esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ensemble Extreme Learning Machine for Data Stream Classification
## 3 The Basic Principles of CELM
### The Data Stream Classification and Concept Drift Detection of CELM  
Data stream is different from the traditional static data, concept drift is often happened, so concept drift detection must be included in the training process. For a data block \(\mathbf{B}_{i}\), the error rate of classifiers is \(p_{i}\) which is a random variable obeying the Bernoulli distribution, so the standard deviation is \(s_{i}=\sqrt{\frac{p_{i}(1-p_{i})}{t}}\) where \(i\) is the number of samples [44; 45]. In this paper, CELM utilizes \(p_{i}\) and \(s_{i}\) to detect concept drift. The change of data stream is divided into three types: stable, error level and concept drift. If \(p_{i}+s_{i}\leq p_{min}+2s_{min}\) and \(p_{i}<\varepsilon\), it suggests that the error rate of classifiers system is in a low level and the concept of data stream is stable where \(\varepsilon\) is a threshold. Thus, the classifiers are suitable for the classification task of the current data stream and they do not need to make any adjustment. If \(p_{i}+s_{i}\geq p_{min}+2s_{min}\) and \(p_{i}<\varepsilon\), it suggests that the error rate of classifiers system is still in a low level, but the performance of classifers has a big fluctuation, the classifiers will give a warning and CELM will use online sequence learning mechanism [30] to update each classifier. At the initial time, let the data block be \(\mathbf{B}_{0}=\left\{\mathbf{x}_{i},\mathbf{t}_{i}\right\}_{i=1}^{N_{0}}\), so the output matrix of hidden layer \(\mathbf{H}_{0}\) and the initial target matrix of \(\mathbf{T}_{0}\) are  
\[\mathbf{H}_{0}= \begin{bmatrix}g(\mathbf{w}_{1},b_{1},\mathbf{x}_{1})&\cdots&g(\mathbf{w}_{L}, b_{L},\mathbf{x}_{1})\\ \vdots&\ddots&\vdots\\ g(\mathbf{w}_{1},b_{1},\mathbf{x}_{N_{0}})&\cdots&g(\mathbf{w}_{L},b_{L},\mathbf{x}_{N_{0}}) \end{bmatrix} _{\mathbf{N}_{0}\times L}\text{ and }\mathbf{T}_{0}= \begin{bmatrix}\mathbf{t}_{1}^{T}\\ \vdots\\ \mathbf{t}_{N_{0}}^{T}\end{bmatrix} _{\mathbf{N}_{0}\times m} \tag{21}\]  
The initial output weight of ELM \(\mathbf{\beta}^{(0)}\) is  
\[\mathbf{\beta}^{(0)}=\mathbf{K}_{0}^{-1}\mathbf{H}_{0}^{T}\mathbf{T}_{0} \tag{22}\]  
where \(\mathbf{K}_{0}=\frac{I}{C}+\mathbf{H}^{T}\mathbf{H}\) and \(\mathbf{T}_{0}=\left[\mathbf{t}_{1},\mathbf{t}_{2},\cdots,\mathbf{t}_{N_{0}}\right]^{T}\). After \((k+1)\)th data block coming into sliding window, the data block is \(\mathbf{B}_{k+1}=\left\{\mathbf{x}_{i},\mathbf{t}_{i}\right\}_{i=\sum_{j=0}^{k+1}N_{j}}^{ \sum_{j=0}^{k+1}N_{j}}\). The output matrix of hidden layer \(\mathbf{H}_{k+1}\) is  
\[\mathbf{H}_{k+1}= \begin{bmatrix}g(\mathbf{w}_{1},b_{1},\mathbf{x}_{\sum_{j=0}^{k}N_{j}+1}) &\cdots&g(\mathbf{w}_{L},b_{L},\mathbf{x}_{\sum_{j=0}^{k}N_{j}+1})\\ \vdots&\ddots&\vdots\\ g(\mathbf{w}_{1},b_{1},\mathbf{x}_{\sum_{j=0}^{k+1}N_{j}})&\cdots&g(\mathbf{w}_{L},b_{L}, \mathbf{x}_{\sum_{j=0}^{k+1}N_{j}})\\ \end{bmatrix} _{\mathbf{N}_{k+1}\times L} \tag{23}\]  
\[\mathbf{T}_{k+1}= \begin{bmatrix}\mathbf{t}_{1+\sum_{j=0}^{k}N_{j}}^{T},\cdots,\mathbf{t}_{ \sum_{j=0}^{k+1}N_{j}}^{T}\end{bmatrix}
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 5 Experimental Verification
### Case 1: Bearing Outer Race Fault Detection  
In case 1, the outer ring defective REBs is mounted on the test rig. The acquired vibration signal is shown in Figure 13a. However, due to the interference of the interference component in the signal, the defect-induced impulse cannot be detected obviously from the time waveform. Meanwhile,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Rollers** & **Pitch Diameter (mm)** & **Roller Diameter (mm)** & **Contact Angle (Degree)** \\ \hline
20 & 180 & 23.775 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of tested bearing in experimental verification.  
Figure 11: The test rig. (**a**) actual installation diagram; (**b**) schematic.  
Figure 10: Fault features extracted by SK. (**a**) Kurtogram of the simulated signal \(y_{1}\); (**b**) denoised signal; (**c**) envelope spectrum.  
no significant FCF is observed in the envelope spectrum as displayed in Figure 13b. Therefore, in order to acquire more useful fault information for bearing fault diagnosis, further signal processing is needed.  
Firstly, the TSLRM method is applied for the extraction of the weak fault features from the measured test data with outer race defect, and the fault features extracted by TSLRM are illustrated in Figure 14. However, the fault-related information is difficult to observe from the waveform. Furthermore, the BPFO and its multipliers are disturbed by other frequencies. Therefore, the denoised signal obtained by the TSLRM method is unsatisfactory. The results obtained do not provide useful information on bearing failure and, therefore, may lead to missed and misdiagnosed bearing failures.  
For comparison, the FISLRA is conducted for the extraction of fault features, and the processed results of the measured test data are demonstrated in Figure 15. In waveform, the repetitive transients are successfully extracted, and the BPFO and its multipliers can be clearly observed in the related envelope spectrum. Therefore, the extracted fault features provide strong evidence for the occurrence of the outer ring fault. The above case analysis shows that TSLRM did not successfully extract fault features, while FISLRA successfully extracted fault features.  
Figure 14: Fault features extracted by TSLRM method. (**a**) Amplitude spectrum; (**b**) envelope spectrum.  
Figure 12: (**a**) The tested bearing with an outer race fault; (**b**) the tested bearing with an inner race defect.  
Figure 13: (**a**) Measured test data; (**b**) envelope spectrum.
## 1 Introduction  
Antimicrobials are widely used in veterinary medicine and can contaminate the environment. The contamination of soil with such antimicrobials, especially agricultural fields receiving frequent manure, is a universal phenomenon. Once in the environment, they may sorb onto soil and sediment, be transported to ground or surface waters, or be degraded. Even though different types of antibiotics have different anticipated environmental exposure routes, the application of animal manure containing excreted antibiotics remains the predominant pathway for veterinary antibiotics (VAs) release into the terrestrial environment [1, 2]. Once in the arable land, VAs can impact soil microbial activity as well as vegetation growth [3]. Plants act as the natural sink for a variety of toxins in the environment, including VAs. Once absorbed into the plant system VAs, depending upon their property, get transformed, conjugated, and then finally sequestrated. Since plants are unable to excrete the toxins and their metabolites, they get sequestrated into vacuoles or cell walls. Therefore, there is a possibility for the transfer of VAs and their metabolites through the consumption of contaminated plants.  
The uptake of VAs by plants is a broad and growing area of research, and several studies have been conducted previously [4; 5; 6]. However, unlike pesticides and herbicides, antibiotics as potential pollutants have not aroused attention until reasonably recently [7]. In addition, the relevance of antibiotic ecotoxicology is scarce due to the minimal possible effects of antibiotics in the environment [8]. Phytotoxicity tests of VAs using seed germination and plant growth test have been conducted previously [9; 10]. Studies have shown the effect of VAs on plants to be different between antibiotic compounds and between plant species [3; 11; 12]. Therefore, studying the environmental implication of VAs associated with animal manure application by considering plant species variability is of immense importance.  
This study investigates the effect of _oxytetracycline_ (OTC), _sulfadimethoxine_ (SDZ), and _streptomycinycin_ (STR) on three types of plants--root (carrot), leafy (lettuce), and fruit (pepper). The purpose of this study was (1) to determine the uptake and accumulation of VAs by lettuce, carrot, and pepper, (2) to determine the time-dependent effects of the VAs on mycorrhizal frequency, proline content, and detoxifying enzymes, (3) to assess environmental risk assessment of the VAs.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 1 Introduction
[31] combined the two-dimensional input image information into an intermediate representation called an observation map to learn Photometric Stereo and used the rotation pseudo-invariance to constrain the network. This method also took the surface normal as the optimization goal. Our method solves the Photometric Stereo 3D reconstruction task from a different perspective. After solving the reflection illumination model, an integration step will be used to restore the three-dimensional topography of the surface, which is also a complicated process. The computational and time cost of this step is also very large, and it may cause cumulative errors and finally cause different degrees of distortion in the reconstructed results. We hope to use depth as the direct optimization goal and obtain the three-dimensional shape of the object surface from end-to-end. In this paper, we built a U-shaped network structure based on FCN that can obtain the 3D topography of the object surface. By training a parameterized model, we can directly simulate the relationship between physical information such as shadows and reflections on the surface of the object and its depth information. The end-to-end learning can make our method more directly obtain the three-dimensional shape of the object. In addition, we added a regularization constraint on the basis of the general L2 loss function, and the experiments prove that, compared with optimizing the depth value of each pixel directly with the simple L2 loss function [27], this constraint can effectively improve the accuracy of prediction. We also adopted a photometric acquisition setup with a specific configuration to collect a real Photometric Stereo dataset, obtained a high-precision ground truth (GT) using structured light scanning and accurately registered it to the 2D image we collected. The experimental results show that the effectiveness of our method has been verified in a real multi-source Photometric Stereo setup. The remainder of this study is organized as follows. We first introduce the principle of multi-source Photometric Stereo and the details of our method, including the network structure, our new loss function including two regularization constraints, and the real Photometric Stereo dataset in Section 2. Then the details of our experiments and the experimental results are shown in Section 3. We end with a discussion of our experimental results in Section 4.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 4 Result and Discussion  
In order to better verify the attack effect, this simulation attacked two satellite key nodes and multiple satellite key nodes under the OSFpv2 protocol and Bellman-Ford protocol, respectively, and analyzed the security performance of LSCNs after being attacked.
## 3 Design of Reinforced Concrete Corrosion Testing
### Preparation of Concrete and UHPC Short Column Specimens  
The UHPC material was supplied by Jiangsu Sobute New Material Co., LTD. For one cubic meter UHPC, the mixture properties were 2232 kg preblended powder, 195 kg steel fiber (2.5% volume ratio), 199 kg water, and 15 kg superplasticizer. Normal concrete (NC) and steel reinforcement were provided by Smart Manufacturing of China Design Group in Yancheng, China. For one cubic meter NC, the mixture properties were 469 kg 42.5 cement, 1041 kg coarse aggregate, 122 kg fly ash, 150 kg water, 618 kg sand, and 6.6 kg water-reducing agent. Based on Chinese code GB/T50152-2012 [33] and Swiss Code SIA 2052 [34], the material properties of concrete were determined. The mechanical properties of normal concrete and UHPC are shown in Table 1, where \(f_{\text{c,n}}\) and \(f_{\text{c,u}}\) represent the cube compressive strength of concrete and UHPC, respectively; \(f_{\text{ck,n}}\) and \(f_{\text{ck,u}}\) represent the cylinder compressive strength of concrete and UHPC, respectively; and \(f_{\text{tu}}\) represents the dog-bone tensile strength of UHPC. Additionally, according to the data provided by these commercial companies, the porosity, sorptivity, and water absorption were 1.31%, 0.013 mm/min\({}^{1/2}\), and 0.29% for UHPC, while they were 9.04%, 0.027 mm/min\({}^{1/2}\), and 3.64% for NC.  
Nine normal concrete specimens (N1-N9) and nine UHPC specimens (U1-U9) were fabricated for the experiment. To accurately reflect the actual corrosion condition of the reinforcement within the columns, the specimens used 16 mm diameter steel bars, the same as those used in the columns. The cover thickness was 30 mm, and no cover was applied at the bottom of the specimens to simulate the joint surface. The specimen dimensions are shown in Figure 2.  
As shown in Figure 3, the short column specimens were placed vertically on either side of the container. The 500 mm high portion of the specimens was fully immersed in a 5% sodium chloride solution. The exposed steel bars at the top of the specimens were connected to the positive terminal of a DC power supply, while the stainless steel mesh was connected to the negative terminal.  
Before corrosion testing, the average initial weight of the specimens was used as a reference. Steel bars with a weight deviation greater than 2% from the average were replaced. Additionally, one steel bar was selected as a reference for subsequent acid-washing calibration. The small specimen molds were made of wood, and the materials were cast using concrete and UHPC from the same batch as the bridge piers, with simultaneous casting. The specimens were cured in a standard curing room for 28 days. Afterward, they were placed in a natural environment for 3 months before testing was conducted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(f_{\text{c,n}}\) (MPa) & \(f_{\text{c,u}}\) (MPa) & \(f_{\text{ck,n}}\) (MPa) & \(f_{\text{ck,u}}\) (MPa) & \(f_{\text{t,u}}\) (MPa) \\ \hline
55.7 & 185.9 & 36.3 & 121.6 & 9.8 \\ \hline \end{tabular}
\end{table}
Table 1: The mechanical properties of normal concrete and UHPC.
## 6 Monte Carlo Simulation
### Commissioning Inertial Alignment Hold  
As a consequence of the Monte Carlo simulation, the random initial states that satisfy Table 7 converge to the desired trajectory within the limited operation time in the staging mode (Figure 12a). The relative error states in the radial and cross-track directions are controlled in the alignment mode, and the states cannot be perfectly controlled because of the restricted degrees of freedom of the thruster (Figure 12b). One sample in Figure 12c shows the deviation in the alignment angle, and it is the failure case of the coarse requirement (2) in Table 8. Comparing the alignment times of the three requirements (Table 1), the alignment time and success probability are the greatest for the coarse requirement (2), coarse requirement (1), and fine requirement (Figure 13 and Table 8). This indicates a 99% chance of success based on coarse requirement (2). The probability of success is 99% based on coarse requirement (2), whereas the probability of success of the fine requirement is remarkably low at 75%. This is because this satellite has a two-degrees-of-freedom propulsion system; therefore, if it orients toward the Sun in the alignment mode the alignment angle is well controlled, but the relative distance is difficult to control. The average impulse consumption is 3.41 Ns, with an average of 2.38 Ns in staging mode and 1.02 Ns in alignment mode (Table 9). Because the staging mode is 40 to 60 min and the alignment mode is 20 min, it exhibits a comparable level of impulse per time.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Mission Mode Starting Conditions**} \\ \hline \hline (1) Along-track distance (\(|y|\)) & \(60<|y|<70\) (m) \\ (2) Staging duration (\(T_{stg}\)) & \(40<T_{stg}<60\) (min) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Starting conditions for C-IAH.  
Figure 12: Controlled and desired trajectory in **(a)** staging mode and **(b)** fine requirement and **(c)** coarse requirement in alignment mode during C-IAH (100 samples: dashed line, desired trajectory; solid line, requirement; gray line, controlled trajectory).  
Figure 13: Aligned time and total impulse for the fine requirement, coarse requirement (1), and coarse requirement (2) during C-IAH.
## 2 Methods and Instruments
### GNSS Maps  
To analyze MSTID and ROTI activity in conjunction with SF, we examined d-TEC and ROTI maps from the DRAWING-TEC project, at a 10 min resolution from 2011 to 2017. These maps are derived from slant TEC data calculated from 30 s carrier phase  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station Lists** & **Latitude (\({}^{\circ}\) N)** & **Longitude (\({}^{\circ}\) E)** \\ \hline Juliusruh & 54.6 & 13.4 \\ \hline Fairford & 51.7 & \(-1.5\) \\ \hline Dourbes & 50.1 & 4.6 \\ \hline Pruhonice & 50 & 14.6 \\ \hline Roquetes & 40.8 & 0.5 \\ \hline San Vito & 40.6 & 17.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coordinates of the European Digisonde stations considered in the present study.  
Figure 1: European Digisonde stations considered in the present study.  
measurements collected by dual-frequency GNSS receivers across Europe. For geographic mapping, ROTI and d-TEC values are projected onto a 300 km altitude ionospheric shell with a pixel size of 0.15\({}^{\circ}\)\(\times\) 0.15\({}^{\circ}\) in latitude and longitude.  
Due to data scarcity in certain areas, the DRAWING project applies a 10-min running average to smooth ROTI and d-TEC values within each pixel. This smoothing process takes into account ionospheric pierce points (IPPs), which span approximately 50 km around a GNSS receiver's zenith in 10 min. In this study, we focused on ROTI and d-TEC maps from 2015 to 2017, achieving a spatial resolution of about (80 \(\times\) 80) km at 10 min intervals. These maps are accessible from the DRAWING project website [18,23].
# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 2 Related Work
### Using Wi-Fi Communication Signals for Sensing  
In the past few years, the use of Wi-Fi signals for sensing has proven to have great potential within the field of device-free sensing. The major power of Wi-Fi sensing is that it is often applied not only to LOS environments but also to NLOS environments. Wi-Fi sensing technologies are widely studied for indoor localization and movement recognition. As an example of conventional Wi-Fi sensing technologies, [PERSON] et al. introduced an indoor localization system utilizing received signal strength (RSS) in a Wi-Fi sensor network [27]. The Chronos system was introduced, in which an indoor localization system achieved decimeter-level localization employing a single Wi-Fi access point [28]. The Chronos system uses CSI derived from commodity Wi-Fi devices to calculate distances between multiple antennas and a target device based on the time of flight (ToF) of Wi-Fi signals. The Chronos system is similar to state-of-the-art localization systems that need four or five access points.  
Sensing techniques based on CSI were also introduced. [PERSON] et al. designed a keystroke recognition technique utilizing CSI [29]. To locate fingers pushing keys on CSI, keystroke features are extracted by playing principal component analysis (PCA). The keystroke recognition technique achieves a word recognition accuracy of greater than 85%. RT-Fall is a fall down detection system using Wi-Fi signals [13]. RT-Fall uses the phase difference between antennas for fall detection and obtains high accuracy. RF-Pose is a through the wall human pose estimation technique utilizing Wi-Fi signals [30]. RF-Pose draws heat maps in two dimensions via a deep neural network to estimate human posture behind walls. Experimental assessments show that RF-Pose can describe human posture in detail [31]. However, post-surgery multiple posture activity detection is still a challenge, so in this research, we detect and monitor multi-pose complex weight lifting activity.
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 5 Experimental Evaluation
### Parameter Tuning of LADRC-Based Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Settling Time (s)**} & \multicolumn{2}{c}{**Settling Time (s)**} \\  & \multicolumn{2}{c}{**Frequency Loop**} & \multicolumn{2}{c}{**Phase Loop**} \\ \cline{2-5}  & \(\mathbf{{}^{129}Xe}\) & \(\mathbf{{}^{131}Xe}\) & \(\mathbf{{}^{129}Xe}\) & \(\mathbf{{}^{131}Xe}\) \\ \hline \(K_{c}=1\)\(mHz\)/\(deg\) & 13.902 & 13.878 & 21.67 & 23.02 \\ \(K_{c}=2\)\(mHz\)/\(deg\) & 13.165 & 12.801 & 16.99 & 17.98 \\ \(K_{c}=3\)\(mHz\)/\(deg\) & 12.934 & 12.569 & 15.41 & 15.71 \\ \(K_{c}=4\)\(mHz\)/\(deg\) & 12.832 & 12.368 & 14.58 & 15.14 \\ \(K_{c}=5\)\(mHz\)/\(deg\) & 12.806 & 12.415 & 14.16 & 14.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed Settling Time of the Tested Method in Figure 7.  
Figure 7: Experimental results of increasing bandwidth of the LADRCs. (**a,c**) are the performance of the \({}^{129}Xe\), and (**b,d**) are the loop of \({}^{131}Xe\). Both kind of isotopes were driven synchronously, and the influence of the parameter \(K_{c}\) was specified.