esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to optimizing medical quality of service (m-QoS) during the COVID-19 situation. Many people could not travel due to the current issues, for fear of spreading the covid-19 virus. Therefore, our paper will provide a very trusted and reliable method of communication between a doctor and his patient so that the latter can do his operation even from a far distance. The communication between the doctor and his/her patient will be monitored by our proposed algorithm to make sure that the data will be received without delay. We test how we can invest buffer space that can be used efficiently to reduce delays between source and destination, avoiding loss of high-priority data packets. The results are presented in three stages. First, we show how to obtain the greatest possible reduction in rate variability when the surgeon begins an operation using live streaming. Second, the proposed algorithm reduces congestion on the determined path used for the online surgery. Third, we have evaluated the affection of optimal smoothing algorithm on the network parameters such as peak-to-mean ratio and delay to optimize m-QoS. We propose a new Smart-Rout Control algorithm (s-RCA) for creating a virtual smart path between source and destination to transfer the required data traffic between them, considering the number of hops and link delay. This provides a reliable connection that can be used in healthcare surgery to guarantee that all instructions are received without any delay, to be executed instantly. This idea can improve m-QoS in distance surgery, with trusted paths. The new s-RCA can be adapted with an existing routing protocol to track the primary path and monitor emergency packets received in node buffers, for direct forwarding via the demand path, with extended features.  
component; quality of service (QoS); buffer optimization; internet of things (IoT); medical surgery; smart-route control algorithm (s-RCA) +
Footnote †: footnote]Footnote : [[https://www.mdpi.com/journal/jsan](https://www.mdpi.com/journal/jsan)]([https://www.mdpi.com/journal/jsan](https://www.mdpi.com/journal/jsan))
## 3 Simulation Results Considering Typical Working Conditions with Mount
### Tip-In/Tip-Out Condition  
The simulation is initiated with the vehicle running stable in the second gear. The step change of the engine output torque is used to simulate the tip-in/tip-out condition at 3 s and 4.5 s from Figure 8a. The simulation results of the vehicle in the tip-in and tip-out conditions with and without the mount are shown in Figure 8.  
Due to the sudden change of the vehicle driveline torque under the tip-in and tip-out conditions, the jerk of the vehicle with and without the mount has a large fluctuation from Figure 8b. Under the tip-in condition, the vehicle jerk with and without the mount are close. Under the tip-out condition, the jerk of the vehicle with the mount has a greater amplitude fluctuation than that without the mount, the maximum fluctuation amplitude of the jerk increases from 17.6 m/s\({}^{-3}\) without the mount to 34.1 m/s\({}^{-3}\) with the mount, which affects the ride comfort of the vehicle. Simultaneously, because the engine adopts the dynamic torque model, the output torque of the engine fluctuates, which can act on the vehicle through the mount. Therefore, when the mount is considered, there is an obvious  
Figure 8: The results of the mount influence on tip-in/tip-out conditions: (**a**) engine torque under tip-in/tip-out conditions; (**b**) vehicle jerk; (**c**) longitudinal force of each mount; (**d**) the longitudinal force of the powertrain on the vehicle.  
high frequency fluctuation in the jerk of the vehicle. It can be seen from Figure 8c that as a result of the sudden change of the system torque under tip-in and tip-out conditions, the longitudinal force acting on the mount changes. Meanwhile, the force of the powertrain acting on the vehicle through the mount increases significantly under the tip-in and tip-out conditions in Figure 8d.
# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
###### Abstract  
Extracting river channels from remote sensing images is crucial for locating river water bodies and efficiently managing water resources, especially in cold and arid regions. The dynamic nature of river channels in these regions during the flood season necessitates a method that can finely delineate the edges of perennially changing river channels and accurately capture information about variable fine river branches. To address this need, we propose a river channel extraction method designed specifically for detecting fine river branches in remote sensing images within cold and arid regions. The method introduces a novel river attention U-shaped network structure (RAU-Net++), leveraging the rich convolutional features of VGG16 for effective feature extraction. For optimal feature extraction along channel edges and fine river branches, we incorporate a CBAM attention module into the upper sampling area at the end of the encoder. Additionally, a residual attention feature fusion module (RAFF) is embedded at each short jump connection in the dense jump connection. Dense skip connections play a crucial role in extracting detailed texture features from river channel features with varying receptive fields obtained during the downsampling process. The integration of the RAFF module mitigates the loss of river information, optimizing the extraction of lost river detail feature information in the original dense jump connection. This tightens the combination between the detailed texture features of the river and the high-level semantic features. To enhance network performance and reduce pixel-level segmentation errors in medium-resolution remote sensing imagery, we employ a weighted loss function comprising cross-entropy (CE) loss, dice loss, focal loss, and Jaccard loss. The RAU-Net++ demonstrates impressive performance metrics, with precision, IOU, recall, and F1 scores reaching 99.78%, 99.39%, 99.71%, and 99.75%, respectively. Meanwhile, both ED and ED' of the RAU-Net++ are optimal, with values of 1.411 and 0.003, respectively. Moreover, its effectiveness has been validated on NWPU-RESISC45 datasets. Experimental results conclusively demonstrate the superiority of the proposed network over existing mainstream methods.  
river channel segmentation; convolutional neural network; attention model; remote sense +
Footnote †: journal:  
0000-0001-999-4000-0001-9999-4000]J.J.J.J.
## 2 Materials and Methods  
In this paper, the single RGB image is used as the input to obtain the virtual view, and the depth estimation network is used to obtain the RGB-D image corresponding to the input image (Section 2.1). Then, the RGB-D image is mapped to the LDI representation. Compared with the previous single-layer depth representation, the LDI was used for multilayer depth representation so as to further utilize the details of the parallax region to generate new geometry for the occluded area (Section 2.2). For newly generated pixels in the occluded area, we filled them with color information and depth information through the GAN (Section 2.3). Finally, the above local color information and depth information after inpainting were integrated in turn, and the integrated view was optimized by using the method of deep learning so that high-quality virtual views were generated (Section 2.4).  
The overall frame of the virtual viewpoint image generation is shown in Figure 1. Firstly, depth-related information was obtained from the input single RGB image. Then, all kinds of constraints extracted were processed through the edge inpainting network, color inpainting network and depth inpainting network, respectively, to obtain the virtual viewpoint image. Finally, the optimized network was used to further optimize the image and output high-quality virtual view image.  
To illustrate the method proposed in this paper more clearly, the implementation logic of virtual view acquisition technology is shown in Algorithm 1.  
```
1. Inputing RGB color image.
2. Generating semantic segmentation image of RGB image and defining semantic segmentation label \(M_{SS}\).
3. Generating depth image of RGB image.
4. Elevating depth image to LDI for multilayer representation.
5. Preprocessing depth image.
6. Sharpening depth image and determining discontinuous pixel segment.
7. Aligning RGB image with depth image.
8. Eliminating spurious response (continuous pixel segment less than 15 pixels) and determining the number of discontinuous layer \(N_{LDI}\).
9. Inpainting layered image.
10. for \(N=1;N<=N_{LDI};N++\)
11. Breaking discontinuous points to generate composite area. The fore ground area, background area and outline of occluded area are determined, and the occluded area is filled by the iterative flood filling algorithm.
12. Inpainting image edge of occluded area.
13. Inpainting of color and depth of occluded area.
14. end for
15. Combining foreground area, background area and occluded area to generate new view angle image.
16. Optimizing virtual view of new view angle image.
17. Determining semantic segmentation label \(M^{s}_{SS}\) in the new view angle image. The semantic segmentation label is applied to the new view angle image and its depth image.
18. for \(M=1;M<=M^{s}_{SS};M++\)
19. Optimizing new view angle image.
20. end for
21. Outputting virtual view image.
```  
**Algorithm 1** Virtual view generation algorithm The proposed virtual view generation technique is described in detail in the following sections.
## 2 Operation and Control
### Fault Detection and System Protection
#### 2.3.1 Fault Detection  
As [PERSON] et al. [54] show, the electric connection circuits of power plants require generator circuit-breakers at the generator terminals. Here, the manner in which various factors influence the delay of the zero passing of the short-circuit current is investigated. The work models the main parameters that affect the failures, and perhaps the main parameter is that of the transient recovery voltage.  
As [PERSON] et al. [55] present, the evolution over time of the sources of insecurity must be known, and how the system will be affected.  
[PERSON] et al. [56] study and propose a regulatory framework to frame protection systems. The work focuses on protection systems and grounding devices in direct current circuits, for the feeding of EV charging stations.  
[PERSON] [57] proposes management of the detection of faults by means of Markov chains. Faults are localized by modeling the data to the invariant plane over time. Therefore, the design of new fault detection systems is necessary, and in addition, systems with self-healing will be critical.
## 4 Optimizing EE- and SE-Based Beam Training  
This section examines the effectiveness of using a CSI predictor realized by an autoregressive predictor and a DL-based time division duplex (TDD)-scheme-based predictor involving pattern extraction realized by a convolutional neural network. These predictors can offer great performance gains, achievable throughput, and a significant improvement in channel predictability. The mMIMO system will be essential to the B5G wireless cellular networks' mm-wave technology. Path loss in mm-wave transmission will be fought by the mMIMO system's high beamforming gain. With interference and non-linear distortions, the DL is successfully used in joint channel estimation and signal detection. Deep CNNs can take advantage of channel correlations to improve the accuracy of channel estimates while reducing the computational cost in mm-wave mMIMO systems. By leveraging the inherent structure of the channel, a deep CNN can efficiently extract and process spatial and temporal features from channel data. This enables the network to generate more accurate channel estimates with less computational overhead, making it a promising approach for optimizing the performance of mm-wave mMIMO systems. With a low-resolution analog to digital converter (ADC), the supervised learning-based sequential interference cancelation is created for mMIMO detection ADCs. An important step in obtaining the channel state data needed for mMIMO transmission is channel estimation. In contrast, an excessive amount of overhead is needed to serve an ever-increasing number of devices due to the exponential increase in devices and the variety of new applications. The maximum number of devices that may be handled in one cell is in the order of \(10^{5}\). The integration of DRL with the AB-HP framework enhances the connection between beam selection and EE/SE outcomes by dynamically optimizing RF chain consumption while minimizing beam training overhead. RBT-DRL intelligently selects beams based on ambient angular information, reducing computing complexity and pilot power consumption while adapting to real-time network fluctuations. By incorporating DNN-based channel estimation, the system ensures accurate channel characteristics, addressing the challenges of high path loss and obstruction in mm-wave communication. Unlike traditional HP systems that focus solely on reducing RF chains or improving training efficiency, RBT-DRL enables dual optimization by dynamically adjusting the number of activated RF chains to balance EE and SE as shown in (6) and (10). This adaptive beam selection approach enhances capacity in multi-user mMIMO systems while maintaining resilience under varying network conditions. The methodology demonstrates how RBT-DRL outperforms existing solutions in efficiency, reliability, and scalability by optimizing beamforming, reducing training overhead, and improving EE/SE trade-offs.
## 3 Materials and Method
### Proposed Method
#### 3.3.1 Data Obfuscation Strategy Based on Probability Density and Information Entropy
Gaussian noise follows the distribution  
\[\eta\sim\mathcal{N}(0,\sigma^{2}), \tag{15}\]  
while gradient perturbation is generated using the gradient information of the task model, defined as  
\[T(x)=x+\epsilon\cdot\
abla_{x}L(f(x),y), \tag{16}\]  
where \(\epsilon\) is the perturbation strength and \(\
abla_{x}L(f(x),y)\) is the gradient of the loss function \(L\) with respect to the input data \(x\). The information entropy fusion module also includes an entropy map generator to apply higher-intensity obfuscation to sensitive regions in specific contexts. For example, in medical imaging tasks, the entropy map identifies lesion areas for targeted protection. Local entropy is computed by combining distribution features and domain priors, using the formula  
\[H_{\text{local}}(x)=-\sum_{j}P_{j}(x)\log P_{j}(x), \tag{17}\]  
where \(P_{j}(x)\) represents the probability distribution of pixel \(x\) within a local window. By combining probability density extraction and information entropy fusion, the proposed method preserves the global distribution of the data while protecting sensitive information. A mathematical analysis of this design demonstrates that the probability density extraction module provides precise modeling of data distributions, ensuring that obfuscated data retain the core characteristics required for analytical tasks. Meanwhile, the information entropy fusion module dynamically adjusts the obfuscation intensity, reducing the adverse effects of over-obfuscation on task performance. In practical applications, this design offers several advantages. First, the efficient modeling capability of the probability density extraction module enhances the obfuscation strategy's adaptability to data distributions, ensuring that obfuscated data align with downstream task requirements. Second, the dynamic adjustment mechanism of the information entropy fusion module significantly reduces the conflict between privacy protection and task performance, making it suitable for various scenarios, such as medical image analysis and financial data prediction. Finally, the inclusion of the entropy map generator allows for the targeted protection of sensitive regions within the data, providing an effective solution for privacy-sensitive domains like healthcare and finance.
## 3 Materials and Methods
### Architectural Enhancements over Original StarGAN
#### 3.2.2 Integration of LeakyReLU Activation  
In our modified architecture, we replaced all of the Generator ReLU activation functions with LeakyReLU activations (with a negative slope coefficient of 0.2) and changed all of the Discriminator LeakyReLU activations to coefficient 0.2, also in the Residual Block. This modification addresses the \"dying ReLU\" problem that can occur in the new adjusted model implementation when handling the subtle intensity variations common in medical images. LeakyReLU allows for small negative gradients to flow through the network, resulting in more stable training dynamics and better preservation of fine details in generated medical images. This change is particularly beneficial in the encoder blocks of the generator network, where preserving gradient information for low-intensity features is crucial for maintaining pathological markers in synthesized medical images.
## 3 Results and Discussion
### Effects of NTP Treatment on Cell Migration  
In order to further explore the effect of NTP treatment on the migration ability of HaCaT cells, the cell scratch test was used to detect the migration ability of HaCaT cells. Compared with the control group, the cell scratch areas in the 15 s treatment group were significantly reduced at 4 h and 24 h after NTP treatment, and the areas in the 10 s treatment group were nearly completely closed after incubation for 24 h. However, we also observed that with the extension of treatment time, the scratch area of cells in the 20 s and 25 s treatment groups did not change significantly (Figure 5A,B). To further verify the results of the scratch test described above, we performed a cell migration test using transwell. Consistent with the scratch test results, the number of cell migrations was significantly greater in the 10 s treatment group than in the control group, and was less for the 25 s treatment group (Figure 5C,D). Therefore, low-dose NTP promoted the migration of HaCaT cells, while high-dose NTP inhibited the migration of HaCaT cells after NTP treatment.  
Wound healing was a complex process with the purpose of restoring skin function. Skin regeneration involved the proliferation and migration of HaCaT cells. Therefore, the migration of HaCaT cells to the wound site was extremely important for wound healing, and many studies have demonstrated the migratory ability of HaCaT cells [49, 50]. For instance, Se-methylselenocysteine stimulates the migration of HaCaT cells [51]. NTP was often used to inhibit the migration of cancer cells [52], but there were few reports suggesting that it promotes HaCaT cell migration. Therefore, we attempted to explore the migration effect of NTP treatment on HaCaT cells. Encouragingly, both cell scratch and transwell experiments showed that low-dose NTP treatment promoted cell migration, while high-dose NTP treatment inhibited cell migration. For migration, intracellular ROS often showed inhibition on cancer cells [53]. However, our results showed that ROS generated by low  
Figure 4: Mitochondrial membrane potential in HaCaT cells. (**A**) Red fluorescence image of mitochondrial membrane potential in HaCaT cells. (**B**) Quantification was performed by measuring the intensity of red and green fluorescence pixels in HaCaT cells using ImageJ J software. (**C**) Green fluorescence image of mitochondrial membrane potential in HaCaT cells. (**D**) The ratio of red to green fluorescent pixels was quantified using ImageJ J software. Data represent the mean \(\pm\) SD of three independent experiments. * \(p<0.05\), ** \(p<0.001\), *** \(p<0.001\), *** \(p<0.0001\) with ANOVA compared with the control.  
dose NTP promoted the migration of HaCaT cells, which was consistent with the results of previous cell viability measurements. The specific mechanism remains to be further explored.
## 2 Materials and Methods
### Interfacial Adaptation Evaluation  
Individual restorations for each preparation were projected in Cerec CAD software (ver. 4.5.2; Dentsply Sirona, York, PA, USA), setting digital spacing = 100 \(\upmu\)m only in axial and occlusal areas. Restorations were milled twice using a calibrated Cerec MC XL (Dentsply Sirona, York, PA, USA) and a hybrid ceramic, Cerasmart (GC, Tokyo, Japan) as material. After finishing and polishing, restorations were bonded onto conventional casts and 3 DC using a radiopaque composite resin flow (Herculite XRV Ultra Flow; Kerr, Orange, CA, USA) and light-cured for 60 s with a light-emitting diode (LED) curing light (VALO; Ultradent, South Jordan, UT, USA). The aim of luting restorations to 3 DC was to evaluate the precision of projecting and milling procedures, as both 3 DC and restorations  
Figure 1: Summary of the study design.  
Figure 2: Random sample analysis. The image on the left represents the comparison between RS and IOS, while the image on the right between RS and 3 DC. The tolerance of 10 \(\upmu\)m is shown in green, while other values scale from yellow to red (positive discrepancy up to 1 mm) and from cyan to blue (negative discrepancy up to \(-\)1 mm).  
were designed and realized starting from the same IOS. The aim of luting restorations to conventional gypsum casts was to evaluate interfacial adaptation of chairside restorations on the gold standard model.  
After re-polishing, samples were subjected to micro-computed tomography (micro-CT) (Skyscan 1172; Bruker, Billerica, MA, USA) to evaluate interfacial fit to conventional casts and 3 DC. High-resolution scans were performed using the following settings: voltage, 100 kV; current, 100 \(\upmu\)a; source to object distance, 220 mm; pixel binning, 292; total scan duration, 40 min; Aluminium and Copper (Al + Cu) filter; pixel size, 15 \(\upmu\)m; and rotation 0.5. An NRecon (Scanco Medical, Bruttisellen, Switzerland) was used to reconstruct samples to obtain DCM files using the same Hounsfield unit (HU) parameters for all samples.  
A novel 3D method was used to analyze overlay fit. Using Mimics software (ver. 20.0; Materialise, Ann Arbor, MI, USA), thresholding of the radiopaque flow was performed automatically in order to generate STL masks for each cast. These files were imported into Geomagic Control X for 3D thickness analysis using default settings in order to determine marginal and internal interfacial adaptation, as shown in Figure 3.