esa-sceva/satcom-chunk-collection huggingface dataset:
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 3 Experiment
### Experimental Setup  
There are several parameters in the proposed method: \(N\), the number of superpixel nodes used in the SLIC model; \(\sigma\) in Equations (1) and (3); \(\alpha\) in Equations (4), (17), and (23); \(\sigma_{x},\ \sigma_{y}\) in Equations (10), (20) and (25); the updating time \(M\) in Equation (28); and the updating weight \(\varepsilon\), \(\tau\) in Equations (28) and (29). For all four datasets, we experimentally set parameter \(N=120,140,160,200,250,300\); \(\sigma^{2}=0.1\); \(\alpha=0.99\); \(\sigma_{x}(\sigma_{y})=0.15\times H(0.15\times W)\) in Equation (10)\(\sigma_{x}(\sigma_{y})=0.20\times H(0.20\times W)\) in Equation (20); \(\sigma_{x}(\sigma_{y})=0.33\times H(0.33\times W)\) in Equation (25); \(M=3\); \(\varepsilon=\tau=0.08\). We carried out a series of experiments to investigate the influence of various factors on the saliency detection. The experiments used the ASD dataset, and the performance evaluation is shown in Figure 9.
## 3 Results and Discussion
### Morphology and Elemental Composition of the Photocatalysts  
The surface shape of materials is predicted using a scanning electron microscope (EVO-80, CARL [PERSON]), and the presenting elements of nanocomposites are located using energy dispersive X-ray spectroscopy (EDX) (AMETEK-EDAX (Zae Analyzer)). Scanning electron microscopy (SEM) was used to examine the structure and morphology of the prepared nanocomposites. From SEM analysis, Dy\({}_{2}\)WO\({}_{6}\), ZnO, Dy\({}_{2}\)WO\({}_{6}\)-ZnO and Dy\({}_{2}\)WO\({}_{6}\)-ZnO/GO nanocomposite, with their respective morphologies, are shown in Figure 2a-f. The unmodified Dy\({}_{2}\)WO\({}_{6}\) catalyst has a structure resembling that of algae (Figure 2a,b). Figure 2c shows an uneven cluster form within the ZnO materials. Figure 2d shows the Dy\({}_{2}\)WO\({}_{6}\)-ZnO (DWZ) composite, which observed two different structures like coupled algae with a cluster shape. The SEM imaging of a Dy\({}_{2}\)WO\({}_{6}\)-ZnO/GO nanocomposite (shown in Figure 2e,f) confirms the presence of Dy\({}_{2}\)WO\({}_{6}\) (algae), ZnO (cluster) and GO (layer) nanosheets. The photocatalytic performance of the catalyst may be improved by these various Dy\({}_{2}\)WO\({}_{6}\)-ZnO shapes [42]. Furthermore, the elemental composition of DWZG is shown in Figure 3 using EDS analysis. When properly deposited on the surface of the GO layer, the composite Dy\({}_{2}\)WO\({}_{6}\)-ZnO can boost the photocatalytic activity of the material. The elemental distribution of carbon (C), oxygen (O), dysprosium (Dy), tungsten (W) and zinc (Zn) in Dy\({}_{2}\)WO\({}_{6}\)-ZnO/GO nanocomposite photocatalysts is depicted in Figure 3 using EDS mapping. The EDS investigation verified that C, O, Dy, W and Zn elements were present in the proper ratios as DWZG's nanocomposites.  
Figure 1: X-ray powder diffraction patterns of graphene oxide (GO), ZnO, Dy\({}_{2}\)WO\({}_{6}\)-ZnO (DWZ) and Dy\({}_{2}\)WO\({}_{6}\)-ZnO/GO (DWZG) nanocomposite.
## 3 Establishment and Validation of Finite Element Model  
### Equation Solving Techniques  
The finite element software ABAQUS 2021 was used to solve the problem [25]. In ABAQUS, the nonlinear contact problem is solved using a direct solver and the Full Newton method, which is known for its high accuracy and stability. The direct solver performs a full decomposition of the stiffness matrix (e.g., LU decomposition) to obtain an accurate solution. Although the computational cost is higher, this method is suitable for handling the high degree of nonlinearity and stiff contact stiffness that can arise in contact problems. In the Full Newton method, the system stiffness matrix is reassembled in each iteration to accurately capture the contact and geometric nonlinearities. The nonlinear contact problem is solved incrementally, with each load increment being decomposed into several sub-steps, and each sub-step is solved using the Full Newton iterative method. In each incremental loading step, the following three primary convergence criteria are used to ensure the accuracy and stability of the computational results: displacement convergence criterion (Inequality (1)), residual force convergence (Inequality (2)) criterion, and contact pressure convergence criterion (Inequality (3) and (4)).  
\[\|\Delta u\|=\sqrt{\sum_{i}^{n}(\Delta u_{i})^{2}}<\kappa_{\rm u} \tag{1}\]  
\[\|R\|<\kappa_{\rm r} \tag{2}\]  
\[P_{\rm n}<\kappa_{\rm p} \tag{3}\]  
\[T_{\rm t}<\kappa_{\rm t} \tag{4}\]  
where \(\Delta u\) is the displacement increment and the displacement convergence tolerance; \(n\) is the total number of degrees of freedom; \(\Delta u_{i}\) is the displacement increment for each degree of freedom; \(R\) and \(\kappa_{\rm r}\) are the residual force and the force convergence tolerance; \(P_{\rm n}\) is the change in the normal contact pressure on the contact surface; \(\kappa_{\rm p}\) is the normal contact pressure convergence tolerance; \(T_{\rm t}\) is the changes in friction force on the contact surface; and \(\kappa_{\rm t}\) is the friction force convergence tolerance.
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
## 3 New Method: Sparse Representations of Wavefields Based on MCA
### Performance of Sparse Representations Using LRT and HRT  
We synthesize the surface waves (Figure 1) of the two-horizontal-layer model (Model 1) described by Table 1 using the staggered-grid finite-difference method [31]. Then, we synthesize reflections (Figure 2) of the three-horizontal-layer model (Model 2) described by Table 2 using ray tracing. Frequency-domain high-resolution forward LRT and time-domain high-resolution forward HRT are applied to the surface waves and reflections to obtain the four panels of the Radon coefficients. Next, we respectively normalize the Radon coefficients to unity divided by the maximum of each panel and apply hard thresholds to them. Finally, the seismic data were reconstructed by the inverse transforms. For a Radon panel, the higher threshold amplitude means that fewer Radon coefficients are used in the reconstruction.  
To confirm the effectiveness of sparse representations for surface waves and reflections, the reconstruction error is calculated as follows:  
\[err=\sum_{x}E_{x}^{r}/\sum_{x}E_{x}^{0} \tag{14}\]  
where \(E_{x}^{r}\) is the root-mean-square error between the reconstruction and the original data at the offset \(x\), and \(E_{x}^{0}\) is the root-mean-square value of the original data at the offset \(x\), and  
\[E_{x}^{r}=\sqrt{\frac{1}{nt}{\sum_{t}}\Big{(}d(t,x)-\widetilde{d}(t,x)\Big{)} ^{2}} \tag{15}\]  
\[E_{x}^{0}=\sqrt{\frac{1}{nt}{\sum_{t}}(d(t,x))^{2}} \tag{16}\]  
where \(\widetilde{d}(t,x)\) is the reconstruction data. The reconstruction errors against the threshold amplitude are illustrated in Figure 3. According to Figure 3a, fewer coefficients can be used to reconstruct the surface waves by frequency-domain high-resolution LRT compared with time-domain high-resolution HRT, which means the former can represent surface waves more sparsely. On the basis of Figure 3b, time-domain high-resolution HRT can represent reflections more sparsely than frequency-domain high-resolution LRT. Comparing the HRT-reconstruction errors shown by dashed lines between Figure 3a,b, time-domain high-resolution HRT leads to a non-sparse representation for surface waves. Similarly, frequency-domain high-resolution LRT leads to a non-sparse representation for reflections when comparing the LRT-reconstruction errors. Thus, the two transforms (dictionaries) are significantly different in the sparse representations for surface waves and reflections, which meets the assumptions of MCA, and it is theoretically feasible to extract the surface waves based on Equation (6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Thickness (m)** & **Vp (m/s)** & **Vs (m/s)** & **Density (kg/m3)** \\ \hline
100 & 1200 & 400 & 2000 \\
150 & 2200 & 1320 & 2250 \\ - & 3300 & 2045 & 2400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of Model 2.  
Figure 2: Synthetic reflections of Model 2.
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 4 Fault Diagnosis Case Investigation Utilizing the Proposed E KNN  
### Dataset Description  
The experiments were carried out on the vibration signals collected from the test rig, and the details are shown in Figure 2. The components include an electric motor, a shaft coupling, a planetary gearbox, a bearing, two rotating plates and a bearing. To acquire the vibration signals, a vibration accelerometer was set on the bearing set between planetary gearbox and rotating plates, and the sampling frequency was 12.8 kHz. Here, the bearings with faults were processed and set in the location of last bearing. Entirely, 10 health conditions are designed:  
1. Normal condition, denoted as NO;
2. Three inner race faults with different fault severities (0.2, 0.6 and 1.2 mm), denoted as IF02, IF06 and IF12, respectively;
3. Three outer race faults with different fault severities (0.2, 0.6 and 1.2 mm), denoted as OF02, OF06 and OF12, respectively;
4. Three roller faults with different fault severities (0.2, 0.6 and 1.2 mm), denoted as BF02, BF06 and BF12, respectively.  
For each vibration signal of each health condition, 400 samples were obtained, and each sample contained 2000 data points; therefore, the whole dataset was composed of 4000 time-domain samples. For network training, 2000 samples were randomly selected, and the rest were used in testing. For clarity, the time-domain samples and their corresponding frequency-domain forms are shown in Figure 3.
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 1 Introduction
It extracts target and clutter features using dedicated encoders and employs a generator to suppress clutter. Unlike supervised methods, DR-GAN does not require paired data for training. Experiments show that it outperforms the RNMF method, improving IF by 17.85 dB. [PERSON] et al. [23] introduced a residual channel attention network (RCAN) that leverages a channel-based attention mechanism to suppress rebar clutter, effectively revealing hidden defects. [PERSON] et al. [24] proposed a Wavelet-GAN network that combines GAN and DWT to remove clutter and noise from GPR images. This method utilizes DWT for decomposition, CNN and GAN for signal reconstruction, and IDWT for generating high-quality GPR images. It demonstrates excellent performance in training on small datasets and processing real-world data. [PERSON] et al. [25] proposed an end-to-end unsupervised deep learning-based network to suppress rebar signals and enhance void defect identification in GPR images. The network integrates feature attention and mix-up modules, optimizes image reconstruction through feature space comparison and cycle perceptual consistency loss, and improves Faster-RCNN for detecting void defects beneath rebars. The method's effectiveness was validated using synthetic and real-world datasets. In this study, we propose an unsupervised learning-based method for suppressing rebar interference in GPR images, enhancing void detection in tunnel linings by learning feature relationships between two sets of incompletely corresponding images. This achieves the transformation from GPR images containing rebar interference to those without it, reducing the interference of rebar clutter with void defect signals and enabling more accurate identification of void signals in the images. The proposed method is based on UNIT [26]. We design a channel and spatial attention (CSA) module for the network to help it better concentrate on critical information in GPR images, allowing for more accurate distinction between rebar clutter and void signals. Specifically, the inclusion of CSA allowsthe network to consider relationships between different channels and spatial positions simultaneously when processing images. In complex situations with overlapping features, this allows the network to adjust its focus on particular channels and spatial regions, depending on the distribution patterns of rebar interference and void signals. This enables the network to better distinguish between and process these two structures and specifically suppress rebar interference signals, thereby more accurately reconstructing complete void defect signals. Experimental results demonstrate that our method effectively suppresses rebar clutter while maintaining the integrity of defect signals, making it well suited for practical engineering applications.
## 4 Proposed System Model  
### Architecture  
In SG, in order to obtain a reliable and efficient operation, DSM is used. Two main objectives of DSM are end-user controlling activities and energy management. A Smart Home (SH) consists of a smart meter along with the EMC, for a reliable bi-directional power and information flow between SG and SH [29]. All appliances, connected sensors, RESs and BSUs are connected to EMC through a Home Area Network (HAN), which is further connected to SG through a Wide Area Network (WAN). Different WAN solutions are available like PLC, Wi-Fi, Wi-Max and GSM [30]. End-users manage their energy consumption activities as per incentives offered by the utilities. In each SH, the end-user puts various parameters of all appliances in EMC. EMC is then responsible for the ON/OFF status of all appliances. Figure 1 shows the proposed system model architecture.
## 4 Discussion  
For the MEC scenario of multi-UAV and multi-edge cloud collaboration, this paper jointly designs the optimization problem of trajectory and computation task allocation to obtain the minimum total execution delay and energy consumption. We improve the MADDPG algorithm by adding communication mechanisms between UAVs and incorporating a safety layer to constrain UAV actions. Experimental results show that the proposed SC-MADDPG algorithm can achieve UAV path planning and provide optimal task offloading solutions. Compared with other optimization methods, it reduces the number of UAV collisions in the retraining process and reduces the overall energy consumption and delay.
## 1 Introduction
A grid of k-points is used to sample the Brillouin zone by using [PERSON]'s theorem applied to the Kohn-Sham wavefunctions. Optimization and single point calculations with PDFT are performed using various self-consistent field (SCF) iteration schemes. While Gaussian basis sets can be used to perform PDFT calculations, plane wave pseudopotential basis sets are computationally less intensive and are used in many PDFT codes [32]. In this review we present a collection of DFT simulations of periodic systems containing porphyrins. While we tried to include many PDFT studies of porphyrins in this review, these are not necessarily a  
Figure 1: Chemical structures of (**a**) freebase porphine/porphyrin, (**b**) metalloporphyrin. Three unique peripheral substituent positions, alpha (C\({}_{\alpha}\)) at 2, 7, 12, 18; beta (C\({}_{\beta}\)) at 3, 8, 13, 18; and meso (C\({}_{\text{m}}\)) at 5, 10, 15, 20 positions are shown the figure. complete collection in the literature. Omission of a particular citation is not a reflection on the quality of that work. Periodic DFT calculations of porphyrins can be broadly classified into two categories. First, simulation of porphyrin crystals, supramolecular compounds, and nanostructures like nanorods, nanowires, and nanosheets. Typical interests for these simulations involve understanding the intermolecular or packing interactions of porphyrins and determining their electronic properties, specifically band structure and density of states. The second category involves porphyrin interactions with solid supports like metals, oxides, carbon, and silicon surfaces. PDFT simulations on surfaces typically involve a molecule or monolayer of porphyrins or metalloporphyrins adsorbed on solid supports to study the adsorption configuration, binding energies, molecule-surface interactions and reactions, electronic structure using density of states (DOS) and band structure, chemical reactivity, interfacial charge distribution, and magnetic properties. Some of the ab initio DFT codes used for porphyrin PDFT simulations include VASP [33; 34; 35], Quantum Espresso [36], CP2K [37], CASTEP [38], Dmol [39], SIESTA [40], CPMD [41], etc. Omission of any code is not a reflection of the quality of the software for PDFT simulations. However, we note that codes involving plane-wave basis sets are more popular than software that are based on Gaussian type orbital (GTO) and natural atomic orbital (NAO) basis sets.
# Handover Management for D2D Communication in 5G Networks
## 2 Related Works
In case neither of the devices can satisfy the minimum requirement of a predefined RSRP threshold, the handover would be conducted immediately. However, this method entirely relies on the selected threshold, and the blocking problem may happen when a threshold is set too low. In [PERSON] et al. (2020), the authors proposed two D2D handover schemes in which the movements of both devices of the same D2D pair were jointly considered. In the situation that both devices are moving in the same direction, the original serving eNB would hand them over together to the target eNB based on their measurements of received signal condition. Another scenario is that both devices are moving away from each other, and one of them has already handed over to a different eNB. In this case, the other UE would set up a countdown timer. This D2D pair would be disjointed into two independent cellular users if the timer countdowns to zero or the signal strength of the D2D link is lower than a predefined threshold.
## 5 Life Cycle Assessment
### Building-Scale Data  
The analyzed variants have different construction data for each building in the neighborhood, as well as varying data concerning the dimensions and types of windows installed. However, the energy consumption, water usage, and waste generation data remained the same for all the variants throughout the entire life cycle assessment analyses. It is important to mention that all the inputs entered into the software were either hypothetical, based on similar studies using the same software, or were simply the default data provided by the software.  
The transportation distances of the materials selected for installation in the buildings in the LCA are as follows: 80 km between the production site and the construction site, and 20 km between the construction site and the discharge site. A material surplus, which corresponds to the average fall rate of the different construction products, of 5% accounting for the average loss rate of different construction products, has been taken into consideration. Additionally, the expected lifetimes are 30 years for frames and windows, 15 years for coatings, and 20 years for overall equipment.  
The energy data were assessed using the default French mix in the software, with a network loss of 9% factored in. In terms of water usage, the drinking water network's efficiency stands at 80%. Cold-water consumption is set at 100 L per person per day, while hot-water consumption is at 40 L per person per day. Waste management involves selective sorting is used, with 90% of glass and 75% of paper and cardboard being sorted for recycling rather than being sent to landfills. An average of 1500 g of daily household waste per person is considered, with 40% of this waste being sent for incineration, yielding an 85% conversion into natural gas. The distances from the site to the garbage dump are 20 km, 100 km to the incinerator, and 20 km to the recycling site.  
Another important parameter under examination is the mobility of the building occupants. For simplicity, it is assumed that all the occupants commute similarly, with 80% commuting daily, either by car or by bus. The distances from home to work, home to the bus station, and home to trade centers are 2500 m, 250 m, and 500 m, respectively.
# Additively Manufactured Inconel 718 Low-Cycle Fatigue Performance
## 1 Introduction
Similarly, [PERSON] investigated the impact of defect size on fatigue performance but did not provide data for the sub-10,000-cycle LCF regime, and the defects studied were not fully representative of the as-printed surfaces typically seen in L-PBF components [23]. [PERSON]'s study also highlighted the fatigue debit caused by surface roughness in L-PBF IN718 but focused solely on room-temperature testing and only completed high-cycle fatigue (HCF) testing [24]. These limitations in prior work underscore the need for further investigation into the influence of surface roughness and defects on fatigue performance, particularly under low-cycle-fatigue conditions and elevated temperatures, to better represent the operational environments of some critical applications. This study aims to build upon prior work and characterize the LCF performance of Inconel 718 produced via L-PBF. Specimens were fabricated in a vertical orientation and evaluated in both as-printed and fully machined surface conditions at room temperature and 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F). The goal is to understand the differences in fatigue behavior between these two surface conditions and to provide insights into the crack initiation and propagation mechanisms under varying test conditions. By building upon prior research, this study seeks to advance the understanding of L-PBF Inconel 718's fatigue performance, paving the way for its reliable application in critical aerospace and gas turbine components.