esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Preprocessing and Evaluation Metric
### Evaluation Metrics  
The signal-to-noise ratio (SNR), \(r\) (correlation coefficient), and root-mean-square error (RMSE) were used to objectively evaluate the denoising performance of the seismic signal denoising and ensure the reliability and accuracy of the denoising experiments. The calculation formulas of the three evaluation metrics are shown in Formulas (9)-(11).  
\[\text{SNR}=10\log_{10}(\frac{\sigma_{s}}{\sigma_{n}}) \tag{9}\]  
\[r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{10}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(X^{\prime}(i)-X(i)\right) }{n}} \tag{11}\]  
The SNR represents the ratio of the clean signal to the noise, in dB. The larger the SNR, the smaller the proportion of noise, where \(\sigma_{s}\) represents the energy of the signal and \(\sigma_{n}\) represents the energy of the noise. In addition, \(r\) represents the correlation degree between the denoised signal and the label signal, and the closer the value is to 1, the greater the overall correlation is between the denoised seismic signal and the seismic signal waveform with labels, where \(X\) represents the reference label signal, \(Y\) represents the denoised seismic signal processed by the model, \(Cov(X,Y)\) represents the covariance of the reference label signal and the denoised signal, \(Var[X]\) represents the variance of the reference label signal, and \(Var[Y]\) represents the variance of the denoised signal. The RMSE describes the error of each sampling point between the model output signal and the label signal. The smaller the RMSE is, the more similar the denoised seismic signal is to the label signal. The waveform distortion degree after signal denoising can be described from the deviation of each sampling point. Here, \(X^{\prime}(i)\) represents the seismic signal that is output by the model fitting, \(X(i)\) represents the reference label seismic signal, and \(n\) is the number of signal sampling points.
## 3 Discussion
### Overall Comparison  
Of the techniques that were applied, SBSE demonstrated the best potential for the evaluation of volatile compounds in sheep milk. In agreement with [PERSON] et al., it should also be noted that SPME could be a complimentary approach when polar compounds are of interest [24]. SBSE allowed small sample volumes to be prepared, extracted, and analysed efficiently and yielded results with good selectivity, sensitivity, and reproducibility compared to the other approaches. It is widely acknowledged that there is no perfect extraction technique for the analysis of volatiles and each method demonstrated biases according to compound polarity and volatility. As expected, the SPME technique did not extract larger molecular weight compounds well but excelled at extracting small polar compounds like dimethyl sulphone and pentanal. The SBSE technique extracted limited concentrations of small polar compounds, yet excelled at extracting larger molecular weight volatiles, in particular lactones. SAFE was able to extract both small polar compounds and larger more non-polar compounds but the reproducibility and efficiency of the method made it less desirable than the other techniques particularly for applications requiring untargeted analysis of multiple samples. SBSE was the most sensitive method overall and identified \(\delta\)-hexalactone, \(\delta\)-octalactone, \(\gamma\)-deca-lactone, \(\gamma\)-dodecalactone, \(\delta\)-tetradcalactone, and \(\delta\)-hexadecalactone for the first time in ovine milk. SBSE should be consideredan effective tool for the extraction and analysis of volatiles, especially lactones, in sheep milk and dairy products in general.
# Experimental Investigation on Self-Excited Thermoacoustic Instability in a Rijke Tube
## 5 Conclusions and Outlook  
An experimental investigation into the thermoacoustic instability of a Rijke tube was presented. To capture the fast dynamics of temperature, a single-ended TDLAS technique was designed and employed. The TDLAS measurement rate was 5 kHz, fast enough to capture temperature fluctuations. The etalon effects encountered during TDLAS measurements were mitigated efficiently by a lowpass filter. The measured temperature oscilliated from 1000 K to 1300 K at the dominant frequency of 230 Hz, which corresponds to the fundamental frequency of the Rijke tube. However, the temperature variations severely deviated the sinuous waveform. This indicates that the entropy waves produced from the unsteady heat release suffer complicated disturbances while propagating downstream. One of the main disturbances might be the heat convection between local gas and the inner wall of the Rijke tube.  
The measurements of flame chemiluminescence demonstrate that the flame front has jagged edges and stretches regularly. Chemiluminescence intensity also fluctuated periodically at 230 Hz. Furthermore, the pressure at the exit of Rijke tube was also found  
Figure 10: Time series and frequency spectra of relative area and pressure. (**a,c**) is the measurements of relative area and its frequency distribution, respectively, (**b,d**) the pressure and its frequency distribution, respectively.  
to oscillate at the same frequency. Additionally, pressure oscillated the most standard sinuous waveform as it underwent the least perturbations in the Rijke tube compared with temperature and chemiluminescence. The dominant frequencies of temperature, chemiluminescence and pressure are consistent with each other, all agreeing with the fundamental frequency of the Rijke tube in the experiment.  
In addition, the successful application of a single-ended TDLAS technique extends the measurement area to where the optical windows are very limited. Only one optical window is enough to employ this optical measurement technique. In future work, the single-ended TDLAS sensor will be improved to be accustomed to a more hostile environment, such as aeroengine combustors or industrial gas turbines, to perform measurements.  
Conceptualization, L.L. and T.L.; methodology, L.L.; software, L.L. and J.D.; investigation, L.L., R.Z. and J.W.; writing--original draft preparation, L.L.; writing--review and editing, T.L.; visualization, all authors.; supervision, T.L. and L.X.; project administration, L.L., T.L. and L.X. funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61827802.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# VirtualFilter: A High-Performance Multimodal 3D Object Detection Method with Semantic Filtering
## 4 Experiments and Results
### Comparison with State-of-the-Art Methods  
The following state-of-the-art methods are compared:  
1. PointPillars introduces an innovative encoder that leverages PointNets to learn representations of point clouds organized into vertical columns, referred to as pillars.
2. SA-SSD employs an auxiliary network that transforms the convolutional features from the backbone network back into point-level representations.
3. PV-RCNN summarizes the 3D scene using a 3D voxel CNN, which processes the scene into a compact set of keypoints through a voxel set abstraction module. This reduces subsequent computational complexity while effectively encoding the key features of the scene.
4. Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region Proposal Network (RPN), and a detection head.
5. CasA consists of a region proposal network (RPN) and a cascade refinement network (CRN).
6. MV3D employs a deep fusion strategy that integrates region-wise features from multiple views, allowing for interactions between the intermediate layers of different paths.
7. 3D-CVF integrates camera and LiDAR features through a cross-view spatial feature fusion strategy.
8. VPFNet aligns and combines point cloud and image data at virtual points to enhance detection accuracy.
9. SFD employs a deep completion network to generate virtual point clouds, thereby enhancing detection performance.
10. VirConvNet uses StVD (Stochastic Voxel Discard) and NRConv (Noise-Resistant Submanifold Convolution) to improve the feature representation of virtual points.  
We have chosen several classic unimodal and multimodal approaches for comparison, and the results of our experiments on the KITTI test set are presented in Table 1. Overall,our VirtualFilter model outperforms the other methods in both 3D and BEV evaluation metrics. We also conducted similar experiments on the KITTI validation set, with the results shown in Table 2. As observed, our model achieves strong detection performance on the KITTI validation set as well.
## 4 Materials and Methods
### Metabolites Measurements  
Total ascorbic acid, reduced ascorbic acid and polyphenol content was analysed in leaves and fruits. For the analysis of the leaf-content, leaves were collected from five plants at the second true leaf stage (approx. 40 days from transplant). For fruit analysis, three fruits per plants were harvested at the following stages: immature (IM), mature green (MG), and ripe-red (R) (Supplementary Materials, Figure S1), based on size and color. Analyses were carried out on three biological replicates. Leaves and fruit tissues, cut in pieces and separated from placenta and seeds, were stored at \(-80\)\({}^{\circ}\)C until use. Frozen tissue (250 mg) was disrupted to a fine powder in a sterile ice-cold plastic tube with grinding beads (two 1 minute (min) bursts at 25 MHz), in 0.2 mL of 6% (\(\
icefrac{{v}}{{v}}\)) of ice-cold trichloroacetic acid (TCA) using the TissueLyser system (Qiagen) in a cold room. Samples were then incubated 10 min in ice with occasional swirling. After centrifugation at 4 \({}^{\circ}\)C (14,000 rpm, 20 min), the clear supernatant was diluted to 0.5 mL with 6% TCA. Total ASA (tAsA) and reduced ascorbic acid (dAsA) were quantified, as previously described [47; 48]. Briefly, an aliquot (50 \(\upmu\)l) was added with 150 \(\upmu\)l 0.2 M phosphate buffer (pH 7.4), 50 \(\upmu\)l double distilled water, 250 \(\upmu\)l 10% TCA, 200 \(\upmu\)l 42% H\({}_{3}\)PO\({}_{4}\), 200 \(\upmu\)l 2,2\({}^{\prime}\)-dipyridyl (dpy), and 100 \(\upmu\)l 3% FeCl\({}_{3}\). An ethanol-no dpy sample was used to evaluate the contribution of interfering plant pigments. After a thorough mix, samples were incubated at 42 \({}^{\circ}\)C for 40 min before measuring light absorbance at 525 nm using a DU-640 UV/Vis spectrophotometer (Beckman Coulter). The AsA concentration was interpolated according to a reference calibration curve covering the 0-0.7 \(\upmu\)mol AsA range. For polyphenol determination, frozen tissue (250 mg) was homogenized in 1 mL of 60% (\(\
icefrac{{v}}{{v}}\)) MeOH with a TissueLyser (Qiagen) as above, and then samples were shaken for 30 min at 25 \({}^{\circ}\)C. After centrifugation (14,000 rpm, 15 min), the clear supernatant was diluted to 5 mL with 60% MeOH and then analyzed, as described [49]. Chemicals were purchased from Sigma-Aldrich.
## 3 Case Study Calculation  
To achieve the necessary pavement asset value, it is preferable to employ environmentally friendly technologies that utilize recycled materials. As an illustration, the structural life expectancy calculation based on pavement design is shown in Figure 4, which features an asphalt concrete (AC) cover material incorporating recycled materials. Deformation characteristics for paving materials are listed in Table 2. The complex modulus of the surfacing material is measured at different frequencies, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**The Number of DAL** & **0** & **1,500,000** & **3,000,000** & **4,500,000** & **6,000,000** & **7,500,000** \\ \hline AC 11 O \(E*\) (MPa) & 10,800 & 6000 & 5760 & 5600 & 5500 & 5450 \\ AC 16 P \(E*\) (MPa) & 8320 & 4550 & 4400 & 4300 & 4220 & 4160 \\ Stress (MPa) & 0.978132 & 0.634328 & 0.613133 & 0.600401 & 0.591348 & 0.584278 \\ Strength (MPa) & 2.4 & 0.769521 & 0.689998 & 0.643549 & 0.610559 & 0.584981 \\ \hline \end{tabular}
\end{table}
Table 1: Radial stress and strength resiliency values in ACM layers based on DAL.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Complex Modulus** & **Strength** & **Poisson Number** & **Layer Thickness** \\ \hline Surface course AC 11 & 7578 & 3.247 MPa & 0.33 & 40 mm \\ Base course AC 16 & 9966 & 2.432 MPa & 0.33 & 80 mm \\ Mechanically bound aggregate, 31.5 & 566 & 0.125 MPa & 0.30 & 180 mm \\ Gravel sub-base, 31.5 & 364 & 0.076 MPa & 0.30 & 200 mm \\ Sub-grade & 100 & - & 0.35 & - \\ \hline \end{tabular}
\end{table}
Table 2: Deformation characteristics of pavement cross-section.  
Figure 4: Pavement cross-section.  
The resulting parameters are expressed in the Wohler diagram shown in Figures 2 and 3. Both diagrams were obtained using the two-point (2 PT) bending test under controlled conditions of a temperature of 10 \({}^{\circ}\)C and a loading frequency of 25 Hz. All tests were conducted on the same equipment and followed identical standard procedures, in accordance with [12]. The \(\varepsilon_{6}\) value, which represents the strain ratio of the samples after 10\({}^{6}\) loading cycles, reveals a 23% difference between the two mixtures.  
For the pavement presented in this case study, the fatigue parameters--determined using the methods described in this section--are shown in Table 4. Its structural life expectancy was computed to be 1.4 \(\times\) 10\({}^{6}\) design axle loads for the new mixture and 1.1 \(\times\) 10\({}^{6}\) design axle loads for the recycled mixture.  
For a more accurate structural life expectancy calculation, specimens taken directly from the pavement and cut to the required test dimensions can be used to determine the fatigue characteristics.
## 5 Conclusions  
A novel scheme that assumes a \(t\) distribution for GNSS measurement errors with an adaptable distribution variance to suit the changing signal conditions was presented. The proposed scheme was tested using realistic adverse environments GNSS signals. The results have shown that the proposed scheme is more robust as compared to the standard filter. The accuracy of the navigation solution was found to improve, although the improvement was not always significant. The reliability of the navigation solution, however, was significantly improved with the proposed scheme. Moreover, as this scheme mostly de-weights the bad measurements instead of removing them, the availability of GNSS measurements was also found to increase. The robustness of the proposed scheme validates the claim that replacing the assumption of normally distributed GNSS measurement errors by \(t\) distribution and changing its covariance using the presented technique to adapt to the changing signal conditions makes the assumed GNSS measurement noise statistics closer to the true statistics thereby making the proposed scheme highly effective for GNSS degraded environments.  
**Conflicts of Interest**  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Absolute Position Errors (m)**} & \multicolumn{2}{c}{**Reliability (\%)**} \\ \cline{2-6}  & **Hor** & **Vert** & **East** & **North** & **Height** \\ \hline
**Standard** & 17.2 & 12.1 & 32.7 & 32.5 & 36.7 \\
**VB** & 23.1 & 12.1 & 56.7 & 50.3 & 38.9 \\
**VB Adaptive** & 16.9 & 11.4 & 57.7 & 50.9 & 42.4 \\ \hline \end{tabular}
\end{table}
Table 4: RMSE and reliability (OEM6 + IMU)—natural canyon.  
Figure 15: Reliability of the position solution (u-blox6 + IMU)—natural canyon.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Standard**} & **VB** & **VB Adaptive** \\ \hline
**\% of Epochs with Integrity** & **OEM6** & 71.9 & 72.0 & 71.9 \\
**Information Available** & **u-blox6** & 65 & 74.5 & 74.5 \\ \hline \end{tabular}
\end{table}
Table 5: Availability of GNSS integrity information—natural canyon.
## 4 Results
### Calibration Results  
Table 1 shows the intrinsic parameters of the upper and lower cameras, respectively. As described in Section 3.1, \(a_{0},a_{1},a_{2},a_{3},a_{4}\) are the coefficients of a fourth degree polynomial that defines the curvature of the parabolic mirror representing the intrinsic parameters of the mirror. \(x_{c}\) and \(y_{c}\) define the optical center of the catadioptric cameras with a resolution of \(1392\times 1038\).  
We obtain the extrinsic parameters of the upper and lower catadioptric cameras with respect to each calibration plane (Figure 2a). That is, six extrinsic parameters (rotation and translation) for each of the 32 images acquired during calibration. The calibration errors of each catadioptric camera are shown in Table 2 which are inside the ranges of typ  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Polynomial**} & \multicolumn{1}{c}{**Coefficients \(f(\rho)\)**} & \multicolumn{3}{c}{**Optical Center**} \\ \hline  & \(a_{0}\) & \(a_{1}\) & \(a_{2}\) & \(a_{3}\) & \(a_{4}\) & \(x_{c}\) & \(y_{c}\) \\ \hline Upper camera & \(-347.7309\) & 0 & 0.0056 & 0 & 0 & 696.3600 & 474.1928 \\ Lower camera & \(-162.3562\) & 0 & 0.0023 & 0 & 0 & 698.3097 & 524.4199 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intrinsic parameters of upper and lower camera.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 2 Materials and Methods
### Research Methodology  
#### 2.3.1 Markov Model  
A stochastic process is said to have Markovianity (no posteriority) if the state \(a_{n}\) of any moment \(t_{n}\) in a finite time sequence \(t_{1}<t_{2}<t_{3}\dots\ t_{n}\) is only related to the state \(a_{n-1}\) of its predecessor \(t_{n-1}\); the process with Markovianity is called a Markovian process. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Original Resolution** & **Data** & **Data Sources** \\ \hline \multirow{4}{*}{Natural Environment Data} & \multirow{2}{*}{1 km} & MAT & National Earth System Science Data Center \\ \cline{3-4}  & & MAP & ([[http://nnu.geodata.cn](http://nnu.geodata.cn)]([http://nnu.geodata.cn](http://nnu.geodata.cn)), accessed on 11 June 2022) \\ \cline{2-4}  & 100 m & Soil property data & 2010–2011 Soil Series Survey \\ \cline{2-4}  & 90 m & DEM, slope, aspect & Geospatial Data Cloud ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 27 June 2022) \\ \hline \multirow{4}{*}{Socioeconomic Data} & \multirow{2}{*}{1 km} & GDP & Resource and Environment Sciences and Data Center \\ \cline{3-4}  & & POP & ([[https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)]([https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)), accessed on 11 June 2022) \\ \cline{2-4}  & & Railway\_d, & \\ \cline{2-4}  & & Freeway\_d, Road\_d, & Open Street Map ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 18 July 2022) \\ \hline \multirow{2}{*}{Statistics Data} & \multirow{2}{*}{Huaibei City Statistical Yearbook} & (http://\(\sharp\).huaibei.gov.cn/ztzl/\(\sharp\)nj/index.html, accessed on 6 September 2022) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
Figure 2: Driving factors.  
LULC change process can be viewed as a Markov process [18]. Therefore, variations in the area of LU types can be predicted using the following equation:  
\[\mathrm{S_{(t+1)}}=\mathrm{P_{ij}}\times\mathrm{S_{(t)}} \tag{1}\]  
where \(\mathrm{S_{(t)}}\) and \(\mathrm{S_{(t+1)}}\) represent the states of the LU system at moments t, t + 1, respectively; \(\mathrm{P_{ij}}\) is the state transfer matrix.
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 4 Proposed Method
### Feature Extraction
#### 4.2.7 Horizontal Profile Projection (HPPP)  
In some AC styles, all words are written on a baseline, whereas in some other styles, words are not subjected to a baseline. Horizontal profile projection is a descriptor that specifies how words are vertically spread within a text image. First, a sub-image that exactly fits the text is cropped from the text image, and then, all pixels are projected to a vertical histogram, using the following Equation (1), to find how pixels are vertically spread.  
\[HPPP=\sum_{N}I(M,N)/\max_{M}(\sum_{N}I(M,N)) \tag{1}\]  
In an image in which words are written on a baseline, HPP will have only one bin with a high value, whilst, in an image in which words are vertically spread, HPP will have more than one bin with high value.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking  
The automatic POI recognition and tracking pipeline forms the core of our methodology. As illustrated in Figure 2, the pipeline consists of several distinct components, each fulfilling a specific role within the overall process. The following sections will provide a detailed discussion of the pipeline and its components.
# Binary Ghost Imaging Based on the Fuzzy Integral Method
## 4 Discussion
With the increase in the hyper-parameter \(w\), the fine structure of the binarization speckle gradually disappears and the resolution of the corresponding reconstruction results decreases. The noise in the reconstruction results is also reduced. Especially when the value of \(w\) is large, the visual effect of the reconstruction result with the proposed method is similar to those of other conventional global binarization  
Figure 8: The curves of SSIM changing with the hyper-parameter \(\alpha\). (**a**) The SSIMs of image Chinese character and panda with the proposed method changing with the hyper-parameter \(\alpha\). (**b**) The SSIM curve of Chinese character with the proposed method when \(\alpha\) changing from 0.005 to 0.055. (**c**) The SSIM curve of image panda with the proposed method when \(\alpha\) changing from 0.005 to 0.055.  
methods, such as MBGI and OBGI, which indicates that the local adaptive binarization is similar to the global binarization when the local window is too large. Furthermore, similar to the above analysis of hyper-parameter \(\alpha\), the SSIMs of the image of the Chinese character and image of the panda are calculated when the hyper-parameter \(w\) selects different values. The calculated results are shown in Figure 10. It can be seen that there is also an optimal value existing in the curve of SSIM. When the hyper-parameter \(w\) selects 10 or 13, respectively, the best reconstruction results of the image of the Chinese character and panda can be obtained. This also means that the contradiction between the noise and resolution of the reconstruction results can be balanced to a great extent when the hyper-parameter \(w\) is set to this optimal value. According to the analysis about hyper-parameters \(\alpha\) and \(w\), it can be concluded that the hyper-parameters \(\alpha\) should be selected as a relatively small value when the prior information of the target is known. The arbitrary value of hyper-parameter \(\alpha\) in the interval [0, 0.03] has little influence on the reconstruction quality. The value selection of hyper-parameter \(w\), which represents the size of local binarization window, should be evaluated according to the requirements of imaging. If the imaging result needs to have a high signal-to-noise ratio, the value of this parameter should be increased as much as possible. If the imaging result needs to have a high resolution and visibility, the value of this parameter needs to be relatively small. Figure 10: The curves of SSIM changing with the hyper-parameter \(w\). Figure 9: The reconstructed results of image panda with the hyper-parameter \(w\).