\({}_{2}\)-catalysed hydration [199]. The addition of sulfuric acid to the 1-haloalkyne with 5 mol% of Ce(SO\({}_{4}\))\({}_{2}\) in DCM, generated 30 \(\alpha\)-haloketones in moderate to excellent yields (Scheme 76). Despite the acidic nature of the protocol, acid-sensitive groups (i.e., OH and NHTs) were well tolerated. The presence of steric bulk close to the triple bond appeared to have a considerable effect on the hydration efficiency.
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 3 Method
### Blind/Referenceless Image Spatial Quality Assessment  
In this study, we use the Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE) to assess the visual-spatial image quality of nSight-1 imagery. The BRISQUE model uses scene statistics of locally normalized luminance coefficients to characterize possible losses of naturalness in the image as a result of distortions [29]. We selected BRISQUE due to its computational simplicity and superior performance over other complex methods. [PERSON] [29] reported that BRISQUE produced better results compared traditional methods involving full-reference peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). BRISQUE uses a Natural Scene Statistics model framework of pair-wise products locally normalized luminance values to compute an automatic measure of perceptual quality in the spatial domain without the use of a reference image [29; 30]. The algorithm does not use distortions specific features such as blur to calculate image quality losses. The results of the image considered to be highly correlated. BRISQUE is increasingly being used in a variety of satellite image quality assessments. The image quality scores computed by the BRISQUE Model are expressed in a grading scale of 0 to 100 where a score of 0 is indicative of the best quality and 100 is considered the worst. Translating this to the equivalent categorical five-level scales (excellent, good, fair, poor, bad) implies 0 is excellent and 100 is poor. This approach is beneficial since other blind image quality assessment methods are distortion specific and are only able to perform that assessment when there is prior knowledge to the distortion affecting the image such as blur, compression, and noise.
## 3 Background
### CSI-Based Attack
#### 3.2.1 Overview of CSI  
Channel state information (CSI) describes the multi-path propagation scattered from the wall and surrounding objects, and it characterizes the variations in the wireless channel and provides more detailed fine-grained information of wireless signals. In our work, we leverage the multipath effect to recognize the input sensitive information, and different CSI modes, such as instantaneous CSI and statistical CSI, are not considered in this paper. That is also because the multipath effect will always exist no matter whether for instantaneous CSI and statistical CSI. CSI can be obtained by WiFi network interface controllers (NICs). Let \(X(f,t)\) be the transmitted signals at different subcarrier frequencies and \(Y(f,t)\) be the received signals at different subcarrier frequencies, and the channel state information \(H(f,t)\) at the receiver end can be calculated via \(H(f,t)=\frac{Y(f,t)}{Y(f,t)}\). Let \(N_{Tx}\) represent the number of transmitter antennas and \(N_{Rx}\) the number of receiver antennas, the attacker received at the receiver end will be \(30\times N_{Tx}\times N_{Rx}\) CSI streams, and 30 means there are 30 subcarriers for each antenna pair.
## 4 Discussions of Engineering Applicability of Theoretical Solutions
### The Influence of the Thickness of Earth Covering  
Different soil types affect the structure differently. In structural analysis, the impact of soil is mainly reflected by soil pressure. Different types of soil produce different soil pressure distributions. Still, the distribution rules of soil pressure have similar characteristics, which can be simplified as trapezoidal distributed loads or segmented trapezoidal distributed loads. Furthermore, the influence of segmented trapezoidal distributed loads on structural response can be considered the superposition of trapezoidal distributed loads in structural analysis.  
This study focuses on analyzing control parameters for the location of TPT openings in complex underground spaces. The research object is a two-layer subway station with a buried depth of no more than 21 m. In the practical engineering of subway stations, the soil properties within this range are almost uniform. This article discusses the primary working conditions for the soil to be homogeneous and focus on the relationship between the opening position and the control bending moment under different thicknesses of earth covering trapezoidal distributed earth pressure.  
Let us take parameters in Section 3.1 as an example, and only the values of thickness of the earth covering are considered as 1.5 m, 2 m, 3 m, 4 m, 5 m, and 6 m. The relationships between the bending moment at the bottom of the column and the thickness of the earth covering are gained by three-dimensional Midas gen software, and analytical solutions are obtained via Equation (38), as shown in Figure 18.  
Figure 17: Relation curve of the column bottom bending moment with the distance between the wall columns.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance between** & **Analytical** & **Simulation** & **The Error between** \\
**Two Wall Columns** & **Solution** & **Simulation** & **Them** \\ \hline
2.5 & 2943.9 & 3369 & \(-\)12.6\% \\ \hline
3 & 3532.68 & 3717 & \(-\)4.96\% \\ \hline
3.5 & 4121.46 & 4101 & 0.49\% \\ \hline
4 & 4710.24 & 4373 & 7.71\% \\ \hline
4.5 & 5299.02 & 4918 & 7.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison between simulation and analytical solution with different distances between two wall columns.  
From Table 4, the error between analytical solutions and the simulation results ranges from 4.9% to 13.7% under the different thicknesses of the earth covering. The error increases with the thickness of the earth covering in the acceptable scope, and the analytical solution has the advantage of high goodness of fit and satisfactory effect.  
In addition, the approximate analytical solutions deduced in this paper are very close to three-dimensional simulation results compared to the above key factors, such as the distance between wall columns, the story height of the metro station, and the thickness of the earth covering in the actual engineering. Equation (38) is engineering applicable and can be quickly and easily applied to most similar projects.
## Nomenclature  
\begin{tabular}{l l} \(N_{0}\) & normal initial positive pressure \\ \(s_{\rm a}(t),s_{\rm v}(t)\) & displacements of the mass in two directions \\ \(k_{\rm u},k_{\rm v}\) & shear spring stiffness in two directions \\ \(k_{\rm n}\) & normal spring stiffness \\ \(w_{\rm u}(t),w_{\rm v}(t)\) & displacements of the friction damper in two directions \\ \(Q_{\rm u}\) and \(Q_{\rm v}\) & external excitations along two directions \\ \(Q_{\rm n}\) & normal external excitation \\ \(\mu\) & friction coefficient \\ \(N\) & pressure \\ \(n\) & distance of the normal motion of the mass block \\ \(f\) & total friction force \\ \(D\) & fractal dimension \\ \(G\) & fractal roughness parameter \\ \(M\) & overlapping ridge number \\ \(\Phi_{\rm m,n}\) & assumed phase \\ \(\gamma\) & fixed value equal to 1.5 \\ \(E_{1},E_{2}\) & Young's modulus \\ \(v_{1},v_{2}\) & Poisson's ratio \\ \(P\) & total contact load \\ \(A\) & actual contact area \\ \(\delta\) & asperity interference \\ \(A\)F\({}_{\rm e}\) & elastic force \\ \(K_{\rm n},K_{\rm t}\) & normal and tangential contact stiffness \\ \(k_{\rm nj},k_{\rm tj}\) & stiffness of each contact pair \\ \({\bf M},{\bf K},{\bf C}\) & mass, stiffness, damping matrix \\ \(N_{\rm h}\) & truncated harmonic order \\ \(\omega\) & external excitation frequency \\ \({\bf U}_{0},{\bf U}_{\rm ck},{\bf U}_{\rm sk}\) & Fourier series corresponding to the \(\odot\)-order harmonic \\ \({\rm N}_{\rm d}\) & degree of freedom of the whole system \\ \({\bf f}(t)\) & nonlinear force \\ \({\bf f}_{\rm nl}\) & external excitation force \\ \({\bf P}(\omega)\) & dynamic stiffness matrix \\ \end{tabular}
## 4 Experimental Analysis
### Analysis
#### 4.4.3 Number of Iterations  
The number of iterations is an important indicator of an algorithm. Figure 11 shows the number of iterations of the four datasets under different situations.  
Compared with Figure 6, we can see that the iteration number of the small datasets is positively correlated to the training time. A small dataset needs more iterations. For large datasets, the change of the iteration number is relatively stable. Besides, Figure 11c shows more confusion than Figure 11a. Real-world data and image data need more iterations than other artificial data, which is because more noise and uncertainty are embodied in real-world data (e.g., dataset 3); in dataset 4, the dimensions that had to be optimized were much higher, thus leading to a relatively large iteration number.  
Figure 10: Training error comparison between traditional PSO, ADMM, CNN and P-PSO–based distributed ESN algorithms. A typical length of data size (\(1\times 10^{5}\)) is selected for the four datasets.  
Figure 9: Correlation coefficient on former three datasets. (**a**) Dataset 1; (**b**) Dataset 2; (**c**) Dataset 3.  
We chose datasets 1 and 4 to compare the iteration numbers between the four different algorithms, since dataset 1 represents time-series data and 4 represents image data. Figure 12 presents the comparison.  
The most significant difference between dataset 1 and 4 is the calculation amount (for ADMM and CNN), and the search range (for PSO and P-PSO). When the dimension of the input data is small (e.g., dataset 1), P-PSO and ADMM can quickly converge, and the traditional PSO needs considerably more iterations. However, once a huge dimension of inputs is fed, the calculation amount/search range would increase sharply, and both P-PSO and ADMM need more iterations. Due to the special network architecture, CNN could converge along with a relatively small iteration number and obtain a  
Figure 11: Number of iterations of each dataset in different lengths and nodes. (**a**) Number of iterations of dataset 1; (**b**) Number of iterations of dataset 2; (**c**) Number of iterations of dataset 3; (**d**) Number of iterations of dataset 4.  
rather good achievement. Even though the traditional PSO uses the least iteration numbers, the testing error is quite high (82.2%, Figure 10). The reason is that traditional PSO is not able to handle the search task within such a high-dimensional space, and falls into suboptimal solutions easily.
# DLI-Based DP-QPSK Reception Scheme for Short-Range Optical Communication
## 4 FSR Optimization
Since the possible set of values for the phase difference \(\Delta\varphi_{n}\) was \(\{0,\frac{\pi}{2},\pi,\frac{3\pi}{2}\}\), according to Equations (6) and (9), the photodetector output of case 1 was larger than that of the situation with an exactly one-symbol delay, which created a deterministic constructive interference for every symbol period. The deterministic constructive interference could suppress the inter-symbol crosstalk that resulted from transmission impairments to a certain extent. It can also be known from Equation (10) and Figure 5 that the decrease of \(t_{2}\) will increase the error rate of the final judgment. Hence, decreasing the differential delay can both increase the tolerance of transmission impairments and increase the BER for receivers. As for case 2, it can be seen from Equations (12) and (13) that the photodetector output did not show any advantage over the situation with an exactly one-symbol delay due to the presence of \(\Delta\varphi_{n+1}\). Therefore, case 2 was not taken into consideration in the following simulation.
## 6 Challenges and Future Works
### Adoption of Hybrid Algorithms  
In term of algorithm implementation, exact, heuristic and metaheuristic algorithms are widely adopted in solving the formulated model of the ridesharing problem. An exact algorithm can guarantee optimality while heuristic and metaheuristic algorithms are able to solve the problem using less computational time. There are several new exact algorithms such as the modern column generation, relaxation, and decomposition approaches which were developed to solve large instances. It can be an interesting field of study to be considered for future work. The widely adopted decomposition algorithm in vehicle-routing-related studies are Dantzig decomposition, Lagrangian Decomposition, Benders decomposition and the other extended variants of the decomposition approaches. Those exact algorithms play an important role in developing the tighter lower bounds in order to be able to benchmark the developed heuristics against the optimal solution for large problem instances. CPLEX is the most common method that is used to obtain the optimal solution but it can only deal with small instances. Therefore, there is a need for a new exact algorithm that can decompose large instance problems into less complex subproblems and achieve optimality within a reasonable time.  
In terms of heuristic and metaheuristic, a hybrid algorithm is largely adopted because it can achieve the balance of intensification (optimality) and diversification (shorter computational time). The hybridisation can be a hybrid between metaheuristic, heuristic, mathematical programming, and other algorithms from different fields of study. Since the ridesharing problem is a NP hard problem, then there is a need to have a hybrid algorithm. Referring to [119], the hybridisation of the decomposition approach with a metaheuristic could be considered for future studies as a powerful algorithm to be used in vehicle-routing-related problems. With regards to the performance of the algorithms, in [120] it was proposed that the culture of \"individual competition\" be switched to one of a \"collective knowledge discovery\". Instead of competing among different algorithms to prove which methods perform better, it is better to understand which of the algorithms are more efficient in solving a specific problem.
# Effect of Baffle Board on Aerodynamic and Stealth Performance of Double S-Duct Caret Intake
###### Abstract  
Intake is not only the main air supply component of an aircraft, but also one of the forward radar scattering sources. The aerodynamic and stealth performance of intake is critical to the serviceability of advanced fighter aircrafts. The effects of baffle boards with different configurations on the performance of the caret intake with a double S-duct diffuser are presented in this article. The multi-level fast multipole method (MLFMM) and the SST k-\(\omega\) turbulence model were respectively used to calculate the surface current and the flow field. It was found that the average RCS value of intake can be effectively reduced by installing the baffle board with vertical orientation in the front diffuser, with the DC60 value and the loss of outlet total pressure both increased slightly. The boundary layer separation and the RCS characteristics of intake were closely related to the configuration of the corrugated baffle board. Compared with the traditional curved board, by installing the corrugated board with optimized corrugation number and shape, the stealth performance of intake can be further improved, and the loss of aerodynamic performance can be also reduced.  
double S-duct caret intake; curved baffle board; corrugated baffle board; total pressure recovery; radar cross-section +
Footnote †: journal: Applied sciences