esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of Sample Preparation  
#### 3.2.1 Sample Extraction  
Three candidate extraction solvents, i.e., methanol, acetone, and acetonitrile, were compared in spiked plasma (50 \(\upmu\)g/L). The extraction recovery (_RE_) was determined by Equation (2) [45]. As shown in Figure 2A, when methanol and acetone were utilized, the recovery of ZEN-14G was above 85%, but ZEN achieved relatively lower recovery levels (only 79.1 \(\pm\) 2.1% for methanol and 78.3 \(\pm\) 3.6% for acetone). The highest _RE_ values (ZEN: 91.7 \(\pm\) 5.8%; ZEN-14G: 90.9 \(\pm\) 7.0%) were found for acetonitrile. Studies have reported that an acidic medium would break the chemical bonds between the analyte and other molecules to benefit their transfer to the organic solvent, contributing to an improved extraction efficiency [46; 47]. Then the extraction effects of acetonitrile containing different contents of formic acid (0.25, 0.5, 0.75, 1, 2, 3, and 4%) were investigated. The highest\(RE\) values were achieved when acetonitrile containing 1% formic acid was chosen as the extraction solution (Figure 2B).  
\[RE=\text{A}/\text{B}\times 100\% \tag{2}\]  
where A represents the peak area of the analyte in the plasma spiked before extraction, and B represents the peak area of the analyte in the plasma spiked after extraction.
Sensors_**2010**, _10_, 4717-4738; doi:10.3390/s100504717  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
Signal Existence Verification (SEV) for GPS Low Received Power Signal Detection Using the Time-Frequency Approach  
**[PERSON] * and [PERSON]**  
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-6-275-7575 ext. 63629; Fax: +886-6-238-9940.  
_Received: 29 March 2010; in revised form: 30 April 2010 / Accepted: 4 May 2010 / Published: 10 May 2010_  
**Abstract:** The detection of low received power of global positioning system (GPS) signals in the signal acquisition process is an important issue for GPS applications. Improving the miss-detection problem of low received power signal is crucial, especially for urban or indoor environments. This paper proposes a signal existence verification (SEV) process to detect and subsequently verify low received power GPS signals. The SEV process is based on the time-frequency representation of GPS signal, and it can capture the characteristic of GPS signal in the time-frequency plane to enhance the GPS signal acquisition performance. Several simulations and experiments are conducted to show the effectiveness of the proposed method for low received power signal detection. The contribution of this work is that the SEV process is an additional scheme to assist the GPS signal acquisition process in low received power signal detection, without changing the original signal acquisition or tracking algorithms.  
**Keywords:** GPS signal acquisition; time-frequency analysis; low received power signal processing
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Power Station Structure and Vulnerability
#### 4.3.5 Steam Boilers  
Each steam boiler system contains a boiler measuring 44 m in height, 28 m in length, and 26 m in width, paired with stacks extending to 80 m. These boilers and stacks, constructed from steel and set upon a concrete slab foundation, are pivotal in the station's energy production process. The station comprises 12 boilers and stacks, evenly divided between two production units, with six boilers each. The main components of the boiler system and their associated fragility curves are detailed in Figure 9.  
The overall failure probability of a single steam boiler system has been determined to be 0.264. For the station to continue operating effectively, at least 8 of the existing 12 boilers must be functional. Therefore, a binomial distribution calculation was implemented to assess the likelihood of four or fewer steam boiler systems failing, ensuring the station's operational reliability. The result of this calculation determined a probability of 0.189, providing the station's operational reliability under these conditions.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### Pseudolite-Based Positioning Algorithm for Legacy GNSS Receivers
#### 3.3.3 Final Estimation of the User Position  
Using given pseudorange measurements, finally the post-calculation module can estimate the user position. From Equation (2), pseudorange measurements can be rewritten using position vectors as in the following equation:  
\[\rho^{j}=\left|R^{j}-R_{r}\right|+\left|R^{\hat{l}}-R_{u}\right|+B+\varepsilon_ {\rho^{j}} \tag{11}\]  
Again, an _a priori_ estimation of the state vector as \(\hat{x}=\left[\hat{R}_{u}^{T}\quad\hat{B}\right]^{T}\) is assumed. The estimated pseudorange measurements may then be described by Equation (12):  
\[\hat{\rho}^{j}=\left|R^{j}-R_{r}\right|+\left|R^{\hat{l}}-\hat{R}_{u}\right|+ \hat{B}+\varepsilon_{\hat{\rho}^{j}} \tag{12}\]  
By subtracting Equation (12) from Equation (11), the pseudorange measurement residual is calculated using the following equation:  
\[\delta\rho^{j}=\rho^{j}-\hat{\rho}^{j}=\left|R^{\hat{l}}-R_{u}\right|-\left| R^{\hat{l}}-\hat{R}_{u}\right|+B-\hat{B}+\varepsilon_{\rho^{j}}-\varepsilon_{ \rho^{j}} \tag{13}\]  
Equation (13) is linearized at the _a priori_ estimation:  
\[\delta\rho^{j}\approx\frac{\partial\delta\rho^{j}}{\partial R_{u }}\Bigg{|}_{R_{u}=\hat{R}_{u}}\left(R_{u}-\hat{R}_{u}\right)+\frac{\partial \delta\rho^{j}}{\partial B}\Bigg{|}_{B=\hat{B}}\left(B-\hat{B}\right)+\left( \varepsilon_{\rho^{j}}-\varepsilon_{\rho^{j}}\right) \tag{14}\] \[= \left[-\hat{e}_{u}^{\hat{l}}\quad 1\right]\left\{\begin{matrix} \delta R_{u}\\ \delta B\end{matrix}\right\}+\delta\varepsilon_{\rho^{j}}\]  
The same equations can be used for \(m\) pseudolites:  
\[\underbrace{\begin{bmatrix}\delta\rho^{1}\\ \vdots\\ \delta\rho^{m}\end{bmatrix}}_{\overline{\delta\rho}}=\underbrace{\begin{bmatrix} -\hat{e}_{u}^{\hat{1}}&1\\ \vdots&\vdots&\vdots\\ -\hat{e}_{u}^{\hat{m}}&1\end{bmatrix}}_{\overline{\delta\pi}}+\underbrace{ \begin{bmatrix}\delta\varepsilon_{\rho^{j}}\\ \vdots\\ \delta\varepsilon_{\rho^{-}}\end{bmatrix}}_{\overline{\delta\sigma}_{\rho}} \tag{15}\]  
\[\delta\overline{\rho}=H\delta\overline{x}+\delta\overline{\varepsilon}_{\rho}\]  
The error state is estimated by Least Square Solution [11]:  
\[\delta\hat{x}=\left[\delta\hat{R}_{u}^{T}\quad\delta\hat{B}\right]^{T}=\left( H^{T}H\right)^{-1}H^{T}\delta\overline{\rho} \tag{16}\]The original state is updated using estimated error state:  
\[\begin{split}\hat{x}=\hat{x}_{old}+\delta\hat{x}\\ \hat{x}_{old}=\hat{x}\end{split} \tag{17}\]  
The final estimation is obtained by repeating these processes until the states converge. The flow chart of the algorithm for the post-calculation module of the user terminal is illustrated in Figure 8.  
In Figure 8, blocks in the dashed box is exactly the same as the position estimation process of general GPS receivers except the nonlinear equation used in Equation (12) (red box). The differences are coming from the existence of the pseudolites between the imaginary receiving point and the user receiver antenna.  
Figure 8: Algorithm for the Final Estimation of the User Position.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 7 Zigbee Network Traffic Dataset and Its Characteristics
### Network Traffic Characteristics
#### 7.2.1 Packet Density Analysis  
When examining the network traffic characteristics, packet density analysis provides important information about the performance of a network. For example, the number of packets sent and received provides information about the network traffic density. If there is a large difference between the number of packets sent and received by a device, it may mean that there is a problem with the device. Another example is related to instantaneous peaks; instantaneous increases in the number of packets can occur when more than one device is transmitting data at the same time, and the capacity of the network can be exceeded at these times. In general, packet density analysis is an effective analysis for observing the traffic density on the network and measuring its capacity.  
Figure 6 shows the graphs of the number of packets in the network (a) per second, (b) per minute, and (c) per 10 min. The x-axis in the figures represents time in seconds, and the y-axis represents the number of packets. When we look at Figure 6a, we observe that the number of packets in the network increases rapidly every second at certain, non-fixed intervals. In Figure 6b, these peak points are seen more clearly. In Figure 6c, we see that the average number of packets every 10 min is around eight thousand. These three sub-figures show the number of packets at different intervals. In Figure 6c, the oscillation is very high, but in (b) and (a), the increase in the number of packets can be easily observed. The traffic density in the network has increased at these times. The results of such analyses lead to the next stage of investigating why the traffic density has increased at these time intervals. The traffic of each network may differ, so the analysis of packet density at different time intervals is important.
# Production and Characterization of Cu/CNT Nanocomposites
## 4 Conclusions  
Cu/CNT nanocomposites were produced by conventional powder metallurgy using ultrasonication as a mixing and dispersion method. Different volume fractions of CNTs were investigated. The best dispersion was obtained for 1.0% vol. of CNTs. The nanocomposites were characterized by CNT agglomerates mainly at grain boundaries.  
The increase of 17% in hardness and 67% in tensile strength attested to the strengthening effect of the CNTs in the Cu matrix. This improvement is a consequence of several mechanisms: (1) load transfer, (2) grain refinement or texture, (3) dislocation, and (4) Orowan strengthening mechanisms. The results show that the load transfer was the strengthening mechanism that played the leading role in these nanocomposites. Elongated and fractured CNTs were observed on the fracture surface of nanocomposites. The introduction of CNTs did not significantly affect the Cu matrix's dislocation density, grain size, or texture.  
The deformation behavior of the nanocomposites was observed during cold rolling under different strains. As expected, the Cu matrix and the nanocomposite show increased hardness values with deformation. At higher strains (greater than 0.36), the nanocomposites have a lower hardness than the Cu matrix. This can be explained due to the damage that can occur during the plastic deformation and the different texture evolution of the nanocomposites. The dislocation density analysis does not show differences between the Cu and Cu/CNT samples. The texture evolution shows various components for the nanocomposites and the Cu matrix. This can be due to differences in the active slip planes between the matrix and the nanocomposite, which affects the lattice rotation.  
Conceptualization, I.C.; investigation, I.C., B.M., B.R. and S.S.; writing--original draft preparation, I.C. and S.S; supervision, S.S. and J.V.F.; formal analysis, S.S and J.V.F.; validation, S.S. and J.V.F.; writing--review and editing, S.S. All authors have read and agreed to the published version of the manuscript.  
[PERSON] was supported by a Ph.D. grant for scientific research from the Portuguese Foundation for Science and Technology (FCT), with the reference PD/BD/143030/2018, and the P2020 | Norte2020 program, with the reference NORTE-08-5369-FSE-000051. This research was supported by FEDER funds through the program COMPETE--Programa Operacional Factores de Competitividade and by national funds through FCT--Fundacao para a Ciencia e a Tecnologia, under the project UIDB/EMS/00285/2020 and ARISE with reference LA/P/0112/2020.  
Not applicable.  
Data Availability Statement: Data can be available upon request from the authors.  
The authors are grateful to CEMUP--Centro de Materiais da Universidade do Porto for the expert assistance with SEM.  
The authors declare no conflict of interest.
## 2 Test Method and Test Materials
### Dynamic Stress Equilibrium  
Based on the one-dimensional wave theory, the wave data were simplified, as recorded in detail by [PERSON] (1963). \(E\) was the [PERSON]'s modulus of the bar. By using the micro strain signal recorded by strain gauges of the incident bar and transmission bar, the incident stress history \(\sigma_{i}(t)\), the reflected stress history \(\sigma_{r}(t)\), the transmitted stress \(\sigma_{t}(t)\) could be recorded, combining the stress history of the incident and reflected waves, and comparing this against the stress history of the transmitted wave to assess dynamic stress equilibrium. Figure 3 shows the stress history during empty beating. It could be found that the dynamic stress curves of the incident wave and reflected wave were basically consistent with the transmitted wave after superposition, and the synchronicity indicated that dynamic stress equilibrium was achieved. It could be seen that the time taken for a complete loading pulse was approximately 475 \(\upmu\)s due to deformation of the rubber sheet.  
Particle crushing must be accompanied by energy dissipation during impact. Based on the one-dimensional stress wave theory, the incident energy, reflected energy and transmitted energy were calculated using Formulas (1)-(3):  
\[W_{i}=\frac{A}{\rho C}\int\limits_{0}^{t}\sigma_{i}^{2}(t)\mathrm{d}t \tag{1}\]  
\[W_{r}=\frac{A}{\rho C}\int\limits_{0}^{t}\sigma_{r}^{2}(t)\mathrm{d}t \tag{2}\]  
\[W_{t}=\frac{A}{\rho C}\int\limits_{0}^{t}\sigma_{t}^{2}(t)\mathrm{d}t \tag{3}\]  
where \(t\) is time, \(A\) is the cross-sectional area of the steel bar, \(C\) is the wave propagation velocity within the steel bar, \(\rho\) is the density of the steel bar. Ignoring the energy lost at the interface between the specimen and the bar, the dissipated energy of the specimen can be expressed as Formula (4):  
\[W_{d}=W_{i}-W_{r}-W_{t} \tag{4}\]  
Figure 2: (**a**) Experimental setup. (**b**) Schematic of steel specimen confinement chamber and wave propagation in the system.  
where \(W_{d}\) is the dissipated energy of the specimen during the impact process, mainly including crushing energy, friction energy, acoustic energy, thermal energy, radiation energy and other forms of dissipated energy.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 4 Experiment and Analysis
### Ablation Studies and Analysis
#### 4.5.2 The Impact of Different Backbones  
To further evaluate the impact of different Backbone networks on the performance of 3D object detection for the Car category in the KITTI validation set, we conducted an ablation study comparing various backbones. Specifically, we tested DLA-102, DLA-102x2, DLA-60, DenseNet, ResNet, and our proposed AuxDepthNet (Ours). Among these, our framework employs DLA-102 as the default Backbone due to its balanced trade-off between efficiency and accuracy. The results demonstrate that while other backbones provide competitive performance, DLA-102 achieves superior precision, validating its suitability for our approach. The results are shown in Table 4.  
The ablation study highlights the impact of different backbones on KITTI's Car category detection. DLA-102 achieves a strong balance between efficiency and accuracy, while DLA-102x2 slightly outperforms it, particularly in the Easy and Moderate settings. DenseNet and ResNet provide competitive results but fall short of DLA-based models, and DLA-60 demonstrates limited performance due to reduced capacity. Notably, our proposed AuxDepthNet achieves the highest accuracy, with an \(AP_{\text{3D}}\) of 18.63 (Moderate) and \(AP_{\text{2 EV}}\) of 25.18 (Moderate), showcasing its superior depth-sensitive feature extraction capabilities for monocular 3D detection. So, our AuxDepthNet Backbone outperforms conventional backbones, highlighting the effectiveness of its design in enhancing depth-sensitive feature representation.
# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 2 Related Work
### Image Matting  
Most existing matting methods exploit low-level properties of image pixels with an auxiliary input as a priori. On the contrary, recent deep learning-based methods utilize high-level semantic information from neural networks to improve the matting results significantly. For example, [PERSON] et al. [17] proposed to combine neural networks with tight form matting [18], KNN matting [19] and KNN segmentation [20]. [PERSON] et al. [21] introduced the first neural network for end-to-end image matting. These methods have different inputs: natural and portrait images with varying categories of objects. [PERSON] et al. [22] developed a multicriteria matting algorithm via the Gaussian process, which searches for the optimal pixel pair by using the Gaussian process fitting model instead of solving the original pixel pair objective function. [PERSON] et al. [23] used an attentional mechanism for natural images with different object classes, and [PERSON] et al. [24] combined foreground and background likelihood maps for alpha prediction. For matting portrait images, [PERSON] et al. [25] used a complete convolutional network to generate pseudo-triangular maps before learning the Laplace matrix using an image matting layer. [PERSON] et al. [26] designed a semi-supervised network to reduce complete dependency on labeled data sets. [PERSON] et al. [19] first predicted low-resolution segmentation maps, which were then used as matting guides. [PERSON] et al. [6] provided an additional background image as an alternative auxiliary cue to predict the alpha matte and foreground. [PERSON] et al. [12] had a good performance for high-resolution image matting but also clarified that the method makes it difficult to produce accurate matting with complex backgrounds. In summary, precise matting suitable for complex backgrounds is necessary.
## 3 Experimental Validation
### Data Augmentation Using WGAN Model  
To validate the proposed method, data augmentation was performed using the WGAN model illustrated in Figure 7. The structure of the WGAN model and model parameters listed in Table 3 are determined through a process of trial and error because there is no specific standard or technique.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Healthy** & **Delamination 1** & **Delamination 2** \\ \hline Number of sensors & P01–P10 & P01 & P01 \\ Number of data & 1000 & 60 & 60 \\ Signal length & 1875 & 1875 & 1875 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of dataset for each health state.  
Figure 6: The ten different accelerometer locations for the research.  
Figure 7: Structures of the generator and critic for the research.  
For the Healthy state, since it takes a significant period to generate synthetic signals for all pairs, signals from sensors in the same location have similar dynamic characteristics. Thus, synthetic signals were generated by only considering sensor location. With data augmentation, with 300 signals per sensor, a total of 3000 signals were generated. For Delamination 1 state, three specimens, D1-1, D1-2, and D1-3, were utilized individually to obtain 1000 synthetic signals per specimen. Similarly, Delamination 2 state used three specimens and acquired 1000 signals per specimen. For delamination cases, delamination in the specimens was manufactured by manually inserting the Teflon films. Therefore, there are slight differences in dynamic characteristics for different specimens. In this perspective, data augmentation for delamination cases was conducted for each specimen. There were 1000 synthetic signals for each specimen; a total of 3000 signals were generated.
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 3 The Proposed Fingerprinting Acoustic Localization Approach
### Location Estimation Based on Iterative Interpolation  
To reduce the sampling effort, global interpolation methods are usually used to improve the positioning accuracy under the sparse sample points collection. However, the global interpolation method usually results in the rapid expansion of virtual RPs quantity, and cannot accurately reckon the quantity of virtual RPs required for the satisfactory positioning accuracy. In this paper, we propose an iterative interpolation method to avoid the unnecessary virtual RPs and further improve the location efficiency.  
In the online positioning process, the virtual RPs can be generated by the iterative interpolation method, as Figure 4 shows, where the iteration interpolation process is based on four adjacent RPs. In the interpolation process, the first generation virtual RPs are defined as the adjacent RPs selected before as \(\textbf{D}_{v}^{1}=\textbf{D}_{a}\). During the iteration interpolation process, the elements of \(\textbf{D}_{v}^{t}\) are refreshed by:  
\[\left\{\begin{array}{l}\textbf{S}_{n}^{t+1}=\omega_{n}^{t}\textbf{S}_{n}^{t }+\omega_{n+1}^{t}\textbf{S}_{n+1}^{t},\quad n<N\\ \textbf{S}_{n}^{t+1}=\omega_{n}^{t}\textbf{S}_{n}^{t}+\omega_{1}^{t}\textbf{ S}_{1}^{t},\quad n=N\end{array}\right. \tag{5}\]  
where \(\textbf{S}_{n}^{t}\) is the \(n\)th element of \(t\)th generation \(\textbf{D}_{v}^{t}\), and \(\omega_{n}^{t}\) is the according weight that can be calculated through IDW method by the feature space Euclidean distance between recent generation virtual RPs and the target point that needs to be located.  
Figure 3: Two-stage RPs matching process: (**a**) the adjacent cluster matching process; and (**b**) the adjacent RPs matching process. The purple, orange, yellow, blue and green dots are the RPs clustered to different clusters marked as \(c1\) to \(c5\); the dark blue dots are the cluster centers marked as \(\mu_{1}\) to \(\mu_{5}\); the red dot denotes the TP; \(DIS\) means the Euclidean distance between TP and each cluster center; and _dis_ means the Euclidean distance between TP and each RP in adjacent cluster.  
As Algorithm 2 shows, online positioning process based on the iteration process continues until the iteration time beyond the maximum iteration value \(T\) or the difference between two-estimation process results is satisfied with \(|I-I^{\prime}|\leq\varepsilon\), where \(\varepsilon\) is the iterative process end threshold value.  
```
Input: adjacent RPs \(\mathbf{D}_{a}\) Output: test point location estimation results \(I\)
1 iteration time \(t=1\);
2 first generation virtual RPs \(\mathbf{D}_{v}^{1}=\mathbf{D}_{a}\);
3 while\(t<T\parallel|I-I^{\prime}|\leq\varepsilon\)do
4\(t=t+1\);
5\(I^{\prime}=I\);
6 new generation virtual RPs \(\mathbf{D}_{v}^{t+1}\) can be generated by Equation (5);
7 location estimation \(I\) can be calculated by the WKNN algorithm ;
```  
**Algorithm 2**The location estimation process based on iterative interpolation.