esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
## 2 Materials and Methods
### Substrate Preparation and Deposition of the Diamond Coating  
This study utilized B-MCD coating, which has shown excellent performance in CFRP machining.  
Prior to the diamond film deposition, the drills were chemically pretreated. The conventionally used WCE method, described by several authors [15; 17], was applied for the selected substrates and included treatment steps with the Murakami reagent and followed a nitric acid solution. All chemical etching steps were performed by immersing the tool substrates into an ultrasonic bath with the corresponding chemicals. Afterward, the pretreated tools were seeded ultrasonically by a purified water suspension with a nanodiamond having a 4 nm average particle size.  
A B-MCD film was deposited on the substrates using the hot-filament chemical vapor deposition (HFCVD) method in an industrial hot-filament reactor (ShinMaywa Industries, Hyogo, Japan). Common deposition parameters are shown in Table 1. A mixture of CH\({}_{4}\) and H\({}_{2}\) was utilized as a precursor gas in the deposition step and nucleation deposition process stages. The boron incorporation was carried out by evaporating a mixture of trimethoxyborane ((CH\({}_{3}\)O)\({}_{3}\)B) with acetone. The HFCVD setup and process details can be found in our previous work [18].  
Figure 2: One-shot WC-Co drill utilized in the current study: (**a**) drill shank; (**b**) reamer cutting portion; (**c**) cutting edge portion; (**d**) magnified view of the drill cutting edge portion; (**e**) primary cutting edge; (**f**) rounded corner; (**g**) secondary cutting edge; (**h**) tertiary cutting edge.  
As a result, the B-MCD coating with a thickness of about 10 \(\upmu\)m \(\pm\) 1 \(\upmu\)m was deposited on the tested drill. After each process stage, the deposited diamond film and chemically and/or ion-etched substrate surface were examined in terms of the surface morphology by a scanning electron microscope (SEM JSM5510, JEOL, Tokyo, Japan). In addition, the surface roughness evaluations for all process stages were conducted with a laser microscope (OLS4100, Olympus, Tokyo, Japan). In the assessment of the adhesion strength between the deposited coating and the WC-Co substrate, indentation tests were carried out (Rockwell hardness testing machine ARK-600, Mitutoyo, Kanagawa, Japan).
# Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots
## 2 Methods
### Simulation Setup  
To evaluate the proposed model, we ran simulation experiments on the ARGoS robotic simulator [37]. Figure 4 shows a screenshot of the ARGoS simulator at the start of a simulation experiment. The simulation environment was a square area of \(L\) meters (\(3\leq L\leq 4\)), and the grey circular region represents the nest with a diameter of d meters (\(1\leq d\leq 2\)). The yellow object in the middle of the simulation area is a light source that allows the robots to return to the nest by sensing the intensity and direction of the light. The blue objects are robots and are placed in the nest randomly before each experiment. The black objects are food tokens and are distributed randomly in the foraging arena. Whenever a robot delivers a food token back to the nest, a new food token is generated randomly in the foraging arena.  
Figure 2: Dynamic response curves for various values of \(\theta\) and \(n\). The probability response curve is steeper with larger \(n\). A robot with larger \(\theta\) goes foraging when the external stimulus is larger.
## 3 Results
### Predictive Modeling--PCA, LASSO, and Mixed Effects  
To explore the multidimensional structure of inter-limb asymmetry variables and their relationship with functional output, Principal Component Analysis (PCA) was performed on the normalized asymmetry dataset. Two principal components were extracted, jointly explaining 73.1% of the total variance. PC1 primarily captured the variance from Impulse and Average Force Asymmetry, while PC2 was driven by Landing RFD and Time to Peak Force asymmetries.  
To visualize the distribution of performance across the asymmetry landscape, RSI (Flight/Contact) values were mapped onto the PCA plane as a color gradient (Figure 2). While the biplot showed some clustering of higher RSI scores toward central regions, no clear linear trajectory or axis alignment was observed, supporting the hypothesis of high inter-individual variability and potentially nonlinear structure.  
Subsequently, these PCA components were entered into a regularized regression model using the Least Absolute Shrinkage and Selection Operator (LASSO), with RSI as the dependent variable. Neither PC1 nor PC2 yielded meaningful predictive coefficients (standardized \(\beta\approx 0\)), suggesting that linear composites of asymmetry indices do not adequately explain variance in explosive reactivity in this population. This result highlights the complexity of neuromechanical performance and justifies the use of nonlinear classification models (Random Forest) in subsequent analyses.  
Two-dimensional projection of the asymmetry dataset using principal component analysis. Data points represent individual limb profiles, color-coded by RSI values. No linear clustering pattern is evident, supporting nonlinear modeling approaches.  
Figure 2: PCA biplot of asymmetry components with RSI-based color gradient.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods
### Simulation Methods  
Currently, numerical methods and computational techniques are commonly used tools in engineering and research to solve complex problems [94]. A significant amount of work in understanding and predicting rockbursts has been performed using numerical modeling. The finite element method (FEM) and dynamic approach have been used to simulate the unstable breakdown of rock masses and the dynamic behavior of rockbursts [105]. The pre-existing numerical simulation work on rockburst assessment is summarized in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Index** & **Formula** & **Method** & **The constitutive Model** & **Researchers (Year)** \\ \hline BPI & \(\text{BPI}=\text{ESR}/e_{c}\) & FEM, FLAC, ABAQUS & Elastic model & [PERSON] et al. (1999) [163]; \\  & & & & [PERSON] et al. (2012) [144]; \\  & & & & [PERSON] et al. (2013) [164] \\ \hline ERR & \(\text{ERR}=\Phi_{k}/\Phi_{0}\) & FLAC, UDEC & Elastic model & [PERSON], 1984 [78] \\ \hline \end{tabular}
\end{table}
Table 8: Summary of pre-existing numerical simulation work.  
However, creating a realistic simulation of rockburst dynamics is challenging using numerical methods. Various approaches have been proposed to predict rockburst hazard, including rock failure process analysis (RFPA) [169] and local energy release density (LERD) [166; 170]. [PERSON] [168] created the bursting potential ratio (BPR) and the out-of-balance index (OBI) has also been developed to assess rockburst potential in joints and discontinuities. To accurately estimate rockburst risk, it is important to use a combination of diverse indicators that consider different physical factors. Numerical models for rockburst are dependent on the inputs provided, some of which may not accurately reflect the real-life situation. Additionally, the lack of a widely accepted energy criteria means that the results obtained from FAI, ESS, LERD, LERR, and ERR indicators cannot definitively classify rockbursts. Further research involving a larger number of project cases is needed to validate these indicators.
## 5 Conclusions  
The investigation into the progressive deformation and energy absorption characteristics of aluminum and CFRP nested-tube specimens under quasi-static lateral loading conditions has yielded significant insights. Notably, the force-displacement behavior of the aluminum and CFRP tubes demonstrated distinct material-dependent characteristics, with aluminum tubes maintaining load-bearing capacity post the initial deformation, whereas CFRP tubes exhibited rapid force escalation followed by a sudden loss of load-carrying capability due to their brittle nature. Aluminum tubes demonstrate a sequential deformation process, with the larger tube deforming first, followed by the inner tube upon contact. This progression results in smooth, stepped force curves characteristic of ductile materials, allowing for efficient energy absorption without sudden failure. Specifically, the energy absorption values for aluminum tubes were found to be 56.4 J, 55.7 J, and 51.1 J for specimens of increasing diameters, showcasing a decreasing trend with larger diameters. Comparative analysis revealed that the NT3-T\({}_{\text{S}}\)+T\({}_{\text{M}}\) sample, which included a combination of one small tube (T\({}_{\text{S}}\)) and one medium tube (T\({}_{\text{M}}\)) within a larger tube, exhibited superior energy absorption performance. Specifically, this configuration enhanced energy absorption by 19.9 J relative to the sum of the individual tubes' energy absorption up to the corresponding displacement threshold. Furthermore, this sample configuration showed a stepped force curve, indicating progressive deformation stages, which is beneficial for controlled energy dissipation during impact. In contrast, CFRP nested samples, while demonstrating higher specific energy absorption due to their lightweight nature, experienced abrupt decreases in load-bearing capacity upon a sequence of cracking occurring on the tube wall due to reaching higher deformation levels. These cracks occur sequentially, first at the upper and lower parts of the tube, followed by the right and left sides. This significantly impacts their load-bearing capacity and results in lower energy absorption values of 22.1 J, 29.4 J, and 32.4 J for increasing diameters.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 3 Spatial Compatibility and Channel Modeling
### Correlation Model
When \(\delta\) follows a Gaussian distribution \(\delta\sim\mathcal{N}(0,\sigma_{\theta}^{2})\), an approximate closed-form expression that reduces the complexity of simulations can be used for \(r_{l,m}\), if \(\sigma_{\theta}\) is small:  
\[r_{l,m}(\theta)=\gamma e^{2\pi jd(l-m)\sin(\theta)}e^{-\frac{\sigma_{\theta}^{ 2}}{2}\left[2\pi d(l-m)\cos(\theta)\right]^{2}}. \tag{18}\]  
Accounting for the spatial channel correlation model above, Equation (12) becomes:  
\[\mathbb{V}\left\{\frac{\mathbf{h}_{l}^{H}\mathbf{h}_{k}}{\sqrt{\mathbb{E}\left\{ ||\mathbf{h}_{l}||^{2}\right\}\mathbb{E}\left\{||\mathbf{h}_{k}||^{2}\right\} }}\right\}=\frac{\text{tr}\left[\mathbf{R}(\theta_{i})\mathbf{R}(\theta_{k}) \right]}{N_{T}^{2}}. \tag{19}\]  
Figure 5 plots the metric defined in Section 3.1 for a uniform linear antenna array of size \(N_{T}\), with the elements placed at half of wavelength and \(\theta_{k}\) the angle of the reference user, set at \(0^{\circ}\). Over a certain interval, e.g., \(\Delta\theta=\theta_{i}-\theta_{k},\Delta\theta\in[-90^{\circ},90^{\circ}]\), the channel separation metric has a monotone variation with the angle between two co-scheduled users. Assuming a precoder requires a 0.1 channel separation (\(\epsilon=0.1\)), Figure 5 shows that this is achieved for any inter-user angle, when the number of transmit antennas (\(N_{T}\)) is greater than or equal to 32. For 16 antennas, the angle required between two co-scheduled users is \(20^{\circ}\) and increases up to \(70^{\circ}\) for the two transmit antennas case.
## 2 Methods
### Monitoring Data Preparation
#### 2.2.2 GNSS and Soil Moisture Data  
SBAS-InSAR is effective in capturing the surface deformation law in the study area, but it lacks real-time monitoring capabilities for key areas. To address this limitation and enable the real-time monitoring of severe deformation points, we installed a GNSS + soil moisture sensor-based monitoring system at a specific location in the study area, as depicted in Figure 3. This system was equipped with solar power supply and signal transmission  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Data** & **Track Number** & **Strip** & **Orbital Direction** \\ \hline
1 & 10 June 2020 & 51 & 009R & Ascending \\
2 & 2 July 2020 & 51 & 009R & Ascending \\
3 & 24 July 2020 & 51 & 009R & Ascending \\
4 & 26 August 2020 & 51 & 009R & Ascending \\
5 & 9 October 2020 & 51 & 009R & Ascending \\
6 & 31 October 2020 & 51 & 009R & Ascending \\
7 & 3 December 2020 & 51 & 009R & Ascending \\
8 & 25 December 2020 & 51 & 009R & Ascending \\
9 & 5 January 2021 & 51 & 009R & Ascending \\
10 & 16 January 2021 & 51 & 009R & Ascending \\
11 & 7 February 2021 & 51 & 009R & Ascending \\ \hline \hline \end{tabular}
\end{table}
Table 1: TerraSAR-x programming data.  
Figure 2: TerraSAR-X data coverage map.  
capabilities allowing for an extended unattended operation. The deployment of this system serves the following primary objectives: first, to monitor the real-time three-dimensional deformation of the selected severe deformation point, providing reliable early-warning reference data for the deformation disaster prevention and control of deep excavation channel slopes; second, to investigate the law and mechanism underlying the influence of soil moisture on the three-dimensional deformation of channel slopes. In order to provide a benchmark for monitoring the surface deformation of channel slopes and minimize monitoring errors, another GNSS monitoring system was simultaneously deployed in the surface deformation stabilization area in the study area. The locations of the two monitoring systems are presented in Figure 3.
## 3 Results  
### Chemical Characteristics, Proximate Analysis, and Bulk Density of Biomass Mixtures  
Cellulose, hemicellulose, lignin, and extract content on mixtures of oil palm residues:pine sawdust ranged from 44.16 to 56.64%, 14.14 to 18.81%, 13.9 to 25.02%, and 4.20 to 22.17%, respectively (Table 1). The highest value of cellulose and lignin was in the mixture 0:100; for hemicellulose, it was in the mixture 100:0; while with extract content, it was in the mixture 80:20.  
ANOVA test values of volatile material and fixed carbon concentration on biomass mixtures showed significant statistical differences (\(p<0.01\)); values ranged from 83.95 to 91.86% and 7.83 to 15.54%, respectively. Oil palm residue (100:0) showed the highest volatile material content (83.95%) as well as the highest fixed carbon content (15.54%). Kruskal-Wallis test of moisture content presented significant statistical differences (\(p<0.01\)); values ranged from 7.58 to 10.14%. Ash did not show statistical differences (\(p>0.05\)), values ranged from 0.31 to 0.58%. However, a non-linear behavior of these variables was recorded, with the highest values of 91.86 in the 80:20 mixture for volatile materials, 0.51 in the 100:0 mixture for the ash content, 10.14 (0:100) for the moisture content, and 15.54 (100:0) for fixed carbon.  
Regarding physical properties, bulk density showed statistical differences (\(p<0.05\)) among biomass mixtures, and values ranged from 0.12 to 0.21 kg m\({}^{-3}\) (Table 1). The higher bulk density values obtained on mixtures were on pine sawdust; proportion increased, since 40:60 (0.20 g/cm\({}^{3}\)), 20:80 (0.21 g/cm\({}^{3}\)), and 0:100 (0.20 g/cm\({}^{3}\)).
## 1 Introduction
Therefore, when increasing the density of vehicles, the state space is dramatically increased in the routing algorithm. This affects the convergence speed of this algorithm negatively. Therefore, QFHR considers intersections as the state set to manage the convergence speed of the routing algorithm. On the other hand, vehicles in each road segment use a fuzzy logic-based greedy routing technique to choose the most suitable next-hop node. As a result, the main contributions of our work are as follows:  
* In QFHR, a traffic detection algorithm is presented for identifying the traffic status of four road sections connected to each intersection. The algorithm provides new traffic information for the Q-learning-based routing process and inform RSUs of the traffic status in the network at any moment. * In QFHR, a Q-learning-based routing scheme called the intersection-to-intersection (I2I) routing algorithm is designed in accordance with a distributed strategy to obtain the best route between different intersections using traffic information. Moreover, The I2I routing algorithm manages network congestion and can quickly discover and replace congested paths. * In QFHR, a greedy routing technique is designed by vehicles to find the best route in each road section. This algorithm addresses the local optimum issue using a fuzzy path recovery algorithm. In the following, the paper is organized as follows: Section 2 demonstrates the related works. Section 3 explains reinforcement learning algorithms, especially Q-learning and fuzzy logic because the proposed method utilizes these techniques for designing the routing process. Section 4 introduces the network system applied in QFHR. Section 5 describes QFHR in VANETs. In Section 6 evaluates the performance of QFHR based on packet delivery rate, end-to-end delay, hop count, and routing overhead. Ultimately, Section 7 presents the conclusions of our paper.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Data Preparation
#### 5.2.2 Transformation  
While testing for stationarity, the Dicky-Fuller test was used to improve the model accuracy. According to the test, the \(p\)-value should be less than 0.05 for the null hypothesis to be true; if greater, the alternative hypothesis should be true. In this study, the \(p\)-value was 2.816, indicating that the time series data were nonstationary.
## 3 Collimating System  
In order to separate symmetric and antisymmetric (along \(X\)-axis) modes of a multimode fiber, we need to collimate the beam from the fiber with a positive lens and then to introduce it in one of the above interferometers. In the simplest case, the fiber optical axis is aligned parallel to the \(Z\)-axis and its \(X\)-coordinate is determined by the interferometer since the projection of the rays on the \(XZ\)-plane must fall normally on the plane mirror. However, the \(Y\)-coordinate of the fiber axis is essentially free. It is only limited by phase changes in the Fresnel retarder and in the plane mirror reflections, but both of them depend quadratically on the \(Y\)-coordinate of the fiber, so we will ignore in our analysis.  
We are interested in coupling the output waves from both ports into new fibers. The beam returning to the source port is focused by the collimating lens symmetrically along the \(Y\)-axis, where the end face of an output fiber must be located (Figure 3a). However, the principal ray of the beam should propagate parallel to the optical axis of this fiber in order the light remains coupled to the same mode than it did in the input fiber. For example, let suppose that the interferometer send the fundamental mode of the input fiber (LP\({}_{01}\)) to the source port. If this beam illuminates the output fiber with a tilt, it will partially couple to the second mode antisymmetric along \(Y\) (LP\({}_{11b}\)) because a tilted illumination is equivalent to a linear phase along the \(Y\)-axis. It does not introduce crosstalk with the light from the other port which couples to the antisymmetric modes along \(X\) (for example LP\({}_{11a}\)). But it does introduce crosstalk between modes when two interferometers (as those presented in the next section) are concatenated. The easiest way to avoid that, is to put both fibers parallel and to accomplish that the principal ray also returns from the interferometer parallel to its input direction. The last condition is achieved when the focal plane of the system composed by the collimating lens, the beam-splitter and the compensating plate matches the plane mirror (Figure 3b). Consequently, the focal plane also contains the vertex of the hollow roof prism (or the Porro's prism) in the other arm.  
Figure 3: Lateral view of an arm of the interferometer and a lens to collimate the light emerging from a fiber. The same collimating lens does not couple correctly the light into an output fiber (**a**), unless the principal ray of the returning beam was aligned with the fiber axis (**b**).  
An identical lens must be symmetrically located respect to the beam-splitter to couple the wave emerging from the other output port of the interferometer in other fiber. A perspective view of this system is shown in the bottom interferometer of Figure 4. As there is a degree of freedom in the Y-coordinate of the fibers, several signals can be (de)multiplexed simultaneously. In this figure, a phase plate was also included in port P1 between the lens and the beam-splitter. Its function is explained in the following section.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 5 Experiment
### Results
As shown in Figure 9a, although our method is slightly lower in the accuracy metric than the NLP-based method, it can acquire more comprehensive packet type information which not only includes the packet type names, but also the component fields of each packet type. As shown in Figure 9b, the total extracted fields accuracy and coverage of our method are all better than the NLP-based method. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Syntax Information**} & \multicolumn{2}{c}{**NLP-Based**} & \multicolumn{2}{c}{**Our Method**} \\ \hline \multirow{4}{*}{Field Information} & Name & ✓ & ✓ \\  & Length & Byte & Byte, Bit \\  & Offset & ✓ & ✓ \\  & Endian & \(\times\) & ✓ \\  & Dependencies & \(\times\) & ✓ \\ \hline \multirow{2}{*}{Packet Information} & Type & ✓ & ✓ \\  & Fields per Packet Type & \(\times\) & ✓ \\ \hline \multirow{2}{*}{Multi-layer Protocol Support} & & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Extracted syntax information comprehensiveness comparison. Wherein, “✓” means it can be extracted, “\(\times\)” means it cannot be extracted. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Processing Time** & **Applicability** & **Comprehensiveness** \\ \hline Manual & high & days & One Target Procotol & packet types, fields \\ Traffic-based & medium & minutes & Most Network Protocols & fields, field boundaries \\ NLP-based & 0.82 & minutes & RFC Protocols & fields, packet format \\ Our Method & 0.94 & seconds & RFC, IoT, ICS Protocols & packet types, fields, field dependencies \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Entire comparison of different methods.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms
#### 4.2.6 Routing Security  
Routing security consists of basic transports and connectivity security mechanisms which are applied to routing protocols as well as the individual nodes. Moreover, nodes must exchange information with their neighbors to construct the network topology in order to apply one of the routing protocols (proactive, reactive and hybrid).  
Routing security involves two aspects: secure routing and secure data forwarding. In secure routing, nodes are required to cooperate in order to share correct routing information, thus keeping the network connected efficiently, whereas in secure data forwarding, data packets must be protected from tampering, dropping, and altering by any unauthorized party. In recent years, many research papers have been presented to supply routing security for UWSNs [83; 84; 85; 86; 87; 88].