esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Drop-Weight Test  
Twelve concrete cylinders with a diameter of 95 mm, a height of 285 mm, and an average 28-day compressive strength of 58.5 MPa were prepared. The preparation of the concrete column and the fabrication of the FRP-confined concrete column were the same as those presented in Section 2.2. Among them, 8 cylinders were wrapped with one layer of the PET FRP, 2 cylinders were confined with one layer of the AFRP, of which the nominal thickness, the tensile strength, and the elastic modulus are 0.169 mm, 3732 MPa, and 115 GPa, respectively, and 2 remaining unconfined concrete cylinders were tested as control specimens (Table 2). All specimens were denoted as \"A-B-C\", where A, B, and C stand for the type of FRP, the number of FRP layers, and the drop height, respectively. Two nominally identical specimens were tested at each drop height to reduce the data scatter.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of**} & \multirow{2}{*}{\begin{tabular}{c} **D \(\times\) H** \\ **Test** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Nominal** \\ **Type of FRP** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Volume** \\ **of FRP** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Tensile** \\ **Fraction** \\ **of Fiber** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number** \\ **of FRP** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Initial** \\ **of FRP** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Unconfined** \\ **Concrete** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Number of** \\ **Identification** \\ **Specimen** \\ \end{tabular} } \\ \hline \multirow{4}{*}{SHPB test} & \multirow{2}{*}{
\begin{tabular}{c} 70 \(\times\) 38 \\ (CFRP) \\ \end{tabular} } & Carbon FRP & \multirow{2}{*}{0.165} & \multirow{2}{*}{15\%} & \multirow{2}{*}{4423} & \multirow{2}{*}{1} & \multirow{2}{*}{10.9} & \multirow{2}{*}{18.5} & \multirow{2}{*}{3} \\  & Polyethylene &  
A large-capacity drop-weight setup was used for the impact test [23], consisting of a hammerhead with a weight of 188 kg, 14 balancing weights each with a weight of 68 kg, a steel frame, a track, a control system, and a lifting device (Figure 4). The maximum drop height was 16 m. Different impact energies can be obtained by adjusting the mass of the drop weight and the drop height. In this test, the drop weight was held the same at 256 kg (the hammerhead + one balancing weight), and different impact energies were achieved by varying the impact height (i.e., 2, 3, 4, and 5 m).
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 3 Karman Vortex Street-Based Energy Harvesting
### Karman Vortex Street-Based EM-FBEHs  
Vortex-induced electromagnetic energy harvesters leverage the principles of electromagnetism to convert VIVs into electrical power. As vortices form and shed behind a bluff body in a fluid flow, the resulting oscillations cause a relative motion between a magnet and a coil. According to [PERSON]'s law of electromagnetic induction, this motion induces an electromotive force (EMF) in the coil, generating electrical current. [PERSON] et al. [56] fabricated an electromagnetic flow-based energy harvester (EM-FBEH), where the pressure variations in fluid flow were applied to a flexible diaphragm with a magnet attached, which was surrounded by a wound coil. As a result, the motion of the magnet with respect to the coil induced EMF in its terminals. Experiments demonstrated that when the excitation pressure oscillated with an amplitude of 254 Pa and a frequency of 30 Hz, an output peak-to-peak voltage of 10.2 mV was produced. [PERSON] et al. [57] reported an EM-FBEH capable of capturing energy from vibrations caused by the Karman vortex street. The periodic pressure oscillations from the Karman vortex street in the flow channel cause the magnet beneath a coil to oscillate, generating voltage. The energy harvester prototype of 37.9 cm\({}^{3}\) was tested at an excitation pressure of 0.3 kPa and frequency of 62 Hz, with an output peak-to-peak voltage of about 20 mV achieved. Recently, [PERSON] et al. [58] also utilized the diaphragm mechanism to develop a flow-based electromagnetic energy harvester.
###### Abstract  
Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.  
imperfect debugging; fault detection; fault correction; testing coverage; software reliability growth model (SRGMs); non-homogeneous Poisson process (NHPP)
## 4 Evaluations of Prediction Results Using Accuracy Metrics
### Evaluations of Prediction Accuracy of Different Training Datasets
#### 4.2.2 Evaluations of Prediction Accuracy Using Percentage-Dependent Metrics  
Table 6 and Figure 9 illustrate the evaluation results of the prediction accuracy of the three groups using percentage-dependent metrics. Due to the existence of zero values in the actual values, the evaluation results of the MAPE were infinite. After removing the zero values from actual values, the value of the MAPE was calculated accurately. The evaluation results of the MAPE of group 1, group 2 and group 3 were, 14.27%, 17.16% and 14.61%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**R\({}^{2}\)**} & **MAPE** & **MAPE** & **SMAPE** & **SMAPE** \\  & & & **(Including Zero** & **(Remove Zero** & **(Including Zero** & **(Remove Zero** & **Values)** \\  & & & **Values)** & **Values)** & **(Remove)** \\ \hline Group 1 & 0.9752 & 0.9443 & Infinite & 14.27\% & 52.02\% & 15.07\% \\ Group 2 & 0.9613 & 0.9190 & Infinite & 17.16\% & 56.17\% & 18.05\% \\ Group 3 & 0.9690 & 0.9318 & Infinite & 14.61\% & 52.94\% & 15.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation results of different training datasets using percentage-dependent metrics.  
Figure 8: Evaluation results of different group datasets using scale-dependent metrics. (**a**) Evaluation results using MSE. (**b**) Evaluation results using RMSE. (**c**) Evaluation results using MAE. The evaluation results of group 1 are minimum, and the evaluation results of group 2 are maximum.  
The existences of zero values in actual values affected the calculation accuracy of the SMAPE as shown in Table 6. When including the zero values in the actual values, the calculation results of the SMAPE of group 1, group 2 and group 3 were 52.02%, 56.17% and 52.94%. After removing the zero values from the actual values, the calculation results of the SMAPE decreased to 15.07%, 18.05% and 15.17%, respectively.  
The evaluation results of the three groups using scale-dependent metrics and percentage-dependent metrics showed that the prediction accuracy of group 1 was the best, group 3 was second and group 2 was third. In other words, the training dataset based on the 300 AE signals collected from the pile-installed sensors (S1, S2 and S3) showed better performance for training the ANN prediction model than the training dataset based on the 300 AE signals collected from the platform-installed sensors (S4, S5, and S6). The accuracy of the training dataset based on the 600 AE signals collected from the mix-installed sensors (S1, S2, S3, S4, S5, and S6) was between those two groups.  
From the evaluation results, engineers can consider that, when detecting pile foundations, it should be the first option to install AE sensors on the pile body to receive AE signals to detect damage locations. The method of mix-installation can be the second option, and the method of platform-installation should be the last option.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Sensitive Analysis  
We conducted the sensitive analysis on driving factors to see how the factors affect the vegetation biomass. Based on these results, we can attribute and quantify the contributions of each factors on vegetation growth. We vary the rainfall of dryland areas. Additionally, we use the values of other parameters in dryland areas. In Figure 2, there are two states in the system. One is stable (solid line), suggesting enough vegetation cover. Another is the unstable state (dotted line), suggesting the bare soil state. There exists one bifurcation when the system approaches a certain value under different climate conditions. We found that the vegetation biomass is sensitive to the CO\({}_{2}\) concentration and land use rate. With the increasing CO\({}_{2}\) concentration, the system is more robust because of the benefits of CO\({}_{2}\) fertilization effects. Furthermore, the land use rate is also one dominate factor for vegetation biomass development. As the land use rate increases, the system more easily approaches the tipping point and more easily shifts to a bare soil state. Based on the analysis above, we try to attribute the vegetation biomass growth to CO\({}_{2}\), climate and land use.
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 3 Feature Extraction
### Computing Shape Parameters  
A local region that is occupied by the input point cloud is repeated divided until the number of points fall into each voxel is equal to the threshold \(N_{p}\). The threshold \(N_{p}\) is related to the horizontal and vertical resolution of LiDAR point cloud. In the process of feature extracting, the densities of features in corresponding voxels vary. We consider the density while searching for the correspondences of the features. The lower the density of the features, the lower the probability of finding enough correspondences within a certain radius is. Our experiment results are achieved when \(N_{p}\) is set to 25. Each voxel contains points that fall into it and voxel centroid. The points that fall into voxel are defined as \(\left\{X_{i}\right\}=\left\{\left(x_{i},y_{i},z_{i}\right)^{T}\right\}\). The first and second-order moments \(\mu,S\) describe the parameters for spatial distribution of the points \(\left\{X_{i}\right\}\):  
\[\mu=\frac{1}{N}\sum_{i=1}^{n}X_{i} \tag{1}\]  
\[S=\frac{1}{N}\sum_{i=1}^{n}\left(  
The two saliency features, named linear feature and surface feature, are linear combinations of the eigenvalues. Figure 1 illustrates the two features used. The quantity:  
\[c=\frac{\lambda_{0}-\lambda_{1}}{\lambda_{0}+\lambda_{1}+\lambda_{2}} \tag{3}\]  
is the linear feature of the points in voxel which ranges from 0 to 1. Similarly the quantity:  
\[p=\frac{\lambda_{1}-\lambda_{2}}{\lambda_{0}+\lambda_{1}+\lambda_{2}} \tag{4}\]  
is the surface feature of the points in voxel which ranges from 0 to 0.5.
## 1 Introduction
### Problem Statement  
Conventional SHM methods, including visual inspections and fixed sensor monitoring, exhibit constraints regarding accessibility, resolution, and dependability. Furthermore, image-based approaches employing conventional computer vision techniques encounter difficulties due to environmental noise, occlusions, and fluctuating illumination conditions, resulting in erroneous positives and negatives in defect identification. The issue is exacerbated by problems in extensive data gathering, high-dimensional feature spaces, and the limited generalisability of AI models across various bridge architectures and contexts. This work emphasises the need for sophisticated, resilient, and interpretable AI models proficient in detecting, localising, and classifying defects such as cracks, spalling, corrosion, and delamination in concrete bridges. It emphasises the necessity of integrating physical knowledge with data-driven methodologies, utilising edge AI, transfer learning, and digital twins to facilitate predictive maintenance and real-time monitoring, thus guaranteeing bridge infrastructures' long-term safety and sustainability.
## 2 Project Overview
### Changing the Coordinates of the Numerical Model  
The third-order primary stress tensor needs to be converted into the third-order stress tensor in the generalized plane coordinate system, since in situ stress measurement typically yields the principal stress and its direction of in situ stress, and the roadway layout is possible in any direction [34; 35; 36]. Using Equation (9), the tensor of the known coordinate system can be changed into the tensor expression of the coordinate system in any direction.  
\[[A^{\prime}]=L[A]L^{T} \tag{9}\]  
\(A\) is the tensor in the original coordinate system. \(A^{\prime}\) is the tensor in the new coordinate system. \(L\) is the transition matrix from the original coordinate system to the new coordinate system.  
Figure 5 shows the model for calculating the roadway in any direction. \(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\) are known ground stresses. The new system's vertical coordinate lines up with that of the original system because a new coordinate framework has been set up. Equation (10) shows the transition matrix.  
\[L=\begin{bmatrix}\cos\theta&0&-\sin\theta\\ 0&1&0\\ \sin\theta&0&\cos\theta\end{bmatrix} \tag{10}\]  
Figure 4: The relationship between the tectonic stress change coefficient and the buckling deflection change coefficient.  
In the equation, \(\theta\) is the angle between the \(\sigma_{1}\) direction of the maximum horizontal principal stress square and the axial direction of the roadway layout. Using the equation above, the stress transformation is performed, and the stress tensor of the new coordinate system is put into the formula for calculating the stress field:  
\[\begin{bmatrix}p_{x}&p_{xy}&p_{xz}\\ p_{yz}&p_{y}&p_{yz}\\ P_{zx}&P_{zy}&P_{z}\end{bmatrix}=\begin{bmatrix}\cos^{2}\theta\cdot\sigma_{3}+ \sin^{2}\theta\cdot\sigma_{1}&0&-\frac{1}{2}\sin 2\theta(\sigma_{1}-\sigma_{3})\\ 0&\sigma_{2}&0\\ -\frac{1}{2}\sin 2\theta(\sigma_{1}-\sigma_{3})&0&\cos^{2}\theta\cdot\sigma_{1}+\sin ^{2}\theta\cdot\sigma_{3}\end{bmatrix} \tag{11}\]  
Here, \(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\) are known ground stresses, and the stress tensor \(P_{ij}\) represents transformed stress components within the new coordinate system.  
The angle \(\theta\) is the variable in the simulation experiment, and there are five working conditions: \(0^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\). We use the results of the in situ stress test in Table 1 to find the \(\sigma^{{}^{\prime}}_{xx}\), \(\sigma^{{}^{\prime}}_{yy}\) and \(\sigma^{{}^{\prime}}_{zz}\) for five different sets of working conditions. The results are shown in Table 2. Using the findings of the calculations above, we look at how different angles affect the rock around the roadway.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 3 The Proposed Method
### Multi-Scale Feature Optimization Based on Improved Coarse-Graining  
To improve the capability of traditional multi-scale arrangement features, this paper starts with the coarse-grained step and establishes an improved multi-scale feature. Taking scale 3 as an example (as shown in Figure 5), different from the original algorithm, under the same time scale \(\tau\), our method can obtain \(\tau\) sets of time series after coarse-grainedprocessing to solve the mutation problem at the 'breakpoint', while there is only one set of time series in the classical algorithm. The mathematical process can be expressed as:  
1. The given signal is processed by the improved coarse-grained process to obtain \(\tau\) sets of time series: \[\left\{\begin{array}{c}Z_{i}^{(\tau)}=\left\{y_{i,1}^{(\tau)},y_{i,2}^{(\tau) },\ \cdots\right\}\\ y_{i,j}^{(\tau)}=\frac{\sum_{j=0}^{\tau-1}x_{j/i+\tau(j-1)}}{\tau}\end{array} \right.,i=1,2,\cdots,\tau\] (39)
2. For each set of new coarse-grained time series \(Z_{i}^{(\tau)}|(i=1,2,\cdots,\tau)\), its characteristic value in time and frequency domains are obtained, and then the average value of \(\tau\) time series eigenvalues is calculated to obtain the eigenvalues under the time scale \(\tau\).
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 5 Methodology
### Data Collection  
The data collection process involved capturing Zigbee communication using a Texas Instruments CC2531 USB dongle, which allowed us to sniff and investigate the traffic in Wireshark. The data collection process is illustrated in Figure 2. The collecting process required that the packet sniffer was connected to a Raspberry Pi 3, which served as the data collection platform. Wireshark, running on the Raspberry Pi, was configured to capture all data from the sniffer on channel 11 (channel of the current Zigbee network configuration) and save it into pcap files. To manage the large volume of data, the recording was set up to save each hour of Zigbee network traffic into a separate pcap file. This data capture process was conducted continuously over 20 days. Additionally, remote access to the Raspberry Pi was configured, allowing for real-time monitoring and management of the data collection process. This setup ensured that the data capture proceeded without interruptions and that any issues could be addressed promptly. The collected packets were saved as pcap files. Each pcap file consisted of approximately one hour.  
The data collection process may be affected by some bias due to hardware and software limitations. Specifically, Wireshark may introduce inaccuracies in data collection, for example, due to limited resources on the host computer, which can lead to packet loss. This may also occur if Wireshark restarts between data recording sessions or following a crash, interrupting the continuity of data capture. In addition, the placement of the sniffer in the smart home environment could impact the accuracy of packet capture. To reduce this effect, we placed the sniffer in a central part of the house to better capture network traffic across the entire space. This position was chosen to avoid areas with potential signal loss or interference that could lower capture quality. Despite these efforts, a small fraction of packets, representing 1.1% of the total dataset, were captured incorrectly. This minor error suggests that while the data collection method was generally reliable, factors like device placement and technical limitations should still be considered when interpreting the results.
## 3 Model Construction  
### Hyperparameter Optimization  
This study evaluates performance using the Root Mean Squared Error (RMSE), coefficient of determination (R\({}^{2}\)), and Mean Absolute Error (MAE) scores. Lower RMSE and MAE values indicate better predictive performance of the model. The R\({}^{2}\) value ranges from 0 to 1, with values closer to 1 indicating a better fit of the model to the data. Figure 6 presents the selection of four hyperparameters: time step, number of iterations, population size, and learning factor. The central value on the \(x\)-axis of each bar chart represents the optimal hyperparameter value used in the model. The average and minimum RMSE values for the four hyperparameters are very similar, so the R\({}^{2}\) score is used for comparison.  
It is worth noting that, while RMSE and MAE provide insights into the absolute errors between predictions and actual values, R\({}^{2}\) offers a measure of how well the model's predictions explain the variability of the actual data. By comparing R\({}^{2}\) scores, we can assess which hyperparameter configuration results in a model that best captures the underlying patterns in the data. Additionally, the similarity in RMSE values across different hyperparameter settings suggests that the model's performance in terms of absolute error may not be highly sensitive to these specific hyperparameters within the tested range.  
Figure 6: Hyperparameter optimization. (**a**) Time step, (**b**) Population size, (**c**) Iteration count, (**d**) Learning factor.  
As shown in Figure 6a, a time step unit value of 10 yields a higher R\({}^{2}\) score. Excessively large values may lead to memory overflow and becoming stuck in local optima, while excessively small values may reduce computational efficiency. For the population size, which typically ranges from 3 to 10, smaller values can reduce the number of iterations in model training but may increase the bias in model validation accuracy. Larger values, on the other hand, increase the number of training iterations but reduce the bias in model validation accuracy. As illustrated in Figure 6b, a population size of 5 significantly improves the R\({}^{2}\) value compared to 10 and 20, making it a reasonable choice for conserving computational resources. In Figure 6c, an iteration count of 30 results in a higher R\({}^{2}\) value. As shown in Figure 6d, selecting a learning factor of 20 yields a significantly higher R\({}^{2}\) score compared to 5 and 10. Therefore, the optimal values for the four hyperparameters are determined to be 10, 5, 30, and 20, respectively.