esa-sceva/satcom-chunk-collection huggingface dataset:
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 5 Discussion  
In the paper, three target tracking associations have been successfully developed for a real-world scenario: measurement-to-measurement (initialization), measurement-to-track, and track-to-track associations. The video of a highway interchange and its surroundings features a variety of moving vehicles, including vehicles driving straight and on curves, occluded, intersected on and under the bridge, and stopping at the traffic signal. Since a minimum number of frames is required for a track to be valid, only targets with a life length over 20 frames (2 s) were considered. There can be incorrect results in the tracking during the last 20 frames because the track is invalid if it is initialized during the last 20 frames.  
The tracks slower than the minimum speed are terminated, which is set at 1 m/s in the paper. It is noted that very slow or stopped targets are not of interest as they do not pose an imminent threat to security and surveillance.  
In all cases, the proposed target tracking scheme does not generate false tracks in a heavy clutter environment. The IMM scheme was able to handle multiple targets with various maneuvers. The directional track association is particularly effective when two vehicles are moving very closely side by side. The average TTL and MTL are 0.917 and 0.842, respectively, for the IMM-CA with the directional track association; the MTL is less than the TTL on 9 targets. They were caused by track breakages as can be seen in the Supplementary Material Video S4. Therefore, the IMM-CA equipped with the directional track association was able to fuse all tracks overlapping on the target. The track breakages were caused by complex reasons including missing detection, wrong association, and high maneuvering.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**IMM-CV**} & \multicolumn{2}{c}{**IMM-CA**} \\  & **Track Association** & **Directional TA** & **Track Association** & **Directional TA** \\ \hline Number of tracks & 61 & 65 & 84 & 64 \\ Avg. TTL & 0.859 & 0.871 & 0.879 & 0.917 \\ Avg. MTL & 0.789 & 0.775 & 0.705 & 0.842 \\ Number of targets with broken tracks & 8 & 9 & 19 & 9 \\ Number of missing targets & 5 & 4 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall tracking performance.  
Figure 13: TTL and MTL of IMM-CA: (**a**) track association and (**b**) directional track association.  
Two targets (Targets 1 and 11) were missed for the IMM-CA with directional track association because the initialization failed; a valid initialization requires two consecutive detections in the direction of the target. The low detection is mainly caused by the low-resolution object. The surveillance area is more than 0.23 km\({}^{2}\) (=640 m \(\times\) 360 m) in the paper. Higher drone altitudes will increase the surveillance area but generate lower-resolution object images and lower detection rates, which remains an issue for future research.
## 2 Materials and Methods
### Spectral Preprocessing  
The average reflectance spectra calculated from ROIs were first transformed to absorbance (log (1/reflectance)). Then, in order to eliminate the undesired impact, such as random noise, light scattering, and baseline shifts [18], three spectral preprocessing techniques, including standard normal variate (SNV), Savitzky-Golay first derivative (1 st Der) (with a second-order polynomialand a five-point window), de-trending (Det) (with a second-order polynomial), two combinations of SNV with 1 st Der (SNV + 1 st Der) and SNV with Det (SNV + Det), were separately adopted and compared to deal with the absorption spectra prior to the model establishment. SNV is normally applied to remove scatter effect. First derivative (1 st Der) removes the baseline shift and amplifies small spectral features [19]. Det is used to eliminate the effects of baseline shift and curvilinearity [20]. The combination of SNV and Det is often used to remove the curvilinearity and absorbance offset from NIR spectra and reveal key information under processes of investigation [21; 22; 23].
## 3 Materials and Methods
### Histogram of Oriented Gradient (HOG)  
The process of extracting features using the HoG method [15] involves several key steps, as shown in Figure 4. Initially, the image is divided into small, connected regions known as cells. For each cell, the gradient of each pixel is calculated. These gradients are then used to create a histogram of gradient directions for each cell. The histograms from all cells are combined to form a comprehensive descriptor for the entire image. To analyze temporal changes, HoG features are extracted at different time intervals and compared. By recognizing patterns in the HoG features, distinct characteristics of each activity can be identified. The temporal changes in HoG features are visualized to provide insights into the dynamics of the activities. Subsequently, the extracted features can be used to train machine learning models such as a support vector machine (SVM). These models are evaluated to determine their performance and accuracy in classifying the activities based on the HoG features. This method proves to be effective in capturing the essential gradient structures that characterize different activities over time.  
Figure 4: The procedure to extract the features of objects using HOG.  
Figure 3: Photos of four types of activities of daily living (ADLs) and three types of falls for one senior.
## 2 Methods  
### Silicon-Based Four Terminal Synaptic Transistor  
The synaptic device used in this work was a silicon-based 4-terminal synaptic transistor previously reported by our group [24; 25]. The synaptic device was composed of an asymmetric dual gate. Only SiO\({}_{2}\) was formed below n\({}^{+}\)-doped polysilicon for gate 1 (G1), which received a pre-synaptic spike, and SiO\({}_{2}\)/Si\({}_{3}\)N\({}_{4}\)/SiO\({}_{2}\) stack was formed below n\({}^{+}\)-doped polysilicon for gate 2 (G2), which was used for STDP. The detailed fabrication process is illustrated in the previous work [24; 25]. Figure 1 illustrates the mechanism of the weight modulation by trapping electrons and holes in the charge trapping layer. If asymmetric spikes were applied to G1 and G2 with a timing difference \(\Delta t\), the type of charge trapped in the charge storage layer and the amount of charge can be controlled. With the positive \(\Delta t\) between the spikes of G1 and G2, as shown in Figure 1a, hot holes generated by impact ionization were injected into the charge storage layer, which makes the threshold voltage of the synaptic device (\(V_{\text{th}}\)) decrease (potentiation). In the opposite case, as shown in Figure 1b, hot electrons were injected into the charge storage layer with the negative \(\Delta t\), which leads to the positive shift of \(V_{\text{th}}\) (depression). The changes of the synaptic weights with the timing difference \(\Delta t\) are plotted in Figure 1c, which was analogous to the biological STDP characteristic [26]. The STDP characteristic was measured with a Keithley 4200-SCS and pulse measurement unit (PMU). Curve fitting for STDP characteristic was performed, and the equations of potentiation and depression were extracted as:  
\[\Delta I_{Source}=\left\{\begin{array}{rl}A_{p}\times e^{-\frac{\Delta t}{ \tau_{p}}},&\text{for }\Delta t>0\ (\text{ potentiation})\\ A_{d}\times e^{\frac{\Delta t}{\tau_{d}}},&\text{for }\Delta t<0\ (\text{ depression})\end{array}\right. \tag{1}\]  
where \(A_{p}\) and \(A_{d}\) correspond to the fitting constant for potentiation and depression, respectively, and \(\tau_{p}\) and \(\tau_{d}\) denote for the time constant of potentiation and depression, respectively. The extracted parameters for the synaptic device are summarized in the inset of Figure 1c. Based on Equation (1), we can calculate the timing difference to adjust the target quantized weights. The detailed scheme for the weight transfer will be explained in Section 3.
## 4 Conclusions  
In summary, the lithiation process of 3D-line-structured Si/C anodes and unstructured Si/C anodes were investigated by in situ SEM techniques. The comparison revealed that through the introduction of channels to release stress brought about by volume expansion, the 3D-line-structured anodes prevent separation from the current collector. Moreover, the channels enlarged the contact area of the electrodes and lithium ionic liquid and improved the utilization of anode materials. This greatly increased the charge capacity of 3D-line-structured anodes. Based on the in situ experimental results, a coarse optimization of 3D-line-structured anodes was proposed. These results shed light on the unique advantages of the in situ SEM technique in studying realistic bulk batteries and in designing 3D electrode structures.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/5/956/s1](http://www.mdpi.com/2076-3417/9/5/956/s1)]([http://www.mdpi.com/2076-3417/9/5/956/s1](http://www.mdpi.com/2076-3417/9/5/956/s1)), Figure S1: Charge-discharge curves of the un-/line-structured Si/C composites at the rate of 100 mA/g, Figure S2: SEM images of (a) pristine and (b) lithiated Si/C composites, Video S1: Video of unstructured Si/C composites during charge and discharge processes, Video S2: Video of 3D-line-structured Si/C composites during charge and discharge processes.  
**Author Contributions:** Conceptualization, Y.Z. and W.P.; methodology, H.S. and Y.L.; resources, Y.Z.; writing--original draft preparation, H.S.; writing--review and editing, X.L. and R.W.; supervision, X.C.; funding acquisition, [PERSON] and W.P.  
**Funding:** This research was funded by the NSFC-DFG joint project (51761135129), National Natural Science Foundation of China (21676005), German Research Foundation (392322200), Beijing Natural Science Foundation (2172002), and Great Wall Scholarship Project (CIT and TCD20170306).  
**Acknowledgments:** The support for laser materials processing was provided by the Karlsruhe Nano Micro Facility (KNMF, [[http://www.knmf.kitel.edu/](http://www.knmf.kitel.edu/)]([http://www.knmf.kitel.edu/](http://www.knmf.kitel.edu/))), a Helmholtz research infrastructure at KIT, and is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Schematic illustrations of morphology variation in (**a**) the initial structure and (**b**) the optimized structure Si/C composites before and after lithiation.
## 2 Materials and Methods
### Gear Test Rig and Experimental Testing
#### 2.3.2 Measuring Devices  
A thermal imaging camera (Hikvision DS-2 TD2637B-10/P, Hangzhou, China) with a resolution of \(\pm\)0.5 \({}^{\circ}\)C was adopted to measure the wheel gear bulk temperature during the test. The thermal imaging camera was calibrated with a portable thermal imaging camera (FLIR E6, Wilsonville, OR, USA) and a reference thermometer (FLUKE Calibration 1524, Everett, WA, USA). To avoid the influence of the flash temperature, the bulk temperature was measured at the tooth root (Figure 7a,b) [42].  
In accordance with [PERSON] et al. [63], an emissivity of \(\varepsilon\) = 0.98 was determined for the used PA polymer. Involute gears are insensitive to small changes in the center distance, and a small increase improves the service life of the gears. Due to increased backlash, the thermal expansion of the gear tooth is allowed and more air is captured in the gaps between the teeth, which improves the heat transfer from the teeth to the environment. All of the gears were tested with a center distance of 78 mm, which was measured before each test using an electronic caliper with an accuracy of 0.02 mm (Yato YT-7201, Shanghai, China).  
Figure 8a shows the optical 3D profilometer (Profilm3D) employed for the non-contact 3D profilometer analysis technique to measure the tooth flank surface profile and roughness. White-light interferometry enabled the measurements of the surface profile and roughness down to 0.05 \(\upmu\)m, and for the line and area surface measurements, ISO 4287 [64] amplitude and ISO 25178 [65] height standards were used. The arithmetic mean of the height S\({}_{\text{a}}\) was used as a stereometric reference parameter within the measured area. The measured area was sized at 4 mm \(\times\) 1.5 mm, and the measurements were carried out perpendicular to the direction of the layer application. The base tangent length was measured using a mechanical counter-disk micrometer (Mitutoyo 223-102, Kawasaki, Japan) with a graduation of 0.01 mm and an accuracy of \(\pm\)0.004 mm. The weight of the gears was evaluated before and after each step load test with a high-precision balance scale (Mettler Toledo JS1603C, Greifensee, Switzerland), with a resolution of 0.0001 g (Figure 8b).  
Figure 7: (a) Thermographic picture and (b) location of the measuring gear bulk temperature.
## 3 Analysis of the Fault State and Establishment of the Fault Data Sample
### Data Processing  
After the sample data is acquired, the fault diagnosis algorithms chosen before are used to sort the data. The specific processing of the two algorithms can be shown with the following flowchart which named with Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rotating** & **Throttle** & **Vibration** & **Vibration** & **Cylinder** & **Cylinder** & **Number** & **Number** & **Number** \\
**Speed** & **Opening** & **Frequency** & **Peak** & **Temperature** & **Temperature** & **One** & **Two-Cylinder** & **State** \\
**(r/min)** & **(\%)** & **(Hz)** & **Value (V)** & **(C)** & **(C)** & **(C)** & **(C)** & **(C)** \\ \hline
3539 & 16.563 & 59 & 2.30 & 158 & 159 & 0.98 & 1.02 & 1 \\
3508 & 14.813 & 59 & 2.82 & 159 & 158 & 0.91 & 0.93 & 2 \\
3494 & 17.813 & 58 & 1.95 & 138 & 151 & 1.13 & 1.26 & 3 \\
3575 & 16.813 & 58 & 2.28 & 157 & 159 & 1.31 & 1.03 & 4 \\
3461 & 16.875 & 59 & 2.23 & 158 & 157 & 1.20 & 1.23 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault sample data.  
Figure 10: Flowchart of the program.  
Firstly, the data sample should be defined with different kinds which are shown in Table 2. After that, due to the different units of the signals, the accuracy of the data classification may be reduced. So, the normalization of the sample is used here. The form is shown with Equation (10).  
\[f:x_{i}\to y_{i}=(y_{\text{imax}}-y_{\text{imin}})\frac{x_{i}-x_{\text{imin}}}{ x_{\text{imax}}-x_{\text{imin}}}+y_{\text{imin}} \tag{10}\]  
where \(x_{i}\) represents the value of each element in the input vector, \(y_{i}\) represents the normalized value. \(x_{\text{imax}}\) and \(x_{\text{imin}}\) represent the maximum and minimum values of the same type of elements in the input vector respectively, and setting the value of the output between 1 and \(-1\), which means the \(y_{\text{imax}}=1\) and \(y_{\text{imin}}=-1\).
## 4 Application Examples for Turbomachinery
### Hybrid RANS/LES Model Menter SST-SAS
#### 4.2.1 Numerical Calculation of LS89  
Due to its extensive experimental data covering high- and transonic-flow conditions, LS89 serves as an ideal simulation target. In the present study, Figure 9 illustrates the computational domain grid for the case study, where the mesh is refined near the leading and trailing edges. The first-layer grid height is set to 0.0015 mm to ensure that \(\Delta y^{+}<1\). [PERSON] [35] used 20.46 million grids with WENO-JS5 in a Delayed Detached-Eddy Simulation (DDES), and [PERSON] adopted 29.7 million grids with a fourth-order centered scheme in Large Eddy Simulation. However, in this case, the total mesh cells are only 7 million with WENO-ZQ5, which are divided into 76 blocks for parallel computation. Tables 3 and 4, respectively provide the geometric data of the LS89 vane and details of two typical high-subsonic operating conditions.  
The distribution of the entropy Mach number for the LS89 airfoil under the MUR129 condition is presented in Figure 10. This figure compares the results obtained in this study with the experimental and LES data, showing complete consistency. This indicates that the use of the high-order novel WENO format coupled with the SST-SAS model, as described in the text, achieves computational accuracy comparable to LES with fewer grid points. Additionally, a monitoring point is placed at the trailing edge to monitor pressure data and is represented by power spectral density (PSD) obtained using Fourier transform. The PSD curves in Figure 11 follow the \(-5/3\) law, indicating that the inertial region of turbulence is correctly resolved by the present SAS. Moreover, the peak frequency of the trailing-edge shedding vortex is shown in the graph as 37.5 kHz, consistent with the shedding frequency of the reference [35]. The grid quantity, the turbulence model, and the numerical scheme adopted in the current case are feasible.  
According to the studies by [PERSON] [36] and [PERSON], [PERSON], and [PERSON] [37], excessive dissipation in the scheme strongly affects the simulation in the separation area, dampening small-scale coherent structures in the LES regions. Figure 12 demonstrates the dimensionless density gradient obtained for flow condition MUR235 from Table 4, comparing the results obtained using different WENO schemes with fifth-order accuracy. The WENO-JS scheme exhibits insufficient resolution in wake vortex aspects, indicating that the scale-resolving capability is not fully utilized. In contrast, the improved finite-volume WENO scheme offers higher accuracy in capturing shock waves, trailing-edge pressure waves, and their reflection waves, while also resolving more small-scale structures in the wake vortex system. Additionally, it is noteworthy that the computational time for the WENO-ZQ format is 10.53% less than that for the classical WENO-JS format, making it more cost-effective.  
Figure 11: Power spectral density of the present SAS under flow condition MUR129.  
Figure 10: Isentropic Mach number distributions of flow condition MUR129.
# MuIm: Analyzing Music-Image Correlations from an
## 4 MuIm Dataset
### Music-Visual Pairing Methodology
#### 4.2.1 Artistic Segment Pairing  
In the artistic segment, emotional content is represented by a 28-dimensional (28D) emotional vector, while semantic associations were derived from media captioning models and further aligned using large language models (LLMs). In Equation (2), for the artistic dataset, \(d\) employs the Mahalanobis distance to measure the similarity between the 28D emotional vectors of music (\(y_{m}\)) and art (\(y_{i}\)) due to the inherent correlations among the emotions.  
For semantic information, we utilized a text embedding model [59] to generate textual embeddings for the semantic tags produced by large language models (LLMs) and enhanced AI caption models. We then employed Equation (3) to compute the similarity score for the semantic content. Given the complex nature of semantic information, which relies heavily on pretrained models, we placed a slight emphasis on emotional representation over semantic content when calculating the music-art similarity within the artistic dataset. If \(w_{e}\) and \(w_{s}\) represent the weights for emotional and semantic similarity, and \(S_{e}\) and \(S_{s}\) denote the emotional and semantic similarity scores, then the final similarity score between music and art is determined by assigning a greater weight to the emotional representation than to the semantic content, mathematically expressed as  
Figure 5: Illustrative figure of the dual-dimension music–image pairing strategy, approximating strong and mixed correlations across semantic and emotional dimensions. Subplots show individual projections on simplified semantic and emotional planes.  
\[\begin{split} S_{\text{final}}=w_{e}\cdot S_{e}+w_{s}\cdot Cosine Sim _{s}\\ \text{where }w_{e}>w_{s}\end{split} \tag{4}\]
# An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations
###### Abstract  
The Weibull distribution is a versatile probability distribution widely applied in modeling the failure times of objects or systems. Its behavior is shaped by two essential parameters: the shape parameter and the scale parameter. By manipulating these parameters, the Weibull distribution adepdly captures diverse failure patterns observed in real-world scenarios. This flexibility and broad applicability make it an indispensable tool in reliability analysis and survival modeling. This manuscript explores five parameterizations of the Weibull distribution, each based on different moments, like mean, quantile, and mode. It meticulously characterizes each parameterization, introducing a novel one based on the model's mode, along with its hazard and survival functions, shedding light on their unique properties. Additionally, it delves into the interpretation of regression coefficients when incorporating regression structures into these parameterizations. It is analytically established that all five parameterizations define the same log-likelihood function, underlining their equivalence. Through Monte Carlo simulation studies, the performances of these parameterizations are evaluated in terms of parameter estimations and residuals. The models are further applied to real-world data, illustrating their effectiveness in analyzing material fatigue life and survival data. In summary, this manuscript provides a comprehensive exploration of the Weibull distribution and its various parameterizations. It offers valuable insights into their applications and implications in modeling failure times, with potential contributions to diverse fields requiring reliability and survival analysis.  
regression models; reparameterization; survival modeling; Weibull distribution  
62N02; 62J99
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 3 Related Work and Introduction of a Novel Approach
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Covered Weather Phenomena** & **Covered Effects** & **Validation Approach** \\ \hline [PERSON] et al. [7] & Rain & Signal attenuation, false negative, ranging error \(d_{error}\), decrease in maximum detection range & Simulation results \\ \hline [PERSON] et al. [8] & Rain, fog, aerosols & Signal attenuation, target reflectivity, range degradation & Simulation results \\ \hline [PERSON] et al. [9] & Rain, fog, snow & Signal attenuation, range degradation & Simulation results, qualitative comparison with real measurements for fog attenuation \\ \hline [PERSON] et al. [10] & Rain & Signal attenuation & Simulation results \\ \hline [PERSON] et al. [11] & Rain, fog, snow, haze & Signal attenuation & Simulation results \\ \hline [PERSON] et al. [12] & Rain, fog, snow, haze & Signal attenuation, false positive & Quantitative comparison with measurements for rain \\ \hline [PERSON] et al. [13] & Rain & Signal attenuation & Qualitative comparison with measurements \\ \hline [PERSON] et al. [14; 15] & Rain & Signal attenuation, false positive & Quantitative comparison with measurements \\ \hline [PERSON] et al. [16] & Rain & Signal attenuation, false positive & Simulation results \\ \hline [PERSON] et al. [17] & Rain & Signal attenuation, false positive & Simulation results \\ \hline [PERSON] et al. [18] & Rain, fog, snow & Signal attenuation, false positive & Quantitative comparison with measurements \\ \hline [PERSON] et al. [19] & Fog & Signal attenuation, false positive & Quantitative comparison with measurements \\ \hline [PERSON] et al. (proposed approach) & Rain, fog & Signal attenuation, SNR, false positive, false negative, ranging error \(d_{error}\) & Qualitative comparison with measurements for all covered effects \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the state-of-the-art LiDAR sensor model working principles and validation approaches. on the point cloud level. In addition, we have generated a virtual rain and fog model that considers the drop size distribution (DSD), falling velocity, gravity, drag forces, turbulent flow, and droplet deformation.