esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 5 Conclusions  
This paper presents a novel defect detection approach leveraging the SA-Pix2 pix network and migration learning to address the issue of insufficient accuracy in detecting blemishes within complex patterned fabrics, especially when dealing with limited sample sizes. The central concept involves treating blemishes as damage to fabric texture, utilizing a conditional generative adversarial neural network to reconstruct the blemished regions within images, aiming to restore them to their normal fabric texture. Subsequently, dissimilarity computation between the reconstructed and defective images is conducted, followedby image segmentation based on the dissimilarity result, ultimately facilitating accurate blemish detection.  
Our contributions revolve around three key points: Firstly, we address the challenge of establishing remote dependencies in images due to the limited receptive field of the convolutional neural network within the generator. We introduce the image self-attention mechanism into the Pix2 pix neural network, exploring its impact on the network's image reconstruction accuracy. This leads to the proposal of the SA-Pix2 pix neural network model, significantly enhancing the accuracy of defective image reconstruction.  
Secondly, to combat the Pix2 pix neural network's limited capacity in processing image details, we introduce the L1 loss function and an improved structural loss function. This exploration assesses the impact of each loss function on image reconstruction accuracy, culminating in the construction of a comprehensive loss function tailored specifically for defective image reconstruction, overcoming the network's limitations in processing image details.  
Thirdly, we introduce migration learning to regulate the training of the target network through feature distribution, mitigating potential overfitting issues arising from small sample sizes.  
Furthermore, a comparative experimental study is conducted, pitting the ReNet-D model, SDDM-PS model, and our proposed model against each other. The study involves the detection of five different complex pattern fabric blemishes, demonstrating that our method outperforms both the ReNet-D and SDDM-PS models in terms of blemish detection accuracy.  
F.H.: Project administration, Funding acquisition, and Writing--Review and editing, J.G.: Conceptualization, Methodology, Formal analysis, and Writing--Original draft. H.F.: methodology. W.L.: data curation. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from Digital textile equipment Key laboratory open fund DTL2023006.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### The Response of hFOB Osteoblast Cells on ECIS after Treatment  
The images of hFOB 1.19 cells after treatment with different concentrations (0, 1, 5, 10, and 50 mM) of MgO and Mg(OH)\({}_{2}\) NPs at 24 h on a transparent single circular 250 \(\upmu\)m diameter active gold electrode of the ECIS system are shown in Figures 2 and 3, respectively. Results in Figure 2 show that after treating hFOB 1.19 cells with 0 mM of MgO NPs at 24 h, cells proliferate, spread, and adhere to the electrode, maintaining a confluent monolayer. At the lower concentration (1 mM) of MgO NPs, hFOB 1.19 cells show similar proliferation as that of the control group (0 mM), representing normal hemostasis of the cells. However, at the concentrations of 5, 10, and 50 mM of MgO NPs, the subsequent reduction in cell number was observed.  
Figure 1: Confluence of human fetal osteoblast (hFOB) 1.19 cells cultured in complete growth media at different time points:(**a**) 20–30% of cell confluence at 24 h; (**b**) 30–40% of cell confluence at 48 h; (**c**) 50–60% of cell confluence at 72 h; and (**d**) 70–90% of cell confluence at 96 h of seeding.  
Figure 3: Images of live and dead hFOB 1.19 osteoblast cells on microfabricated electrodes after treatment with different concentrations of Mg(OH)\({}_{2}\) NPs at 24 h: (**a**) 0 mM Mg(OH)\({}_{2}\) (control group); (**b**) 1 mM Mg(OH)\({}_{2}\); (**c**) 5 mM Mg(OH)\({}_{2}\); (**d**) 10 mM Mg(OH)\({}_{2}\); and (**e**) 50 mM Mg(OH)\({}_{2}\).  
Figure 2: Images of live and dead hFOB 1.19 cells on microfabricated electrodes after treatment with different concentrations of MgO nanoparticles (NPs) at 24 h: (**a**) 0 mM MgO NPs (control group); (**b**) 1 mM MgO NPs; (**c**) 5 mM MgO NPs; (**d**) 10 mM MgO NPs; and (**e**) 50 mM MgO NPs.  
Results in Figure 3 show that after treating hFOB 1.19 cells with 0 mM of Mg(OH)\({}_{2}\) NPs at 24 h, cells proliferate, spread, and adhere to the electrode and maintain the uniform monolayer. At the lower concentration (1 mM) of Mg(OH)\({}_{2}\) NPs, hFOB 1.19 cells show adhesion; however, the reduction in cell growth was observed as compared to the control group (0 mM). With the concentrations of 5 and 10 mM of Mg(OH)\({}_{2}\) NPs, subsequent reduction in cell growth and number of the live cells was observed. With the exposure of the 50 mM concentration of Mg(OH)\({}_{2}\) NPs on the hFOB 1.19 cells, a drastic reduction in the cell growth was observed.  
Comparing the reactivity of hFOB 1.19 cells in response to the exposure of 50 mM concentrations of MgO and Mg(OH)\({}_{2}\) NPs for 24 h, results depict major reduction in cell proliferation and spread for Mg(OH)\({}_{2}\) NPs.
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 3 Results and Discussion
### Subjective Assessment  
Visual results of various MRI and CT fusion methods are shown in Figures 5-7. A good MRI- and CT-fused image should contain both the soft tissue information and dense structure information of the MRI and CT images. We can draw the following observations by examining the visual quality of the four sets of MRI-CT fusion results using various methods.  
Figure 5: Visual quality analysis of various fusion algorithms for MRI-CT (set-7). (**a**) MRI image, (**b**) CT image, (**c**) ASR, (**d**) CVT, (**e**) DTCWT, (**f**) MSVD, (**g**) CSMCA, (**h**) NSST, (**i**) proposed method.  
Figure 6: Visual quality analysis of various fusion algorithms for MRI-CT (set-11). (**a**) MRI image, (**b**) CT image, (**c**) ASR, (**d**) CVT, (**e**) DTCWT, (**f**) MSVD, (**g**) CSMCA, (**h**) NSST, (**i**) proposed method.  
Figure 7: Visual quality analysis of various fusion algorithms for MRI-CT (set-15). (**a**) MRI image, (**b**) CT image, (**c**) ASR, (**d**) CVT, (**e**) DTCWT, (**f**) MSVD, (**g**) CSMCA, (**h**) NSST, (**i**) proposed method.  
1. Compared to all the other methods, our proposed algorithm provides a brighter outer region representing the CT image's dense structure;
2. From Figures 5-7, it can be seen that the fused images of methods (c)-(g) are yielding poor contrast;
3. Though the method (h) in all the Figures 5-7 provides better contrast details; still, it is suffering from artifacts, especially in the CT region.  
From Figures 5-7, it can be noticed that the ASR method transfers both the CT and MRI information partially with low contrast. Next, coming to the CVT contains more MRI details than the CT. In the DTCWT method, we can find a few fusion artifacts in and around the CT region. Similarly, we can observe information fusion loss in the MSVD method. Compared with the methods mentioned above, CSMCA gives better visual quality, but the overall contrast of the image is reduced. The fused images with the NSST method are visually degraded due to both the fusion loss and artifacts. Overall, our proposed method retains the necessary information from the MRI and CT with minimum fusion losses. The comparison results of
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 3 System Models
### Network Model  
Drone clustering is an important model for UAV network applications to enhance the destructive resistance of UAV networks with node redundancy and provide mission completion rates. The large-scale mission environment and numerous nodes make the scale UAV network has higher requirements for scalability and security. Based on the threats that exist, the mission-oriented UAV network is essentially an asynchronous network with Byzantine General problem at scale, containing different network models containing two phases of mission preparation and mission execution.  
* Centralized network: The centralized network in the mission preparation phase completes the initialization of the nodes and the network in a secure environment with the registration server as the center. All UAV nodes participating in the mission provide their own unique information, such as IP, NIC address, etc., and submit registration applications to the center. The registration server allocates the corresponding public and private keys of the UAV nodes and generates 48-bit hash values for each UAV node based on the public key as the UAV identity \(ID_{i}\). The registration center will accumulate all IDs into a fixed-size identity vector commitment \(\overrightarrow{ID}\) based on the size of \(ID_{i}\), and calculate the witness \(W_{i}\) of the identity existence corresponding to \(ID_{i}\), and initialize an empty one for storing the untrusted node word vector commitment, and randomly select \(M\) authorized proxy nodes as the consensus committee. The registry creates the Genesis block and deploys the membership information of the consensus committee, the identity vector commitment and the corresponding identity authentication smart contract in it. The blockchain program is deployed on each drone node \(ID_{i}\), allocating the corresponding key pair \(PK_{i}\), \(SK_{i}\), and identity witness \(W_{i}\), and synchronizing the creation block to complete node initialization, and \(ID_{i}\) becomes a blockchain network node. The final composition of the UAV network with \(N\) nodes is denoted as \(\{ID_{i}\}\),\(i\in\{1,2,\ldots,N\}\).
* Distributed network: The UAV network in the mission execution phase is an asynchronous distributed network. After the mission starts, the registration server is not involved in the mission and the UAV network is handed over to the blockchain system for management, which is mainly responsible for node identity authentication and node trustworthiness decision during communication. Authorized agent points constitute the upper logical network \(ID_{A^{\prime}}^{i}\), \(\{j\in[M]\}\), which is responsible for consensus computing and inter-zone routing. Combined with the ZRP routing protocol. By detecting the forwarding behavior of nodes, an adaptive reconfigurable network model with hierarchical architecture is constructed based on a continuously updated blockchain.
## 3 Results and Discussion
### Delignification Step via Ultrasound-Assisted Extraction (LIAE)
Despite this limitation, ChLAGly was comparable in terms of delignification efficiency with the treatment using NaOH for 60 min (27.06% vs. 30.06%, respectively, \(p>0.05\)). Moreover, when sonication time was prolonged up to 2 h, this NaDES proved even higher efficiency than NaOH (33.38% vs. 25.05%, \(p<0.05\)). Figure 1: Grape stalk (GS) delignification by alkaline and NaDESs treatment under UAE (40 kHz, 200 W). Results are expressed as mean \(\pm\) SD. Values within each variable with different letters are significantly different (\(p<0.05\)). Regarding ChLA, it should be noted that no significant difference was observed at 60 min compared to the tricomponent ChLAGly at 120 min (\(p>0.05\)). Since sonication prompts mass transfer and cell rupture due to the solvent penetration into the solid matter, a less viscous fluid would favor the process and improve delignification yields even at shorter times. Moreover, at 120 min, delignification was significantly favored (\(p<0.05\)), as expected, with a delignification yield higher than 50%. Finally, the results achieved using ChLevA were noteworthy (Figure 1), showing an efficiency around 70% in terms of GS delignification, which was significantly higher compared to the other results (\(p<0.05\)). Considering bicomponent NaDESs, despite the lower LevA acidity compared to LA, an excellent extraction capability for this NaDES at 1:2 molar ratio was unveiled. Furthermore, it was also worth noting that no statistical significance was found for 60- and 120-min UAE (73.99% vs. 72.31%, \(p>0.05\)), thus highlighting the possibility of employing shorter times for a more sustainable valorization process. Overall, the applicability of NaDESs for delignification purposes in combination with US was efficiently demonstrated for GS. Particularly, ChLevA proved an excellent candidate in this way. Moreover, it is worth noting the properties of LevA in terms of cost and availability [36], as well as its easier production from renewable sources as lignocellulosic biomass, making it available for NaDES production.
### The Analysis of the Impact of Mining Size on Fault Activation
#### 2.4.2 Analysis of the Changes in the Stress of Fault Plane Caused by Mining  
The shear stresses distribution on the fault plane under various excavation sizes is depicted in Figure 21. It can be observed that the shear stress and additional shear stress rise when the distance between the mining area and the fault is fixed. Regardless of the increase in the length or height of the mining area, the possibility of fault activation also increases.  
When the mining size increases along the length direction, the shear stress distribution rule is obvious. The peak shear stress appears near the depth of \(-450\) m, and the value increases with the increase of mining length. Such as the maximum shear stress is 2.434 MPa for a mining size of 100 m \(\times\) 50 m. The maximum shear stress is 2.817 MPa when the mining size increases to 200 m \(\times\) 50 m. When the mining size increase to 300 m \(\times\) 50 m,  
Figure 21: Shear stress distribution at the fault plane with different mining sizes.  
Figure 20: Absolute displacement distribution of surrounding rock under different mining sizes (unit: m), they should be listed as: (**a**) Mining size is 50 m \(\times\) 100 m; (**b**) Mining size is 50 m \(\times\) 200 m; (**c**) Mining size is 50 m \(\times\) 300 m; (**d**) Mining size is 100 m \(\times\) 50 m; (**e**) Mining size is 200 m \(\times\) 50 m; (**f**) Mining size is 300 m \(\times\) 50 m.  
the maximum shear stress increases to 3.543 MPa near the depth of \(-450\) m. While the shear stress that is 0 MPa in the depth range of 0 m to \(-100\) m, demonstrating that the mining-affected fault has been pulled away near the surface.  
When the mining size increases along the height direction, the shear stress increases compared with the initial equilibrium state between 0 m and \(-350\) m deep above the goaf. Positive additional shear stress is present, and it rises with the increase of mining depth. Indicating that the larger the mining size along the vertical direction, the higher the probability of fault activation. When the mining size is 50 m \(\times\) 100 m and 50 m \(\times\) 200 m, the maximum shear stress near the depth of \(-50\) m is 0.673 MPa and 0.796 MPa respectively, and when the mining size increases to 50 m \(\times\) 300 m along the height direction, there is a tendency for the maximum shear stress to extend downwards, the maximum shear stress is 2.006 MPa and has a large increase. Below the depth of \(-350\) m, the additional shear stress is negative and the shear stress decreases.
## 4 Discussion
### Limitations and Generalizability  
This study has several limitations that should be acknowledged. First, the heterogeneity of ATS systems--ranging from differences in system logic and interface design to terminology and operational rules across cities--constrained the selection of subjects and limited the sample size. These variations, while reflective of real-world conditions, may affect the generalizability of the findings. Second, due to sample size constraints, the study focused on a limited set of factors derived from pre-experiment interviews and questionnaires. Future research with larger and more diverse samples could explore a three-dimensional automation trust model encompassing system factors (e.g., failure history and safety redundancy), dispatcher factors (e.g., individual tendencies and skill levels), and environmental factors (e.g., task complexity and multitasking demands). Finally, the study's focus on Chinese subway dispatchers raises questions about cross-cultural applicability, necessitating further validation in other regions and operational contexts.
## 3 Results and Discussion
### Mechanical Properties of EP/BCI/BSEA Composites
#### 3.2.3 Impact Properties  
As summarized in Figure 9b and Table S2, the impact strength of EP/BCI/BSEA-2 achieved a peak value of 72.44 kJ\(\cdot\)m\({}^{-2}\) at RT and 31.85 kJ\(\cdot\)m\({}^{-2}\) at 77 K, with an improvement of 201.83% and 128.81% compared with those of pure EP (24.00 kJ\(\cdot\)m\({}^{-2}\) at RT and 13.92 kJ\(\cdot\)m\({}^{-2}\) at 77 K), respectively. Moreover, the impact strength decreased when the amount of BSEA added continued to increase. The increase in impact strength helped the sample resist local impact forces in the ultra-low-temperature liquid oxygen environment, thereby reducing the impact sensitivity of liquid oxygen and improving the LOC of EP. This result is in line with the conclusion of the previous IRS test. This was mainly due to the mechanism whereby the free volume increased with the addition of BSEA. The free volume could absorb energy by distorting itself, as well as providing a space for molecular chains to move in the crosslinking networks during the process of being impacted by the external forces, and thus increasing the impact strength. However, the microstructural integrity of modified EP could not be maintained because too much BSEA was introduced into the crosslinking networks, leading to a decline in impact strength. Moreover, the impact strengths at 77 K were all decreased compared with those at RT in the same components. This was because molecular segmental motion occurred more easily at RT than at 77 K, thus dissipating more impact energy and exhibiting higher impact strength.  
Figure 10: SEM images of the fracture surfaces of the cured EP: (**a**) pure EP, (**b**) EP/BCI-25, (**c**) EP/BCI/BSEA-2, (**d**) EP/BCI/BSEA-3, (**e**) EP/BCI/BSEA-4, (**f**) EP/BCI/BSEA-5 at RT, (**g**) pure EP, (**h**) EP/BCI-25, (**i**) EP/BCI/BSEA-2, (**j**) EP/BCI/BSEA-3, (**k**) EP/BCI/BSEA-4, (**l**) EP/BCI/BSEA-5 at 77 K.
## 3 Discussion
### Future Research Directions  
While this study confirmed the effectiveness of PSD simplifications and FEM models for predicting fatigue life, several avenues for future research remain. There is a need to explore non-linear behavior in mechanical systems under random vibrations, particularly for components like dampers, which may experience significant non-linearity under higher loads. Additionally, developing more adaptive PSD simplification techniques that can dynamically adjust to different loading conditions would further enhance the precision and applicability of these methods.  
Expanding the scope of this research to other mechanical systems and varying types of dynamic loads could also provide broader insights and help generalize the findings to a wider range of industries.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 6 Results and Discussion  
This section investigates the performance metrics of route selection, namely cumulative end-to-end delay and packet delivery rate.  
The first investigation in Section 6.1 aims to understand the effect of PUs' activities to traffic offload by measuring the traffic traverses along each D2D route in the presence and absence of a backbone route. Next, the aim is to understand the D2D and backbone routes, which consist of nodes with different processing capabilities in addition to the presence of PUs' activities in D2D routes and the absence of PUs' activities in the backbone route. The second investigation in Section 6.2 compares the packet delivery ratio of the traffic traverses along a D2D route with PUs' activities and the backbone route without PUs' activities. The third investigation in Section 6.3 compares the cumulative end-to-end delay of the traffic traverses along the D2D routes with different PUs' activity levels and the backbone route without PUs' activities.
## 5 Accuracy and Uncertainty of COSMIC-2 Refractivity, Temperature, and Water Vapor Retrievals
### Accuracy and Uncertainty of COSMIC-2 Refractivity in the Troposphere
The mean COSMIC-2-RS41 water vapor difference from surface to 5 km altitude for each SNR groups (also see Table 4) are equal to \(-\)1.34 g/kg (0-500 \(\
u\)/\(\
u\)), \(-\)1.17 g/kg (500-1000 \(\
u\)/\(\
u\)), \(-\)1.33 g/kg (1000-1500 \(\
u\)/\(\
u\)), \(-\)0.93 g/kg (1500-2000 \(\
u\)/\(\
u\)), and \(-\)1.52 g/kg (\(>\)2000 \(\
u\)/\(\
u\)). Except for the \(>\)2000 \(\
u\)/\(\
u\) group, the high SNR measurements from COSMIC-2 seem to improve the mean water vapor difference for higher SNR groups slightly (especially for the 1500-2000 \(\
u\)/\(\
u\) group) comparing with those from lower SNR groups.
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 2 In Vivo Confocal Laser Scanning Microscopy
### Pre-Surgical Mapping  
While an accurate analysis of melanocytic lesions encompasses the use of the wide-probe in order to explore the entire lesion, the handheld RCM device [59; 60] is employed not only for diagnostic purposes in difficult-to-reach areas, but also for pre-surgical mapping of LM/LMM borders [61].  
Indeed, the management of LM/LMM may be challenging for ill-defined margins, possible subclinical spread, and common localization of these lesions on areas with an aesthetic impact, such as the face. Therefore, the possibility to non-invasively explore and mapping lesions before surgery represents an advantage in terms of healthy tissue sparing, thus reducing the number of skin biopsies and in order to plan an adequate treatment of LM/LMM and counsel patients appropriately.  
The most recent techniques for non-invasive pre-surgical mapping are the Radial Videomasicking RCM [62] and the smart approach [33]. The first one consists of confocal exploration from the center of the lesion, going radially to the periphery, using adhesive paper rings for the definition of the margins while the latter employs small skin cuts are used for the same purpose, proceeding radially outward the identified segment by 2 mm intervals until an RCM-negative margin is observed [33; 62]. A prospective study involving 72 patients performed a comparison of the subclinical extension of LM/LMM identified through RCM and histopathology, revealing an overall agreement of 85.9% between RCM margin assessment and final histopathology [63].  
Studies including a lower number of cases have investigated the role of RCM for margins evaluation in BCCs [64; 65].