esa-sceva/satcom-chunk-collection huggingface dataset:
# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 4 Experiments
### Homemade Dataset
#### 4.1.2 Tarim River Mainstem Dataset  
The dataset comes from the NASA Landsat8 satellite, which carries an OLI land imager, which includes nine bands with a spatial resolution of 30 m and an imaging width of 185 \(\times\) 185 km. In this paper, we gathered data from six scenes within the Tarim River mainstream area. Considering the dichotomous nature of river segmentation, we opted for a combination of bands 5, 6, and 4 to enhance the differentiation between the river and background information. The resulting remote sensing image, synthesized from 564 bands, forms a non-standard false-color image by combining infrared and red bands. ENVI 5.3 and ArcMap 10.2 software were employed for remote sensing image processing and labeling. During the labeling process in ArcMap 10.2 software, the river was assigned a label of 255, while the non-river background was labeled as 0. After manually labeling the rivers in the six remote sensing image views, each view was randomly cropped into 400 512-pixel by 512-pixel river remote sensing image datasets containing the rivers, based on the location of the rivers. Finally, we selected 1900 datasets with a high proportion of rivers as the  
Figure 7: Schematic diagram of experimental area.  
dataset. Some images in the dataset are shown in Figure 8. The dataset was then divided into training and validation sets with a 9:1 occupancy ratio.
## 4 Numerical Modeling of Da'anshan Landslide
### Inputs and Parameters for Simulations  
Digital elevation maps (DEMs) before and after the landslide are needed in the simulations. By comparing the elevation difference between these two DEMs, the extent and height of the mobilized mass in the source area were determined. The pre-sliding digital terrain was reconstructed using an 8 m resolution DEM, combined with high-resolution remote sensing images two months before the landslide. The same method was used to obtain the digital terrain after the landslide, which occurred a few days after the landslide occurred. The digital elevation maps used in the current analysis have a final resolution of 2 m.  
The bulk density \(\rho\), internal friction angle \(\varphi\), and basal friction angle \(\delta\) are determined based on the empirical values in similar landslide analyses. These values remain constant in both dynamic models. The turbulent coefficient is needed only in the depth-averaged model. Referring to similar landslide simulations [44; 59] and via trial calculation, the entrainment coefficient is taken as \(10^{-6.2}\) in this study. For the sliding block model, the size of the block is 6 m, which means nine blocks form one group (see Figure 1). The size of the group needs to be determined via on-site investigation combined with the distribution of rock joints and the degree of weathering. The values of some parameters have referred to similar landslide analyses and the actual geological conditions of this landslide. Special parameters, such as cohesive stress, are determined via trial calculations. The model parameters and corresponding values are summarized in Table 1.
## 4 Results
### Continental-Scale FRP  
MODIS FRP is much smaller than VIIRS FRP on a continental scale in Africa. Linear regression shows that the daily sum FRP estimates from contemporaneous fire detections between MODIS and VIIRS are highly correlated (Pearson r > 0.95). However, their FRP value differs significantly. In comparison with the VIIRS sum FRP from VIIRS fire detections observed both inside and outside the MODIS swath gaps, MODIS sum FRP is approximately 49% smaller according to the regression slope, with a significant bias of 20124.43 MW that accounts for 13.5% of the daily mean of MODIS sum FRP or 8.3% of the daily mean of VIIRS sum FRP (Figure 5a). Comparing with only VIIRS sum FRP from VIIRS fire detections sensed outside MODIS swath gaps, MODIS sum FRP is approximately 26% smaller with a bias of 25116.66 MW that explains 16.8% of the daily mean of MODIS sum FRP and 11.8% of the daily mean of VIIRS sum FRP outside MODIS swath gaps (Figure 5b).  
Figure 4: Variations of the minimum (min), mean, and maximum (max) fire radiative power (FRP) per fire detection (or pixel) of the MODIS (**a**) and VIIRS (**b**) sensors in different satellite view zenith angles (VZAs) from nadir to the scan edge. Note that the \(y\)-axis is on a logarithmic scale.  
Figure 5: Comparison of daily continental sum FRP from daytime active fire detections observed contemporaneously by MODIS and VIIRS I-band across Africa in 2017. (**a**) MODIS FRP versus VIIRS FRP from all VIIRS fire detections, and (**b**) MODIS FRP versus VIIRS FRP inside MODIS granules (VIIRS detections in swath gaps were excluded). The color legend on the right represents the date (day of year, abbreviated as DOY) of each sample observation. The red solid line represents the best-fitted model line and the dashed line is the 1:1 line.
## 3 Results and Discussion
### SEM and XRD Examinations
As a result, a small amount of ferrite stays at the austenite grain boundary even after the nugget cools down to room temperature, as confirmed by the SEM inspection in Figure 9. To clarify the phase structure of the AISI 304 joint, X-ray diffraction was used to analyze the spectrum and phases generated after AISI 304 was welded; the XRD phase analysis spectrum is given in Figure 10. The XRD results proved that the \(\gamma\)-Fe and \(\delta\)-Fe phases were found in the welded joint. Figure 9: SEM of the similar stainless steel 304 RSW joint with different magnifications. (**a**) 2000\(\times\); (**b**) 4000\(\times\); (**c**) 8000\(\times\). Reducing the grain coarsening of the dissimilar AISI 304/Ti grade 2 joint through heat input control has an inverse effect on the shear strength, which in turn affects the performance of the weld joint. This explains why, in contrast to the results reported in [81], the shear strength of the RSW joints in the present study of the dissimilar joint is superior. Implementing all of the RSW parameters at varying levels and without excluding any variable also leads to the previously indicated improvement in shear strength. The results from an SEM analysis confirm the aim of improved welding characteristics (Figure 11). According to the SEM findings, the two metals completely diffused into each other since the diffusion line joining them is unseen. Figure 11: SEM of the titanium-stainless steel 304 dissimilar RSW joint. Figure 10: XRD analysis of the RSW AISI 304 stainless steel joint (case G). The XRD test conducted on the fusion line of the optimum Ti/ AISI 304 joint (case I) reveals the produced phases in the welded joint, as shown in Figure 12. According to the XRD data, dissimilar welded titanium and stainless steel joints with the NiTi phase are desirable because they help to decrease the amount of brittle IMCs like FeTi, Fe2 Ti, and CrTi that formed in the joint under study. The combined strength of the welded joint is improved by this. Thus, the presence of IMCs such as FeTi and Fe2 Ti stimulates brittleness and functions as a stress concentrator, thereby facilitating the initiation and propagation of cracks under shear stresses. Furthermore, the inclusion of IMCs like CrTi introduces localised hardness and fracture sensitivity, hence lowering the shear strength. These three IMCs reduce the joint's ductility, decreasing its ability to resist deformation before failure. The existence of the NiTi phase in the dissimilar joint is advantageous because it improves joint quality and decreases the influence of these three IMCs on the shear strength. The XRD examination also revealed the presence of \(\gamma\)-Fe as a phase. It was shown that this phase improves the shear strength and ductility of the welded joint; this claim is supported by [86].
# Application of Machine Learning to Express Measurement Uncertainty
## 3 Experimental Data, Comparative Analysis, and Discussion  
The considered experiment is related to testing and monitoring of power equipment, i.e., determining PD location in the mineral oil by the non-iterative, all-acoustic method. The non-iterative algorithm gives a solution for a system of nonlinear equations describing PD location. The algorithm has 19 parameters (16 inputs and 3 outputs), i.e., a relatively sizeable nineteen-dimensional state space. The location of PD is determined indirectly. All of the parameters, both input and output, can influence the combined measurement uncertainty of the non-iterative algorithm [30].  
The correlation between the two input parameters of the algorithm, the mean distance of PD and AE sensors, and the temperature coefficient of sensitivity was observed in [30]. The correlation was expressed via the mean values of the parameters. Calculating the mean value of the parameter does not consider the value on which it depends. The mean values were calculated using the Monte Carlo (MC) method, which was time-consuming. Additionally, not all possible values were generated through MC randomization.  
Figure 2: The proposed analogy between the ML model and measuring device for expressing type B measurement uncertainty.  
To overcome the mentioned shortcomings, supervised learning technique is proposed. The dataset generated through MC randomization is used as a training dataset of a model that can subsequently predict even the \"unseen\" data. The chosen metric for ranking the models' performance is \(R^{2}\). The \(R^{2}\) compares the model's hypothesis variances and the label's mean value from the training dataset. In this way, the improvement over the existing calculation of the mean value of the parameter can be directly evaluated.  
The tradeoff of this approach is the appearance of additional uncertainty generated by the ML model. The tradeoff can be made more acceptable by quantifying and controlling the given uncertainty by the procedure proposed in Section 2.4.
# Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects
## 3 Implementation of the Proposed Framework
### Step 3: Implementation of Phase 2
 \tag{23}\]  
(5) Check the sum value (SV) of all columns. If the value of SV is larger than 1, the weight values should be normalized. For the \(W_{1}\) matrix, the checking process is shown in Equation (24). \[\text{SV}=(0.339+0.230+0.487+0.119)=1.174>1 \tag{24}\]  
As shown in Equation (8), the matrix should be normalized. Each value of matrix W\({}_{1}\) is divided by 1.174, and the final weighted matrix for fuzzy pair-wise comparison matrix is generated as illustrated in Equation (25). \[\text{W}_{2}=\text{Weighted Sum Value for W}= \begin{bmatrix}0.289\\ 0.196\\ 0.414\\ 0.101\end{bmatrix}  \tag{25}\]  
Using the same step-by-step calculation procedure, the local weights for the sub-criteria can be obtained. Then the global weight of each criterion was calculated by multiplying its local weight and the weight of the corresponding main criterion, which is summarized in Table 5.  
In this phase, the respondent experts were asked to evaluate all the potential alternatives obtained from Phase 1 based on the numerical value (1-9) measurement among the sub-criteria. Therefore, the weighted normalized decision matrix is generated through Equation (7). Following the steps of the TOPSIS method, \(d_{i}{}^{+}\) (positive ideal solution) and \(d_{i}{}^{-}\) (negative ideal solution) are calculated, and the separation distance of each alternative from \(d_{i}{}^{+}\) and \(d_{i}{}^{-}\) is determined. The weighted normalized decision matrix and separation distance are illustrated in Table 6. The closeness coefficient (\(CC_{i}\)) of each alternative to the ideal solution is evaluated. The results are shown in Table 7.  
According to the results from Table 7, A2 shows super qualities that meet the requirements of assisting the tasks and ranks first. In addition, A1 and A3 have similar performance, and rank second and third, respectively.
## 2 Experimental Methodology
### Strain Measurements and Validation: Experimental vs. Simulation  
Strain measurements were conducted on the textile samples to evaluate their mechanical behavior under controlled conditions. The compression garments were designed to apply pressure in clinically relevant ranges: mild (1-1.9 kPa), moderate (2.66 kPa), and firm (4 kPa). These pressure ranges are aligned with clinical applications for the management of medical conditions such as venous insufficiency, lymphedema, and postoperative recovery. To compare the experimental and simulated strain measurements, we applied a standardized grid system to both the physical samples and their virtual counterparts. The grid was marked at 1 inch intervals in both the wake and course directions, as depicted in Figure 2a. A template was employed to mark the grid on the textiles [18]. This method allowed for the precise calculation of changes in the grid dimensions before and after the fabric was stretched using the following equation [6]:  
\[\text{Strain (\%)}=\frac{\text{Stretched length -- Original length}}{\text{Original length}}\times 100\]  
For the experimental setup, strain measurements were performed by stretching a compression waistband in the course direction on a manikin, at strain levels of 15% and 20%. The strain measurements were conducted in three directions: the course direction (axial strain), the wake direction (transverse strain), and the bias direction, considering that the waistband was stretched in the course direction accordingly (Figure 2b). The textile's strain was evaluated by applying the load around the manikin, with the stretch facilitated using Velcro (as depicted in Figure 2c). Specifically, a strain measurement tool in CLO3D was used to simulate and record the local strain corresponding to the applied strain levels in the axial, transverse, and bias directions (Figure 2b) at consistent 1 inch intervals between the grid points, ensuring alignment with the physical measurements (Figure 2d). By using both real-world grids and virtual strain predictions, this study validated CLO3D's accuracy in simulating textile deformation under controlled conditions.  
\begin{table}
\begin{tabular}{c c} \hline
**Single Jersey (S))** & **Interlock (INT)** \\ \hline \hline
**Single Jersey Plaited (SJP)** & **Interlock Plaited (INTP)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Knit materials’ structure used in this study.
# A Component-Based Model for Novel Modular Connections with Inbuild Component
## 2 Parameter Analysis
### Parameter Analysis Results  
#### 2.3.1 Thickness of Side Plate and Bolts Preload  
The thickness of the side plate was selected from 6 mm to 16 mm with an interval of 2 mm, and the preload of split bolts was selected from 80 kN to 120 kN with an interval of 20 kN. The moment-rotation curves were shown in Figure 8. There was no obvious difference among all curves, indicating that the side plate thickness and bolts preload have little influence on the mechanical properties of modular connections.  
Figure 6: Parameters setting of finite element model.  
Figure 7: Parameters setting of finite element model: (**a**) Relative dislocation between units; (**b**) Relative rotation between units.  
The influence rule of side plate thickness and bolts preload on relative dislocation between upper and lower columns were shown in Figure 9. When the moment is below 200 kNm, the curves almost coincide and increase linearly, indicating that the thickness of the side plate has little influence on the relative dislocation of the upper and lower columns in the elastic stage. When the bending moment reaches 200 kNm, curves entered into the elastic-plastic stage and began to separate. The higher the thickness of the side plate, the smaller the relative dislocation of the side columns. There is no significant difference between the curves with different bolt preload.  
The influence rule of side plate thickness and bolts preload on continuity index were shown in Figure 10. As the thickness of side plate increases, the curve is closer to the horizontal axis, which means that the rotation difference between the upper and lower columns is smaller. For bolt preload influence, the connections with higher bolt preload have better continuity in the elastic stage, but there was no obvious difference in elastic-plastic range.  
In general, the thickness of the side plate has little influence on the overall moment-rotation curve of connections but plays a positive role in enhancing the continuity between modules. The Mises stress distribution of side plates with thickness of 6 mm and 16 mm is shown in Figure 11. The yield area of S16 was obviously smaller than that of S6, indicating that the plate with larger stiffness can prevent the relative dislocation of the lower and upper columns more effectively. The bolt preload has little effect on the global mechanical properties of the connection, but it can improve the continuity index in the elastic stage. However, if the bolt preload is too high, the column flange will enter the yield stage before vertical force is formally applied, as shown in Figure 12.  
Figure 8: Moment-rotation curves: (**a**) Thickness of side plate; (**b**) Bolts preload.  
Figure 9: Moment-relative dislocation curves of connections: (**a**) Thickness of side plate; (**b**) Bolts preload.
###### Abstract  
Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor-shaft-disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.  
order tracking; fault diagnosis; CEEMDAN; Hilbert transformation
earthquakes have been historically recorded towards the northeast, while strike-slip and normal fault events have been localised on the southeast and west sides. In particular, the Guidonia earthquakes that occurred on 23 December 2022 and 1 January 2023 could have been produced by a strike-slip fault proposed by [PERSON] et al. [1] starting on the Cornicolani Mounts and ending at the Colli Albani volcano district, crossing the city of Guidonia city (dashed line in Figure 1 of their paper, depicted here as the green dashed-dot line in Figure 1b). In the box delimited by 41.9\({}^{\circ}\)N \(\leq\) latitude \(\leq\) 42.1\({}^{\circ}\)N and 12.6\({}^{\circ}\)E \(\leq\) longitude \(\leq\) 12.8\({}^{\circ}\)E, the highest magnitude event between 1985 and 2023 was Md = 3.3 on 16 February 1999, 2 km north of Tivoli, which seems located on a northeast parallel fault. Other magnitude 2.9 earthquakes were recorded on the same fault close to Tivoli town on 22 September 2005 (doublet) and on 20 August 2013. After 1 January 2023, an event of magnitude Mw = 3.2 was recorded 8 km northwest of Colonna town on 22 January 2023 at 1:46:11 UT ([[http://cnt.rm.ingv.it/event/33960061](http://cnt.rm.ingv.it/event/33960061)]([http://cnt.rm.ingv.it/event/33960061](http://cnt.rm.ingv.it/event/33960061)), accessed on 29 January 2023). This event was localised on the southern side of the same strike-slip fault (dashed-dot green line in Figure 1b). In conclusion, the ML3.3 Guidonia earthquake of 1 January 2023 is presently the strongest event ever reported with the instrumental catalogue (from 1985) on this fault.  
One basic question is whether the events are parts of a seismic sequence or a swarm. A seismic sequence is characterised by a higher magnitude event (the mainshock) followed by aftershocks, and could be preceded by foreshocks of lower magnitude. In contrast, a seismic swarm is a chain of comparable and low-magnitude earthquakes without a clear mainshock [2]. If this is a seismic sequence, it is crucial to know if the event of magnitude ML = 3.3 of 1 January 2023 is the mainshock or if a future event of higher magnitude is likely to occur. Furthermore, for seismic sequences, the decay of the aftershocks generally follows the Omori-Utsu law [3]; in contrast, a seismic swarm has an almost constant seismic rate. The peculiarities of the seismic earthquakes of 22 December 2022 and 1 January 2023 localised close to Guidonia (Rome, Italy) motivates a further investigation that we report in this short paper. The research is based on our previous experience in the field of lithosphere, atmosphere, and ionosphere analysis prior to seismic events.  
Figure 1: _Cont._
## 2 Management of Biomass from Phytoremediation Processes
### Biochemical Conversion of Biomass  
The biochemical conversion, i.e., composting, is applied to reduce the level of organic matter in phytoremediation waste and concentrate the metal-enriched fraction via composting or digestion [48; 49]. Its advantages are that it is a low-energy method, extensively used for the treatment of many agricultural wastes for centuries, and that it only liberates emissions of already captured carbon (closed cycle) [50]. Some disadvantages are the probable immobilization of metals by microbes that is well known to occur naturally through microbial action (notably Hg; [51]) and the production of dead-end solid waste that still needs confinement and poses a risk of leaching metals.  
These latter problems can be overridden by coupling biochemical conversion with thermal treatment; nonetheless, the carbon lost during composting must be considered as energy loss for the upstream process. Another alternative is the composting and recovery of biogas from post-phytoremediation material. Interestingly, the plant-species-specific characterization indicates that not all plants are equally optimal for this process, since differences in accumulation and biodegradability affect the biogas yield and the final metal concentrations under normative limits [52].  
Plant compost can also be used in a virtuous cycle to limit Hg leaching and increase fixation during phytoremediation in plant aerial parts [53], potentially setting a base to study Hg phytoextraction. It has been observed that Cd is easily removed from the plant material and efficiently phytoextracted even during repetitive harvesting using tobacco plants [54]; the leached biomass can find further safe uses like composting or bioenergy recovery.  
Composting should not be discarded a priori based on its apparent simplicity; it has a valuable and potential use in cases where the metal concentration in the final compost is below normative standards for traditional sludge, therefore becoming fertilizers of the same phytoremediation site [27]. In this regard, Kidd et al. [55] reviewed the extensive agronomic experience of phytoremediation to manage the metal concentration in aerial tissue with periodical harvests and soil amendments. In this way, limiting the plant uptake and extending the remediation time can be counterbalanced by implementing up-stream operations, i.e., composting, that diversify the local bioenergy production economy.