esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Model Performance
[40], but again, no real EC measured value-based comparisons were conducted. In another study, [PERSON] et al. [41] constructed a multiple imputation algorithm based on ex pectation and maximization algorithms but the predicted results lacked consistency across various forest ecosystems. There is another ANN-based gap-filling algorithm originally proposed by [PERSON] et al. [42], but was largely designed for hydrological modeling not dedicated for general EC flux data. In some most recent study, [PERSON] et al. [43] designed three non-linear regression approaches for gap-filling NEE, but yielded variable prediction accuracy and limited reliability. [PERSON] et al. [44] indicated that a simple parametric model of missing storage incorporated with a PAR-extrapolated advective respiration loss approach could yield comparable, even slightly better results for gap-filling forest NEE flux data than REddyProc. However, its applicability and effectiveness in row-crop or pasture-based ecosystem studies remain unclear. Likewise, an earlier popular EC gap-filling model developed by [PERSON] et al. [45] also had a strong focus on forest ecosystems. Before the advent of REddyProc, ANN-based algorithms were generally regarded as one of the best gap-filling approaches [46]. However, as indicated in this study, the uncertainties associated with its structure parameter setting and lack of stability might hinder its popularity in the future. We considered SVM as the optimal machine learning algorithm for EC gap-filling. Overall, REddyProc-based algorithms provided limited performance while predicting NEE but was relatively reliable for LE.
## 4 Concerns about and Encounters with Underwater Wireless Sensor Network
### Underwater Acoustic Communications Characteristics  
Based on the direction of the sound beams, acoustic connections are categorized as either vertical or horizontal. Acoustic communications are reliable regarding time dispersion, multi-path spreads, and delay variance. According to the theory of sound transmission, normal molecular motion within an elastic medium spreads to nearby particles. This circumstance is based on [PERSON]'s explanation of acoustic wave theory. A sound wave is a form of mechanical energy that moves swiftly across the ocean and from particle to particle. Diverse environmental factors, such as the ocean's surface and the seafloor, can impact sound transmission in underwater acoustic communication. Because the impedance  
Figure 4: Flowchart of the process flow in clustering.  
of the aquatic and terrestrial environments differs, the reflection of the acoustic waves from the water surface is virtually flawless. Nevertheless, surface waves differ from one another. The figure and residues of the seafloor could similarly change [37]. The marine atmosphere is not an isotropic atmosphere because of the pressure and density of seawater [38]. There are extra considerations while transmitting underwater, including the noise formed by aquatic life, ships, external noise, fall noise, and clutter brought on by changes in hydrostatic pressure.
## 4 Hardware Implementation
### DDR3 Read/Write Module  
The DDR3 read/write module is connected to the register manager, the FIFO module and the address generator within the controller. First, the module determines the mode of data access (e.g., read/write, range/azimuth) by reading the data of each bit of the register from the register manager, and obtains relevant data parameters (e.g., starting coordinates, read/write length). Secondly, the module reads the data required for a burst transmission from the FIFO and obtains the corresponding address signal from the address generator. Finally, the dual-channel DDR3 data reading and writing is completed by establishing the correct MIG IP user interface timing. The module consists of two parts: the DDR3 write sub-module and the DDR3 read sub-module.  
In the DDR3 write sub-module, the values in the register are constantly polled, and the write data process is initiated when the user issues a write data instruction. During the writing process, the write module obtains eight data from the input FIFO as a burst data transmission. After that, an address request signal is sent to the address generator to generate the write address. When the write address is output correctly, it is connected to the address signal of the MIG, and the control signal in the MIG is set up with the correct timing to perform the write operation. When all write operations are completed, a wr_over signal is sent to the register manager, the value of the register is zeroed, and the write process is completed.  
In the DDR3 read sub-module, the values in the register are constantly polled, and the read data process is initiated when the user issues a read data instruction. In the read process, an address request signal is sent to the address generator, requiring a read address to be generated. When the read address is output correctly, it is connected to the address signal of the MIG and the control signal is set with the correct timing. When accepting a valid signal to read data from MIG, eight continuous data from MIG, which can be transmitted at one time, are written to the FIFO. When all read operations are completed, an rd_over signal is sent to the register manager, the value of the register is zeroed, and the read process is completed.
## Results  
### Characteristic of GO and CNC  
The individual nanosheets were 200-500 nm in diameter and most likely consisted of a few layers, as shown in Figure 1a. The diffraction patterns indicated a roughly hexagonal crystallinity (Figure 1b). Furthermore, the length and the width of the CNCs were about 100-200 nm and 10-20 nm, respectively. The XRD spectra (Figure 2) showed that the 20 of the graphite flakes (26.4\({}^{\circ}\)) shifted to 9.6\({}^{\circ}\) of the GO nanosheets [18; 19; 20]. This indicates that the d spacing of the GO nanosheets increased (1.73A of the graphite flakes expanded to 4.62 A of the GO nanosheets) after the oxidation and intercalation process of the graphene nanosheets. In addition, the XPS spectra support the conclusion that GO was oxidized (Figure 3). The C1s of the XPS spectra (Figure 3a) were drawn by curvefitting each spectrum to four peaks, corresponding to the functional groups: carboxylates (O-C=O, 289.0 eV), carbonyl (C=O, 287.8 eV), epoxy/hydroxyls (C-O, 286.2 eV), and carbon sp\({}^{2}\) (C=C, 284.8 eV) [21; 22; 23]. The O1s of the XPS spectra (Figure 3b) displayed a peak in the range of 530-537 eV, showing that the oxidized functional group was formed at the GO nanosheets.  
Figure 1: **(a)** TEM images and **(b)** selected area electron diffraction (SAED) patterns corresponded to graphene oxide (GO) nanosheets; **(c)** TEM image of a cellulose nanocrystalline (CNC).  
The colloidal suspensions of the CNC exhibited a nematic liquid crystalline alignment after the acid hydrolysis conditions were optimized [24]. The XRD spectra (Figure 4) showed that the crystallization of the pristine cellulose at 20 = 15.9\({}^{\circ}\), 22.4\({}^{\circ}\) and 34.6\({}^{\circ}\). However, the half-height width at 20 = 22.4\({}^{\circ}\) in the pristine cellulose decreased from 1.97\({}^{\circ}\) to 1.37\({}^{\circ}\) after the cellulose was hydrolyzed by sulfuric acid (CNC). This indicated that the pristine cellulose fibers were decomposed to nanoscale fibers, but still retained the crystallization. The CNC displayed six crystalline peaks at 20 = 14.9\({}^{\circ}\), 16.3\({}^{\circ}\), 22.4\({}^{\circ}\), 30.8\({}^{\circ}\), 38.4\({}^{\circ}\), and 44.6\({}^{\circ}\), showing that it had a higher crystallization than in pristine cellulose.  
Furthermore, from FTIR spectra (Figure 5), the splitting peak at 1110 cm\({}^{-1}\) was found in the CNC/GO nanocomposites, showing the interaction between CNC (-OH) and GO (-COOH). Further, the characteristic functional groups of O-H and C-O-C still existed after the cellulose was hydrolyzed by sulfuric acid (CNC).  
Figure 4: XRD patterns of pristine cellulose, CHC, GO and CHC/GO/CNC.  
Figure 3: (a) C1s and (b) O1s XPS spectra of GO nanosheets.
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 3 Results and Discussion
### Parameterization of the MNAG Model  
In this section, we used some model molecular dynamics simulations to test the new analytical form of the MNAG model against the void evolution data obtained from these simulations. The MNAG model was also compared with other commonly used models later in the paper. It is important to note that the parameters in Tables 1-3 were specific to the simulation conditions used to obtain them. If the loading conditions were altered, there should be a change in a few of the parameters. The goal here was to check the robustness of the analytical form and not so much the specific values of the parameters. However, in order to check the robustness of the analytical form, we also attempted to interpret the physical meaning of the obtained parameters against the simulations and known damage mechanisms as a function of microstructure. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MNAG Parameters** & **Regime** & **Initial Value** & **Calibrated** \\ \hline \(\dot{N}_{o}\) & Void nucleation & \(1.5\times 10^{22}\) m\({}^{-3}\) ps\({}^{-1}\) & No \\ \(\sigma_{n0}\) & Void nucleation & \(P_{spall}\times 0.7\) & No \\ \(\sigma_{n}\) & Void nucleation & \(5\) GPa & Yes \\ \(k_{VS}\) & Void nucleation & \(1\) & Yes \\ \(\sigma_{g0}\) & Void growth & \(5\) GPa & Yes \\ \(\eta\) & Void growth & \(20\) GPa ps & No \\ \(k_{V}\) & Void coalescence & \(1.5\times 10^{24}\) m\({}^{-3}\) & No \\ \(k_{R}\) & Void coalescence & \(1\) & Yes \\ \(R_{c}\) & Void coalescence & \(5\times 10^{-9}\) m & Yes \\ \(V_{c0}\) & Void coalescence & \(10^{25}\) m\({}^{-3}\) ps\({}^{-1}\) & Yes \\ \(t_{offset}\) & Stress evolution & \(3\) ps & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The initial parameters in the MNAG model obtained from shock simulations of single crystal Ta. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **(001)** & **(110)** & **(111)** \\ \hline \(\dot{N}_{o}\) & \(1.5\times 10^{22}\) m\({}^{-3}\) ps\({}^{-1}\) & \(1.5\times 10^{22}\) m\({}^{-3}\) ps\({}^{-1}\) & \(1.5\times 10^{22}\) m\({}^{-3}\) ps\({}^{-1}\) \\ \(\sigma_{n0}\) & \(14.32\) GPa & \(14.47\) GPa & \(15.24\) GPa \\ \(\sigma_{n}\) & \(4.38\) GPa & \(2.34\) GPa & \(2.72\) GPa \\ \(k_{VS}\) & \(2.39\) & \(2.05\) & \(2.06\) \\ \(\sigma_{g0}\) & \(6.32\) GPa & \(7.01\) GPa & \(7.44\) GPa \\ \(\eta\) & \(20\) GPa ps & \(20\) GPa ps & \(20\) GPa ps \\ \(k_{V}\) & \(1.5\times 10^{24}\) m\({}^{-3}\) & \(1.5\times 10^{24}\) m\({}^{-3}\) & \(1.5\times 10^{24}\) m\({}^{-3}\) \\ \(k_{R}\) & \(1.12\) & \(1.28\) & \(1.51\) \\ \(R_{c}\) & \(4.63\times 10^{-9}\) m & \(7.41\times 10^{-9}\) m & \(8.23\times 10^{-9}\) m \\ \(V_{c0}\) & \(3.05\times 10^{25}\) m\({}^{-3}\) ps\({}^{-1}\) & \(3.01\times 10^{25}\) m\({}^{-3}\) ps\({}^{-1}\) & \(2.95\times 10^{25}\) m\({}^{-3}\) ps\({}^{-1}\) \\ \(t_{offset}\) & \(4\) ps & \(6.5\) ps & \(7.5\) ps \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fitted MNAG parameters for different orientations in shock loaded single-crystal Ta.
# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 4 Simulation Experiments
### Simulation Platform and Data  
To study the critical problems of mission scheduling, link optimization, collaborative computing, and network topology discovery of spatial information networks, we simulated satellite data, including satellite load data, satellite orbit data, inter-satellite visible time window, and satellite ground visible time window data, satellite resource capacity, and satellite resource quantity. In addition, we simulated data of common Earth observation application scenarios, including Earth observation area, mission execution time demand, mission resource type, mission resource demand, etc. The system created a multi-satellite and multi-task earth observation scene. The scene start time is \"13 June 2021 04:00:00.000 UTCG\", and the end time is \"14 Jun 2021 04:00:00.000 UTCG\". The three-dimensional and two-dimensional views of the satellite and the earth in the Earth observation simulation scene are shown in Figure 8. As shown in Figure 8, we depict the track of the subsatellite point in the two-dimensional plane. We denote the satellite coordinates in its orbital plane as cr=(\(x_{0},y_{0},z_{0}\)). The calculation formula (1) is as follows,  
\[cr=\begin{cases}x_{0}=-a\big{(}1-e^{2}\big{)}\times\frac{\cos(\theta)}{1+c\cos (\theta)}\\ y_{0}=-a\big{(}1-e^{2}\big{)}\times\frac{\sin(\theta)}{1+c\cos(\theta)}\\ z_{0}=0\end{cases} \tag{1}\]  
where \(a\) denotes semi major axis, \(\theta\) indicates trueanomaly, \(e\) represents eccentricity.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Blockchain Overview  
Blockchains are often confused with conventional databases or Distributed Ledger Technology (DLT). There are three main types of blockchains, and they are private, public, and consortium blockchain. In the public blockchain, any node may join or interact in the blockchain network without authorization, and no one has control over the blockchain network [60]. In the private blockchain, permission from the administrator is required in order to join the network and the administrator has control over the activities of the nodes. The private blockchain are not decentralized systems, because there is a strong hierarchy concerning network control. The consortium blockchain is a'semi-private' network and it has a control user group, which operates through many organizations. In blockchain, consensus is a fault-tolerant technique that is used to achieve the necessary agreement among distributed multiple nodes on a single state of the network. It is a sequence of rules, which define the contributions made by the participating nodes of the blockchain. In [61], different categories of consensus mechanisms that were used in a different types of blockchain were discussed. In addition, smart contract functionality in blockchain provides enhanced security functionality that sets protocols, and provides user data management, decision-making, and access rights monitoring. It is a programmable application that is automatically implemented when a set of predefined conditions is met [62].
## 3 Theoretical Development  
### Geometry of Fractured Media  
It has been shown that the fractal geometry can be applied in the analysis of flow and transport properties in porous media in general and specifically in fractured media, e.g., [64; 65; 66; 67; 68]. Fractured media are assumed to be composed of the fractures and the surrounding matrix, e.g., [69]. The matrix permeability is normally much smaller than that of the fractures and thus the matrix can be considered as impermeable and no fluid exchange through the fracture walls. In this work, fractures are conceptualized as a bunch of parallel capillary slits as shown in Figure 1. The fracture is approximately considered as plane with rectangular cross-section, whose widths follow the fractal scaling law, e.g., [70; 71; 72]. The aperture and width of parallel fractures are \(2a\) (m) and \(w\) (m), respectively. In order to derive electrokinetic properties at macroscale, a representative elementary volume (REV) as a cube with the length of \(L_{v}\) and the cross-sectional area \(A_{REV}\) that is perpendicular to the flow direction is considered as shown in Figure 1. As mentioned, the REV is conceptualized as a bundle of fractures with width varying from \(w_{min}\) to \(w_{max}\). It is shown that the number of fractures whose widths are in the range from \(w\) to \(w+dw\) in the REV is given by, e.g., [71; 72; 73; 74]:  
\[-dN=D_{f}w_{max}^{D_{f}}w^{-D_{f}-1}dw, \tag{5}\]  
where \(D_{f}\) (no units) is the fractal dimension for pore space, \(0<D_{f}<2\) in two-dimensional space and \(0<D_{f}<3\) in three dimensional space. The minus sign in Equation (5) implies that the number of fractures decreases with an increase of fracture width. The fractal dimension for fracture space (\(D_{f}\)) can be estimated from properties of porous media using the following relation, e.g., [71; 72]  
\[D_{f}=2-\frac{\text{ln}\phi}{\text{ln}a}, \tag{6}\]where \(\phi\) is the porosity of porous media and \(\alpha\) is the ratio of the minimum width to the maximum width of the fractures (\(\alpha=w_{min}/w_{max}\)).  
It has been shown that the aperture \(a\) (m) is related to the width \(w\) (m) of the fracture by the following linear scaling law, e.g., [71; 72; 75; 76]:  
\[a=\beta w, \tag{7}\]  
where \(\beta\) is the fracture aspect ratio.
## 2 Related Work
#### Identified Literature Gaps and Open Challenges  
Even though there are a large number of studies in this field, the reviewed state-of-art models indicate that the redundancy of features and high dimensionality are still open challenges. Herein, therefore, we propose an effective model for optimal feature extraction and reduction of training time complexity. Most studies focus on improving the IDS model to classify the result into the binary classification or the multi classification. In general, the IDS models that fits both attack, non-attack classes, and a high-dimensionality environment are still weak and incomplete. Existing anomaly detection models suffer from a high rate of false alarms. Currently, these studies suggest that any pattern which deviates from the normal pattern is an anomaly, even when this is not the case (i.e., a false indicator). This prediction error is due to the negative correlation in irrelevant features.  
Thus, based on our literature survey and the identified gaps, our study here investigates and proposes an (more) intelligent IDS system that treats and removes redundant features from the dataset in an earlier phase of the process. We also study the effects of redundant features on the characterization of the general representation of the data. Accordingly, this study's outcome has both reduced the higher false-alarm rates compared to those found in the literature, as well as reduced training time complexity through dimensionality reduction. Key to this study is the application of a unique feature selection method that combines two entropy-based techniques (IG and GR) to select the best features.
## 6 Experimental Evaluation
### Evaluation of the Proposed Approach with VM Distribution  
Simulation reservation cycles are grouped into three categories, namely RAC, RBC and RMC, to evaluate the effectiveness of migration. For every eight RAC, three RBC are allowed to create a demand for migration, followed by one RMC. Figure 11 presents the CPU demand considered for VM distribution with all reservation cycle categories.  
Scenario III: Mapping of heterogeneous VM types to heterogeneous machines with different distribution policies  
In Scenario III, the overload host selection is based on MAD, IQR and static threshold (ST). Similarly, VM selection is based on MMM and maximum migration with least resource request (MML) policy for task distribution. Figure 12a-c presents the number of migrations, RVA% and power consumption of different combinations of host and VM selection policies. Figure 12a-c concludes that IQR-MML has an average of 1% increase in acceptance rate and considerable increase in power reduction than other approaches. IQR-MML based host and VM selection policy has resulted in a power reduction of 0.1-0.4% more than ST-MMM, 0.2-0.5% more than IQR-MMM, 0.3-0.4% more than MAD-MMM, 0.2-0.4% more than MAD-MML, and 0.1-0.4 %more than ST-MML. The placement of  
Figure 11: Generated processor demand with request migration cycles.  
Figure 10: CPU utilization of scenario II for 100% RVA for first 14 cycles.  
VMs without task distribution policy has resulted in increased an power consumption of 0.13% in AHSA, 0.49% in PA, 0.44% in FFD, and 0.38% in BFD than IQR-MML.  
Figure 12: Impact of migration (**a**) Number of migrations (**b**) RVA% (**c**) Power consumption.  
In this work, non-parametric Mann-Whitney U/Wilcoxon rank-sum test is used to check whether there is a significant difference in the results obtained. Figure 13 demonstrates the power consumed by different types of VM-placement approaches. After running the test on two samples, AHSA with FFD, AHSA with BFD, PA with FFD, PA with BFD, the \(p\) value is less than 0.0001, and therefore it can be concluded that AHSA- and PA-based allocation has a significant difference with other approaches towards energy consumption. Figure 14 demonstrates the power consumed by different types of load balancing approaches. IQR-based host selection and MML-based VM selection have a minimum power consumption compared to methods. The FFD approach has more power consumption in all distribution policy and VM selection approaches. MMM-based VM selection has more power consumption irrespective of host selection, due to migration of more VMs. The MML-based VM selection works better with IQR than in ST- and MAD-based host selection.  
Figure 14: Power consumption with load balancing approaches.  
Figure 13: Power consumption.
## 3 Types of Supplementary Dampers and Their Characteristics
### Metallic Yield Dampers  
#### 3.4.1 Operating Principle  
Metallic yield dampers function by dissipating energy through the plastic deformation of metallic materials. Often termed hysteretic dampers, these devices are classified as passive energy dissipation systems that utilize the plastic deformation characteristics of metals to reduce seismic energy [84; 85]. Typically constructed from specially designed metal elements, these dampers often employ mild steel or other ductile alloys, which undergo controlled yielding during seismic activities. This yielding process converts kinetic energy into thermal energy via plastic deformation, thus diminishing the seismic forces exerted on primary structural components [86; 87]. There are several configurations of metallic yield dampers, such as added damping and stiffness (ADAS) devices, triangular-plate-added damping and stiffness (TADAS) devices, and buckling-restrained braces (BRBs). Their fundamental simplicity, dependability, and cost-effectiveness have led to their widespread use in both new construction projects and seismic retrofitting efforts around the world [88; 89].  
One of the primary advantages of metallic yield dampers is their consistent and predictable hysteretic behavior, which enhances the accuracy of performance evaluations and design methodologies. These devices can be effectively incorporated into various structural systems, including moment frames and braced frames, providing engineers with significant adaptability in their application [90; 91]. Moreover, metallic yield dampers demonstrate exceptional resistance to fatigue, allowing them to withstand numerous loading cycles without experiencing considerable performance decline. This characteristic is especially beneficial in regions prone to aftershocks or frequent seismic events [92; 93]. In addition, current research efforts are focused on the creation of advanced alloys and refined geometric configurations to enhance the energy dissipation properties and durability of these dampers, thus expanding their use in seismic engineering and improving the overall resilience of structures against earthquake hazards [94; 95].