esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The strain rate and temperature histories obtained from the numerical model are stored on the tracers. The relationship among the grain size, microhardness, strain rate, and temperature is obtained using the Zener-Hollomon parameter and Hall-Petch relationship. [PERSON] [14] simulated the temperature field of friction stir welding by ABAQUS software. The computing time is saved by non-uniform mesh. The results show that the temperature field is asymmetrical in relation to the center of the weld, and the temperature of the advance side of the weld is slightly higher than that of the retreat side of the weld. The welding process of friction stir welding can be divided into three stages: the plunging stage, the dwelling stage, and the welding stage. The heat produced by the interaction between the shaft shoulder and the workpiece interface is friction heat when calculating the temperature field. The heat production of the agitated needle is mainly caused by the shear stress [15; 16; 17]. The preheating process helps the material reach the plastic state as soon as possible and reduces welding defects [18; 19]. Due to the uncertainty of parameter interval setting in the test process, the difficulty and workload of the test are greatly increased. In 2A14-T6 aluminum alloy friction stir welding, the influence of welding speed, rotating speed, and pressing down on the welding quality was analyzed by changing the welding process parameters. When the welding process parameters are appropriate, a welding joint with beautiful shape, smooth and compact appearance, no internal defects, and excellent quality can be obtained [20; 21; 22]. In this paper, the finite element theory of friction stir welding is analyzed, and the FEM is modified by the test data. The modified FEM is used to simulate the full working conditions of FSW. It can effectively determine the process parameters of better welding quality and has a guiding significance in the real welding process. To sum up, the weld quality is related to factors such as spindle speed, welding speed, welding inclination, etc. Therefore, in order to study a better-quality friction stir welding process, it is necessary to analyze and verify the process parameters. The method of numerical simulation can be used to carry out the verification work in the early stage, which can provide an effective reference for the later test stages. The microstructure and mechanical properties of the joint were analyzed. The welding quality was determined by considering the strength and plasticity of the welded joint on the basis of a large number of process experiments. Welding process parameters with good quality were applied to the roof welding of rocket fuel tank.
## 4 Micro-Cracking Behaviors of Shale under Different Bedding Parameters
### Effects of Shear-to-Tensile Strength Ratio of Beddings
#### 4.2.3 Micro-Cracking Morphology  
Figure 14 presents the final fracture morphology of shale under different STRs. The variation of the STR hardly changes the fracture morphology if the dip angle is 0\({}^{\circ}\). The failure of the shale specimen mainly occurs in the shale matrix rather than the beddings when the dip angle is 0\({}^{\circ}\). When the dip angle is 30\({}^{\circ}\), a higher STR promotes a cluster generation of matrix cracks rather than outward extension. The matrix cracks mainly occur at the top and the middle of the shale specimen. When the dip angle is 60\({}^{\circ}\), the dominant failure is the shear slip of the beddings, and the variation of the  
Figure 13: Effects of shear-tensile strength ratio (STR) on the crack types in uniaxial compression. (a) STR = 0.8; (b) STR = 1.0; (c) STR = 1.2; (d) comprehensive comparison of matrix cracksSTR only affects the number of sliding beddings. Owing to the constant tensile strength of beddings, the tensile failure zone of beddings is basically unchanged when the dip angle is 90\({}^{\circ}\). Under this circumstance, the tensile-sliding combination failure of beddings gradually transforms into the tensile failure of beddings as the STR increases from 0.8 to 1.2.  
Figure 14: Distributions of crack types at different STRs: (**a**) STR = 0.8; (**b**) STR = 1.0; (**c**) STR = 1.2.  
The influence of the STR on the complexity of micro-cracking morphology is quantitatively analyzed through the perspective of fractal dimension. As the STR increases from 0.8 to 1.2, the average of the fractal dimension is 1.229, 1.219 and 1.268 for the matrix crack (Pbt+Pbs) and 1.186, 1.242 and 1.293 for the tensile crack (Pbt+Sjt), respectively. The STR cannot play a role in the main fracture morphology, but the increase of the STR can enhance the complexity of sub-fractures, especially when the STR is larger than 1.0.
## 3 Results and Discussion  
### Hyperspectral Image Analysis  
#### 3.1.1 ROIs Extraction Based on PCA and Masking  
Processing on image data of D7 was shown as an example. After PCA was applied, the first three PCs had a total variance percentage of 99.57%, while individual variance percentages were 92.31%, 6.34%, and 0.92% for PC1, PC2 and PC3, respectively. Comparing the score images of the first PCs, although PC1 had a maximum value of variance percentage, maize kernels seemed to be more distinct from the background on PC2 and PC3 score images. Therefore, data of PC2 and PC3 were used to make a scatter plot (Figure 1a) to extract object of maize kernels. Five classes were selected based on density slice imagery (Figure 1b), the corresponding pixels of each class were shown in a score image (Figure 1c) where the red class represented the whiteboard, the magenta class the background, the purple class the label, sea green for most parts of the Petri dishes, and yellow for kernels and lesser parts of the Petri dishes. Some parts of the labels produced specular reflection. Hence, they were gathered with the whiteboard in the scatter plot. Since kernels and parts of the Petri dishes were gathered together, it was difficult to separate them clearly in this step. Then, the yellow class was exported to ROI tools and used to make a mask to remove other classes' information. After the mask was applied, retained data also contained parts of the Petri dishes. A scatter plot of PC3 vs. PC4 (Figure 2a) was drawn based on the masked data. As shown in Figure 2a, two distinct clusters were apparent. The green cluster was for the parts of Petri dishes (Figure 2b), and exported to make an inverse mask to remove the parts of Petri dishes. After that, a clean image (Figure 2c) with only maize kernels was obtained. Image data of the other treatments (D1, D2, D3, D4, D5, D6, and DC) were processed with the same process steps.  
Figure 1: (a) PCA scatter plot of PC2 vs. PC3 (6.34% and 0.92%) of the image of D7; (b) density scatter plot of PC2 vs. PC3; and (c) corresponding score image where the red class represented the whiteboard and lighting sections of labels, the magenta represented the background, the purple the label, sea green for most parts of the Petri dishes, and yellow for kernels and lesser parts of the Petri dishes.
# Photon-In/Photon-Out X-ray Free-Electron Laser Studies of Radiolysis
## 2 Methods
### X-ray Pump/X-ray Probe
Figure 4: Simplified schematic of an X-ray pump/X-ray probe transient absorption experimental setup to study water radiolysis. Two broad bandwidth X-ray pulses, pump (green) and probe (blue) are produced collinearly from the accelerator and overlapped in a thin liquid sheet jet. Pump/probe delays between 0–100 fs are set with attosecond precision by the magnetic chicane. The transmitted beam is energy dispersed with a grating and detected on a 2D pixel array. Figure 3: Schematic of a potential X-ray pump/X-ray probe transient absorption study of water radiolysis. Left: X-ray pump photons (green) eject an electron from any of the valence or inner-valence orbitals \(1b_{1},3a_{1},1b_{2},2a_{1}\). X-ray probe photons (blue) selectively induces transitions from the 1s core level to the vacancies, (e.g., \(1a_{1}\to 1b_{2}\)). Right: Absorption spectrum of liquid water for a thickness of 800 nm [42] in solid gray. Calculated positions for absorption based upon the liquid phase binding energies [43]. The several eV separation of the bands allows to distinguish hole dynamics in different orbitals. It is desirable to study radiolysis in the isolated event regime. The concentration of ionization events can be readily controlled by the fluence of the X-ray pump pulses, i.e., the pulse energy/focal spot area. An additional consideration is to remain in the linear regime of X-ray interactions. As an example, for a pump pulse energy of 5 \(\upmu\)J on target at 260 eV, water jet thickness of 0.4 \(\upmu\)m, spot area of 100 \(\upmu\)m\({}^{2}\), one obtains an ionization fraction of 2% and a transmission of 77%. The relatively large area of the converging sheet jet (1 \(\times\) 3 mm, H\(\times\)V) can accommodate a substantial defocusing from the above size to achieve isolated event conditions. Our earlier X-ray transient absorption studies operated with an ionization fraction of \(<\)1% and observed a change in absorption of \(\sim\)10% for the analogous \(1a_{1}\to 1b_{1}\) transition-validating the feasibility of the X-ray pump/X-ray probe approach for studies of the physico-chemical timescale of water radiolysis.
## 4 Discussion
### Spatial Inhomogeneity of Accuracy
The accuracies of cold spots are generally lower than those of hot spots and areas without significant clustering, with average \(R^{2}\) of 0.91, 0.93, and 0.93, respectively. The lowest accuracy is observed in cold-spot regions during summer, with an \(R^{2}\) of 0.89. In general, the CNN-STK model we constructed can generate high-precision spatiotemporal continuous datasets based on sparse satellite data. Since data distribution from satellite collection during spring and autumn is relatively uniform, resulting in high-quality XCO2 reconstruction datasets. Moreover, the accuracy is higher in the northern regions compared to the southern regions. However, during summer, particularly in the northeastern region where low-value aggregation occurs, accuracy is lower, possibly due to significant disturbances from biosphere activities. Future research could consider vegetation characteristics and conduct separate modeling analyses for this region.
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 5 Hydrogels in Transdermal Drug Delivery  
Hydrogels possess remarkable qualities that make them well-suited for transdermal drug delivery. These characteristics encompass biocompatibility, biodegradability, elasticity, non-allergenicity, ease of application, soft consistency, and high-water content [77]. The application of hydrogels induces hydration effects, thereby improving the transport of drugs across the skin [77]. The basic component of the transdermal drug delivery system is a drug-loading matrix, as described in the earlier section. Hydrogels are frequently employed as matrices for Transdermal Drug Delivery Systems (TDDSs). [PERSON] et al. utilized polyethylene glycol diacrylamide (PEG-DA) hybrid hydrogel in mouse embryonic fibroblast cell lines (NIH 3T3). The hydrogel demonstrated favorable mechanical properties, swelling capacity, biocompatibility, and non-toxicity to the skin [78]. Hydrogels composed of gelatin polyacrylamide (Gel-PAAm) applied to human skin exhibited non-toxicity, high stretchability, and favorable swelling properties [79]. In another study involvingat abdominal skin, a hydrogel composed of polyacrylamide-grafted-chondroitin sulfate (PAAm-g-CS) was employed [80]. A hydrogel based on natural materials, chitosan-azelaic acid (CS-AZ), specifically the CS-AZ hydrogel, was utilized with mouse fibroblast L929, demonstrating outstanding swelling, water vapor permeability, high porosity, and low cytotoxicity [81]. Additional instances of hydrogels in transdermal drug delivery encompass the utilization of carboxymethyl chitosan-silk fibroin peptide/oxidized pullulan (CMCS-SFP/OPL) hydrogel in newborn porcine Tskin [82] and carboxymethyl chitosan-grafted-2 hydroxyethyl acrylate (CmCHT-g-pHEA), which was administered on the dorsal skin of micropigs [65; 83].
## 3 Hierarchical Blockchain-Empowered Cloud-Edge Orchestrated FL Architecture
### Overview of Proposed HBCE-FL Architecture  
The architecture of HBCE-FL for IoT consists of the local model training layer, the edge blockchain sanitizing layer, and the cloud aggregation layer, as visually represented in Figure 1. The local model training layer consists of IoT edge devices such as smart vehicles, which have a large amount of local private data. These devices employ in-built programs and algorithms to process and train on these data, ultimately generating essential knowledge in the form of FL parameters such as gradients. These devices can establish communication channels with the nearest edge server, enabling parameter exchange and the acquisition of updated model parameters. The edge blockchain sanitizing layer consists of edge servers such as communication base stations, where the knowledge blockchain is deployed. These edge servers play a pivotal role in linking smart devices to cloud servers, assuming responsibility for message sanitization and broadcast. Finally, the cloud aggregation layer includes multiple independent servers, each tailored for specific learning tasks. Servers at different levels serve different clients and do not collaborate or communicate with each other, facilitating data and client privacy isolation. To illustrate this within a practical context, consider a smart home environment, where the local model training layer represents an array of smart devices, including personal computers, wearable devices, and household appliances. In this scenario, routers serve as the servers within the edge blockchain sanitizing layer, while the cloud aggregation layer is the data center under the management of smart device manufacturers and smart home service providers.
## 3 Hybrid Visual SLAM with Deep Keypoints, Depths, Poses, and Line Features
### Virtual Stereo Visual SLAM
#### 3.3.2 Virtual Features Derived from Learned Depth  
In low-texture areas, ensuring an even distribution of detected keypoints across the entire image is challenging. Consequently, monocular SLAM struggles to achieve consistent scale in pose estimates through local feature matching alone. As shown in Figure 2a, this is a typical area with sparse texture. There is a lack of outdoor scenes such as vegetation and buildings. In close proximity to the camera, there are fewer feature points on the road. Therefore, it is difficult for the SLAM front-end to track sufficient feature points in close proximity areas. However, there are objects with obvious pixel gradients on the horizon. This enables the front end to stably track these distant features. It is worth noting that monocular SLAM exhibits scale ambiguity. It cannot distinguish between nearby and distant objects from a single image. Specifically, the positions of keypoints near the horizon remain largely unchanged as the camera moves. This leads SLAM to mistakenly assume that the camera has not moved significantly, resulting in degradation of scale estimation. As shown in Figure 2b, monocular SLAM exhibits scale degradation in the low-texture area. SLAM cannot correctly predict the depth of spatial points. Incorrect depth estimation of spatial points can also lead to incorrect keyframe pose estimation. At the same time, it is impossible to correct scale errors in the scene without loop nodes.  
To enable monocular SLAM to achieve scale awareness of the scene, we integrate a self-supervised visual ego motion estimator into the proposed monocular SLAM framework. Depth estimates from DepthNet in the estimator serve as a pseudo-depth sensor. These depth estimates provide a relative depth value \(D_{i}\) for each detected keypoint \(u_{L}\). While this depth value lacks an absolute scale, it accurately represents the relative distance of each object in the image. The use of relative depth values enhances the performance of positioning and mapping.  
As shown in Equation (12), we incorporate self-supervised depth labels into monocular SLAM to create a pseudo RGBD SLAM system. For each keypoint \(u_{L}\), we use the virtual baseline \(b_{u}\) and depth value \(D_{i}\) to compute a virtual matching point \(u_{R}\) in the right view, where \(f_{u}\) represents the focal length.  
\[u_{R}=u_{L}-\frac{f_{u}b_{u}}{\mathbf{D}_{i}} \tag{12}\]  
The stereo keypoints and line features obtained through the pseudo depth sensor are referred to as virtual local features. The post-processing of these virtual local features follows the same procedures as in stereo SLAM. In the back-end optimization, we construct a re-projection error function based on both point and line features.  
We combine the observed local features with the projected virtual features to formulate the optimization function.
## 4 Design Procedure and Application Example
### Verification of the SMC Stability  
The next step is to evaluate the SMC's stability, which requires verifying the transversality and reachability conditions. Evaluating the transversality value given in Equation (28) using the characteristics of Table 2 and the parameters previously calculated produces the average value \(\frac{d}{dt}\left(\frac{d\Psi}{dt}\right)=-2.9\times 10^{5}\
eq 0\), which confirms the transversality condition (26).  
Taking into account that the transversality value is negative, the reachability conditions that must be evaluated are inequalities (34) and (35). Evaluating those inequalities with the characteristics of Table 2 and the parameters previously calculated leads to the restrictions \(-145\) A/ms \(<\frac{d\mu}{dt}<145\) A/ms. Since the reference signal \(i_{R}\) is generated by a high-pass filter processing the load current \(i_{o}\), as discussed in Section 4.2, the maximum derivative of \(i_{R}\) is the same as that of \(i_{o}\): \(\max\left(\left|\frac{d\mu_{R}}{dt}\right|\right)=\max\left(\left|\frac{d\mu_{ R}}{dt}\right|\right)=70\) A/ms. Therefore, the previous calculations confirm that the reachability conditions (33) are fulfilled. It must be noted that, in case of a positive transversality value, inequalities (31) and (32) must be evaluated.  
In conclusion, the proposed design of both the power stage and SMC ensures a stable operation of the system, which guarantees the desired support for the battery.
## 4 Experiment and Discussion
### Experiment Results
With the improved strategy for PSO, IPSO can obtain better parameters for the FNN and the overall performance of the IPSO-FNN model is better than the other models for health condition estimation. However, the curves in Figure 8 present different performance. The six algorithms will consume more and more training time with the increase of training sample numbers. Due to the use of intelligence algorithms for optimizing the parameters of the FNN, the training times of the IPSO-FNN, PSO-FNN, and GA-FNN are obviously shorter than other methods. In addition, it can be observed from Table 3 and Figures 4-7 that the proposed method provides the best evaluation performance than other methods when the number of the training samples changes from 300 to 800. Four indices of the IPSO-FNN-based, PSO-FNN-based, GA-FNN-based, FNN-based, BPNN-based, and SVR-based evaluation methods decreases at first and then increases or have faint fluctuations with the increase of training samples size. The reason for this phenomenon is that too many training samples may over-train the network and the generalization performance of the network will weaken. Furthermore, the evaluation results of the shearer health condition are not always ideal. Therefore, on the premise of ensuring evaluation performance and running time for the shearer health condition, the number of training samples should be determined according to the actual experiment. Figure 8: Comparison of training time. Figure 7: Comparison of [PERSON]’s inequality coefficient. In order to further prove the optimization performance of the proposed IPSO in training the FNN model, the basic PSO [26], quantum-behaved PSO (QPSO) [27], opinion leader-based QPSO (OLB-QPSO) [28], and Laplace PSO (LPSO) [29] were employed to optimize the parameters in the FNN model. The parameters were set according to the relevant literatures. Based on the previous analysis, the dataset of S4 was selected for training and testing the FNN model. Three indices of MSE, MAPE, and TT were used to measure the optimization capacity in the FNN model and the comparison results are listed in Table 4.  
As seen from Table 4, the PSO with some improvements performs better training and testing properties than the basic PSO in optimizing the FNN model. In comparison of the four improved PSO algorithms, the OLB-QPSO can obtain the best MSE and MAPE values, both in the training phase and testing phase, while the training time (10.24 s) is not ideal. Although the MSE and MAPE values of the proposed IPSO are a little larger than those of OLB-QPSO and LPSO, the training time (8.96 s) is obviously better than other three improved PSO algorithms. Offering comprehensive consideration of the prediction error and running time, the proposed FNN-IPSO predictor can perform better in evaluating the shearer health condition.