esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 2 Related Work
[15] proposed an SDN-FANET architecture that utilizes multiple wireless link access technologies. Furthermore, they devised a resilient multi-path routing protocol that identifies various disjoint routes for UAV pairs to improve the resiliency of the network. Moreover, their approach reduced the outage rate of end-to-end connections in the presence of malicious UAVs and achieved a moderate reduction in end-to-end delay compared to traditional algorithms. However, this work does not consider the battery limitations of the UAVs and the weight needed to carry different wireless link access technologies that could impact the performance of the UAVs. [PERSON] et al. [27] implemented a dual-controller cooperative SDN-based FANET scheme and design a Dyna-Q-based reinforcement learning algorithm using power allocation and track planning collaborative optimization against smart jamming. The authors used two different controllers to keep track of the UAVs' behavior and the other to be aware of the network status. In our proposal, we adopt the usage of a single controller responsible for managing the network as a whole, including the UAVs' trajectories and their status. [PERSON] et al. [28] proposed an SDN-based solution for FANET to manage a group of UAVs for an Internet of Things (IoT) coverage. Flying nodes are used simply as switches to establish communication between terrestrial segments and a flying data collector. An additional node is considered as the SDN controller to update the other nodes' routing tables. Their work showed the efficiency of the SDN-based solution through simulation. [PERSON] et al. [20] presented a centralized Traffic-Differentiated Routing (TDR). The protocol groups UAVs into several cluster domains. It runs on an SDN-based FANET and is executed in each cluster. Besides, it focuses on the data plane by guaranteeing the specific QoS requirements of delay-sensitive and performance-requisite applications. However, in our proposal, we provide reliable transmission of the management and coordination messages in the control plane, which leads to precise coordination and predictability of the UAVs positioning. Moreover, our work also highlights the method used to compute the clusters and choose the cluster heads. Table 1 summarizes the main characteristics of existing works intended to provide control plane Message management for SDN-FANET. It compares the control plane message management for  
SDN-FANET described in this article to various others described in the literature concerning the application, routing approach, and control message transmission performance. Therefore, to the best of our knowledge, efficient control messages management together with a clustering algorithm has not yet been provided by a unified solution.
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
###### Abstract  
Cross-laminated timber (CLT) is an innovative wood panel composite that has been attracting growing interest worldwide. Apart from its economic benefits, CLT takes full advantage of both the tensile strength parallel to the wood grain and its compressive strength perpendicular to the grain, which enhances the load bearing capacity of the composite. However, traditional CLT panels are made with glue, which can expire and lose effectiveness over time, compromising the CLT panel mechanical strength. To mitigate such shortcomings of conventional CLT panels, we pioneer herein nail-cross-laminated timber (NCLT) panels with more reliable connection system. This study investigates the flexural performance of NCLT panels made with different types of nails and explores the effects of key design parameters including the nail incidence angle, nail type, total number of nails, and number of layers. Results show that NCLT panels have better flexural performance than traditional CLT panels. The failure mode of NCLT panels depends on the nail angle, nail type, and quantity of nails. A modified formula for predicting the flexural bearing capacity of NCLT panels was proposed and proven accurate. The findings could blaze the trail for potential applications of NCLT panels as a sustainable and resilient construction composite for lightweight structures.  
structure; composite; cross-laminated; timber; nail; bending; model; prediction; fracture +
Footnote †: journal: Applied Sciences
# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 2 Review of Related Algorithms
### Review of the CNN Algorithm  
CNN is developed from the neural perceptual machine concept, inspired by animal visual systems [25], and are a form of feedforward neural networks featuring convolutional operations within a deep structure [26], making CNN one of the representative algorithms in deep learning. The prediction principle of a CNN model lies in its layered structure comprising convolutional, pooling, and fully connected layers, which sequentially extract features from the data to make predictions.  
(1) Convolutional layer: Extracts local features such as edges and textures by scanning the input data with convolutional kernels [27]. The kernels slide over the input data with a specified stride, performing dot products and weighted summations to compute the feature maps. The convolution operation is given by the following formula:  
\[y_{i,j,k}=\sum_{m,n}x_{i}+m,j+n\cdot w_{m,n,k}+b_{k} \tag{7}\]  
where \(x\) represents the input, \(w\) is the convolutional kernel, \(b\) is the bias, and \(y\) is the output feature map [28].  
(2) Pooling layer: Reduces the dimensionality of the convolutional results, decreases the number of parameters, retains essential features, and enhances the model's computational efficiency and robustness. Common pooling methods include max pooling and average pooling [28].  
(3) Fully connected layer: Combines the local features extracted by the previous layers into global information through fully connected operations, providing the final prediction output.  
The CNN model leverages the convolutional layer for local feature extraction, the pooling layer for dimensionality reduction and noise filtering, and the fully connected layer for integrating global features. This structure offers significant advantages in predicting the SOH of lithium-ion batteries. Compared to other time-series prediction models, CNN demonstrates superior computational efficiency and the ability to capture local features, making it particularly suitable for analyzing the nonlinear changes and dynamic characteristics of battery health.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.7. XGBoost  
XGBoost is a specific implementation of gradient boosting, an ensemble learning method that combines the predictions of multiple weak models (decision trees) to create a strong predictive model. The key idea is to fit a new model to the existing model's residuals (differences between actual and predicted values). This process continues iteratively [54; 55].  
The XGBoost algorithm was employed to model the dataset, utilizing a learning rate (\(\eta\)) of 0.3 to balance convergence speed and generalization. Each decision tree in the ensemble had a maximum depth of 10, enabling the model to capture intricate patterns in the data. The objective function was set to \"_reg-linear_\" for regression tasks, and the ensemble was trained over 60 boosting rounds. Default hyperparameters were used for subsampling and regularization to maintain computational efficiency and avoid overfittingThe key mathematical equations in XGBoost relate to the objective function and the gradient of the loss concerning the model's predictions. The objective function, for instance, illustrates the loss for the entire dataset and the ensemble of trees.  
\[L=\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}+\gamma T(f)+\lambda|w| \tag{11}\]  
where \(N\) is the number of data points, \(y_{i}\) is the actual target value, \(\hat{y}_{i}\) is the predicted value, \(\gamma\) and \(\lambda\) are regularization terms, \(T(f)\) measures the complexity of the tree, and \(|w|\) is the magnitude of the weights on the leaves.
# Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
## 2 Circuit Principle
### LD Pulse Drive  
Figure 7a illustrates the schematic diagram of the laser diode pulse drive circuit. In the figure, LMG1025 is a high-speed gate driver from Texas Instruments (TI), capable of achieving an extremely fast propagation delay of 2.5 ns and a minimum pulse width of 1 ns. The IN+ pin of the LMG1025 serves as the positive input pin for the pulse signal, with the signal originating from the output of the pulse narrowing circuit. The output terminals OUTH and OUTL of the LMG1025 are connected to the gate of the GaN MOSFET switch EPC2215 via 0 \(\Omega\) resistors. The MOSFET utilizes the GaN field-effect transistor EPC2215 from Yipu Energy Conversion Company (EPC). GaN field-effect transistors exhibit higher switching speeds compared to silicon field-effect transistors, making them ideal for high-speed switching applications [37]. Figure 7b shows the layout of a portion of the printed circuit board (PCB) for the drive circuit. Due to the high current generated during the conduction of the GaN MOSFET, a transient voltage fluctuation may occur on the ground plane, which can couple into the gate of the MOSFET, preventing it from fully conducting. Therefore, it is necessary to segment the circuit's ground plane, separating the power ground (PGND) and signal ground (GND) layouts. As shown in Figure 7b, the source of EPC2215 has three electrodes, with two sources connected to the PGND and one source connected to the GND. Inside the EPC2215 chip, both the PGND and GND are connected to the source, achieving common grounding. This prevents the impact of high currents on the PGND, which could cause a potential rise on the GND. A Schottky diode \(D_{1}\) is used to suppress the back electromotive force. The resistor \(R_{5}\) can quickly discharge the charge in the semiconductor LD, thereby accelerating the turn-off speed. Additionally, when the circuit is not operating, it connects the positive and negative terminals of the semiconductor LD together, preventing the PN junction from being broken down due to external static electricity, thus protecting the semiconductor LD. The capacitor \(C_{ES}\) is soldered near the semiconductor LD, serving the functions of filtering and energy storage.  
Figure 6: LD power supply circuit.  
We have chosen 0 \(\Omega\) for \(R_{3}\) and \(R_{4}\), 4.7 \(\Omega\) for \(R_{5}\), and 100 \(\mu\)F for \(C_{ES}\) in the circuit. When the operating voltage of the pulse LD we selected is 18 V, the peak power of the output pulse light can reach 75 W, so the \(V_{LD}\) is set to 18 V.
# An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference
## 2 Related Work  
### Absolute Moment Block Truncation Coding (AMBTC)  
As a variant of BTC, AMBTC was introduced by [PERSON] and [PERSON] [13] in 1984. During the encoding procedure, the algorithm divides the original image into a set of non-overlapped blocks with a size of \(k\times k\). Assume that \(x_{ij}\) is the pixel value in location \((i,j)\) of the block, the mean value \(u\) and its standard deviation \(\alpha\) are computed by Equations (1) and (2), respectively.  
\[u=\frac{1}{k\times k}\sum_{i=1}^{k}\sum_{j=1}^{k}x_{ij}, \tag{1}\]  
\[\alpha=\sqrt{\frac{\sum_{j=1}^{k\times k}\left|x_{ij}^{2}-u^{2}\right|}{k\times k }}. \tag{2}\]Then, each block is converted to two quantizers and a bitmap image \(bm\). The two quantizers \(bm\) and \(lm\) for the block are computed by:  
\[lm=u-\alpha\sqrt{\frac{q}{k\times k-q}}\, \tag{3}\]  
\[hm=u+\alpha\sqrt{\frac{k\times k-q}{q}}\, \tag{4}\]  
where \(q\) is the number of pixels that are greater than or equal to the mean value \(u\). The bitmap \(bm=\left\{m_{ij}|m_{ij}\in\{0,1\},1\leq i,j\leq k\right\}\) is created as follows:  
\[m_{ij}=\left\{\begin{array}{ll}1,\text{ if }x_{ij}\geq u\\ 0,\text{ if }x_{ij}<u.\end{array}\right. \tag{5}\]  
Figure 1 shows an example to describe the procedures for the AMBTC scheme. Figure 0(a) shows the original image block of \(4\times 4\) pixels. Then, the block mean \(u\) and standard deviation \(\alpha\) are computed, respectively, by using Equations (1) and (2). In this example, \(u=110\) and \(\alpha=10\). The bitmap generated by AMBTC is shown in Figure 0(b). Finally, the quantization levels, namely \(lm\) and \(hm\), are calculated by using Equations (3) and (4), respectively. Figure 0(c) shows the reconstructed image block.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 3 Pipeline Overview
### Dual Contouring for Point Clouds  
The final step extracts the mesh from our data structure using recursive octree traversal initially introduced by [PERSON] et al. [17], which we extend to include diagonal connections (Section 4.1). This part is performed on the CPU using work-stealing-based simplification (Section 6.3.1). Since dual contouring generates quad meshes, we perform triangulation, duplicate removal, and vertex list compaction in a final series of GPU kernels called _Finalization_.
## 3 Establishment of a Prediction Model of Segment Fracture Width  
### The Establishment of Geometric Models  
A geometric model is established based on the working conditions and segment dimension parameters of the October Square Station and Jinling Petrochemical Station of Nanjing Metro Line 6. The ground elevation of the working area is approximately 16.57-35.59 m, and the main strata through the section are moderately weathered argillaceous sandstone, silty clay and silty clay, strongly weathered anddesite and moderately weathered anddesite.  
Figure 1: The nonuniform thrust produces a certain inclination angle with the normal of the torus of the segment.  
Figure 2: The segment is misaligned.  
The strength of the rock strata is 10-80 MPa, and the quartz content of the argillaceous sandstone is 40-50%. The upper soft and lower hard strata account for approximately 16%, the soil layer accounts for approximately 10%, and the rock layer accounts for approximately 74%. Groundwater has no influence on the construction of the work area. The physical and mechanical parameters of each rock and soil layer are listed in Table 1.  
The size parameters of the segment are as follows: inner diameter 5500 mm, outer diameter 6200 mm, and ring width 1200 mm. The parameters of the segment structure are as follows: splicing block F, adjacent blocks L1 and L2, and standard blocks B1, B2 and B3. The center angle corresponding to the splicing block is 21.5\({}^{\circ}\), and the center angles corresponding to the adjacent block and the standard block are 68\({}^{\circ}\) and 67.5\({}^{\circ}\), respectively. The segment material is high-strength concrete with an elastic modulus of 3.45 \(\times\) 10\({}^{4}\) MPa, a Poisson's ratio of 0.2, density of 2500 kg/m\({}^{3}\), a tensile strength of 1.89 MPa and a breaking energy of 147.6 N/m [24]. The segment design and a model diagram are shown in Figures 3 and 4.  
The internal steel mesh of standard block B1 of the segment is shown in Figure 5. There are 20 inner annular ribs and 106 longitudinal ribs of a single-ring segment, as shown in Figure 6. The steel bars are grade 3 steel with a diameter of 16 mm and an elastic modulus of 200 GPa.
## 1 Introduction
Considering these challenges, in this study, we propose a suitable weighting model and a joint detection of clock jump and cycle slip algorithms for the single-frequency GPS and BeiDou navigation satellite system (BDS)-combined KOD of LEO satellites using the sun-pointing attitude mode. Based on the careful analysis of the satellite attitude's impact, a weighting model based on exponential function is extended for correctly processing the observations of low or negative elevations to improve the availability of the KOD solution. The proposed cycle slip algorithm can jointly process receiver clock discontinuity and cycle slip. This paper is organized as follows. The GPS/BDS-combined KOD model is first introduced in Section 2, and then the impact of the LEO satellite attitude and the improved weighting model are discussed. The joint single-frequency receiver clock jump and cycle slip detection algorithm are also presented in Section 2. The improved single-frequency KOD strategy is validated on the Luojia-1A satellite, which is a scientific experimental low-cost small LEO satellite adopting the sun-pointing attitude mode. Section 3 first introduces Luojia-1A satellite information and a basic data processing configuration. The experiment validation results based on Luojia-1A onboard GPS/BDS data are then described and analyzed. The conclusions are given in Section 4.
## Appendix A
### Pseudocode of Logistic Regression Algorithm  
```
1:Input:\(D_{\text{train}}\): Training dataset; \(D_{\text{test}}\): Test set; \(\alpha\): Learn rate; \(n\_epochs\): Training epochs
2:Output:\(D_{\text{predicted}}\): Predicted probabilities for the test dataset
3:Program Body
4: Initialize \(\theta\) (Parameter vector) to zero or random values
5:for epoch = 1 to \(n\_epochs\)do
6: let \(gradients\) be a vector of zeros
7:for each \((x,y)\) in \(D_{\text{train}}\)do
8: let \(z\) be the dot product of \(\theta\) and \(x\); let \(\hat{y}\) be the sigmoid function of \(z\), \(\hat{y}=\frac{1}{1+e^{-z}}\)
9: let \(error\) be \(\hat{y}-y\); update \(gradients\) by adding \((error\cdot x)\)
10:endfor
11: update \(\theta\) by subtracting \(\alpha\cdot gradients\)
12:endfor
13:Prediction for \(D_{\text{test}}\)
14:for each \(x_{\text{test}}\) in \(D_{\text{test}}\)do
15: compute \(\hat{y}\) as the sigmoid of the dot product of \(\theta\) and \(x_{\text{test}}\); assign \(\hat{y}\) to \(D_{\text{predicted}}\)
16:endfor
17:Return \(D_{\text{predicted}}\)
```  
**Algorithm A2** Logistic Regression Algorithm [50]
# Multi-Scale Localized Perturbation Method in OpenFOAM
## 4 Discussion
### Limitations of Assumptions  
The MSLPM is derived under the assumption that there is sufficient scale separation between the background and perturbation, and that the perturbation does not have a significant influence upon the background. This assumption limits the perturbations that may be studied, for example, a large temperature or velocity perturbation to the entire domain, as these perturbations may have a significant impact on the dynamics of the background. Because the one-way coupling of the background and perturbation is the result of this assumption, the implementation of two-way coupling may partially relax this assumption and permit a greater range of perturbations to be studied.  
Certain solution methods may also limit the applicability of the MSLPM. The case that is described in Sections 2 and 3 above assumes that the time scale of the background processes is so large compared to the simulation duration that the background can be assumed to be constant in time. This requirement may be restrictive, depending on the background flow used, but two other solution methods are described in Section 2. The simultaneous solution of background and perturbation fields on the same domain may also be used for this case and, in particular, may have been more appropriate given the nature of the background shear flow. Interpolating the background fields temporally and spatially from a separate simulation could also be used. This method may be of interest to nested multi-scale simulations; however, one must ensure that proper interpolation is used.  
The 1D and 2D+t simulations were also conducted under the assumption that the flow evolves slowly in certain directions. The 1D simulation was performed in order to provide initial conditions for the background shear in the 2D+t simulation, and as such it loses any evolution in other directions, particularly the \(y\) direction in which it flows. Within the 2D+t simulation, any significant changes in the direction in which the axisymmetric current flows is lost. While this mainly affects the evolution of the axisymmetric current, the interactions between internal gravity waves and the background shear layer may lose any 3D effects that may be important in the interactions. Similarly, the choice of a \(k-\varepsilon\) type model may also affect the quantitative values that are seen in Figures 6-10. In this case,the results seen here may only be qualitatively correct. As a final note, this case is demonstrative of the capabilities of the MSLPM, so no quantitative conclusions should be drawn from these results.
## 3 Multi-Strategy Improved Sparrow Search Algorithm
### Proposed ISSA Algorithm
#### 3.2.4 General Framework of ISSA  
The ISSA algorithm first initializes the population using tent chaotic mapping to enhance population diversity. Next, it integrates the sine-cosine concept and introduces a nonlinear learning factor to dynamically adjust the update mechanism of the discoverer's position, thereby accelerating the solution rate. Finally, the ISSA algorithm introduces the Levy flight strategy into the follower's position updating method to perturb and mutate the current optimal solution, enhancing local escape capabilities.  
Step 1: Initialize algorithm parameters, including population size \(n\), dimension \(d\), discoverers \(PD\), sentinels \(SD\), warning value \(ST\), upper bound \(ub\), lower bound \(lb\), and maximum iteration number \(T\).  
Step 2: Initialize the entire population using the tent chaotic sequence from Equation (12), generating \(nd\)-dimensional vectors \(z_{i}\), then map them to the variable range of the original problem space using Equation (13).  
Step 3: Compute the fitness \(f_{i}\) of each sparrow, then select the current optimal fitness value \(f_{g}\) and its corresponding position \(X_{best}\).  
Step 4: Select the top sparrows with strong adaptability as discoverers, and the rest as followers. After incorporating the sine-cosine algorithm (SCA) idea and introducing a dynamic learning factor, update the discoverer's position according to Equation (15).  
Step 5: The remaining sparrows act as followers, introducing the Levy flight strategy and updating the follower's position based on Equation (16).  
Step 6: Randomly select sparrows from the sparrow population as sentiries and update their positions according to Equation (11).  
Step 7: After one iteration, recalculate the fitness value \(f_{i}\) of each sparrow and the average fitness value \(f_{av}\) of the entire sparrow population.  
Step 8: Based on the current status of the sparrow population, update the optimal position \(X_{best}\) and its fitness value \(f_{g}\) experienced by the entire population.  
Step 9: Check if the algorithm meets the termination conditions. If yes, end the algorithm and output the results; otherwise, return to Step 4.  
The flowchart of the ISSA is illustrated in the Figure 3.