esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
In Figure 5, the phase angles of the harmonics obtained for the four groups are shown. It can be seen that for the third harmonic, regardless of whether the group of associated lamps consists of all LEDs, CFIs or mixed, the phase angle obtained is between 240\({}^{\circ}\) and 270\({}^{\circ}\). For the fifth harmonic, the angle obtained moves between 120\({}^{\circ}\) and 150\({}^{\circ}\), between 15\({}^{\circ}\) and 60\({}^{\circ}\) for the seventh and between 260\({}^{\circ}\) and 310\({}^{\circ}\) for the ninth. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lamp** & **THDi (\%)** & **P (W)** & **Q (Var)** & **D (VAd)** & **S (VA)** \\ \hline G-L & 104.16 & 38.3 & \(-\)19.5 & 44.9 & 62.1 \\ G-C & 53.32 & 62.1 & \(-\)29.2 & 79.2 & 104.9 \\ G-M-1 & 104.78 & 59.5 & \(-\)30 & 70.2 & 96.8 \\ G-M-2 & 64.97 & 104.2 & \(-\)40.9 & 98.9 & 149.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Values measured with the Fluke parsed in the four groups. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lamp** & **THDi (\%)** & **P (W)** & **Q (Var)** & **D (VAd)** & **S (VA)** \\ \hline G-L & 97.44 & 38.28 & \(-\)18.67 & 41.50 & 59.46 & 0.64 \\ G-C & 104.78 & 63.42 & \(-\)30.93 & 73.93 & 102.20 & 0.62 \\ G-M-1 & 100.91 & 59.33 & \(-\)28.94 & 66.61 & 93.78 & 0.63 \\ G-M-2 & 84.10 & 104.32 & \(-\)42.15 & 94.62 & 147.01 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Calculated values for the four groups. Figure 3: Measurements obtained for the G-M-2 grouping: Harmonic spectrum (**a**), waveform (**b**) and phasor diagram (**c**). [PERSON] et al. state in the conclusions of their work published in 2019 [34], that the distortions that are generated in the network because of the operation of several lamps, have a considerable effect on the electricity bill. Based on the findings of this paper, the statement is the same, but the fundamental difference with [PERSON] is that our conclusions are drawn from a linear voltage system. In addition, they analyzed the loads individually, and in this case it is found that the conclusions can also be applied to load associations, and that there is no compensation for the distortion phenomena that are generated. The best solution to minimize the problem mentioned would be the incorporation of notch or absorption filters in the loads. In this way, excess consumption would be significantly reduced and the introduction of distortions into the electrical network would be avoided. This solution would have to be incorporated by the manufacturer, and since it would entail an extra cost, it is unlikely that this will occur unless there is legislation that regulates it. Another possibility would be to incorporate these  
Figure 4: Phase angles of 3 rd, 5 th, 7 th and 9 th harmonic currents of LED and CFL lamps. Figure 5: Phase angles of 3 rd, 5 th, 7 th, and 9 th harmonic currents of lamp groups. filters not to the lamps individually, but to the residential community. In this case, only the problem of distortions introduced in the electrical network would be improved.
## 4 Results and Discussion
### Information-Enriched Long-Term Vegetation Change
We therefore presume that our methodology is transferable to similar coregions with comparable data availability (although tuning of the endmember model might be necessary), e.g., to Australia, South Africa, or the Mediterranean at large. Due to the consistency of the contemporary and upcoming data from Landsat 8, Landsat 9, as well as Sentinel-2A/B and Sentinel-2C/D with the existing Landsat legacy data records, extending our approach temporally to the current era will be straightforward, too, although the change analysis may need to be extended (or interchanged with other tools, e.g., BFAST) to accommodate for more than one breakpoint. Our approach could also be extended using other novel approaches, e.g., by incorporating object-based segmentation of phenologically similar pixels to increase spatial coherence of spatially similar pixels [92] or by incorporating new insights of statistical trend estimates in large remote sensing data [93]. Entirely different drivers may relate to changes predominantly occurring in the herbaceous or woody cover, or a combination thereof (Figure 9). Thirty-eight percent of the areas with steadily decreasing woody cover (yellow left) do not show substantial changes in the herbaceous cover (gray right); 56% show signs of increasing herbaceous cover (green right). Thus, when only analyzing total vegetation cover as measured via the peak of season metric (Figure 5b), only the first case (yellow left, gray right) was observable. In the second case (yellow left, green right), the long-term decrease in woody cover was compensated by the  
Figure 8: Long-term change of vegetation components over the study period 1984–2006. (**a**) RGB representation of the major vegetation components for 1985; stretched from 0–100%; an animation for the complete time series is available in the supplemental material. (**b**,**c**) Syndrome-based labeling of land-use change classes for woody and herbaceous vegetation trajectories, respectively. White areas are non-natural areas or above 1500 m a.s.l., which were out of scope of this study. increase in the herbaceous component. Consequently, the overall absence of decreasing total vegetation cover is not synonymous with the absence of vegetation dynamics. Similarly, the change-and-trend analysis on decomposed woody and herbaceous cover allows making more informed interpretations of the vegetation dynamics concerning the ecological meaning of related changes. As an example, areas with steadily increasing woody cover (green left) unanimously show no (gray right, 56%) or negative (yellow right, 32%) changes in the herbaceous vegetation. This might be caused by shrub encroachment, which might be considered a degradation process [94]. However, whether the decrease in the herbaceous cover is due to selective grazing or occlusion of the herbaceous layer by a growing canopy cannot be answered with this approach.
## 1 Introduction
[24] measured the electromagnetic radiation signals of sandstone samples with different water contents under uniaxial compression conditions and noted that as the water content increases, both the signal and mechanical strength decrease. Currently, scholars at home and abroad have achieved fruitful research results in the aspects of rock mechanical response characteristics and fracture mechanisms in water-rich environments. However, further in-depth exploration is still needed in terms of rock mechanical response characteristics, damage evolution laws, and fracture mechanisms under different moisture content conditions. At the same time, there are relatively few studies considering the influence of moisture content on the distribution laws of different types of cracks and their fracture modes during the rock deformation and failure process. Based on the above-mentioned literature analysis, it can be seen that currently scholars at home and abroad have achieved fruitful results in the aspects of rock mechanical response characteristics and fracture mechanisms in water-rich environments. However, further research is still required in terms of rock mechanical response characteristics under different moisture content content conditions, and the law and mechanism of the change in rock stress, strain, and elastic modulus with moisture content. In view of this, an in-depth discussion is provided in this study of the infiltration and deformation mechanism of limestone cores in deep-water environments, through the simulation of the mechanical response under different water content conditions, revealing the behavioral characteristics of the rock in the high-pressure water environment. In this study, saturated limestone cores are taken as the research objects. Firstly, through the fine processing of the test specimens, the consistency of test conditions and the accuracy of data are ensured. Secondly, in order to deeply investigate the mechanical properties of rocks in the water-containing state and their relationship with stress and strain, four different working conditions are analyzed, as follows: dry condition, 50% water content, 80% water content, and 100% water content. Under these three working conditions, the water pressure undergoes a specific cycle process, namely \"pressurization-depressurization-pressurization-depressurization-depressurization\", aiming to explore the deformation laws and mechanisms of the cores during the cyclic pressurization and depressurization processes under different water content conditions. Finally, to ensure the accuracy and reliability of the data, the time was held for 60 s during each stress stabilization phase. Through the detailed recording and analysis of these data, the deformation laws and mechanisms of the cores under different water content conditions can be deeply studied, thus providing strong support for research in related fields.
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 2 System Model with Chirp-BOK Modulation
### Chirp-BOK System Model  
Chirp-BOK uses a quasi-orthogonal chirp signal with up- and down-frequency to carry different information bits. The up-chirp signal represents the bit \"1\", and the down-chirp signal represents the bit \"0\". After passing through the communication channel, the receiver uses a down-chirp and up-chirp matched filter to detect and judge for demodulation. The schematic diagram of the chirp-BOK modulation system is shown in Figure 2 and Algorithms 1 and 2.  
```
Input:\(d=[d_{1},d_{2},\ldots,d_{n}]\) (data series), \(\mu\) (frequency modulation slope) Result:\(s(t)\) Data: initial phase \(\phi_{0}\) for\(i=1\)to\(n\)do
1\(s_{i}(t)\gets A\cdot\cos(2\pi f_{0}t+\phi(d_{i}))\) if\(d_{i}=1\)then
2\(\phi(d_{i})\leftarrow\phi_{0}+\pi t\mu^{2}\)/* up-chirp signals */
3
4 else
5\(\phi(d_{i})\leftarrow\phi_{0}-\pi\mu^{2}\)/* down-chirp signals */
6
7\(s(t)\leftarrow\sum_{i=1}^{n}s_{i}(t)\) Output: Final modulated signal \(s(t)\)
```  
**Algorithm 1**Chirp-BOK modulation algorithm.  
Figure 2: Scattering communication system based on chirp-BOK modulation.  
```
Input: Received signal(the signal transmitted through the channel after \(s(t)\)) Result: Decoded binary data /* Match the filtering and get the \(up\_chirp\_filtering\) result */
1\(up\_chirp\_filtering\) = MatchedFilter(_received_signal, \(up\_chirp\)_) /* Match the filtering and get the \(up\_chirp\_filtering\) result */
2\(down\_chirp\_filtering\) = MatchedFilter(_received_signal, \(down\_chirp\)_) /* Compare the \(up\_chirp\_filtering\) and \(down\_chirp\_filtering\) results to determine the binary value of the output */
3 if\(up\_chirp\_filtering>down\_chirp\_filtering\)then
4 Output \"1\"
5 else
6 Output \"0\"
```  
**Algorithm 2**Chirp-BOK demodulation algorithm.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 3 Proposed Method
### Degradation Estimation Subnetwork  
As shown in Figure 2, the degradation estimation subnetwork takes the real blurred image \(y\) as the input and outputs the estimated degradation representation \(E\), whose structure is inspired by the work of [PERSON] et al. [25]. In order to encourage the subnetwork to better learn and estimate degradation representation, discrete wavelet transform (DWT) pairs are designed at the beginning and end of the subnetwork. \(y\) is converted to a smaller size with an increasing dimensionality through DWT, followed by initial feature extraction through a \(3\times 3\) convolutional layer and learning in a cascade of 10 convolutional blocks. Then, symmetrically with input, the image is passed through one \(3\times 3\) convolution layer and one inverse discrete wavelet transform (IDWT) layer in order to achieve size recovery. Finally, a \(1\times 1\) convolution layer is used to transform the output after IDWT into 64 channels of high dimensionality. Compared with the single explicit blur kernel estimated in the general method, implicit degradation representations of 64 channels can better adapt to the complex spatially variant degradation representations in real blurred images and possesses a stronger expression of them.
## 3 Regulation of DAPG Biosynthesis
### Environmental Factors Influencing DAPG Production  
Several environmental factors can influence DAPG production by plant-beneficial _Pseudomonas_ spp. First, the type of carbon source available can greatly affect DAPG production. For example, the presence of sucrose, fructose, galactose, or mannitol promotes DAPG production by _P. kilonensis_ F113, while the presence of glucose and sorbose negatively impacts its production [22; 72]. On the other hand, glucose promotes DAPG production in _P. protegens_ CHA0 and in several other DAPG-producing strains [73], indicating that carbon sources have differential effects on DAPG production depending on the strain genotype. Furthermore, the presence of specific metabolites, such as fusaric acid, can also reduce DAPG production. Fusaric acid is a mycotoxin produced by several _Fusarium_ species, including _Fusarium oxysporum_[74]. Fusaric acid was shown to strongly repress DAPG production by plant-beneficial _Pseudomonas_ spp. both in vitro and in the rhizosphere of tomato and wheat [15; 75; 76]. Since an isogenic mutant lacking the pathway-specific repressor PhlF was found insensitive to fusaric acid, it was determined that this mycotoxin likely acts via the modulation of PhlF activity [15; 75]. Finally, plants can also influence DAPG production by plant-beneficial _Pseudomonas_ spp. colonizing the rhizosphere in several ways. For example, [PERSON] and colleagues used a split-root system to demonstrate that infection by _Pythium ultimum_ resulted in a change in root exudate composition, which leads to an increase in DAPG production by _P. protegens_ CHA0 colonizing the rhizosphere of the infected plant [77]. Recently, two plant flavonoids, apigenin and phloretin, were shown to repress DAPG production by rhizosphere-inhabiting _Pseudomonas_ sp. 2P24 [78]. These two metabolites promote _phlG_ expression by modulating PhlH-mediated repression, which ultimately results in the degradation of DAPG.
## 3 Results and Discussion
### The Effect of Operation Parameters on AC-CoPcS Catalytic Performance
#### 3.2.5 The Effect of Oxygen Pressure (\(P\)(O\({}_{2}\)))  
As is shown in Equation (6), the synthesis reaction of DMDS is a volume reduction reaction, so the increase of \(P\)(O\({}_{2}\)) will benefit the process. The effect of \(P\)(O\({}_{2}\)) on the \(\mathit{CPP}_{\mathrm{SMM}}\), \(\mathit{Yield}_{\mathrm{DMDS}}\), and \(\mathit{Purity}_{\mathrm{DMDS}}\) was investigated, and the result is shown in Figure 10. As is shown in the figure, the \(\mathit{Yield}_{\mathrm{DMDS}}\) increases sharply from 42.5% to 86.8% with the \(P\)(O\({}_{2}\)) increasing from 0.5 MPa to 0.9 MPa, and it decreases slightly at 1.0 MPa. However, at the same time, the \(\mathit{CPP}_{\mathrm{SMM}}\) only increases from 90.5% to 98.7% when the \(P\)(O\({}_{2}\)) increases from 0.5 MPa to 0.9 MPa, and then also decreases slightly to 96.8% when \(P\)(O\({}_{2}\)) continues to increase to 1.0 MPa. These results indicate the fact that SMM is much easier to react with oxygen, but the reaction condition is much harsher to obtain a high yield of the DMDS product. Nevertheless, the purity of the DMDS was not affected by the \(P\)(O\({}_{2}\)), and the \(\mathit{Purity}_{\mathrm{DMDS}}\) is still as high, above 99.5%.
## 2 Proposed Method
### Label Classifier Based on Local Minimum Mean Discrepancy
#### 2.3.2 LMMD Loss Function  
In traditional domain adaptation approaches, a primary consideration is employing the MMD or JMMD loss functions to synchronize either the global distribution across different domains [21; 22]. However, the alignment of category distributions between these domains is often overlooked, which leads to the loss of fine-grained sample information and subsequently results in reduced accuracy for bearing fault diagnosis methods. To tackle this issue, this study incorporates LMMD as one of the loss functions for the label classifier. LMMD gives precedence to aligning the distributions of the same categories in both the source and target domains [11]. The formula can be represented as follows:  
\[L_{lmmd}\triangleq\mathbb{E}_{c}\Big{\|}\mathbb{E}_{p^{(c)}}[\phi(x^{s})]- \mathbb{E}_{q^{(c)}}[\phi(x^{t})]\Big{\|}_{H}^{2}=\frac{1}{C}\sum_{c=1}^{C} \left\|\sum_{x_{i}^{s}\in D_{s}}\omega_{i}^{sc}\phi(x_{i}^{s})-\sum_{x_{i}^{s} \in D_{t}}\omega_{i}^{tc}\phi(x_{i}^{t})\right\|_{H}^{2} \tag{2}\]  
where \(x^{s}\) and \(x^{t}\) are samples from \(D_{s}\) and \(D_{t}\), respectively, \(q^{(c)}\) and \(p^{(c)}\) are the distributions of subdomains \(D_{s}^{(c)}\) and \(D_{t}^{(c)}\), respectively. By minimizing Equation (2), the distribution of related subdomains within the same category is narrowed. Parameters \(w_{i}^{c}\) are defined as weights belonging to the sample \(x_{i}\). Given a sample \(x_{i}\), the calculation formula of \(w_{i}^{c}\) is as follows:  
\[w_{i}^{c}=\frac{y_{ic}}{\sum_{(x_{j},y_{j})\in D}y_{jc}} \tag{3}\]  
where \(y_{ic}\) is the \(c\)th element of the vector \(y_{i}\), and the source domain sample \(x_{i}^{s}\) is calculated through the real label \(y_{i}^{s}\). However, there is no label \(y_{j}^{t}\) in the target domain sample \(x_{j}^{t}\). In the proposed RASAN-W model, pseudo labels \(\hat{y}_{j}\) are used to calculate \(w_{j}^{Tc}\).
## 5 Experimental Results
### RoSS vs. B-R and N-R  
In Figure 4, we can see that the gap between the proposed approach and the other algorithms increases with \(P\), particularly with large \(N\). For \(P\leq 4\) and all three of the values for \(N\) that are considered, RoSS is comparable with N-R and roughly four times as fast as B-R. However, for \(P=256\), RoSS is up to eight times faster than N-R (which is slightly faster than B-R, as shown in [7]) and B-R. We also note that these fully-balanced approaches may also stop scaling when the computation shrinks and the communication becomes too intensive: e.g., here, RoSS stops scaling at \(N=2^{16}\) and \(P=64\).
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology
### Proposed Digital Twin Architecture
#### 3.2.1 Data Acquisition Module  
This module collects multi-source information, which forms the foundation of the digital twin. The data sources are broadly categorized into construction site data and equipment data. Construction site data consist of subsurface, surface, and design data, such as target models, cut/fill zones, and as-built construction data collected during operation. Subsurface data represent the underground material conditions and their characteristics, which can be collected through geotechnical investigations. Boreholes provide data on the earthwork material at different depths, along with other in situ and laboratory test results, to achieve geotechnical stratigraphy and properties such as material type, strength, cohesion, friction, and ground level.  
Surface data, which represents the existing ground conditions, can be obtained from publicly available datasets such as the National Geographic Institute of Korea (NGII),  
Figure 3: Digital twin development process for earthwork construction.  
the United States Geological Survey, OpenStreetMap, and GIS. However, these datasets are inaccurate because of scale changes and outdated formats. Advanced technologies, such as laser scanning and photogrammetry, can be used to capture surface data with millimeter-level accuracy. In addition to accuracy, it is important to obtain the current state of information for realistic simulations, which is lacking in the public datasets. In addition, earthwork design data, including BIM models, CAD data, and earthwork quantity reports, provide targets and cut-and-fill information.  
Construction data is acquired regularly using cameras or laser scanners, ensuring that both static and dynamic field data are captured for downstream use. Another critical data source is the equipment used for model manipulation. Equipment data are obtained either by scanning onsite or from equipment specifications, such as equipment-specific dimensions, capacity, and mobility parameters that influence the simulation performance. In addition, kinematic and dynamic equipment data can be obtained from the onboard sensors and telemetry systems. Moreover, automated machine guidance (AMG) can provide significant information about the equipment at a construction site.
## 5 Experimental Results and Discussion
### Dual-Robot Collaborative Grinding Experiment
#### 5.2.2 Rotational Collaborative Movement  
In this scenario, the workpiece is initially held horizontally, and then the workpiece clamping manipulator changes the attitude of the end to change the gripping attitude of the workpiece. At the same time, another manipulator follows the surface of the workpiece. The experimental scenario is shown in Figure 8.  
The motion time of each planned trajectory point is still set to 0.5 s, the end of the manipulator 1 is set to rotate 90 degrees, and the rotation is set between each attitude to be 1 degree, so that the motion process is 45 s in total. Since there is only the end rotation, it can be seen that the workpiece rotates around the \(X\) axis in the coordinate system of manipulator 1, and the corresponding series of rotation matrices can be calculated, and the position of the trajectory point that manipulator 2 needs to reach in different periods can be described accordingly.  
The movement of the two manipulators in their respective desired directions was analyzed. For manipulator 1, the movement in the desired direction is shown in Figure 9:  
For manipulator 1, the error in the direction of rotation is small, the average rotation angle error is 0.0094, about 0.538 degrees, and the maximum rotation error is 0.02356, about 1.350 degrees. The error is in a small range and the distribution is relatively uniform, which indicates that the impedance control is better in the effect of tracking attitude.  
For manipulator 2, the movement in the desired direction is shown in Figures 10 and 11:  
In the Y-direction, the overall motion error is roughly in a downward trend, with the maximum error in the Y-direction being 2.77 mm, the minimum error being 0.36 mm, and the average error being 1.37 mm. The error distribution in the Z-direction is relatively uniform; the minimum error is 0.42 mm at the beginning, increasing rapidly within 6 s, and then remaining stable; the maximum error is 2.65 mm; and the average error is 2.05 mm. It can be seen that, in the process of grinding tasks, the error in the Z-direction is relatively stable, but the error in the Y-direction becomes relatively larger with more complicated  
Figure 8: Dual-arm rotational collaborative experiment.  
Figure 9: The rotational motion and error of manipulator 1.  
rules. In general, however, the tracking effect in the Y-direction is better than that in the Z-direction.