esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure and Sound Absorption Properties of Spiral Vane Electrospun PVA/PEO Nanofiber Membranes
## 2 Materials and Methods  
Polyvinyl alcohol (1788) and Polyethylene oxide (\(M_{\mathrm{w}}\) = 1,000,000) were purchased from Changzhou Fengyuan Textile Assistant Co. Ltd. (Changzhou, China) and Shanghai Liansheng Chemical Co. Ltd. (Shanghai, Chian), respectively. PVA (8 wt %) and PEO (3 wt %) were blended by electrospinning with different solution mass ratios of 100/0, 90/10, 80/20, and 70/30, respectively. Then the nanofiber membrane was composited with needle-punched nonwovens (DN30D1, 4 mm, Shenzhen Dongfang Nonwove Co. Ltd. (Shenzhen, China)).  
Spiral vane electrostatic spinning machine (Kunshan Tongray) (Figure 1), which is composed of a spiral generator, an acceptor and a high-voltage power source. High-voltage power can provide up to 70 KV voltage, strong electric force was produced on the surface of the metal spiral to overcome the surface tension of solution, the solution Taylor cones were stretched into superfine jets. Then water vapor evaporated and formed PVA/PEO nanofibers on the acceptor.  
Fourier Transform-Infrared (FT-IR) transmittance were carried out for different nanofibers membranes by using Fourier transform-infrared spectrometer (Nicolet5700, Nicolet, Madison, WI, USA) in the spectra range of 4000-800 cm\({}^{-1}\). X-ray diffraction (XRD) scans were obtained by using PANalytical X-Pert Pro MPD XRD system (Chemplex, Palm, FL, USA), the diffraction angle in the range of 5\({}^{\circ}\)-45\({}^{\circ}\). The morphology of the nanofiber membranes were measured by using scanning electron microscopy (S4800, Hitachi, Tokyo, Japan). The three-dimensional characterization of the nanofiber membranes were tested using a depth of field three-dimensional microscopy system (VHX-1000, OLYMPUS, Osaka, Japan). According to ISO 10534-2:1998 standards, the acoustical properties of samples were measured with a transfer-function method. Sound absorption testing instruments were SW477 and SW422 impedance tube (BSWA Technique Company, Beijing, China) in the range of 80-6300 Hz. Testing conditions: relative temperature 20 \(\pm\) 1 \({}^{\circ}\)C, relative humidity 65 \(\pm\) 2%.  
The structure of impedance tube was showed in Figure 2. The impedance tube method is on the basis of the two-microphone transfer-function method. There are sample materials and loudspeakers at both ends of the impedance tube, which is airtight, rigid, and straight. Different frequencies of sound waves are generated by the sound source along the plane to the material direction, the two sound pressure measuring devices near the sample obtain the acoustic transfer function of the signal,  
Figure 1: Schematic diagram of spiral vane electrostatic spinning machine.  
then the sample's normal incident absorption coefficient is calculated, the sound absorption coefficient spectrum displayed on the computer screen finally.
###### Abstract  
Background: Children born with single-ventricle hearts require surgery in order to survive. Liver fibrosis is a known complication of Fontan surgery for univentricular hearts. Methods: In this study on 13 post-Fontan and 21 post-Glenn patients, we used elastography (shearwave and transient elastography) as well as serum biochemical fibrosis markers to evaluate the degree of liver fibrosis in comparison to 32 controls. Results: The mean \(\mathrm{E_{median}}\) and \(\mathrm{V_{median}}\) values determined by shear wave elastography in the Fontan Group were significantly higher than the controls (4.85 kPa vs. 3.91 kPa and 1.25 m/s vs. 1.12 m/s, respectively). Fontan patients had significantly increased Fibrotest, Actitest, AST-to-Platelet Ratio index, ALT and GammaGT levels compared to controls. For post-Glenn patients, the mean \(\mathrm{E_{median}}\) and \(\mathrm{V_{median}}\) values were similar to healthy controls, whereas the Fibrotest, Actitest and AST-to-Platelet Ratio index were significantly increased. Using transient elastography, we found significantly higher values for \(\mathrm{E_{median}}\) and \(\mathrm{V_{median}}\) in Fontan patients compared to Glenn patients. Conclusions: Elastography and biochemical fibrosis markers are valuable non-invasive tools for screening and monitoring liver fibrosis in patients with Fontan and Glenn interventions.  
Fontan; Glenn; noninvasive; liver; fibrosis; elastography; Fibrotest Article  
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# The Influence of Heat Input on the Surface Quality of Wire and Arc Additive Manufacturing
## 2 Materials and Methods
### Surface Evaluation Method of WAAM  
In order to quantitatively analyze the surface smoothness and surface profile shape characteristics of the specimen, the optimal function profile is selected according to the actual profile shapes and the principle of minimum error to establish an ideal model, and the actual specimen models are established with a three-dimensional scanning device. The standard deviation \(S_{sd}\) and the root mean square deviation \(S_{rms}\) of each specimen to characterize the surface quality were calculated.  
Standard deviation \(S_{sd}\):  
\[S_{sd}=\sqrt{\frac{1}{M-1}\frac{1}{N-1}\sum_{i=1}^{M}\sum_{j=1}^{N}\left(X_{ ij}-\overline{X}\right)^{2}} \tag{2}\]  
In the formula, \(M\) and \(N\) are the number of points in the grid in two directions, \(X\) is the actual value of each point, and \(\overline{X}\) is the average value of each point.  
The root mean square deviation \(S_{rms}\):  
\[S_{rms}=\sqrt{\frac{1}{mn}\sum_{i=1}^{m}\sum_{j=1}^{n}\eta_{ij}^{2}} \tag{3}\]  
In the formula, \(m\) and \(n\) are the number of points in the calculated area, and \(\eta_{ij}\) is the distance from the actual point to the reference point.  
The standard deviation reflects the smoothness of the specimen. It focuses on the surface roughness of the specimen, but it cannot reflect the local shape of the specimen. The root mean square value reflects the deviation between the specimen and the ideal model,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Heat Input** **(\(\mathbf{J}\)-\(\mathbf{mm^{-1}}\))** & **Current** **(\(\mathbf{A}\))** & **Voltage** **(\(\mathbf{V}\))** & **Travel Speed** **(\(\mathbf{m}\)-\(\mathbf{min^{-1}}\))** & **Wire Feed Rate** **(\(\mathbf{m}\)-\(\mathbf{min^{-1}}\))** \\ \hline
12,250 & 100 & 14 & 0.08 & 1.123 \\
10,197 & 95 & 13.8 & 0.09 & 1.263 \\
8568 & 90 & 13.6 & 0.10 & 1.404 \\
7248 & 85 & 13.4 & 0.11 & 1.544 \\
6160 & 80 & 13.2 & 0.12 & 1.685 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Process parameters during WAAM under different heat input.  
Figure 1: Placement of thermocouples.  
and can reflect the degree of deviation of the surface profile from the ideal surface, which can better characterize the surface quality of these specimens. The specific evaluation steps are shown in Figure 2.
# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 3 The Local Rules of ESUSTPM
### The Parameters of the Local Rules  
In order to maintain a stable communication connectivity and collision avoidance, each agent of UAV swarm should keep a given distance from the other agent as far as possible. Thus, the design of the local rules should consider the distance and direction factors concurrently. The relationship between the distance and \(\overset{\rightharpoonup}{F}\) is reflected by the coefficient \(\beta_{i}\) in Equation (9). Equation (9) demonstrates that the interaction strength scale varies with the distance between agents. \(\beta_{i}\) is a constructed function based on two exponential functions shown in Equations (7) and (8).  
\[g_{i}(d_{i})=t_{i}\Bigg{(}1-\exp(\frac{-(d_{i}-c_{i})^{2}}{{\delta_{i}}^{2}}) \Bigg{)}, \tag{7}\]  
\[a_{i}(d_{i})=k_{i}(d_{i}-c_{i})\exp(\frac{-(d_{i}-c_{i})^{2}}{{\delta_{i}}^{2} }), \tag{8}\]  
\[\beta_{i}=a_{i}(d_{i})g_{i}(d_{i}), \tag{9}\]  
where \(a_{i}(d_{i})\) was designed to avoid collision between agents and keep a given distance for the connectivity maintenance [20; 21]. \(k_{i}\) is the scale factor of \(a_{i}(d_{i})\), and the parameters \(\delta_{i}\) allow for the adjusting range of the \(a_{i}(d_{i})\). The curve of the \(a_{i}(d_{i})\) is shown in Figure 3a. The curve slope at the center point \(c_{i}\) is so high that the UAV agent tends to oscillate at the center point \(c_{i}\). In order to avoid the state oscillation of the agents at \(c_{i}\), the adjusting function \(g_{i}(d_{i})\) shown in Figure 3a is firstly designed in Equation (7) to adjust the curve slope of \(a_{i}(d_{i})\). \(t_{i}\) and \({\delta_{i}}^{\prime}\) in \(g_{i}(d_{i})\) present the factor of the adjusting strength and the adjusting range of the \(g_{i}(d_{i})\). \(c_{i}\) in Equations (7) and (8) is the given distance that the agents should maintain.  
According to Equation (9), the curve of \(\beta_{i}\) shown in Figure 3c is smooth at the center point \(c_{i}\). This operation ensures that UAV states change smoothly at the center \(c_{i}\), and the agents will be subject to resistance gradually when leaving the center \(c_{i}\).  
Figure 2: The sketch map of the local rules.
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 3 Evolutionary and Clean Slate Approaches
### The Clean-Slate Approach
#### 3.2.2 MobilityFirst  
MobilityFirst is a future internet architecture that was proposed by NSF as a part of their clean-slate project program. The initiative faces challenges such as mobile access and scalability, as detailed in [39]. The architecture is built around key components, including names from the address separation, public key-based names, delay-tolerant routing that can handle wireless link quality fluctuations, large protocol data units, and location-aware services (see Figure 6). The service layer is designed to serve as the narrow waist, facilitating the development of mobile-centric services while the addressing security and privacy concerns that were outlined in [40]. To enhance the network functionality, the paper incorporates specific routing techniques, particularly storage-aware routing.  
The core paradigm of MobilityFirst emphasizes the use of mobile devices to enable communication between the applications and entities that are identified by a global unique identifier. Unlike IP addresses, which are associated with fixed endpoints and do not account for mobility, MobilityFirst routers can leverage a distributed name resolution service to overcome mobility-related challenges [41].  
The authors were able to develop a prototype of the MobilityFirst protocol stack on a GENI testbed in 2011, with contributions from various US universities (Rutgers, UMass,  
Figure 5: Comparison of IP and NDN at narrow waist [37].  
MIT, Duke, University of Michigan, UNC, University of Wisconsin, and University of Nebraska), as well as research centers in the industry. GENI provides support for existing projects, infrastructure models for scaling networks, and a framework for device control between different users, as well as environments to validate, analyze, assess, and record the network outcomes.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 2 Likelihood-Based Modulation Recognition Methods  
Likelihood-based (LB) methods represent a category of modulation recognition approaches that calculate the likelihood ratio between the received signal and known modulation signals (as illustrated in Figure 2). While these methods are typically optimal in a Bayesian sense, they suffer from high computational complexity. Although LB methods were proposed earlier and have been extensively studied in traditional wireless-signal  
Figure 1: Modulation format recognition in VLC systems and its fundamental principles.  
modulation recognition, their application in the domain of VLC modulation recognition remains relatively limited. Below, we provide a brief introduction to several primary LB methods mentioned in [15; 16].
# Genetic Engineering Approaches for the Microbial Production of Vanillin
## 1 Introduction
Over the past three decades, considerable progress has been made in the molecular and biochemical characterisation of the genes and enzymes involved in the conversion of these various precursors into vanillin, and the diverse pathways that can be used for bioconversion have been the subject of several reviews [11; 12; 13]. These reviews have focused mainly on pathway characterisation and gene identification in vanillin-producing microorganisms. However, few details on the genetic engineering approaches to improve vanillin biosynthesis have been provided. After briefly describing the current knowledge of the genes and enzymes involved in vanillin biosynthesis in plants (Section 2) and in vanillin-producing microorganisms (Section 3), our review focuses on the major genetic engineering or synthetic biology approaches used to produce vanillin in microorganisms (Section 4). These approaches aim either at improving vanillin yields in vanillin-producing microorganisms and at expressing, in these microorganisms, genes that enable them to use economically viable, water-soluble, and nontoxic precursors, or at introducing bioconversion (mainly from phenylpropanoids) or de novo biosynthetic pathways (from simple precursors such as sugars or from wastes such as polyethylene terephthalate residues) in non-producing strains that can be easily manipulated genetically.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 2 Corrosion Mechanism and Experimental Study of Anchor Bolts  
### Corrosion Theoretical Model of Anchor Bolts  
The corrosion process model is an important reference for identifying the corrosion of anchor bolts in rock masses. The corrosion forms of anchor bolts include uniform corrosion, pitting corrosion, and stress corrosion, among others. As shown in Figure 1, when anchor bolts are used for a long time in environments with high chloride ion content, they are prone to corrosion by chloride ions. The evolutionary process of anchor bolts being corroded by chloride ions is as follows.  
Initial stage: Chloride ions infiltrate the interior of the anchor bolt anchoring system, eroding the surface of the anchor bolt and gradually breaking down the protective layer of the anchor bolt.  
Accelerated stage: The deterioration of the protective layer on the surface of the anchor bolt intensifies, resulting in an increased exposed area of the anchor bolt. Chloride ions continuously penetrate and migrate, further corroding the anchor bolt.  
Stable stage: The sectional area of the anchor bolt reaches a certain extent, causing a reduction in the cross-sectional area of the anchor bolt and resulting in a loss of the load-bearing capacity.
## 4 Case Study
### Data Collection and Sentiment Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentiment** & **Corresponding Words** \\
**(Examples)** & **Number of Words** \\ \hline Positive & \
\begin{tabular}{c} Satisfying, clean, comfortable, \\ friendly, kind, luxurious \\ Unkind, dirty, lousy, \\ uncomfortable, old, narrow, nasty \\ \end{tabular} & 130 \\ Negative &
\begin{tabular}{c} Unkind, dirty, lousy, \\ uncomfortable, old, narrow, nasty \\ \end{tabular} & 121 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sentiment-word lexicon used for the case study (translated from Korean to English) (adapted with permission from Ref. [55]; published by Elsevier, 2023). \begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Corresponding Words (Examples)** \\ \hline Furniture and appliances (FA) & Furniture, bed, sofa, desk, cabinet, mattress, pillow \\ Bathroom condition (BC) & Bathroom, toiletries, amenity, lotion, towel \\ Room condition (RC) & Cleaning, dust, noise, air conditioning, view \\ Dining and food (DF) & Room service, food, restaurant, diner, buffet \\ Service and staff (SS) & Staff, front desk, check-in, concierge, receptionist, internet access \\ Facilities and conveniences (FC) & Atmosphere, parking, swimming pool, spa and \\ sauna, gym, lounge, banquet hall \\ Location and surroundings (LS) & Location, airport shuttle, subway, taxi, landmark, \\ tour, beach \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Attribute lexicon used for the case study (translated from Korean to English) (adapted with permission from Ref. [55]; published by Elsevier, 2023).
## 3 Experiments and Analysis
### Experimental Settings
#### 3.1.3 Implementation Details  
In this experiment, all road extraction networks use the same hardware setup, loss function, optimizer, learning rate, dataset division, and data augmentation methods.  
The hardware setup includes a 13 th Gen Intel(R) Core(TM) i5-13500 HX CPU running at 2.50 GHz, 16 GB of RAM (Intel Corporation, Santa Clara, CA, USA), and an NVIDIA GeForce RTX 4060 Laptop GPU with 8 GB of VRAM (NVIDIA Corporation, Santa Clara, CA, USA). The algorithm programming environment utilizes PyTorch 1.10.2 with GPU acceleration via CUDA 11.3. The model employs Cross-Entropy (CE) Loss as the loss function, and Stochastic Gradient Descent (SGD) as the optimizer. The learning rate follows a Cosine schedule, starting at a maximum of 7 \(\times\) 10\({}^{-3}\) and gradually decreasing to a minimum of 7 \(\times\) 10\({}^{-5}\). The batch size is set to 8, with a total of 100 epochs for training.
## 4 Case Study and Proof of Concept
### Test System 1
The economic load dispatch equation is minimized in 30 individual trials using all the three algorithms and thereafter the minimum cost, maximum cost, and total number of times the minimum cost is yielded by each algorithm is recorded. The suggested CSAOA has a greater level of robustness, as shown by the smallest value of the standard deviation and the maximum hits. Figure 16 shows the boxplot that is drawn using the data mentioned in Table 15. The cost convergence characteristics in Figure 17 show the change in generation cost with the change in iterations for various algorithms. A sensitivity assessment is also operated to comment on the effects of change in tuning parameters _fl_, and \(z\). _fl_ is changed from 1.5, 2 and 2.5 for \(z\) ranging between 1, 5, and 9, as shown in Table 16. The generation cost remains unchanged, whereas the effect of change in tuning parameters is only shown in the execution time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & _fl_ & \(z\) & **Cost with DSM (USD)** & **Execution Time (s)** \\ \hline
1 & 1.5 & 1 & 74,774.7027 & 4.8003 \\ \hline
2 & 2 & 5 & 74,774.6995 & 4.8003 \\ \hline
3 & 2.5 & 9 & 74,774.6984 & 4.8003 \\ \hline
4 & 1.5 & 1 & 74,774.7054 & 3.2005 \\ \hline
5 & 2 & 5 & 74,774.6986 & 3.2005 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Sensitivity analysis for tuning parameters of CSAOA. Figure 15: Three-dimensional plot for cost and emission of test system 2 with \(\mu\) ranging from 0.1 to 0.9. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Minimum** & **Maximum** & **Average** & **Standard** & **Hits to** & **Execution Time** (s)** \\  & **Attained Cost** & **Attained Cost** & **(USD)** & **Deviations** & **Minimum Cost** & **Execution Time** (s)** \\ \hline CSA & 74,792 & 74,829 & 74,806.80 & 18.4361 & 18 & 5.06 \\ \hline AOA & 74,775 & 74,822 & 74,789.10 & 21.9063 & 21 & 4.05 \\ \hline CSAOA & 74,774 & 74,782 & 74,775.60 & 3.2547 & 24 & 3.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Statistical analysis of generation costs for test system 2 after 30 trials. Figure 16: Boxplot of generation costs using various algorithms. Figure 17: Convergence curve characteristics for the change of generation cost per iteration using various algorithms.
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 3 A Periodic Watermark-Based Detection of Replay Attacks
### Analysis of System Performance Degradation  
This paper focuses on analyzing the impact of this method on system control. Assuming no additional signals are present, the true state variables of the UMVs system are expressed by \(x(k)\). The state error is then described as \(\epsilon(k)=x(k)-\hat{x}(k)\). Employing the methodology presented herein, the state variables for the system during normal operation are given by \(\hat{x}^{\prime}(k)\), and the undisturbed steady-state error as defined within this section is \(\hat{\epsilon}(k)\), thus yielding \(\delta(k)=\hat{x}^{\prime}(k)-\hat{x}(k)\). Upon constructing the matrix \(\hat{x}(k)=\left[x(k)\ \ \epsilon(k)\ \ \delta(k)\right]^{\mathrm{T}}\), the following relation is established:  
\[\hat{x}(k+1)=\sum_{p=1}^{3}\begin{bmatrix}A_{dp}+B_{dp}F&-B_{dp}F&0\\ 0&A_{dp}-LCA_{dp}&0\\ 0&0&A_{dp}\end{bmatrix}\hat{x}(k)+\begin{bmatrix}w(k)\\ Hw(k)-Lv(k+1)\\ \sum_{p=1}^{3}B_{dp}u(k)\end{bmatrix} \tag{28}\]  
where \(H=I_{n}-LC\), \(\Theta\) and \(\gamma(k)\) can be achieved as:  
\[\Theta=\sum_{p=1}^{3}\begin{bmatrix}A_{dp}+B_{dp}F&-B_{dp}F&0\\ 0&A_{dp}-LCA_{dp}&0\\ 0&0&A_{dp}\end{bmatrix},\gamma(k) =\begin{bmatrix}w(k)\\ Hw(k)-Lv(k+1)\\ \sum_{p=1}^{3}B_{dp}u(k)\end{bmatrix}\]  
Consequently, the covariance of the system is expressed as:  
\[cov(\hat{x})=\Theta cov(\hat{x})\Theta^{\mathrm{T}}+\iota \tag{29}\]  
Wherein, \(\iota\) is characterized by:  
\[\iota=\begin{bmatrix}W&WH^{T}&0\\ HW&HW(I_{n}-LC)^{T}&0\\ LCW&0&\sum_{p=1}^{3}(B_{dp}\Sigma_{o}B_{dp}^{T})\end{bmatrix}\]  
In the scheme proposed in this paper, the system control performance index can be represented as:  
\[\begin{split} J&=\lim_{\ell\to\infty}\frac{1}{\ell+1}\sum_{k=1 }^{\ell}\mathbb{E}\Big{\{}x^{*\tau}(k)Qx^{*}(k)+(u(k)+\Delta o(k))^{T}\\ &\times R(u(k)+\Delta o(k))\big{\}}\\ &=\lim_{\ell\to\infty}\frac{1}{\ell+1}\sum_{k=1}^{\ell}\mathbb{E} \Big{\{}\tilde{x}(k)\Omega\tilde{x}^{T}(k)+\Delta o^{T}(k)R\Delta o(k)\Big{\}} \\ =& tr(\mathrm{cov}(\tilde{x}(k))\Omega+\Sigma_{o}R)\\ \text{where }\Omega=\begin{bmatrix}Q+F^{T}RF&Q&-F^{T}RF\\ -F^{T}RF&0&F^{T}RF\\ Q&Q&0\end{bmatrix}.\end{split} \tag{30}\]  
In the scheme proposed in this paper, the system performance degradation is expressed as:  
\[\Delta J=J-J^{*} \tag{31}\]  
wherein, the optimal system control performance index \(J^{*}\) is derived using (10).  
**Remark 6**.: _According to (31), it can be observed that an increase in the covariance of the watermark signal \(\Sigma_{o}\) results in a corresponding rise in the control performance function \(J\). The methodology adopted in this paper, which utilizes the relative positions of watermark signals for attack detection, enables the application of smaller \(\Sigma_{o}\) values, effectively minimizing control costs in UMV systems._