esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Information System Software Failure and Its Causes  
#### 4.2.1 Switch Failure and Its Causes  
The application failure of information system communication technology is also closely related to the software system, in which switch failure is relatively common. The switch system failure is mainly due to the software operation of the corresponding switch not being smooth, which can easily cause a greater threat to the application of information data [27]. For this reason, it is necessary to comprehensively strengthen equipment maintenance, reduce various hidden dangers, and avoid data loss.
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 2 Models and Methods
### The Existing Time Functions and Their Flaws
#### 2.2.4 Other Time Functions  
In addition to the Knothe time function and its improved forms, there are other time functions: Logistic time function, Gompertz time function, Sigmoidal Function [35] and so on [22; 36; 37].  
(1) Logistic time function  
The Logistic time function has been widely used in ecology, demography and other fields. It reflects the process by which things occur, develop, mature and tend to saturation [13; 26; 38; 39]. It is expressed as:  
\[W(t)=\frac{W_{0}}{1+fe^{-gt}} \tag{8}\]  
where \(g,f\) are the coefficients related to overburden lithology. \(W_{0}\) and \(W(t)\) are the same as above.  
Figure 4: (**a**) The subsidence and velocity of the segmented Knothe time function; (**b**) The acceleration of the segmented Knothe time function.  
The expressions of \(v(t)\) and \(a(t)\) for Logistic time function are shown in Equations (9) and (10), respectively:  
\[v(t)=\frac{W_{0}gfe^{-gt}}{\left(1+fe^{-gt}\right)^{2}} \tag{9}\]  
\[a(t)=\frac{W_{0}g^{2}fe^{-gt}\left(fe^{-gt}-1\right)}{\left(1+fe^{-gt}\right)^{ 2}} \tag{10}\]  
The time-subsidence curve of the Logistic time function also has a \"S\" shape, while when \(t=0\), \(W(t)=W_{0}/(1+f)\
eq 0\), \(v(t)\
eq 0\), \(a(t)\
eq 0\). Moreover, considering \(a(\tau)=0\), then \(e^{-gt}=1/f\), \(W(\tau)=W_{0}/2\), it means the subsidence amount of the Logistic time function at the inflection point is constant, which is equal to \(1/2\) of the final subsidence. These cases do not accord with the measured data.
## 5 Discussion
### Comparison of Theoretical and Experimental Values  
A fundamental aspect of our investigation involved comparing the theoretical values of time measurements with the experimental data obtained through the smartphone audio recording methodology. Theoretical calculations based on the principles of classical physics provided expected time intervals for the free fall of a steel ball from varying heights, while experimental measurements using the smartphone's onboard microphone recorded the actual time intervals during the descent. The comparison between the theoretical and experimental time measurements revealed a consistent trend and a high degree of alignment across varying experimental conditions. The table of results exhibits this alignment, indicating that the measured time intervals closely corresponded to the theoretical predictions for the descent of the steel ball from different heights. While a slight discrepancy between theoretical calculations and experimental measurements was evident, the relative error remained within a narrow margin. The observed relative error ranged between 2%  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Sensor** & **Accuracy** & **Uncertainty** & **References** \\ \hline Pendulum & Smartphone ambient & 99.215\% & 9.729 \(\pm\) 0.01 m/s\({}^{2}\) & [28] \\  & light sensor & 99.215\% & 9.729 \(\pm\) 0.01 m/s\({}^{2}\) & [28] \\ \hline Free fall & Microphone and sound & 99.90\% & 9.82 \(\pm\) 0.03 m/s\({}^{2}\) & [32] \\  & personal computer & & & \\ \hline Falling ball with the pendulum & Microphone and sound & 99.75\% & 9.776 \(\pm\) 0.005 m/s\({}^{2}\) & [34] \\  & computer & & & \\ \hline Free fall & Smartphone’s onboard & 97.50\% & 9.57 m/s\({}^{2}\) & [38] \\  & microphone & 99.82\% & 9.8274 \(\pm\) 0.01 m/s\({}^{2}\) & This work \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._and 3.27% across different experimental trials. This discrepancy could be attributed to factors such as minor variations in the experimental setup, imperfections in the timing of the switch actuation and the steel ball's impact, or inherent limitations in the audio-based time measurement system. However, it is noteworthy that despite this minor discrepancy, the calculated value of \(g\) derived from the experimental time measurements exhibited a remarkably small relative error when compared to the theoretical \(g\) value. The experimental \(g\) value of 9.8274 m/s\({}^{2}\) evidences a mere 0.17737% relative error when juxtaposed with the theoretical value, emphasizing the accuracy and reliability of the smartphone-based methodology in determining \(g\). The consistency in the correlation between theoretical and experimental values further validates the efficacy and precision of the smartphone audio recording method in capturing accurate time intervals during free fall experiments. Despite the minor variations observed, the overall agreement between theoretical predictions and experimental measurements underscores the methodology's robustness and reliability in determining gravitational acceleration.
## 1 Introduction
(2020) used a particle swarm algorithm to solve the beyond-frequency analytical equations of two-end cemented hangers to identify the cable force and bending stiffness of the hangers; [PERSON] et al. (2019) used a multilayer perceptron and long-short-term memory network to learn the nonlinear mapping between the arch rib line shape and the cable force, and utilized a DNN model to predict the desired construction cable force corresponding to the target line shape; [PERSON] et al. (2017) used a QPSO-SVM model, which accurately predicted the contact resistance increment and mass loss of SF6 circuit breakers; [PERSON] et al. (2018) identified the sensorless cable-stayed bridge cable-stayed force through a back-propagation neural network in combination with a finite element model; [PERSON] et al. (2019) optimized the generalized regression neural network using a sparrow search algorithm and combined it with finite element simulation to predict the cable force of the bridge, which is better compared with other algorithms. [PERSON] et al. (2020) used the BPVMM technique to amplify the small vibration of the cable, used the line tracking algorithm to measure the cable displacement of the amplified video, and then obtained the frequency of the cable to find out the cable force. In summary, while deep learning models demonstrate strong performance in predicting bridge cable forces, their efficacy is contingent upon larger datasets and longer training times. Additionally, other algorithms face challenges related to limited model generalization ability and high computational complexity. In contrast, the QPSO-SVM model exhibits unique advantages, including stronger generalization ability, higher computational efficiency, and better robustness. As such, it holds great promise for application in the field of bridge cable force prediction. In practical engineering, the design and construction of sub-bearing cable-stayed arch bridges present numerous challenges, particularly with regard to the accuracy of cable force calculations, which directly impact bridge safety and stability. The traditional formulas for calculating cable forces often result in significant errors due to the complex interplay of factors such as materials, environment, and construction conditions on bridge structures. This study proposes a novel cable force prediction method based on QPSO-RBF-SVM, offering a fresh perspective for identifying cable forces in arch bridges and effectively reducing testing errors.
# Information-Aware Secure Routing in Wireless Sensor Networks
## 3 Model and Methods
### Attack Probability
#### 3.5.2 Attack Probability of a K-Hop Path  
As shown in Figure 3, a WSN is very similar to the serial-parallel system in classical reliability theory. A route between two nodes in a WSN can be seen as a serial system, and the WSN can be viewed as a complex parallel system because there is more than one route for each pair of nodes. Data can be transmitted along parallel routes. Data can be successfully transmitted to the sink node  
Figure 1: Calculation process of the attack probability.  
Figure 2: Direct or indirect attack probability.  
along a specific route only if all nodes in the route work normally. Therefore, the nodes in a route can be viewed as a serial system of independent components.  
According to the serial system theory, the attack probability of a path (\(l=\langle m_{1}\cdots,m_{K+1}\rangle\)) can be calculated as follows:  
\[A_{L}=1-\prod_{k=1}^{K}(1-Attack_{\langle m_{k}m_{k+1}\rangle}), \tag{4}\]  
where \(K\) is the number of hops.
## 2 Related Work
In [10], authors propose a multilayer hierarchical architecture to cover more sensing area and to distribute energy across the network. The work focusses on hierarchy creation in the network without incorporating benefits of a scheme like cooperative MIMO suitable for long-range wider area coverage. Further, various aspects such as cluster-head rotation and scaling with number of nodes have not been addressed. [PERSON] _et al_. [11] provide a clustering mechanism for spatially distributed data in wireless sensor networks using deterministic and probabilistic approaches to unsupervised learning. Again, the work is distinctive for its focus on distributed data aggregation and clustering than energy efficiency and long-range cooperative MIMO communication. In this work, we propose an architecture for energy efficient clustering to be used with cooperative MIMO communication in WSNs. Our scheme is hierarchical, offers an efficient reclustering approach and scales to large networks. In subsequent sections, we provide details of proposed scheme and use hierarchical MIMO/hierarchical cooperative clustering interchangeably given our proposed framework attains benefits of both MIMO and cooperative clustering.
## 2 Research Methodology and TMD Fundamentals  
### Overall Research Methodology  
This study adopts a comprehensive numerical simulation framework to evaluate the control effect of TMD on the dynamic response of deep-water jacket platforms under typhoon-wave loads. The overall research process is shown in Figure 1, with the main process depicted in Figure 1a, involving the following key steps. In terms of platform structure modeling, a three-dimensional nonlinear finite element model of the deep-water jacket platform is established in OpenSees based on actual engineering parameters. The model strictly considers soil-pile interaction (SSI) and water-pile interaction, where SSI effects are accurately simulated by the nonlinear BNWF model (PySimple1, TzSimple1, and QzSimple1) (Figure 1b).  
For an environmental load simulation, it can be divided into typhoon wind loads and typhoon wave loads. The typhoon wind loads use the WRF model to simulate extreme typhoon scenarios (Section 4.1) to obtain key wind parameters. Dynamic wind loads acting on the structure are generated by combining the exponential wind speed profile fitted based on WRF results and the wind spectrum model calibrated with measured data. The typhoon wave loads adopt random wave theory combined with the JONSWAP spectrum (Section 4.2) to characterize the extreme wave field. Wave particle kinematics are calculated based on linear wave theory, and [PERSON]'s equation is applied to compute wave loads on the pile foundation (Figure 1c). The implementation of the TMD control strategy will be introduced in the subsequent sections of this chapter.  
Finally, dynamic response analysis and evaluation are conducted. Nonlinear time-history analysis is performed under the simulated typhoon-wave loads (Section 5). The peak values and decay rates of key response indicators (deck acceleration, displacement, and base shear) are compared to quantify the vibration reduction efficiency of TMD.
###### Abstract  
Pansharpening is a critical technique in remote sensing, particularly in ecological and environmental monitoring, where it is used to integrate panchromatic (PAN) and multispectral (MS) images. This technique plays a vital role in assessing environmental changes, monitoring biodiversity, and supporting conservation efforts. While many current pansharpening methods primarily rely on PAN images, they often overlook the distinct characteristics of MS images and the cross-modal relationships between them. To address this limitation, the paper presents a Dual-Stream Cross-modality Fusion Network (DCMFN), designed to offer reliable data support for environmental impact assessment, ecological monitoring, and material optimization in nanotechnology. The proposed network utilizes an attention mechanism to extract features from both PAN and MS images individually. Additionally, a Cross-Modality Feature Fusion Module (CMFM) is introduced to capture the complex interrelationships between PAN and MS images, enhancing the reconstruction quality of pansharpened images. This method not only boosts the spatial resolution but also maintains the richness of multispectral information. Through extensive experiments, the DCMFN demonstrates superior performance over existing methods on three remote sensing datasets, excelling in both objective evaluation metrics and visual quality.  
attention mechanism; computer vision; deep learning; feature extraction; image fusion; nanotechnology; pansharpening; spectral-spatial interaction Article
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ1: What Are the Different Phases of Forming an IoT Botnet?
Likewise, in the article [83], the authors divided IoT botnets into four different phases as follows: selection of the target; device fingerprinting and infection; detection of avoidance and persistence; and activation. In a different manner, [84] considered that IoT botnets have three phases: host scanning, system acquisition and service attack denial. In summary and from the above, it can be noted that each study divided the phases of development for a botnet in a different way. They provided acceptable phases based on the objectives of the solutions proposed, which could be the discovery of the malware file, the discovery of suspicious activities carried out by the botnet or the discovery of the devices attached to the botnet. As seen in Figure 7, the results show that most of the selected studies, approximately 62%, concentrated on proposing solutions for detecting the IoT botnet in the attack phase, which is considered a late phase in the IoT botnet's lifecycle. Only 16% of the selected studies focused on proposing solutions for detection in the scanning phase, and 22% focused on the propagation phase for their solutions. Thus, fewer papers concentrated on detection in the early phases. Hence, it appears that there is little interest in the early stages. Table 9 displays the numbers of studies that focused on the various phases of IoT botnet development. \begin{table}
\begin{tabular}{c c} \hline \hline
**Phase of Botnet** & **Number of Studies** \\ \hline Scanning & 6 \\ \hline Propagation & 8 \\ \hline Attack & 23 \\ \hline Sum & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Numbers of studies focusing on the different phases of IoT botnet development. Finally, we believe that the most appropriate for proposing a solution of detection the IoT botnet is detecting the IoT botnet in the early phase of the lifecycle i.e., Scanning and Propagation, since that the IoT botnet in these phases does not start their harm activities.
## 3 DAMP-YOLO Network  
As illustrated in Figure 4, the proposed DAMP-YOLO network architecture enhances the YOLOv8 baseline by incorporating a deformable CSP bottleneck (DCB), aggregated triplet attention (ATA), random meter data augmentation (MDA), and network pruning (NP). The DCB module effectively captures the features of objects with similar shapes and structures through adaptive adjustment of deformable convolutional kernel offsets. The ATA module enhances focus on attention across all dimensions by aggregating features from different branches. The MDA module aims to simulate data distribution in real-world scenarios, and network pruning is employed to reduce network parameters and computational costs. These techniques collectively ensure that our network is robust in detecting objects in complex environments with high accuracy while significantly reducing model size.  
The following part of this section will describe, in detail, the implementation process and methods for improvement.
## 3 Experiment Results and Analysis of Dynamic Characteristics
### Influence of Loading Frequency on Reinforced Grawelly Soil
#### 3.2.3 Effect of Frequency on Excess Pore Water Pressure  
Figure 13 shows the \(u_{d}\)-\(N\) curves of the samples loaded under different frequencies. The comparison shows that excess pore water pressure in the samples increase nearly logarithmically with loading cycles before stabilizing. The higher the loading frequency, the longer it takes the excess pore water pressure to stabilize, and the higher the excess pore water pressure. As loading frequency increases from 0.5 Hz to 5 Hz, the excess pore water pressure ratio of the samples increases from 0.3 to 0.57.
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 4 Simulation and Verification
### Link Load Comparison  
Figure 5 shows the comparison of link load between the relevant literature and this design in cross-domain networks with the same routing request requirements. Due to the fact that this algorithm simultaneously establishes the adaptability of time-varying and fixed type networks, it has more flexible routing links and path planning compared to previous literature methods, with the minimum average load degree of around 0.4, which is reduced by 31% compared to the literature methods. In order to ensure communication quality, the design of the SDN-WSN continuously increases the load as the number of applications increases; the DFWA chooses a more conservative routing strategy to reduce data loss, which makes it difficult to reduce load. Due to the use of exhaustive search method in the ORPT, the load degree presents a random state with different subnets, with weak regularity.  
Figure 4: Link stability corresponding to different maximum route delay times with fixed route application times.
## 1 Introduction
### Paper Overview  
In this study, we explored the feasibility of using an RTK quadcopter (Phantom 4 RTK) and SfM photogrammetry for fine-scale (e.g., submeter) monitoring of water-worked coastal topography. We carried out a variety of tests of interest for the application because of their influences on data collection time and measurement quality (e.g., resolution and precision). More specifically, we were interested in enhancing the survey efficiency to surpass tide-related constraints while ensuring the results' effectiveness through rigorous error characterization at two macrotidal coastal field sites with contrasting survey areas and morphologies. The DEM error evaluation at localized check points (ChkPIs) was supplemented where possible by full DEM comparisons and the collection of over 2000 survey points with RTK-GNSS, whilst the availability of repeat surveys and invariant features enabled the assessment of the complete workflow replicability.  
Our results show that at least five GCPs, in addition to camera information, were necessary to achieve the optimal model quality that minimized vertical bias and random errors. Under this configuration, the standard deviation of error representing measurement precision (\(\sim\)1 GSD) was approximately two times better than that when camera information was unused. First, the geometric analyses are presented, highlighting the potential of the method for submeter bedform characterization over entire beach systems.
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Microstructure Characterization Methods  
#### 2.4.1 Scanning Electron Microscope (SEM) Observation  
The Scanning Electron Microscope (SEM) operates on the principle of generating and manipulating a focused beam of high-energy electrons to interact with a sample's surface, thereby generating various signals (secondary electrons, reflected electrons, X-rays, etc.) that are subsequently detected and converted into visual images. The intensity of these signals is related to the properties of the sample's surface, such as topography, composition, and crystal structure. Consequently, it facilitates the observation and analysis of the microstructure, composition, and other material properties with remarkable precision [31].  
The microstructure of CuW composites, both prior to and following the addition of graphene, was observed utilizing a SUPRA55 (ZEISS, Jena, Germany) type scanning electron microscope. This allowed for a comparative analysis of the grain size and microstructure between the two samples, thereby elucidating the impact of graphene addition on the microstructure. Additionally, energy dispersive spectroscopy (EDS) was employed to observe and analyze the elemental distribution of Cu, W, and C within the material.
# A New Multi-Physics Coupled Method for the Temperature Field of Dry Clutch Assembly
###### Abstract  
The temperature field of the clutch assembly is critical for the clutch design and operation life. Current modeling methods of the temperature of the clutch assembly suffer from insufficient accuracy or a limited time scale for the complicated multi-physics coupling between the contact force, friction-generated heat, heat transfer, and thermal deformation in the clutch assembly in harsh operation conditions. In order to improve the accuracy of temperature field simulation and achieve long-term time scale, a new approach to modeling the temperature is proposed based on CFD simulation and decoupling technology. Firstly, the flow-thermal bi-directional coupling method is employed to determine the convective boundary conditions between the clutch assembly and the ambient air, improving the model's accuracy. Secondly, the thermal-solid decoupling method is then used to reduce the computational time. The temperature of the clutch assembly during the continuous engagement and disengagement process is performed using this approach and verified by the rig test. The results demonstrate that the temperature of the outer, middle, and inner diameters of the pressure plate by the model agrees well with that by the rig test. For the first engagement and disengagement processes, the proportion of simulated temperature deviations exceeding 5 \({}^{\circ}\)C from the measured data is only 3.03%. For the last engagement and disengagement process, while the maximum temperature of the clutch is above 350 \({}^{\circ}\)C, the maximum temperature deviation between simulation and measurement is 4.99%. It proves that the approach proposed for modeling the dry clutch assembly temperature field has high accuracy while achieving long-term time-scale simulation.  
d  
ryd clutch assembly; temperature field; multi-physics coupling; CFD simulation; decoupling technology