esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Classification Comparison of Volatile Compounds in Rose Beverage
Notably, SFR and LSFR samples had relatively high levels of decanoic acid and octanoic acid, while LFR and LSFR samples had higher levels of nonanoic acid and citronellonic acid. This suggests that L. plantarum may enhance long-chain fatty acid formation, while _S. cerevisiae_ may promote short-chain fatty acid formation in rose beverages. [PERSON] et al. [74] and [PERSON] et al. [21] also reported that yeast and LAB fermentation increased the content of fatty acids, respectively. Phenylpropanoids and benzoyl compounds originate from the aromatic amino acid phenylalanine and were significant contributors to the aroma of plant-based foods [75]. The most dominant volatiles of the phenolics in rose beverage were eugenol (4-allyl-2-methoxyphenol) (strong aroma of cloves), methyleugenol (4-allyl-1,2-dimethoxybenzene), and 2,4-di-tert-butylphenol (2,4-bis(1,1-dimethylethyl)phenol), which were important aroma compounds in rose plants (Figure 4) [76]. After fermentation, its content decreased by 69.1-93.6% in _S. cerevisiae_ monoculture rose beverage, while it could maintain a high relative content in the _L. plantarum_ associated samples, especially for eugenol and 2,4-ditert-butylphenol. The content of them under LAB treatment increased by 6.16- and 3.74-fold, and 2.69- and 1.69-fold in LFR and LSFR, respectively, compared with unfermeted rose beverage. Previous studies showed that many LAB produce \(\beta\)-glucosidase, and the increase in eugenol content in LFR and LSFR may be closely related to \(\beta\)-glucosidase hydrolysis [77]. Generally, environmental variables, such as temperature, dissolved oxygen, and pH, are crucial in controlling microbial metabolic pathways. However, the metabolic products of microorganisms play a decisive role in the formation of flavor [78; 79]. Therefore, in the process of exploring the flavor formation mechanism of fermented rose beverages, the influence of environmental variables should also be considered. In future research, we will further explore the influence of environmental variables on the flavor of fermented rose beverages, thereby comprehensively analyzing the rules by which different strains regulate the formation of characteristic flavors.
## 4 Results
### Accuracy Assessment of The InSAR Measurements  
In order to assess the uncertainty of InSAR measurements, independent InSAR observations from the Sentinel-1 satellite images covering the same area and during the same time interval are utilized. As the overlapped region is stable, the horizontal displacements are not considered in this step. Four InSAR deformation rates in LOS direction from 2016 to 2019 are all projected in the vertical direction with respect to their corresponding incidence angles at each pixel. Please note that an identical DEM is applied for the geocoding of every SAR measurement, which guarantees that the cross validation is taken under the same ground coordinate system.  
Histograms of the differences in deformation rate between different measurements are shown in Figure 3, where Figure 3a is the difference between descending Sentinel-1 Path 3 Frame 470 and ascending Sentinel Path 98 Frame 118, and Figure 3b is the difference between descending Sentinel-1 Path 3 Frame 470 and Path 76 Frame 467. The standard deviations for two differences are about 0.9 and 1.3 cm/a, respectively.
# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 4 Experiments  
### Experimental Datasets  
To validate the performance of CoNet-MS, this paper conducted experiments on multiple datasets, including the CIFAR10 and ISIC2019 datasets.  
For CIFAR10, the dataset contains 60,000 images across 10 balanced categories (6000 per class). We followed a standard split with 50,000 images for training, 5000 for testing and 5000 as metadata.  
For the medical image analysis, we adopted the imbalanced ISIC2019 dataset for skin lesion classification. The training set consists of 6120 images with the following class distribution: Melanoma (MEL, 1124), Melanocytic nevus (NV, 3191), Basal cell carcinoma (BCC, 798), Benign keratosis (BKL, 606), Squamous cell carcinoma (SCC, 129), Actinic keratosis (AK, 192), Vascular lesion (VASC, 36) and Dermatofibroma (DF, 44). This reflects a significant class imbalance, where the majority class (NV) has over 70\(\times\) more samples than the smallest classes (DF and VASC). Additionally, we used 640 images for validation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Computational Complexity** \\ \hline Cross dual-branch classifier network architecture & \(O(Tnlogn)\) \\ \hline Meta-guided module & \(O(T(n+m))\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity of each module in the CoNet-MS framework.  
and 4291 images for testing. The imbalance in training data extremely challenges model generalizability, which aligns with our focus on robust performance under real-world clinical scenarios.  
In our experiments, the training data labels are corrupted by two kinds of noise types: uniform noise and pair-flipping noise. For uniform noise, labels are flipped uniformly into all the other classes with a given error rate. For pair-flipping noise, labels are flipped into similar classes with a given error rate. Specifically, the training datasets are manually corrupted by the noise transition matrix \(Q\), where \(Q_{ij}=P\left(\widetilde{y}=j\left|\;y=i\right.\right)\), given that noisy \(\widetilde{y}\) is flipped from clean \(y\). The precise definitions of uniform noise and pair-flipping noise transfer matrices are given as Equations (14) and (15).  
\[\text{Uniform noise}:Q=\begin{bmatrix}1-\epsilon&\frac{\epsilon}{n-1}&\dots& \frac{\epsilon}{n-1}&\frac{\epsilon}{n-1}\\ \frac{\epsilon}{n-1}&1-\epsilon&\frac{\epsilon}{n-1}&\dots&\frac{\epsilon}{n-1 }\\ \vdots&&\ddots&&\vdots\\ \frac{\epsilon}{n-1}&\dots&\frac{\epsilon}{n-1}&1-\epsilon&\frac{\epsilon}{n- 1}\\ \frac{\epsilon}{n-1}&\frac{\epsilon}{n-1}&\dots&\frac{\epsilon}{n-1}&1- \epsilon\end{bmatrix} \tag{14}\]  
\[\text{Pair flipping}:Q=\begin{bmatrix}1-\epsilon&\epsilon&0&\dots&0\\ 0&1-\epsilon&\epsilon&&0\\ \vdots&&\ddots&\ddots&\vdots\\ 0&&&1-\epsilon&\epsilon\\ \epsilon&0&\dots&0&1-\epsilon\end{bmatrix} \tag{15}\]  
where \(n\) is the number of the total classes, and \(\epsilon\) is the noise rate. Note that the above uniform noise and pair-flipping noise transfer matrices are totally the same as those in co-teaching [9].
## 4 Design of an MRE-Based Adaptive Seismic Isolator
### Magnetic Distribution under Compressive and Shear Loading  
Magnetic field is sensitive to mechanical change or the motion of the device and it varies accordingly when external loading is applied. The location and the shape of MRE (or MRE with MWCNTs) layers may displace and change under compressive and shear loading. Under different shear (0 mm, 3 mm, 6 mm, 9 mm) and compressive (0 mm, 2 mm, 4 mm, 6 mm) displacement loadings, a comparative investigation of magnetic flux density is conducted for both MRE and MRE with MWCNTs. When the MRE (or MRE with MWCNTs) is sheared, Payne effect may occur as the strain achieves a threshold. When it is subjected to compressive loading, MRE (or MRE with MWCNTs) layers will approach the bottom plate where larger magnetic flux density occurs, and decreased distance between the magnetic dipoles increases the magnetic energy. In order to explain the relationship between strain and magnetic flux, a flux diagram of 5-layer MRE (or MRE with MWCNTs) are shown in Figure 15.  
For both MRE (or MRE with MWCNTs) layers, the magnetic flux density has the largest value when the compressive displacement is 6 mm, and the smallest value when the shear displacement achieves 9 mm. This phenomenon agrees well with the aforementioned Model 5 of MRE.  
Specifically, when the compressive displacement is 2 mm, with the shear displacement increasing from 0 mm to 9 mm, the magnetic flux density of layer 5 of MRE with MWCNTs decreases from 0.321 T to 0.262 T. This clearly demonstrates when the compressive effect is larger, the magnetic flux density increases. Both compressive and shear effects change  
Figure 15: Magnetic Flux Density under Shear and Compressive Loading.  
the magnetic-induced structural state. The magnetic field decreases along with an increase in shear displacement and increases along with an increase in compressive displacement. Magnetic flux circuit is dependent on structural deformation and motion. It also shows that the larger the layer number of MRE (or with MWCNTs) or laminated steel sheet, the more magnetic energy loss happens due to the gap between the top plate and the yoke.
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data  
### Emissivity  
#### 2.1.1 The Combined ASTER MODIS Emissivity Over Land  
The CAMEL product is available from 2000 to 2016 at a monthly mean, 0.05\({}^{\circ}\) (\(\sim\)5 km) resolution for 13 hinge points within the 3.6-14.3 um region. It is created using both the ASTER GEDv4 and MODIS UW BF emissivity datasets as input. A standalone CAMEL High Spectral Resolution (HSR) Algorithm included with the dataset is also available, which extends the 13 hinge point CAMEL product to 417 infrared spectral channels using a principal component regression approach. Additional details of the CAMEL dataset are available in Part 1 of this two-part paper series ([PERSON] et al. 2018). The CAMEL dataset, including the uncertainty product discussed in this paper, is available from the NASA online archive at [[https://lpdaac.usgs.gov/about/news_archive/release_nasa_measures_camel_5_km_products](https://lpdaac.usgs.gov/about/news_archive/release_nasa_measures_camel_5_km_products)]([https://lpdaac.usgs.gov/about/news_archive/release_nasa_measures_camel_5_km_products](https://lpdaac.usgs.gov/about/news_archive/release_nasa_measures_camel_5_km_products)) ([PERSON] et al., 2018).
# Multizone Leak Detection Method for Metal Hose Based on YOLOv5 and OMD-ViBe Algorithm
## 2 Leak Detection Methods
### Foreground Frequency Calculation Method  
The binary image generated by OMD-ViBe is utilized to compute the frequency of foreground points within each subregion. This involves summing up the frequency of changes in foreground points for each pixel coordinate within the subregion. The average frequency of changes \(A_{i}\) is computed based on the number of images, and the subregions exhibiting excessive changes are filtered out using a threshold. The percentage of subregions exhibiting excessive changes is then computed to determine the extent of leakage. The formulae are as follows:  
\[X_{i}^{t}=\sum_{t\in[1,m]}I(f_{i,(x,y)}^{t}=foreground), \tag{18}\]  
\[A_{i}=\frac{X_{i}^{t}(x,y)-(X_{i}^{t})_{min}}{(X_{i}^{t})_{max}-(X_{i}^{t})_{ min}},\ X_{i}^{t}(x,y)\in X_{i}^{t}, \tag{19}\]  
\[F_{i}=I(A_{i}>\varepsilon)\times\sum_{t\in(n,m]}I(f_{i,(x,y)}^{t}==255), \tag{20}\]  
\[P_{i}=\frac{F_{i}}{\sum F_{i}}\in[0,\ 1]. \tag{21}\]  
where \(m\) is the final frame number of the video, \(X_{i}^{t}\) is the sum of the pixel change frequency of the subregion, \(A_{i}\) is the average pixel change frequency of the subregion after the maximum normalization, \(F_{i}\) is the frequency of the corresponding region, \(P_{i}\) is the percentage of the leakage subregion, \(I(*)\) is the conditional function when the conditions are met, and \(I(*)=1\) or \(I(*)=0\).
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Object Detection Results  
In order to provide the evidence benefits on harsh road scenes, an experiment is done to prove whether our method affects the results of object detection. YOLOv3 [5], an object detection method, is used for detection which is trained with COCO dataset for the experiments.  
Figure 12a shows object detection results on degraded input images, where (b) shows object detection results after applying DerainNet on input images, (c) shows object detection results after applying MSCNN on input images, and (d) shows object detection results after applying FHRR-Net. The first row of Figure 12a shows that object detection fails in the case of degraded images of distant vehicles. In contrast, the first row of Figure 12d shows that even distant vehicles can be detected as objects. Figure 12b,c show the object detection results of DerainNet and MSCNN: object detection results are better than in Figure 12b,c, the object detection result is better than Figure 12a, but the overall accuracy is lower than in Figure 12c.  
Table 3 shows detection accuracy results after using state-of-the-art image restoration methods. We use 20 real-world test images to measure and compare average mAP and confidence for each object in the images. It is shown that our algorithm positively affects object detection in degraded images via Table 3 and Figure 12.  
Figure 11: Comparison results of the combined SOTA methods with FHRR-Net. (**a**) Ground truth. (**b**) Our results. (**c**) Results of applying the (DerainNet \(+\) MSCNN) method. (**d**) Results of applying the (MSCNN \(+\) DerainNet) method.  
Figure 12: YOLOv3 object detection results on real-world road restored images. (**a**) Input images. (**b**) Object detection results after applying DerainNet. (**c**) Object detection results after applying MSCNN. (**d**) Object detection results after applying FHRR-Net.
## 4 Structure Optimization
### New Structure
#### 4.1.2 Numerical Simulation Verification  
The new structure is analyzed using the same thermal-structural coupling method in Section 3. The temperature distribution of the tool is shown in Figure 11. Compared with the original structure, the cutting edge's temperature does not exceed the heat-resistant  
Figure 10: Schematic diagram of the new energy-absorbing tube cross-section.  
Figure 9: A schematic of mixed fracture modes involved during the cutting process [21].  
temperature of HSS during the cutting process and has a more uniform distribution. Only the temperature in the middle of the cutting edge is slightly higher than in other areas. It will reduce the possibility of tool failure caused by excessive and uneven temperatures.  
The tool's maximum temperature versus displacement curve is shown in Figure 12. The tool's maximum temperature reduces to 670 K, which is 65.10% lower than the original structure (1920 K). Moreover, the temperature in the original design increases sharply in the initial phase (0 to 30 mm). In contrast, the temperature rises gently in the new design all the time, which helps reduce tool's thermal failure due to thermal shock.  
The cutting force versus displacement curve is shown in Figure 12. The fluctuation of cutting force is significantly weaker than the original from 0 to 75 mm. The steady-state force slightly decreased by 1.85% from 55 kN to 54 kN, and energy absorption decreased by 0.94% from 8.49 kJ to 8.41 kJ. This means that eliminating the tearing fracture mode does not significantly affect the cutting force and energy absorption but enables a more stable cutting process.  
The above numerical simulation results indicate that the new structure addressed the problem of the excessive temperature of the tool's tip at the expense of a small amount of performance. What is more, these improvements help reduce the possibility of tool failure caused by thermal wear and moderate relief of mechanical wear simultaneously.  
Figure 11: Tool temperature distribution of the new structure during the cutting process.  
Figure 12: Curves of force and temperature versus displacement of the new structure.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 3 Multiple Target Tracking
### State Estimate and Covariance Update  
The state estimate and the covariance matrix of targets are updated as follows:  
\[\hat{\mathbf{x}}_{t}(k|k)=\hat{\mathbf{x}}_{t}(k|k-1)+W_{t}(k)\
u_{th_{k}}(k), \tag{18}\]  
\[P_{t}(k\big{|}k)=P_{t}(k\big{|}k-1)-W_{t}(k)S_{t}(k)W_{t}(k)^{T}. \tag{19}\]  
If no measurement can be associated with target \(t\) at frame \(k\), they merely become the predictions of the state and the covariance as  
\[\hat{\mathbf{x}}_{t}(k|k)=\hat{\mathbf{x}}_{t}(k|k-1), \tag{20}\]  
\[P_{t}(k|k)=P_{t}(k|k-1). \tag{21}\]
## 7 Results
### Architecture
#### 7.7.2 Network Architecture  
If the number of peers per organization increases and the number of endorsers is kept constant, the maximum throughput increases. The same occurs if the number of organizations increases and the other two variables remain constant. For this setup, the optimum throughput was two peers per organization with one endorser. Therefore, employing the right peers could enhance throughput by up to 31% and 21% in public and private transactions, respectively.
## 4 Results and Analysis  
### Dataset  
#### 4.1.1 Tangerine Peel Moisture Loss Dataset  
A total of 480 groups of original moisture loss data were acquired from the tangerine peel drying tests described in Section 2.3.3, with each group accompanied by corresponding images. All experiments were conducted under strictly controlled environmental conditions to ensure the repeatability and reliability of the data. During testing, moisture variations for each sample group were measured using a high-precision electronic analytical balance (Leqi CZ3003, accuracy 0.001 g) and synchronized with image data to facilitate subsequent image feature extraction and deep learning analysis.  
To ensure data standardization, preprocessing steps including outlier detection, data smoothing, and normalization were applied. Each record included test time (min), tangerine peel mass (g), dry basis moisture content (g/g), moisture ratio (MR), and test environmental parameters (temperature, wind speed, humidity, and light). This comprehensive dataset served as the basis for subsequent deep learning-based model optimization for real-time moisture content prediction during tangerine peel drying.
## 2 Cluster Head Selection
### Assisted Schemes
#### 2.2.1 BS Assisted Schemes  
The inexhaustible resources of energy and high processing capabilities of BS are considered as a powerful and reliable source for sensor nodes to which they can shift the burden of CH selection and cluster formation phases. This also improves the capabilities of end-user to control the placement and number of CHs through the BS in accordance with the characteristics of the network and type of applications. However, these entail the periodic update of the BS with necessary information by sensor nodes.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 2 Traditional Methods before Deep Learning  
Before the application of deep learning techniques in image processing, traditional machine learning methods were widely used in various image semantic segmentation tasks. These methods typically relied on manually designed feature extractors and classifiers such as support vector machines (SVMs), decision trees, K-means, and conditional random fields (CRFs). These algorithms usually depend on manual feature extraction methods such as SIFT and HOG to achieve image segmentation through feature matching or classification. These traditional methods still have advantages in certain cases such as when processing small-sample data or images with minimal noise. However, with the development of deep learning techniques, deep learning-based image semantic segmentation methods have gradually become mainstream. Deep learning methods automatically learn feature representations by utilizing multilayer convolutional neural networks (CNNs), thus avoiding the tedious process of manually designing feature extractors. Overall, traditional machine learning methods have laid the foundation for the development of deep learning methods in image semantic segmentation tasks.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 3 Risk Analysis of Constellation Collision
### Phase 1: Climbing from 300 km to 550 km  
After the release of each batch of Starlink satellites, the climbing process and orbital height changes are shown in Figure 1. The first batch lasts about 41 days, the second batch lasts about 65 days, and the third batch lasts about 115 days. The average climbing days of 73 days are selected as the time parameter. During the climbing phase, the evolution of the flux and orbital height of Starlink satellites encountering \(\geq\)6 cm objects over time is shown in Figure 2. At this stage, the overall objects flux fluctuates between \(2.52\times 10^{-6}/\mathrm{m}^{2}/\mathrm{a}\) to \(2.59\times 10^{-6}/\mathrm{m}^{2}/\mathrm{a}\), and the highest flux is about \(2.59\times 10^{-6}/\mathrm{m}^{2}/\mathrm{a}\) during the adjustment waiting for orbit ascent.  
The average number of collisions and the relationship between the collision probability and the debris diameter of a single satellite and all satellites in the orbital layer during the climbing phase are shown in Table 2. The collision probability of debris larger than 6 cm  
Figure 1: Starlink satellite orbit altitude.  
Figure 2: Flux and satellites altitude over time during Phase 1 with objects \(\geq\)6 cm.  
to a single satellite is less than 0.01%, and the collision probability of 1628 satellites in the orbital layer is 1.89%.
# Perceptual Characteristics of Voice Identification in Noisy Environments
## 3 Perceptual Characteristics for Different Frequency Distributions
### Experiment 2: Impact of Frequency Distribution  
The experimental process of experiment 2 is the same as that in Figure 1. The frequency distribution and SNR, procedure, and participants are described below.  
(a) Frequency Distribution and SNR  
To determine the influence of the frequency distribution on voice identity perception, the eight-order Butterworth bandstop filter was used to generate four kinds of speech noise by removing different frequency bands. The center and cut-off frequencies are given in Table 2, which were obtained according to the approximate constant Q property of the cochlea [23]. Figure 5 shows the spectrograms of four kinds of speech noise.  
Based on the results of experiment 1, the SNR for this listening experiment was selected as \(-4\) dB and \(-8\) dB in a low SNR (<0 dB) environment. There were eight noise interference environments (four kinds of speech noise \(\times\) two kinds of SNR). Finally, ten pre-recorded sentences from five speakers were mixed with eight noise inte  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Center Frequency/Hz** & **Bandwidth/Hz** \\ \hline
1 & 438 & 300–640 \\ \hline
2 & 936 & 640–1370 \\ \hline
3 & 2004 & 1370–2930 \\ \hline
4 & 4279 & 2930–6250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency distribution of bandstop filters.  
Figure 5: Four spectrograms of speech noise after removing a certain frequency band. (**a**) 300–640 Hz; (**b**) 640–1370 Hz; (**c**) 1370–2930 Hz; (**d**) 2930–6250 Hz.  
voice signals masked by different speech noises with various frequency bands removed were obtained.  
Procedure  
Participants  
Sixteen Mandarin speakers (age: 20.44 \(\pm\) 0.73 years) were recruited. All the participants reported no known hearing problems. During the hearing test, the JND of the loudness at 80 dB SPL is less than 2.5 dB. The JND of 1 kHz frequency is less than 10 Hz. Before the formal experiment, the participants took part in a simple pre-experiment to understand the experimental setup. A total of 1920 (16 participants \(\times\) 120 voice pairs = 1920) stimulus-response events occurred. In order to guarantee the reliability of the experiment, the Cronbach alpha values were calculated for all the data. The result shows that the experiment has good internal reliability (Cronbach's Alpha = 0.843; N = 16).