esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Residual Strength of Nonlinear Fatigue-Damaged Pipeline Structures
## 4 Results and Discussion  
### Residual Strength Characteristics of the Pipe Body under the Influence of Low-Cycle Fatigue  
Under the influence of low-cycle fatigue after repeated \"bending-straightening\" alternating deformations, the strength of the CT110 steel coiled tubing material has undergone significant changes after a certain amount of damage, and the changes in the residual strength and damage degree are shown in Figure 11. When CT110 steel-coiled tubing is not damaged, the residual strength is still the initial strength of 758 MPa. When the damage level reaches 2%, the residual strength drops rapidly to 657 MPa, and the strength loss is as high as 13.3%. When the alternating deformation low-cycle fatigue is continued, the calculation results show that the residual strength of CT110 steel coiled tubing is within 20% of the damage degree, indicating a rapid decline trend. When the damage degree reaches 20%, it drops to 581 MPa, and the residual strength is 76.6% of the initial state strength. When the damage degree exceeds 20% and reaches 80%, the residual strength shows a slow and slight decline trend. The relationship between the residual strength and the damage degree in this process is approximately linear, resulting in a final residual strength loss of about 29%.  
Figure 10: Plastic strain results from pipe cross-sections at different times: (**a**) t = 0.7 s, (**b**) t = 0.8 s, (**c**) t = 0.9 s, and (**d**) t = 1.0 s.
## 2 Materials and Methods
### Estimation of Fat Content and Oxidation Parameters
#### 2.2.3 Antioxidant Capacity  
Antioxidant capacity was determined according to the DPPH (2,2-diphenyl-I-picrylhydrazyl) method [17]. An extract of a sample was prepared according to [PERSON] et al. [18] using a homogenisation sample (3 g) with 5% trichloroacetic acid (15 mL), after which chloroform (10 mL) was added. DPPH crystalline powder (Sigma-Aldrich Product, Steinheim, Germany) was dissolved in methanol (at a ratio of 0.025 g\(\cdot\)L\({}^{-1}\)) for the preparation of fresh DPPH solution (radical stock). The absorbance value of the DPPH solution was measured using the GENESYSTM 6 spectrophotometer (Thermo Electron Corporation, Beverly, MA, USA) as A\({}_{0}\) against a blank at 515 nm. The mixture was measured by a spectrophotometer (515 nm) after 10 min of sample extract (0.2 mL) reaction with DPPH solution (3.8 mL), and the absorbance was recorded as A\({}_{10}\). The inhibition percentage (%) of the DPPH radical was calculated according to the formula (A\({}_{0}\)-A\({}_{10}\)/A\({}_{0}\)) \(\times\) 100, where A\({}_{0}\) was the absorbance of the control at time = 0 min and A\({}_{10}\) was the absorbance of the antioxidant at time = 10 min.
## 5 Simulation Results and Discussions  
In this section, the performance of the proposed secure localization algorithm is evaluated. The algorithm was implemented using MATLAB. In the experiments, the deployment area is set to 500 m \(\times\) 500 m \(\times\) 500 m. There are 500 unknown nodes randomly deployed in the 3D space. The communication range of unknown nodes is set to 100 m. Other parameters used in simulations are listed in Table 2. The performance of CSLT is compared based on the following three metrics: (1) detect ratio of malicious nodes, which is used to evaluate the security of CSTL; (2) localization accuracy, which is one of the most important evaluation indicators of localization algorithms; (3) localization ratio, which is used to evaluate whether all the unknown nodes can be successfully localized; (4) energy consumption, which is used to evaluate energy efficient of the proposed algorithm in energy-limited UWSNs.
## 5 Discussion  
When using the real-world system, the images taken contain several added noises, such as the image background (the table) and the object's texture, shadows, reflections, and change in coloration due to the different light sources used, as compared to our previous work, where the images are taken inside a simulated environment. These noises decrease the detection confidence and increase the positioning error in the real environments and may pose a problem when performing robot sorting operations.  
The best results are obtained when using natural light, under which the model was trained, and for the circular object due to its symmetry, but the robot system successfully picks the object in every case.  
Compared with the industrial software, the confidence in detection is reduced when using the cloud service, but the position errors remain similar. Regarding the response time, the local solution is faster, as to be expected, but the cloud response is not slow either.  
The response time varies with Internet speed, but it remains consistent under the same test conditions. The network used has a receive and transmit speed of 433 Mbps, the size of the images varies from 1.83 MB up to 2.04 MB, and the resolution is \(2616\times 1960\).  
In comparison with classical identification methods, which take into account the shape and contour of the object, the presented method can be used successfully for identification in cases when the objects are a bit dirty (Figure 17) or they overlap (Figure 18).  
Figure 16: (**a**) Time histogram for natural light for the industrial software. (**b**) Time histogram for artificial light for the industrial software.  
Figure 15: (**a**) Time histogram for natural light. (**b**) Time histogram for artificial light.  
The results are better when using the industrial software, trained for the same images, but when using the detection images to improve the cloud solution model, the detection results are improved. In this case, we used the detection images with natural light to retrain and improve the model, obtaining the results presented in Figure 19a for natural light, showing a 7.2% increase in detection confidence, and Figure 19b for artificial light, with a respectable 5.2% increase. Aside from the increase in confidence, a reduction in the standard deviation of 10.8% for natural light and 4.1% for artificial light is present.  
Figure 17: Detection results of dirty objects.  
Figure 18: Detection results of overlapping objects.
## 4 Data and Methodology
### SAR Data
Our sequence of processing is given in a Figure 4. We created interferograms of unwrapped phase for each subswath in case of Sentinel-1A, merged them after debursting, and continued with a single product in differential SAR processing and unwrapping the phase. We did the unwrapping with the SNAP's plugin _staplut_ outside of SNAP toolbox, but later on, we computed the displacements separately using well known equations for displacement computation from the unwrapped phase (more details in [12; 15]). After the determination of the Line of Sight (LOS) displacement, we corrected the values for the topographic correction and finally computed the estimation of vertical ground displacement after the earthquake. The vertical ground displacement was computed by LOS displacement and incident angle \(\theta\) using the expression ([71; 72]):  
\[DISP_{VERT}=\frac{LOS_{disp}}{\cos\theta}; \tag{1}\]  
As for the elevation data for the removal of topographic phase, we used \(1\) arc second Shuttle Radar Topography Mission (SRTM) [37] digital elevation model (more details on: [[https://lpdaac.usgs.gov/products/srtmgl1v003/](https://lpdaac.usgs.gov/products/srtmgl1v003/)]([https://lpdaac.usgs.gov/products/srtmgl1v003/](https://lpdaac.usgs.gov/products/srtmgl1v003/)), accessed on 10 January 2021).
## 2 Wind Speed Modeling
### Data-Based Black Box Model  
Measured and historical data of wind speed, wind speed prediction by the local meteorological center, and other measured environment data are used to build the black box model of wind speed by data-driven modeling methods. A time-series wind speed model is a kind of data-based black box model that depicts the dynamic changes of wind speed over time by using historical wind speed data. This model is the precondition of wind power integration reliability assessment in the planning stage, and the first link in wind power credible capacity evaluation [27; 28; 29; 30; 31; 32]. Autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) are the methods to build a time-series model. The literatures [28; 29] using an ARMA model to fit the wind speed spectrum function and to establish the wind speed model. The literature [14] has applied a time-series modeling theory based on a higher-order statistics method. The reconstructed ARMA model for a short time series performs good robustness and high precision.  
The Markov random process model is another kind of data-based black box model. Based on the statistical analysis and transition probability calculation, the wind speed can be obtained [27]. A discrete-state Markov chain model (DSMC), continuous state model, and birth-and-death Markov process model are the commonly used Markov random process models. As representatives of these two types of models, the ARMA model and DSMC model are compared, as shown in Table 1. In addition to the aforementioned models, many data-driven methods, such as neural network and support vector machine, combined with multi-source data, are used to build black box models of wind speed [33; 34; 35].
## 4 Experiments
### Qualitative Comparison with State-of-the-Art Methods
#### 4.3.2 Visual Analysis on Flare7K++ Dataset  
Figure 8 presents the qualitative comparison results on the Flare7K++ dataset. As shown, EnlightenGAN, Zero-DCE, and URetinex-Net all amplify glare regions, while RUAS exhibits severe overexposure, resulting in significant detail loss in road scenes. PSENet shows a limited ability to suppress glare, making the light source in the third image appear more focused, but it fails to address linear glare artifacts. The IAT method only slightly reduces the extent of glare in the second and third images. Benefiting from the proposed GPA module, which accurately identifies glare regions, our method effectively suppresses glare around light sources while preserving the integrity of the light source itself. As seen in the third image, large halo areas surrounding the light source are significantly reduced. Additionally, the efficient and lightweight sampling modules facilitate the recovery of background details previously obscured by glare. For example, in the fourth image, the building structures covered by glare are fully restored. Overall, these qualitative results further demonstrate the unique advantages of our method in glare suppression.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 4 Results
### Editing Results  
To evaluate the effectiveness of our approach, we conducted experiments on various videos, demonstrating its ability to edit local content components and transfer textures to specific regions. Figure 4 illustrates the editing results, where we showcase two representative cases to highlight the flexibility and robustness of our method.  
In the first row of Figure 4, we used a source video featuring a woman holding a red apple and speaking without any accessories. A canonical image was extracted from the video, and a reference image of a woman wearing sunglasses was provided. By editing the canonical image to include sunglasses, our method propagated this modification across the entire video sequence. The resulting edited video demonstrates a smooth and temporally coherent transfer of the accessory (sunglasses), where all frames depict the woman wearing sunglasses while maintaining consistency with the original video's motion and content.  
In the second row of Figure 4, we performed texture transfer by using a reference image of a green tangerine. The canonical image was edited to replace the texture of the red apple with that of the green tangerine. This modification was then propagated across the entire video sequence. The resulting video frames show that the red apple is consistently replaced by the green tangerine across all frames, preserving the spatial alignment and the natural motion of the hand and object.  
By editing a single canonical frame, our method successfully disseminates modifications across the entire video sequence while maintaining spatiotemporal coherence. This demonstrates the efficacy of our approach in handling localized edits. Two cases serve as evidence that our approach supports a wide range of edits, from adding new visual features (e.g., sunglasses) to replacing object textures (e.g., apple to green tangerine). This versatility underscores the generalizability of our method to various video-editing tasks. The results show that by simply providing different reference images, users can achieve highly customized edits, which offers significant flexibility and control.
## 3 Results and Discussion
### Aerosol Optical Properties
#### 3.3.1 Aerosol Scattering Properties
The backscatter ratio (b) is defined as the ratio of the coefficient of aerosol backscattering to the scattering coefficient. When light reaches the aerosol surface, it is partly scattered back into space, resulting in a cooling effect on the atmosphere. This scattered part is known as the up-scatter fraction (\(\beta\)), which can be considered as the ratio of light scattered back into space to the total scattered light [23]. Here, b can be measured using a nephelometer. The aerosol asymmetry parameter and up-scatter fraction can both be retrieved based on the backscatter ratio. These three physical quantities reflect the specific scattering effects of aerosols within the total scattering. Thus, investigations into g, b, and \(\beta\) can help improve the understanding of aerosol optical properties during haze periods. Figure 4b shows b_550 obtained by nephelometer at the green wavelength (550 nm) during the experimental period. Before the haze occurred, b_550 was \(\sim\)0.12 on 10 June; it then decreased to 0.10 on 11 June, before recovering to \(\sim\)0.12 following the disappearance of the haze. [PERSON] _et al._ also reported a mean value of b of 0.10 for a haze episode [23]. The value of b_550 is closely related to the particle size and hygroscopicity. [PERSON] and [PERSON] concluded that the lower value of b (0.11) during haze events is indicative of the aged character and particle growth of smoke aerosols [56]. As mentioned above, aerosols undergo an aging process during the time of solidification and condensation, hydrophilic aerosols increase during transportation, and the decreased value of \(\alpha_{\rm s}\) indicates an increasing trend in particle size, all of which contributed to the lower value (0.10) of b_550 during the haze in Wuhan. The up-scatter fraction \(\beta\) and asymmetry parameter can be calculated according to the Henyey-Greenstein phase function, as shown in the following formulas [57]:  
\[\beta=0.082+1.85\rm b-2.97\rm b^{2} \tag{4}\]  
\[\rm b=\frac{1-g^{2}}{2g}\biggl{(}\frac{1}{\sqrt{1+g^{2}}}-\frac{1}{1+g}\biggr{)} \tag{5}\]  
Changes in \(\beta\)_550 and g_550 during the experimental period are shown in Figure 4c. It can be seen that the value of g_550 was higher (_i.e._, remaining at \(\sim\)0.65) during the haze days of 10-12 June. This shows that aerosol forward scattering played a dominant role during the haze. In the NCP, the value of g was found to be 0.66 due to the growth of aged particles, which is similar to our results [23].
## 3 Mission Overview
### Measurement Sequence and Coverage
The largest impact is over the tropics, although gaps are still less than 800 km, but coverage remains excellent at mid-latitudes and over the poles. The wider swath of the multispectral radiometric imager leads to a much swifter global coverage by the passive instrument. A limitation of the prescribed polar orbit is the convoluted sampling of the diurnal and seasonal, or intra-annual, water vapour cycles for any given location. In other words a full 24-h cycle will only be sampled over the course of a year. This is not ideal for a tracer that is highly variable both in space and in time; however, for a given latitude belt, at least two diurnal observations will be available within a narrow zonal region. Because of the Earth's rotation between times \(t_{0}\) and \(t_{3}\), the measurements taken by the nadir and limb looking LiDARs will not exactly cover the same track. However, the nadir instrument is pointing slightly cross-track or off-nadir to avoid specular reflection, which mitigates this effect. Furthermore, collocation should be defined as a function of the characteristic spatial scales of variability of the observed phenomenon. For tropopause folds and tropical pumping of water vapour into the UTLS,  
Figure 3: Ground tracks of collocated nadir and limb measurements from a configuration using five retroreflectors, coloured by time of day UTC, for (**a**) one day, (**b**) ten days, (**c**) 21 days and (**d**) 21 days, with only four retroreflectors. this characteristic scale is of the order of 100 km. Our analysis (shown in Figure 4) produced a typical measurement track offset at the equator at ground level (the worst case) of between 33 and 200 km for an off-nadir angle of 5\({}^{\circ}\).
# On Transient Stability Problems in DC Microgrids
## 5 Conclusions  
This article has comprehensively investigated the transient stability problems in DC microgrids, providing valuable insights into their characteristics, analysis, and mitigation strategies. Through theoretical analysis and experimental validations, the critical role of power control strategies in maintaining stability has been demonstrated. The key findings reveal that DC microgrids are susceptible to transient instability when power-transfer limits are exceeded. The equilibrium points, power-transfer limitations, and stability conditions for both PI-power-controlled and droop-controlled DC generators have been derived. Comparative analysis highlights the superiority of droop control in terms of transient stability and robustness against disturbances. This article provides a comprehensive foundation for understanding and enhancing transient stability in DC microgrids, paving the way for further advancements in the field of DC microgrid stability and control. Future extensions of this work include exploring multi-microgrid interactions and incorporating machine learning for real-time stability assessment in DC microgrids.  
Conceptualization, J.F.; Methodology, M.C.; Software, Z.W.; Validation, D.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Jinan Municipal Government under Grant 20 New University Items, Grant Number 202228069, in part by the Department of Science and Technology of Shandong Province under Grant ZR202210270088, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant Guangdong-Shenzhen Joint Youth Fund, Grant Number 2022A1515110422.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Related Work
Sometimes it is also the only option due to the limitations of the interface used for the control [11; 22]. However, using a fixed level of autonomy introduces the problem of finding an optimal balance in the arbitrating control between the user and the system [10]. If the user is primarily in control at all times, without any automating, it defeats the purpose of having semi-autonomous control in the first place. On the other hand, a high level of autonomy where nearly everything is automated may not be a satisfying experience for the user either [24]. Automating may even counteract what the user is trying to achieve in cases where the automating performs the wrong action, e.g., reaching for the wrong object, and it may impose a safety risk. Even cases where the automating acts as intended may result in lower satisfaction for the user as they may no longer feel in control, especially for individuals with movement impairments [19]. One way to avoid or minimize many of these issues is to rely on an adaptive level of autonomy instead of a fixed one. Several studies on teleportation of robots have successfully demonstrated semi-autonomous control with an adaptive level of autonomy based on a confidence-measure [25; 26]. This confidence-measure is an expression of how certain the system is in its own prediction of the intent of the user, such as interacting with a certain object. The system will hence offer a lot of assistance in scenarios where it has a high confidence of being able to correctly assist the user. The opposite is also true; the system will offer no or little assistance in cases of low confidence where it is unclear what the user is trying to accomplish. A benefit is hence that it can adapt its level of autonomy to fit different scenarios. Hence, in the current study, three different control schemes were implemented: a non-autonomous (i.e., manual) control, a semi-autonomous control with a fixed level of autonomy, and a semi-autonomous control using a confidence-based adaptive level of autonomy. The implementation of each is described in further detail in Section 3.4. These three different control schemes are evaluated and compared against each other, as described in Section 4. The purpose is to determine whether using computer vision for tongue-based control of an upper limb exoskeleton is beneficial or not and whether semi-autonomous control with a fixed or adaptive level of autonomy is preferable in this context.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Gas Phase Method
#### 4.3.1 Chemical Vapor Deposition (CVD)  
Chemical vapor deposition (CVD) [183; 184] is a method of forming a thin film by chemical reaction to one or more vapor phase elements or compounds on the surface of a substrate. CVD can be used to deposit metals, carbides, nitrides, oxides, borides, and the like. CVD can be applied to the surfaces of complex geometric shapes and has a good adhesion to the film base. Therefore, the development of CVD is very rapid. Depending on different reactants and control conditions, CVD can be further divided into atmospheric pressure chemical vapor deposition, low pressure chemical vapor deposition, plasma CVD, laser CVD, and metal-organic CVD. Thanks to the development of thin film manufacturing technology, CVD has been continuously developed and improved. However, the equipment and cost required for CVD is relatively high. In addition, CVD possesses many disadvantages, such as uneven microscopic surface roughness of the resulting film and particle size (50-150 nm), as well as a relatively large surface roughness of the film.