esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Environment
### End to End Delay Calculation  
The End to end delay is the overall delay of the network. Figure 11 compares the end to end delay performance of the nodes for various protocols namely GA-LEACH, MW-LEACH, CSOGA, and MWCSGA. For this scenario, the simulation experiment is carried using 100 nodes. The end to end delay performance of MWCSGA protocol is 99.86 ms. Whereas the earlier protocols such as GA-LEACH, MW-LEACH, and CSOGA protocol produces energy efficiency as 275.12 ms, 233.35 ms, and 157.36 ms, respectively. According to analyzes it proves that the performance of MWCSGA protocol produces low delay when compared with the others.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline _Simulator_ & NS-2.34 \\ _Simulation Period_ & 100 ms \\ _Coverage Area_ & 1000 \(\times\) 1000 \\ _No of Nodes_ & 100 \\ _Initial Energy_ & 0.5 J \\ \(E_{DA}\) & 5 nJ/bit/signal \\ \(d_{0}\) & 87 m \\ _Packet Size_ & 4000 bits \\ E\({}_{\text{energy}}\) & 50 nJ/bit \\ _Free space (E\({}_{\text{fm}}\))_ & 10 pJ/bit/m\({}^{2}\) \\ _Multi-path (E\({}_{\text{am}}\))_ & 0.00013 pJ/bit/m\({}^{4}\) \\ _Percentage of CHs_ & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters details of the proposed model.  
Figure 10: Energy efficiency calculation.
## 2 Dynamic Model of Drilling System
### Dynamic Equations of Drilling System
So, we can synthesize the bit-rock interaction into bit weight \(W\) and torsion \(T\), see Figure 3 and the disturbance cutting force could be written as follows:  
\[\Delta W(t)=-\zeta saN(d(t)-d_{0}) \tag{5}\]  
\[\Delta T(t)=-\frac{1}{2}sa^{2}N(d(t)-d_{0}), \tag{6}\]  
where \(\zeta\) and \(s\) are the parameters that characterize the angle of cutter and the coal properties, \(a\) is the radius of the bit, \(N\) is the identical blades number of bit, \(d\) and \(d_{0}\) are the instantaneous and stable cutting depth per revolution of the drill bit, respectively. The axial displacement of bit from time \(\widetilde{t}\) to time \(t\) is \(d(t)\) in Figure 4. \(Z(t)\) and \(\Phi(t)\) are the axial and torsional position of the bit at time \(t\). \[d(t)=Z(t)-Z\big{(}\widetilde{t}\big{)}, \tag{7}\]  
where \(\widetilde{t}=t-t_{n}\). \(t_{n}\) is a state-dependent delay time required for the bit to rotate an angle \(2\pi/N\) to its current position at time \(t\) and determined by the following equation:  
\[\Phi(t)-\Phi\big{(}\widetilde{t}\big{)}=\frac{2\pi}{N}. \tag{8}\]  
The spatial location at any time of the drill bit can be expressed as:  
\[Z(t)=V_{0}t+z(t),\Phi(t)=\Omega_{rot}t+\phi(t). \tag{9}\]  
The cutting depth can be obtained by Equations (7)-(9), which is calculated by the delay time and disturbed value:  
\[d(t)=z(t)-z(t-t_{n})+V_{0}t_{n} \tag{10}\]  
\[d_{0}=\frac{2\pi V_{0}}{N\Omega_{rot}}. \tag{11}\]  
Figure 2:0 PDC bit in deep hole drilling: (**a**) The PDC bit; (**b**) single blade cutting model. On the other hand, the delay time of the drilling system can be sorted into Equation (12), which is a function determined by time and torsional state, called state-dependent delay (SDD). It is also possible to see the importance of torsional vibration of the drilling system through the form of SDD. At the same time, we can simplify the SDD into a constant delay (CD), as follows:  
Figure 2:1  
Figure 2:2  
Then the SDD model Equations (14) and (15) and CD model Equations (16) and (17) can be obtained by the substitution of Equations (5), (6) and (10)-(13) into the perturbation dynamic equations, respectively. Figure 2:3  
Figure 2:4  
Figure 2:5  
Figure 2:6  
where \(z_{\tau}=z(t-t_{n})\), \(\phi_{\tau}=\phi(t-t_{n})\). It is worth noting that the drilling system will have a bit bounce and the drill bit will be separated from the coal when \(d<0\). So the analysis is based on \(d>0\) in this paper.
## 2 Frequency Factors and Constraints
### Readout Displacement Noise and Sideband Frequency Constraints
Because the beat-note frequency signal \(f_{\rm het}\) varies with time, \(\delta x^{\rm sideband}\) is usually calculated by replacing \(f_{\rm het}\) with \(f_{\rm upper}\). From Equations (3) and (4), the total \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Electron charge constant & \(e\) & \(1.6\times 10^{-19}\) C \\ Heterodyne interference efficiency & \(\epsilon_{\rm het}\) & 0.8 [20] \\ Photodiode responsivity & \(R_{\rm pd}\) & 0.68 A/W [20] \\ Relative intensity laser noise & RIN & \(1\times 10^{-8}\)[21] \\ Photodetector phase number & \(N_{\rm pd}\) & 4 \\ Photodetector voltage noise & \(\delta U_{\rm pd}\) & \(2\,\rm nV/\sqrt{Hz}\) \\ Photodiode capacitance & \(C_{\rm pd}\) & 10 pF \\ Current noise & \(\delta I_{\rm pd}\) & \(1.5\,\rm pA/\sqrt{Hz}\)[22] \\ Laser power transmitted through the telescope & \(P_{\rm tel}\) & 2 W [7] \\ Total optical efficiency & \(\epsilon_{\rm opt}\) & 0.853 [23] \\ Arm length & \(L\) & \(3\times 10^{9}\) m [6] \\ Diameter of telescope & \(D\) & 40 cm [7] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Taiji program parameters. readout displacement noise may be minimized by reasonably setting\(f_{\text{upper}}\),\(f_{\text{USO}}\), \(m\), \(P_{\text{receive}}\), and \(P_{\text{local}}\).
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### The Need for Blockchain and IoT in the Coffee Supply Chain
#### 1.3.4. Enhanced Consumer Trust Through Verifiability  
By linking IoT-generated data with blockchain storage, consumers can gain access to trusted and tamper-proof information about the coffee's journey. Scanning a QR code on the product package can reveal details such as farm origin, harvest date, processing steps, and storage conditions. These data are anchored in the blockchain, assuring consumers that there has been no alteration [16]. This level of transparency allows brands to differentiate themselves through verifiable ethical practices and quality assurance, thereby increasing customer loyalty and trust.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
###### Abstract  
Software-defined networks (SDNs) are improving the controllability and flexibility of networks as an innovative network architecture paradigm. Segment routing (SR) exploits an end-to-end logical path and is composed of a sequence of segments as an effective routing strategy. Each segment is represented by a middle point. The combination of SR and SDN can meet the differentiated business needs of users and can quickly deploy applications. In this paper, we propose two routing algorithms based on SR in SDN. The algorithms aim to save the cost of the path, alleviate the congestion of networks, and formulate the selection strategy by comprehensively evaluating the value of paths. The simulation results show that compared with existing algorithms, the two proposed algorithms can effectively reduce the consumption of paths and better balance the load of the network. Furthermore, the proposed algorithms take into account the preferences of users, actualize differentiated business networks, and achieve a larger comprehensive evaluation value of the path compared with other algorithms.  
software-defined network; segment routing; routing algorithm; performance evaluation 1  
[PERSON], [PERSON], [PERSON]
# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
## 2 Methodology
### Low-Resolution GAN  
In the low-resolution GAN, the generator consists of a fully connected (FC) layer followed by two U-Nets in series [34]. Among them, the first U-Net and fully connected layer are called the global feature extraction network, which is responsible for extracting and encoding global features of crosshole GPR data. Afterwards, the features are fed to the second U-Net to be decoded into a low-resolution defect reconstruction image of enlarged size.  
A crosshole GPR image is first input into a fully connected layer composed of 1024 neuron nodes, in which each neuron is connected with all pixels of the input data with a weight assigned to each connection. Thus, the fully connected layer processes the global information of the input data, combines the outputs of the 1024 neurons, and generates a complex high-dimension global feature map of the input images with sizes 64 \(\times\) 64 \(\times\) 1024.  
Then, we use a U-Net structure named global feature extraction network to merge the features obtained from the fully connected layer and reduce the dimensionality of the feature map. The global feature extraction network employs the first 100 layers of ResNet101 V2 as the encoder and uses 12 cascade deconvolutional layers (Deconv2d) as the decoder. Once processed by the global feature extraction network, the dimensionality of the feature map is reduced to 64 \(\times\) 64 \(\times\) 32.  
Afterwards, a low-resolution inversion network is used to further filter redundant features and infer the defect reconstruction image based on the features created by the global feature extraction network. The decoder and encoder of the low-resolution inversion network are symmetrically arranged with 10 cascaded convolutional (Conv2d) and deconvolutional layers, respectively. Meanwhile, in order to suppress over-fitting and speed up the convergence rate during network training, we placed a batch normalization (BN) layer and a dropout layer between every two deconvolutional layers. In addition, we set horizontal connections to link same-size features between the convolutional and deconvolutional layers. This operation utilizes low- and high-order feature maps comprehensively to suppress possible gradient vanishing. Once having passed through the low-resolution inversion network, the feature maps are converted into a low-resolution defect reconstruction image with the size of 64 \(\times\) 64 \(\times\) 1.  
Finally, we utilize a neural network with a fully connected layer of 256 neurons and five cascaded convolutional layers as the low-resolution discriminator, which receives both crosshole GPR data and the low-resolution defect image and then gives a 4 \(\times\) 4 score matrix to the low-resolution defect image.
# Experimental Study on the Mechanical Properties of Deep Granite under Gradient-Confining Pressure
## 4 FEM Numerical Simulation Analysis
### Analysis of Simulation Outcomes  
In line with the Mohr-Coulomb strength criterion, the failure of the rock is shear failure resulting from shear stress. Thus, the study of the shear stress distribution within the rock is highly important for explaining the failure of the rock. Figure 10 presents the shear stress nephogram of the established deep granite numerical model under gradient confining pressure. It can be observed from the figure that, under the action of the axial load, there is an obvious shear stress concentration within the rock, and it exhibits a certain symmetric distribution along the transverse central axis. With increasing confining pressure, the position of the shear stress concentration notably expands. Under the same numerical model, the stress concentration positions under each confining pressure are basically consistent, and they are more in line with the position of the rock failure surface in the real physical test results.  
To probe the impact of the confining pressure on the axial deformation of the rock specimen, the axial displacement nephograms of the numerical model under the action of the axial load at both low and high confining pressures were separately derived, with a total of four stages selected from the beginning to the end, as depicted in Figure 11. As seen from the figure, upon being subjected to the axial load, distinct axial deformation emerged  
Figure 10: Cloud diagram of the shear stress distribution of the numerical model under gradient confining pressure.  
within the numerical model, and the amount of deformation decreased from the end of the model towards the middle, with the overall deformation presenting a layered distribution. With increasing axial load, this distribution trend became increasingly evident. The axial deformation of the model under high confining pressure was also somewhat restrained, and the overall deformation amount was smaller than that under low confining pressure, while the layered distribution effect of the deformation is also weaker than that under low confining pressure.
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
###### Abstract  
Smoothed Particle Hydrodynamics (SPH) is a mesh-free numerical method that can simulate metal cutting problems efficiently. The thermal modeling of such processes with SPH, nevertheless, is not straightforward. The difficulty is rooted in the computationally demanding procedures regarding convergence properties and boundary treatments, both known as SPH Grand Challenges. This paper, therefore, intends to rectify these issues in SPH cutting models by proposing two improvements: (1) Implementing a higher-order Laplacian formulation to solve the heat equation more accurately. (2) Introducing a more realistic thermal boundary condition using a robust surface detection algorithm. We employ the proposed framework to simulate an orthogonal cutting process and validate the numerical results against the available experimental measurements.  
metal cutting simulation; thermal modeling; Laplacian; boundary condition; surface detection; SPH +
Footnote †: journal: Applied
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 2 Materials and Methods
### DMD/PiDMD/mpEDMD for Channel Estimation in Wireless Systems  
DMD/PiDMD/mpEDMD can be applied to channel estimates in wireless systems, including CF-MMIMO scenarios, to capture the dynamic behavior of the wireless channel, which highly improves the accuracy of CSI estimation. The following steps outline how DMD can be used for Channel Estimation following this procedure:  
1. Data Collection: Collect a series of snapshots of the received signals from the wireless channel over a period of time. These snapshots can be obtained through the transmission of known pilot symbols or training sequences.
2. Data Matrices: Organize the received signal snapshots into two data matrices, often referred to as \(X\) and \(Y\). These matrices represent the received signals at consecutive time steps.
3. Singular Value Decomposition (SVD): Perform a singular value decomposition (SVD) on matrix \(X\) to factorize it into three matrices: \(U\), \(\Sigma\) (sigma), and \(V^{T}\). The SVD captures the dominant modes of the received signals.
4. DMD/PiDMD Modes and Eigenvalues: Calculate the DMD/PiDMD modes by using the relationship between matrices \(X\) and \(Y\). These modes represent the coherent spatial patterns or oscillations in the channel behavior. The associated eigenvalues provide information about the growth rates and frequencies of these modes.
5. Channel Estimation and Prediction: The DMD/PiDMD/mpEDMD modes and eigenvalues can be utilized for Channel Estimation and prediction. The DMD/PiDMD/mpEDMD modes help in identifying the spatial characteristics of the channel, while the eigenvalues offer insights into the temporal evolution of these characteristics.
6. Adaptive Strategies: DMD-based Channel Estimation can be integrated into adaptive communication strategies. For instance, in MIMO systems, the identified modes can guide the selection of appropriate beamforming vectors, precoding matrices, or transmit power allocation, based on the changing channel conditions.
7. Interpolation and Extrapolation: DMD/PiDMD/mpEDMD also enables interpolation and extrapolation of the channel behavior between and beyond the observed time instances. This can lead to more accurate CSI estimates during periods without pilot transmissions.  
Performance Evaluation: Validate the effectiveness of DMD-based Channel Estimation through simulations or real-world measurements. Compare the performance of DMD-based estimation with conventional methods to assess its benefits in terms of accuracy and adaptability.  
It is important to note that while DMD-based methods have the potential to improve Channel Estimation in wireless systems, their success depends on factors such as the quality and quantity of data, the dynamic range of the channel variations, and the computational resources available for DMD calculations. In scenarios where the wireless channel experiences rapid variations, DMD and its variants can offer advantages by capturing the temporal evolution of the channel, leading to more accurate CSI estimates and enhanced system performance.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 4 Results and Discussion
### Variation of Compressive Strength after the Sulfate Resistance Test  
The compressive strengths for the specimens subjected to the sulfate resistance capability test were also investigated and the results are shown in Figure 6a-c for specimens with various w/b ratios. Each ratio of bentonite replacement has two columns to represent the compressive strength acquired before and after the sulfate resistance test. Figure 6 shows the trend of the compressive strength corresponding to the replacement ratio of bentonite and the reduction phenomenon of strength due to reactions of the sodium sulfate solution. The phenomenon of strength reduction was found for all specimens subjected to sulfate resistance testing in three different w/b ratios. The reduction rate of strength was as high as 30%, such as that of the 0.485 w/b ratio without bentonite replacement. The phenomenon of strength reduction was mitigated corresponding to the increase of the replacement ratio of bentonite, as observed from specimens with 0.445 and 0.485 w/b ratios. In the extreme case with the 0.75% replacement ratio of bentonite, the reduction rate of the compressive strength of specimens subjected to sulfate corrosion was decreased by as much 1% compared to that of specimens with a 0.445 w/b ratio. Therefore, the usage of bentonite can effectively reduce the strength-reduction phenomenon if proper replacement ratios for the bentonite are applied. Generally, the mitigation effect of strength reduction of slag cement mortar due to sulfate attack was more significant for the specimens with lower w/b ratios (i.e., 0.445 and 0.485) and higher replacement ratios of bentonite (i.e., ratio of 0.75).  
Figure 6: Comparison of compressive strength before/after the sulfate resistance capability test: (a) w/b = 0.445, (b) w/b = 0.485, and (c) w/b = 0.550.
## Materials and Methods
### Forest-Non-Forest Classification  
Forest-non-forest (FNF) classification was performed on PALSAR-2 polarizations images to delineate the forests from other land cover. This process is critical to define the boundary of forests and to ensure that the estimated AGB did not include other types of vegetation. The reason was that the forests are often confused with rubber, teak, and other timber tree plantations, which are common in Peninsula Malaysia and they appear almost identical on both HH and HV polarizations. Instead of using only the original backscatter HH and HV polarizations, an attempt has been also made to derive other image variables derived from PALSAR HH and HV images. Image variables, namely (i) simple band ratio (HH/HV), (HV/HH), (ii) average (HH + HV/2), and (iii) square root of products (\(\sqrt{\text{HH}\times\text{HV}}\)) were produced.  
The incorporation of texture measure also can improve classification of spatially distributed pixels on an image. Gray-level co-occurrence matrix (GLCM) uses a gray-tone spatial dependence matrix to calculate texture values. This is a matrix of relative frequencies with which pixel values occur in two neighboring processing windows separated by a specified distance and direction. For this purpose, texture has been defined as repeating pattern of local variations in image intensity which is too fine to be distinguished as separate class at the observed resolution. Thus, a connected set of pixels satisfying given gray-level properties which occur repeatedly in an image region constitute a textured region. A mean-type GLCM was applied to the original HH and HV polarizations to produce textured images with clearer definitions of the objects on the images [30].  
These inputs were used for the FNF classification and the Maximum Likelihood Classifier algorithms with nearest neighbor technique was applied. The forests were then further classified into several forest types by using the DEM from SRTM.
###### Abstract  
In the simulation analysis of the lily harvesting process, the intrinsic parameters of the lily bulb and the contact parameters between the lily bulb and the lily mechanized harvesting equipment (Q235 steel) are deficient. Thus, the three-axis size, density, moisture content, Poisson's ratio, elastic modulus, and other parameters of lily bulbs are measured in this paper with lily bulbs as the research object. Moreover, the discrete element model of the lily bulb was established using 3D scanning technology. The contact parameters between the lily bulb and Q235 steel were calibrated through bench test and simulation parameter test. The relative error between the measured value of the lily bulb accumulation angle and the simulated value is taken as response value to calibrate three parameters (collision recovery coefficient, static friction coefficient, and dynamic friction coefficient between lily bulbs). A regression model of the relative error of the stacking angle and three parameters is established, and the response surface is optimized. The results demonstrate that collision recovery coefficient, static friction coefficient, and dynamic friction coefficient between lily bulb and Q235 steel are 0.301, 0.423, and 0.063, respectively; these coefficients between lily bulbs are 0.455, 0.425, and 0.158, respectively. Additionally, a better combination of parameters is adopted to perform the simulation stacking test. The measured stacking angle is 32.31\({}^{\circ}\), which is 0.34% in error with the stacking angle obtained by the physical stacking test. The test results suggest that the discrete element model and contact parameters of the lily bulb can be used in the discrete element simulation test. Furthermore, these research results could provide references for simulation tests, such as mechanized harvesting and post-harvest processing, of lily.  
lily bulb; discrete element method; contact parameter; model; parameter calibration +
Footnote †: journal: Applied Sciences