esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Thermal and Heat Insulation Properties of Aerogel Heat-Insulating Reflective Coatings
## 5 Conclusions  
Simulating two working conditions for thermal testing of plasterboard, aerogel board, and XPS board, the temperature distribution curves of the specimens under the two working conditions were obtained. The following conclusions can be drawn by analyzing the temperature change curves and characteristic data and combining them with the calculated values of radiative cooling thermal power and barrier thermal power.  
Under Condition 1 and Condition 2 conditions, the aerogel board can enlarge the air temperature difference between the hot and cold ends by 32.6% and 44% compared with the blank board. From the data curve of the thermal insulation test, the thermal insulation ability of a 20 mm thick XPS board is more robust than that of a 1 mm thick aerogel coating. Still, the high reflective property of aerogel coating in Condition 2 makes the surface temperature of the hot end of the aerogel board significantly lower than that of the remaining two samples. The heat transfer efficiency calculations show that the radiative cooling power of the aerogel coating is 145.9 W/m2, and the radiative insulation power of the white coating is 48.6 W/m2, which increases the cooling efficiency by two times. When the temperature difference between the two sides of the sample is 10 \({}^{\circ}\)C, 1 mm aerogel coating can reduce the heat transfer of plasterboard by 1.42 W/m2 (>10%), and the heat transfer of 20 mm XPS by 9.33 W/m2, but their thicknesses differ by 20 times. This is of great significance to the energy-saving design of building facades.  
Conceptualization, writing--review and editing, Y.-L.L.; writing--original draft preparation, and methodology, Y.-L.L. and D.-G.Y.; validation, D.-H.W. and D.-G.Y.; project administration, Y.-L.L., D.-H.W. and X.L.; supervision, Y.-L.L.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University Natural Science Research Project of Anhui Province (CN), grant number KJ2020 ZD43, KJ2021A0607, 2022 AH050261; Anhui Housing Urban-Rural Development Scientific and Technological projects (2022-RK057); the Anhui Jianzhu University Doctoral Initiation Fund (2020 QDZ17, 2022 QDZ23).  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 5 Numerical Simulation Experiment
### Object Detectable Analysis
#### 5.2.3 Observation Frequency  
Figure 12 shows the relationship between the number of observable space objects in the different constellations and the detectable frequency of these objects. As can be seen from the figure, for the constellations with a small number of satellites, the detectable frequency for most space objects was approx. 30-50 times. For large-scale constellations with more satellites, the detectable frequency of space objects increased considerably by approx. 70-80 times. Therefore, the larger the constellation size, the more observation opportunities, and the easier it is to observe the key targets.
# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 3 Method Details
### Notation Assembly  
The purpose of notation assembly is to calculate and combine the results of the stave detection model and musical symbol detection model according to the semantic rules of music sheets, and finally obtain the pitch and type of notes. Specifically, notation assembly includes three tasks:  
1. _Determining the note pitch._ Note pitch is decided by the relative position of the note heads and the positions of accidental, clef, and staff lines.
2. _Determining the note type._ Note type is judged by the classification of note head duration and note duration.
3. _Combining output note sequences._ Identify the fused output sequence of notes by note pitch and note type.  
The logic of notation assembly is shown in Figure 8. The (a) and (b), respectively, describe the five basic elements that serve the notation assembly: stave position, note head position, note head type, note head duration, and note duration. To determine the pitch of notes (as shown in Figure 8c), we interpolate the notes' relative position on the staff based on the absolute position of the staves and notes. Then, we obtain the note pitch according to the types of note heads, accidentals, and clefs. To determine the note type (as shown in Figure 8d), we compare and judge the classification results of note head duration and note duration. Specifically, we use three types as the duration of the note head: \"Black\" corresponds to a quarter (or shorter) note, \"Half\" corresponds to a half note, and \"Whole\" corresponds to a whole note. The types of note duration are divided into two categories: dotted notes and non-dotted notes, which can be used to judge the existence of dotted notes. The strategy for determining the type of note is as follows: when the note duration matches the note head duration, the note type is the same as the output of the network; when they do not match, we consider the final note type as a combination of the note head duration and the dotted note.  
Figure 7: Overview of note type classification. (**a**) Note type classification model. (**b**) RepVGGBlock for training. (**c**) RepVGGBlock for inference.
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 5 Discussion
Using 120 and 430 TECUs as benchmarks, relative ionospheric corrections of approximately 35 and 125 m can be expected for group delay altimetry during low and high solar activity. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline  & \multicolumn{4}{c}{**Very Low: 5\({}^{\circ}\)–10\({}^{\circ}\)**} & \multicolumn{4}{c}{**Low: 10\({}^{\circ}\)–20\({}^{\circ}\)**} & \multicolumn{4}{c}{**Mid-Low: 20\({}^{\circ}\)–30\({}^{\circ}\)**} \\ \cline{3-10}  & & \(\Delta_{
# Segmentation and Classification of Zn-Al-Mg-Sn SEM BSE Microstructure
## 3 Results and Analysis
### Procedure B from Figure 7 (the Technical Filter)  
The technical filter could be applied to the image from Table 8 (1% Sn, Zoom4000). Figure 10 shows a part of the BG/LD(Shapes) histograms before and after the application of the technical filter (the bin indices between 650 and 900 from the total number 1000). The stimulus for the application of technical filter is the rel. high valley between two peaks marked as the point A. The technical filter transformed the data of the input image (red line) into a new image (blue line) whose histogram's peaks are now more detached from each other, and the position of the appropriate valley (point B) is lower. The main aim of this transformation is to deepen the histogram. The courses of the histograms are intentionally unfiltered in this case.  
The result of the technical filter application meant the inclusion of about 38,000 pixels in other classification classes. Besides minor unwanted effects on the contours of some  
Figure 10: Histograms of BG/LD(Shapes) with and without the technical filter application.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sn** & \multicolumn{2}{c}{**Zoom4000**} & \multicolumn{2}{c}{**Zoom2000**} & \multicolumn{2}{c}{**Zoom1000**} \\ \hline \multirow{3}{*}{**[\%]**} & **PQ** & **Accuracy** & **PQ** & **Accuracy** & **PQ** & **Accuracy** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline \multirow{3}{*}{**0**} & BG: 67.57 & \multirow{3}{*}{99} & BG: 65.43 & \multirow{3}{*}{98} & BG: 66.89 & \multirow{3}{*}{98} \\  & BW: 4.67 & & BW: 4.38 & & BW: 4.05 & & \\  & L: 27.76 & & L: 30.19 & & L: 29.06 & \\ \hline \multirow{3}{*}{**1**} & BG: 71.50 & \multirow{3}{*}{92} & BG: 69.21 & \multirow{3}{*}{85} & BG: 73.54 & \multirow{3}{*}{91} \\  & BW: 6.05 & & BW: 3.59 & & BW: 3.32 & \\  & L: 14.44 & & L: 16.82 & & L: 15.95 & \\  & D: 8.01 & & D: 10.38 & & D: 7.19 & \\ \hline \multirow{3}{*}{**2**} & BG: 76.76 & \multirow{3}{*}{95} & BG: 71.00 & \multirow{3}{*}{95} & BG: 70.94 & \multirow{3}{*}{82} \\  & BW: 5.40 & & BW: 3.27 & & BW: 3.25 & \\  & L: 9.59 & & L: 17.51 & & L: 13.59 & \\  & D: 8.25 & & D: 8.22 & & D: 12.22 & \\ \hline \multirow{3}{*}{**3**} & BG: 73.66 & \multirow{3}{*}{95} & BG: 80.03 & \multirow{3}{*}{93} & BG: 79.63 & \multirow{3}{*}{95} \\  & BW: 3.87 & & BW: 4.73 & & BW: 5.50 & \\  & L: 9.99 & & L: 4.63 & & L: 4.30 & \\  & D: 12.48 & & D: 10.61 & & D: 10.57 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Typical results of the classification and phase quantification.  
shapes, there was a positive removal of errors, especially between the classes BG and LD(Shapes) and between L and D as well. The classification accuracy thus increased from the original value of 92% to 94%.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Different Methods for Anomaly Score Calculation  
In classification-based ASD systems, pseudo-labels are typically generated from machine attributes, and anomaly scores are computed using classification confidence. However, these methods have significant limitations. Pseudo-labels often only capture the superficial characteristics of normal data, while classification confidence is not well suited for detecting anomalies that are sparsely distributed or significantly deviate from normal patterns. As a result, these methods may fail to achieve the desired accuracy and robustness in anomaly detection.  
To address these limitations, this study proposes calculating anomaly scores using multiple methods, including cosine similarity, K-Means, GMMs, and LOF. Each method provides a unique approach for anomaly evaluation: cosine similarity measures the difference in similarity between samples, K-Means identifies outliers by clustering data, GMMs detect anomalies based on probabilistic distribution characteristics, and LOF identifies anomalies through local density analysis. By employing these methods, this study offers a more comprehensive and nuanced framework for anomaly detection, compensating for the limitations of traditional classification-based approaches.
## 4 Influence of Axial Joint Transfixion Degree on Energy Dissipation
### Analysis of Energy Composition  
The incident energy \(E_{\rm I}\), reflective energy \(E_{\rm R}\), transmitted energy \(E_{\rm T}\), and dissipative energy \(E_{\rm D}\) of different joint transfixion degree specimens obtained by the SHPB test are shown in Table 1. The relationship curve of the reflected energy, transmission energy, and dissipation energy ratio of the specimen with the joint transfixion degree is shown in Figure 6. According to the analysis in Table 1 and Figure 6, it can be seen that when the stress wave propagates in the water-saturated frozen specimen, with the increase of the transfixion degree of the axial joint, the proportion of reflection energy first increases and then decreases. The growth rate between the 0-40% transfixion degree joint specimens is less than the decay rate between the 40-100% transfixion degree joint specimens, and the proportion of reflection energy fluctuates within the range of 10-25%. However, axial joints have little effect on the penetration ability of stress waves. With the increase of joint transfixion degree, the proportion of transmission energy in \(E_{\rm T}/E_{\rm I}\) has no obvious upward or downward trend, and the proportion of transmission energy is only 40.40-46.47%. The proportion of dissipated energy shows a trend of first decreasing and then increasing with the increase of axial joint transfixion degree. Taking the dissipated energy proportion of the 40% transfixion degree specimen as the intermediate point, the decreasing rate is approximately the same as the increasing rate. The reason for no obvious change trend in the proportion of transmission energy is that the specimens are frozen with saturated water before each impact test. The water-containing area inside the specimen is in the state of ice crystals, and the transmission area of the stress wave is always cement mortar and ice crystals. The increase in the proportion of reflection energy is due to the small transfixion degree of the specimen and the enhanced load-carrying capacity after freezing in saturated water, resulting in a gradual increase in the proportion of reflection energy of the 0-40% transfixion joint specimens. The reflection energy of 40% of transfixion joint specimens accounted for the largest proportion. The reason for the gradual decrease in the proportion of reflection energy of 40-100% transfixion joint specimens is the effect of increasing transfixion degree.
# Fluidization Behavior of Soft Soil Induced by Shield Construction
## 5 Discussion  
Soft soil such as mucky, silty clay has the characteristics of shear-thinning. For example, in Shanghai and Suzhou, there will be a shear strength reduction of 2-4 times for intact soft soils after being disturbed [27]. In addition, the yield stress and viscosity of the soil also decrease significantly under the disturbance of the shield. The proposed method is suitable for ideal plastic fluid conditions, and the Bingham constitutive model used cannot describe this characteristic of the soil. Therefore, it is necessary to consider the shear-thinning effect of soft soil to further understand the fluidization behavior of soil during shield construction.  
Based on the proposed theoretical method, a closed four-stage shear path to describe the shear behavior of soft soil during shield tunneling was presented in Figure 11, which fully considers the shear-thinning effect of the soil. Based on previous studies, a large sheared region will occur first in the intact soil when the cutting wheel arrives with a designed revolution \(n_{0}\), as the curve of \"Path I\" shows in Figure 11. As a result, the strength and viscosity of the sheared soil will be greatly reduced due to the shear-thinning effect of the soil. To balance the applied torque, the sheared region will be expanded to compensate for the decrease in the yield stress of the sheared soil. Additionally, the revolutions will also be increased during the stage of \"Path II\", as shown in Figure 11. In practice, it is advisable to maintain a stable cutting wheel revolution during tunneling. To keep the revolution from increasing too much, it is necessary to reduce the torque of the cutting wheel appropriately. As the curve of the \"Path III\" shows in Figure 11, T\({}_{1}\) is the required torque to maintain the designed revolution \(n_{0}\) in the remolded soil, where the corresponding sheared region will also be reduced. With the shield tunneling, the cutter head contacts the intact soil again and returns to the shear path I, as the curve of the \"Path IV\" shows in Figure 11. Therefore, the complete process of the soil behavior during the shield tunneling is constituted by the above four shear paths.  
It can be seen that the sheared region is significantly expanded due to the shear-thinning characteristics of the soil. Moreover, under the combined action of the shield machine excavation and advancing, the parameters of the soil that the shield encounters are constantly changing, which leads to large fluctuations in the tunneling parameters, and makes it more difficult to maintain parameter stability. Therefore, to minimize the disturbance of the cutter head to the soil and maintain the stability of the excavation parameters, it is recommended to use a lower cutter head revolution and advancing speed when the shield tunnels in soft soil.
## 3 Results and Discussion
### Institution Analysis
Moreover, the colors of network connections provide qualitative insights, where purple connections between U.S. and Indian institutions suggest relatively earlier collaborations from 2000 to 2002 between them [39; 40]. Collaborations among European and Chinese institutions commenced later compared to those in the United States. Utilizing the spectral clustering algorithm in CiteSpace, nodes with close associations are grouped into four clusters, each represented by a distinct color (Figure 4c), which aids in the clear identification of node categories. In Figure 4c, the green, red, blue, and purple clusters roughly correspond to the dashed ellipses of the same colors in Figure 4b. After clustering, keywords for each cluster are automatically extracted. The keyword extraction algorithm determines whether the frequency of keywords in a given text corpus significantly exceeds what would be expected by random chance. The Log-Likelihood Ratio (LLR) algorithm was utilized to derive the keywords. The red cluster comprises keywords including \"global warming potential\", \"aviation\", \"arctic haze\", \"France\", and \"free troposphere\". Keywords associated with the blue cluster include \"aerosol optical depth\", \"Indo-Gangetic plain\", \"forest fires\", and \"India\". The purple cluster features keywords including \"Tibetan plateau\", \"aerosols\", \"WRF-Chem\", \"absorption enhancement\", and \"China\". Lastly, the keywords for the green cluster include \"aerosols\", \"aerosol optical depth\", \"snow\", \"nuclear winter\", and \"remote sensing\". In the red cluster, the presence of keywords such as \"arctic haze\" and \"France\" indicates European characteristics [41; 42]. Similarly, keywords including \"Indo-Gangetic plain\" and \"India\" in the blue cluster denote features specific to India [43; 44]. Meanwhile, keywords like \"Tibetan plateau\" and \"China\" in the purple cluster highlight characteristics unique to China [45; 46]. And, the green cluster contains terms such as \"aerosol optical depth\" and \"remote sensing\", indicative of satellite remote sensing features, likely associated with contributions from NASA and NOAA in the United States [47; 48]. These clustering results are consistent with the conclusions drawn from our analysis of Figure 4b. Figure 4: (**a**) The yearly count of publications for the top ten institutions, (**b**) the institutional cooperation network, with the color of the links between nodes representing the time when two institutions started collaborating, and (**c**) the grouped clusters of institutions. Table S5 contains the full names corresponding to the institutional abbreviations.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring
### Signal Pre-Processing
#### 5.1.2 Noise Filtering  
Band-pass filtering is commonly employed for baseline distortion removal. However, severe morphological distortions from the low perfusion and baseline drift in PPG signals are difficult to remove with frequency filtering. Additional filtering or feature extraction methods, such as moving average filter and wavelet decomposition, could be useful to regulate the signal. On the other hand, the filtering procedure should be minimized, since this process alters the positions of peaks and PPG and EBI waveform analysis for HR estimation is mostly based on exact peak position [36]. A sample routine of filtering noise from an EBI signal conducted in [16] is shown in Figure 10. The order of filters producing a  
Figure 9: Workflow of heart rate estimation form the measured biological signals.  
smoothed signal Sm was as follows: high-pass cut-off = 0.5 Hz \(\rightarrow\) low-pass cut-off = 50 Hz \(\rightarrow\) notch 50 Hz \(\rightarrow\) moving average window = 60 ms.  
In [68], a two-stage noise filtering process was employed. In the first stage, a band-pass filter composed of infinite impulse response (IIR) with a frequency band of 0.1 Hz-1.5 Hz was chosen owing to the fact that the fundamental frequency of PPG signal is about 1 Hz. In the second stage, a 50 Hz notch filter was used. In [69], the raw PPG signal and the acceleration signal in a given time window were filtered with a band-pass filter from 0.4 Hz to 5 Hz using a second-order Butterworth filter. Source [70] reports utilizing a fourth-order Butterworth filter with cut-off frequencies 0.1 Hz to 1.2 Hz for filtering the EBI.
## 3 Data and Method
### RS Images Processes
#### 3.1.1 Image Pre-Processing  
To reduce the effects of atmospheric scattering and absorption, atmospheric correction was conducted using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm. The FLAASH is a kind of physical method based on a radiative transfer model considering different atmospheric optical properties during the image acquisition and is observed to be more precise than the dark-object subtraction (DOS) method, which is an image-based technique, especially in grass sites [30; 31].  
After atmospheric correction, a false color composite was performed: TM images--band 4, 3 and 2; MSS images (Landsat 1-3)--band 7, 5 and 4; MSS images (Landsat 4, 5)--band 4, 2 and 1, in order to depict different LULC classes more accurately, especially vegetation types.  
Geometric correction of the images was carried out using a master-slave approach [6]. The 2004 images were previously geo-referenced using topographic maps (1:100,000 scale); thereafter, with the aid of the 2004 images as references, satellite data of other three periods mentioned above were rectified to a common geometric system. A minimum of 9 evenly distributed points was used in the correction process for every image. In addition, the patches that kept stable should be overlaid accurately. Images were geo-referenced using the quadratic polynomial and finite element method and were resampled using the nearest neighbor method. The root mean squared error (RMSE) of geometric correction was less than 1.5 pixels (45 m for TM and HJ-1 images and 120 m for MSS images).  
The histogram equalization method was conducted to perform image enhancement, which was meant to improve the visual interpretability of the RS images by increasing the apparent distinction between features [14].