esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Traditional Train Vibration Data Analysis Methods  
A type of mechanism-based method has been proposed based on the theory of vehicle system dynamics [18; 19; 20]. Particularly, compared with the bogie and car body, the track geometry estimation using the axle-box vibration data can obtain relatively improved results because of the presence of a suspension system between the axle boxes, bogies, and car bodies, as shown in Figure 2.  
The suspension system acts as a filter for the original waveform of the track geometry, thereby aggravating the nonlinearity between the track geometry and bogie vibration or track geometry and car-body vibration. However, sensors installed in the axle boxes are operated under sub-optimal working conditions, and thus, their timely maintenance is a challenging task. Therefore, researchers prefer to utilize the vibration data of bogies and car bodies for studying the characteristics [21]. Another type of mechanism-based method has been proposed, based on the theory of vehicle-track dynamics implemented via simulation models [22; 23; 24; 25] in which the vehicle-track system is represented as a coupled system [21]. However, owing to heterogeneous influencing factors, such as the track structure, wheel-rail contact condition, and nonlinear suspension, the vehicle-track relationship features complex nonlinearities. Therefore, the mechanism-based method is unable to consider the various influencing factors with relative ease comprehensively [16], and the accuracy depends on the authenticity and reliability of the mode of system-parameter selection [26].  
The signal analysis-based method directly analyzes the vibration signals [27; 28; 29]. However, this method is essentially linear processing of vibration data, and the running speed is generally assumed constant during processing [26]. In addition, considering that different TGDs have different features in the vibration signal after passing through the vehicle, determining the most feasible and effective feature types for each type of TGD is a challenging task [16].
# A New Top-Mounted Shear-Hinge Structure Based on Modal Theory and Rubber-Pad Damping Theory
## 3 Finite-Element Modeling
### TMSH Strength-Check Calculation
The curved section is a shear-hinge damage-prone section so the strength of the shear hinge on the curved section is calibrated by considering the lateral load. The lateral load of the train is determined by the static wheel weight, derailment coefficient, and wheel-rail friction coefficient \(\mu_{k}\). The load of a train wheel pair with only one wheel rim and rail transverse contact that produces a transverse force is calculated as the value of the static wheel weight multiplied by the derailment coefficient. For the other wheel with the wheel tread and rail top contact, the load is calculated as the maximum transverse force of the static wheel weight multiplied by the dynamic friction coefficient. The derailment coefficient is 0.8, and the dynamic friction coefficient is 0.2. The larger value of transverse force is calculated using Equations (7)-(9) to be 64 kN, and the transverse force on the other side is 16 kN. Since three floating-slab tracks are built in this validation model, only the adjacent bogie loads of two adjacent carriages are considered, and the load is applied using 1/2 fastener spacing as the step length, running uniformly and continuously along the \(x\)-axis. Figure 8 shows the stress value of the shear rod under metal-pad conditions, with a maximum strength of 180.9 MPa, and Figure 9 shows the stress values with an additional rubber-pad layer. The shear rod is subjected to a maximum Mises stress value of 150.9 MPa. From a static point of view, the rubber pad reduces the stress value of the shear rod by 16.7%. According to the material definition, the yield strength of the 60 Si2 Mn used in the shear hinge is 1293 MPa. Taking a safety factor of 2, the permissible strength stress is calculated as follows:  
\[[\sigma]=\frac{\sigma_{b}}{n} \tag{10}\]  
Figure 8: Shear-rod stress values for top-mounted shear hinges mounted on metal pads. Figure 9: Shear-rod stress values for top-mounted shear hinges mounted on rubber pads. where \(\sigma_{b}\) is the material yield strength, \(n\) is the safety factor, and the calculated permissible stress is 646.5 MPa. \([\sigma_{max}]<[\sigma]\) under the two operating conditions, which is fully compatible with user requirements.
## 4 Experimental Results
### Dataset  
In our research, two types of dataset have been developed. The first type includes still images (snapshot images), and the second one includes the video dataset. We developed two snapshot image datasets named DB1 and DB2 along with the video dataset named DB3. DB1 includes 6000 images that have been captured by us from different angles, distances, and backgrounds in a real wind turbine farm. In this wind turbine farm, more than 300 WTTs exist, and on average, 20 images/WTT are available in DB1. This dataset is used for the object detection problem (Hub detection). In addition, DB2 includes about 10,000 snapshot images uniformly distributed in two different classes (Blade/Non-Blade) for the second problem (Binary classification). DB2 includes 5000 blade images with diverse angles (0 to 360 degrees), different thicknesses and lengths, as well as 5000 non-blade images (please see Figure 5). For velocity estimation, we have deployed our third dataset DB3 including 4 video files. These four videos were captured with 30 frames per second (fps) with different distances and angles from a shared object (a wind tower target), synchronously. Each of these includes 3600 images with 120 s time duration. It is worth mentioning that we measured the ground truth velocity (reference rotational speed) in a time window about 120 s (time duration of each video file) using Long Range Finder Sensor UART(TTL) with USB RB605B 703A U81. The resolution of our B605B laser distance meter sensor was 1 mm, with measuring range from 0.03 to 100 meters for constant conditions.
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 3 Results and Discussion
### Physical Properties  
The viscosity and surface tension analysis results of the TM oil are shown in Table 5. As expected, no significant differences were observed in either parameter between batches. However, viscosity values are significantly lower, 5 times less, than those previously reported in _Tenebrio molitor_ oil [37; 38] but similar to some edible vegetable oils such as corn, soy, or sunflower [84; 85]. These differences could be attributed to the shear rate applied and the filtration step during the obtaining procedure.  
As expected, TM oil samples showed a Newtonian behavior at different temperatures (Figure 2). This behavior was also observed in different vegetable edible oils [86] and those obtained from insects [38; 41]. Surface tension results were also similar to some cold-pressed edible oils like flaxseed, avocado, or EVOO [85].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Batch 1** & **Batch 2** & **Mean \(\pm\) SD** \\ \hline Viscosity (cP) at 25 \({}^{\circ}\)C & 61.47 \(\pm\) 0.58 \({}^{\text{a}}\) & 61.90 \(\pm\) 0.95 \({}^{\text{a}}\) & 61.68 \(\pm\) 0.74 \\ Surface tension (mN/m) & 36.50 \(\pm\) 0.87 \({}^{\text{b}}\) & 36.00 \(\pm\) 1.00 \({}^{\text{b}}\) & 36.25 \(\pm\) 0.88 \\ \hline \hline \end{tabular} Different letters in each row indicate significant differences (\(p<\) 0.05) between batches.  
\end{table}
Table 5: Physical properties of the _Tenebrio molitor_ oil (n = 6).  
Insect oil for human consumption is studied little for application in the food industry. It is mainly used in cosmetics and pharmaceutical products [12]. However, from a practical point of view, knowledge about physical properties such as viscosity or surface tension of TM oil is necessary for assessing their behavior in water/oil food emulsions, oleo gels, or even as a frying medium [87; 88; 89; 90; 91].
###### Abstract  
As the market penetration rate (MPR) of autonomous vehicles increases, it is expected that the safety of mixed traffic situations will change due to interactions between vehicles. A proactive safety analysis of mixed traffic situations is needed for future intelligent transportation systems; thus, it is necessary to determine the driving safety evaluation indicators that have a significant impact on identifying hazardous sections of actual roads by each MPR. The purpose of this study is to simulate autonomous vehicle behavior by analyzing real-world autonomous vehicle data and to derive a promising integrated driving safety evaluation index for mixed traffic. Autonomous vehicle driving data from an autonomous mobility testbed in Seoul were collected and analyzed to assess autonomous vehicle behavior in VISSIM. The simulation environment was established to match the real road environment. Decision tree (DT) analysis was adopted to derive the indicators influencing the classification of hazardous sections of real roads by MPR. The vehicle-vehicle interaction indicators used to evaluate driving safety were applied as the input variables of the DT, and the classification of real-world hazardous road sections was the output variable. An integrated evaluation index was developed using the promising evaluation indicators and information gains derived for each MPR. The most hazardous section and the factors affecting the driving safety of the section based on the integrated evaluation index for each MPR were then presented. The results of this study can be utilized to proactively identify hazardous road sections in the real world through simulations of mixed traffic conditions.  
autonomous vehicle data; autonomous vehicle MPR; decision tree; driving safety indicator; integrated evaluation index +
Footnote †: journal: Applied Sciences
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 3 Partition Method Based on Spatio-Temporal Correlation
#### 3.1.2 The DBSCAN Clustering Algorithm  
DBSCAN is a density-based clustering algorithm. The algorithm divides the entire sample set quickly by using the sample sets which are closely connected in each group. Simultaneously, the algorithm groups closely connected sample sets into one class to form a cluster category, while not requiring the prior input of the number of samples for clustering. The basic idea of DBSCAN clustering algorithm is that, for each measurement in the partition cell, the neighborhood of its given radius contains no less than a given number of measurements.  
Therefore, DBSCAN only needs two input parameters: the size of the radius and the minimum number of measurements in the neighborhood of each measurement in the partition cell. For a sample set of any shape, there exists some point from which any point in the sample set can be reached by a \"dense\" path. In other words, the points in the cluster must be density-connected.  
Suppose that, at time \(k\), it exists a set of measurements \(Z=\{z^{i}\}_{i=1}^{N_{z}}\) (subscript \(k\) is ignored for simplicity). If a measurement is a core point, then its neighborhood \(\varepsilon\) (see reference [19] for a definition of neighborhood) contains at least \(M\) measurements.  
The steps of the DBSCAN algorithm are described as follows:  
1. Set the cluster count \(\mathcal{C}=0\) and the set of core points \(C=\{\}\);
2. Traverse the measurement \(z^{i}\) values in the measurement set \(Z\), and determine whether it is a core point. If it is, add it to the set \(C\), i.e., \(C=C\cup\{z^{i}\}\);
3. Traverse the set of core points \(z^{i}\) in \(C\) that have not been visited. Increment the cluster count \(\mathcal{C}=\mathcal{C}+1\). Set the category of \(z^{i}\) as \(C\) and marked it as visited.
4. According to the current core point \(z^{i}\) and the count of clusters \(\mathcal{C}\), traverse all points \(z^{i}\) in the \(\varepsilon\) neighborhood of \(z^{i}\), setting the category of \(z^{i}\) as \(\mathcal{C}\) and marking them as visited. If \(z^{j}\) is not visited and is a core point, set \(z^{i}=z^{j}\) and repeat step 4 until the condition is not satisfied;
5. Repeat steps 3 and 4 until all the core points are visited;
6. The points in the measurement set \(Z\) that do not belong to the core points and have not been visited are set as noise points.  
After the above steps, the measurement set \(Z\) is divided into the measurement set of the target and the noise point.
## 2 Experimental Section
### Characterization  
Transmission electron microscopy (TEM) was performed using a thermal field emission transmission electron microscope (Tecnai G2 F30 S-TWIN, FEI, Hillsboro, OR, USA). X-ray diffraction (XRD) patterns and Fourier transform infrared (FTIR) spectra were measured using an X-ray diffractometer (XRD-7000, Shimadzu, Kyoto, Japan) with a Cu Kr radiation (\(\lambda\) = 1.5405 A) and FTIR spectrometer (670-IR, Varian, Palo Alto, CA, USA), respectively. X-ray photoelectron spectroscopy (XPS) spectra were obtained using an X-ray photoelectron spectrometer (ESCALAB 250 Xi, Thermo Fisher Scientific, Waltham, MA, USA). Ultraviolet-visible (UV-vis) absorption and PL spectra were recorded using an ultraviolet-visible spectrophotometer (Cary 60, Agilent Technologies, Santa Clara, CA, USA) and fluorescence spectrophotometer (F4600, Hitachi, Tokyo, Japan), respectively. The time-resolved PL decay curves were measured with a spectrometer (FLS980, Edinburgh, UK). The PLQY was measured using our customized system which is composed of a spectrometer (QEPRO, Ocean optics, Tianjin, China) and an integrating sphere. The electroluminescence (EL) spectra and optical properties of the LED devices were tested using an analyzer system with an integrating sphere (HPCS6500, HOPOO, Hangzhou, China).
## 4 Simulation
### Influence of SNR
#### 4.2.4 Calculation Time Comparison  
In addition to sound source identification error, computational efficiency is an essential indicator for evaluating algorithm performance. Taking the comparison conditions in the previous section as an example, Table 5 compares the average calculation times of different methods. It should be noted that factors such as the location, frequency, and amplitude of the sound source will not affect the calculation time. Although the SNR will make a difference in the calculation time, it will not affect the efficiency comparison conclusion between different methods.  
As can be seen from Table 5, the calculation time of off-grid-\(GL_{1}\) and FISTA is relatively long, while other methods are within 10 s. Although VG-SBL requires multiple iterative calculations and takes longer than the original SBL, its calculation time is still acceptable based on significantly improving calculation accuracy.
## 5 Discussion and Conclusions  
### Temperature  
The observed temperature behavior in the winter and spring seasons distinguishes the mesopause annual temperature variations above Eastern Siberia from the normal one. It can be assumed that this behavior is driven by the influence of processes developing in the underlying atmospheric layers. Winter is characterized by a high frequency of sudden stratospheric warmings, which occur and rapidly develop over Siberia. These processes might cause significant variations in average nighttime temperatures and a related slowdown of the decrease in monthly average temperature [25].  
The summer-autumn discrepancies in the observed temperature can be indirectly induced by differences in the intensity behavior during this time interval for satellite and ground-based instruments, because the effective temperature is calculated using the 557.7 nm emission height profile. In addition, we should note that the temperature from model data for this time interval matches well the results of satellite observations.
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 1 Introduction
However, this method needs the global network information, and has high computational complexity. In this paper, we solve the problem of communication overhead reduction for the distributed estimation, where the communication overhead problem is transformed into the network topology optimization problem. In order to optimize the network topology, we propose a method according to the local structure parameters of network nodes, in terms of node degree, average path length, and clustering coefficient. Since sensor nodes are power-limited, it is essential to constrain the communication range between sensor nodes. The main contributions of this paper are as follows:  
* The problem of communication overhead reduction in the KCF algorithm is converted into the optimization of network topology, which achieves a good trade-off between the mean square estimate error and network communication overhead. * A network topology optimization method is proposed according to nodes' local structural parameters information, in which the constraint on the communication range of two nodes is incorporated. To the best of our knowledge, there are few works on reducing the communication overhead of the KCF algorithm from the perspective of network topology optimization. Moreover, due to the unstableness of WSNs, it is more desirable to adjust communication links for the performance improvement. In addition, the communication links are adjusted based on local structural parameters of the network, in terms of the degree of network nodes, average path length, and clustering coefficient. Compared with some existing heuristic search algorithms, the proposed method in this paper outperforms them in terms of the computational complexity and communication overhead. The remainder of the paper is organized as follows. Section 2 introduces the system model and formulates the problem of network topology optimization. In Section 3, a network topology optimization method is proposed. Simulation results are given in Section 4. Finally, we conclude the paper in Section 5.
## 3 Results and Discussion
### Bioactive Properties
#### 3.4.2 TPC and TFC
[42] found losses of bioactive compounds in raspberries convection-dried at 60 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C compared to fresh fruit: total phenols at 32-40%, while flavonoids were in the range of 3-25%. Concerning polyphenols, these are similar losses to those obtained in this study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Fresh** & **Freeze-Dried** & **Conv-40** & **Conv-60** \\ \hline Total polyphenol content & & & & \\ (mg GEA/100 g DM) & 259.68 \(\pm\) 23.26 a & 233.56 \(\pm\) 5.96 ab & 205.87 \(\pm\) 12.90 b & 141.79 \(\pm\) 9.83 c \\ \hline Total flavonoid content & & & & \\ (mg EPI/100 g DM) & 40.17 \(\pm\) 0.48 a & 36.79 \(\pm\) 0.74 b & 25.99 \(\pm\) 0.21 c & 22.41 \(\pm\) 0.70 d \\ \hline ABTS scavenging activity & & & & \\ (\(\mu\)M Trolox/100 g DM) & 448.62 \(\pm\) 4.23 a & 364.51 \(\pm\) 9.12 b & 283.56 \(\pm\) 8.26 c & 232.12 \(\pm\) 11.47 d \\ \hline DPPH scavenging activity & & & & \\ (\(\mu\)M Trolox/100 g DM) & 328.85 \(\pm\) 16.98 a & 258.78 \(\pm\) 5.16 b & 172.85 \(\pm\) 15.51 c & 144.24 \(\pm\) 5.83 d \\ \hline Fresh fruit; Freeze-Dried—freeze-dried fruit; Conv-40—convection-dried fruit at 40 \({}^{\circ}\)C; Conv-60—convection-dried fruit at 60 \({}^{\circ}\)C; within each line, values with the same letter are not significantly different at \(p<0.05\) ([PERSON]’s post hoc test). & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bioactive properties of fresh and dried quince.
## 3 Results and Analysis
### Estimation Model of Chlorophyll Content in Plant Leaves Based on Spectral Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Parameter** & **Regression Model** & **Decisive Coefficient** \\ \hline \multirow{3}{*}{RES} & y = 75.5e–37.56x & \(R^{2}\) = 0.0908 \\  & y = \(-\)2107.9x + 76.099 & \(R^{2}\) = 0.0852 \\  & y = 639,164x2 - 15,453x + 140.95 & \(R^{2}\) = 0.1403 \\ \hline \multirow{3}{*}{BES} & y = 23.476e–891x & \(R^{2}\) = 0.2892 \\  & y = \(-\)46,608x + 13.537 & \(R^{2}\) = 0.2357 \\  & y = \(-\)5E+06x2 - 55,192x + 10.031 & \(R^{2}\) = 0.2359 \\ \hline \multirow{3}{*}{YES} & y = 87.435e–3192x & \(R^{2}\) = 0.0634 \\  & y = \(-\)19,288x + 86.605 & \(R^{2}\) = 0.0690 \\  & y = \(-\)6E+06x2 + 154x + 69.045 & \(R^{2}\) = 0.0701 \\ \hline \multirow{3}{*}{YEP} & y = 1E + 06e–0.019x & \(R^{2}\) = 0.0147 \\  & y = \(-\)0.9134x + 530.86 & \(R^{2}\) = 0.0097 \\  & y = \(-\)0.0474x2 + 48.545x - 12,373 & \(R^{2}\) = 0.0097 \\ \hline \multirow{3}{*}{RGP} & y = 220.99e–11.36x & \(R^{2}\) = 0.8523 \\  & y = \(-\)563.33x + 128.09 & \(R^{2}\) = 0.7368 \\  & y = 3124.9x2 - 1411.9x + 181.94 & \(R^{2}\) = 0.7840 \\ \hline \multirow{3}{*}{RWSB} & y = 92.18e–1.88x & \(R^{2}\) = 0.4570 \\  & y = \(-\)100.42x + 85.736 & \(R^{2}\) = 0.3885 \\  & y = 172.71x2 - 220.49x + 103.77 & \(R^{2}\) = 0.4046 \\ \hline \multirow{3}{*}{REA} & y = 80.529e–1.114x & \(R^{2}\) = 0.1094 \\  & y = \(-\)62x + 79.517 & \(R^{2}\) = 0.1009 \\  & y = 420.51x2 - 403.98x + 144.75 & \(R^{2}\) = 0.1412 \\ \hline \multirow{3}{*}{YEA} & y = 107.49e–19.58x & \(R^{2}\) = 0.2037 \\  & y = 1160.1x + 98.23 & \(R^{2}\) = 0.2129 \\  & y = 72.497x2 + 6679.1x + 198.72 & \(R^{2}\) = 0.2811 \\ \hline \multirow{3}{*}{BEA} & y = 83.579e–13.29x & \(R^{2}\) = 0.0657 \\  & y = \(-\)793.36x + 83.563 & \(R^{2}\) = 0.0698 \\  & y = 5083.8x2 - 1204.2x + 91.552 & \(R^{2}\) = 0.0700 \\ \hline \multirow{3}{*}{LCI} & y = 2839.6e–5.298x & \(R^{2}\) = 0.1629 \\  & y = \(-\)240.95x + 236.02 & \(R^{2}\) = 0.1121 \\ \cline{1-1}  & y = \(-\)5636.2x2 + 8389.1x–3064 & \(R^{2}\) = 0.1648 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression model of chlorophyll content and spectral parameters (sample number = 60). Figure 9: Comparison between the measured value and the predicted value (sample number = 60).
# An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background
## 4 Discussion  
This paper proposes the mean-curvelet transform denoising algorithm to address the issues of uneven background intensity, complex noise and interference, and diverse target shapes in images of space targets captured by visible light cameras under the constraints of the imaging system and observation environment. The algorithm applies mean filtering and curvelet transform filtering to the noisy space target images separately and then applies the inverse curvelet transform to the filtered results to generate the final image. The experimental results show that this algorithm effectively improves the signal-to-noise ratio of images.  
Furthermore, since the fixed threshold is used in the curvelet transform in this paper, we will study the application of the variable threshold curvelet transform to the algorithm in future experiments.  
However, since the curvelet transform is mostly implemented on hardware platforms such as X86 and X64, which cannot tolerate long-term operation in space radiation environments, the algorithm proposed in this paper currently does not have the capability of real-time processing in orbit. In future studies, we will select some hardware platforms with radiation resistance indicators, such as DSP or FPGA, and apply the algorithm on these hardware platforms to achieve real-time processing in orbit.