esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, high-temperature creep properties at 760 \({}^{\circ}\)C and 80 MPa of Alloy 800H and its weldments using Inconel 625 and Haynes 230 filler materials were investigated and compared. The microstructures before and after the creep test and the fracture mode were reported. Creep mechanisms, dislocation structure, and precipitates evolution were explicated. The following conclusion can be drawn:  
* High-temperature apparent tensile yield strength and creep resistance of Incoloy 800H welds at 80 MPa and 760 \({}^{\circ}\)C were significantly enhanced by the addition of Inconel 625 and Haynes 230 into the Alloy 800H weldments. Both of the weldments showed longer creep rupture time but lower rupture strain compared with the BM specimen.
* Significant dislocation slip and interaction with precipitates were observed in the microstructure, indicating a high-temperature power-law creep mechanism. Dislocation bypassing through the Orowan mechanism accompanied by climb and cutting facilitated dislocation slips during high-temperature creep deformation.
* Microstructural characterization revealed that extensive precipitation took place after the prolonged creep testing at high temperature. A large number of sub-micron-sized carbides (MC and M\({}_{23}\)C\({}_{6}\)) were observed in the microstructure of FZ, HAZ adjacent to the fusion boundary, and base metals under various conditions (as-received, as-welded, and creep-tested). The varied sizes and locations of the M\({}_{23}\)C\({}_{6}\) and MC carbides suggest a complex microstructural evolution during the creep test.
* The weldments with Inconel 625 filler material exhibited detrimental \(\delta\) and Laves phases in the weld metal after the creep test. Although the failure occurred in the base metal rather than in the fusion zone under the current test conditions, the presence of these phases could cause potential crack initiation after prolonged high-temperature ageing.
* The weldment with Haynes 230 filler material demonstrated superior phase stability and improved creep rupture properties compared to the one with Inconel 625 filler material. This suggests that Haynes 230 could be a promising filler material for further investigations into Alloy 800H applications.  
In conclusion, microstructural evolutions and phase stability of filler materials and base metals are important aspects in high-temperature creep deformation. They should be evaluated and understood when studying the high-temperature materials and their weldments in high-temperature reactor applications.  
W.L.: conceptualization, investigation, methodology, funding acquisition, writing--original draft; L.X., L.W. and Q.D.: methodology, validation, writing--review and editing; M.I. and R.S.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Atomic Energy of Canada Limited's Federal Nuclear Science & Technology Work Plan.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Game-Theory-Based Multimode Routing Protocol for Internet of Things
## 3 Proposed Mechanism
### Assumptions and Features
#### 3.2.7 Nodes' Mobility Ratio  
The network's nodes are mobile and have variable mobility ratios. The \(k\)th location of a node \(n_{i}\) is \(Loc_{k}(n_{i})=(x_{k},y_{k})\) where \(x\) and \(y\) are the coordinates. If a node \(n_{i}\) moves from location \(k\) to location \(l\) in a time period \(t\), then it can be denoted as \(\Delta Loc_{k,l}^{t}(n_{i})\) and computed as in Equation (12).  
\[\Delta Loc_{k,l}^{t}(n_{i})=\frac{\sqrt{\left(x_{k}-x_{l}\right)^{2}+\left(y_{ k}-y_{l}\right)^{2}}}{t} \tag{13}\]  
The same can be said for the \(n_{i}\) node's mobility ratio, which is indicated by \(\mathit{MobRatio}(n_{i})\). A smart node requires a threshold value for mobility ratio, \(ThM\), during default protocol selection, which can be calculated using Equation (13).  
\[ThM=Med\big{(}\mathit{MobRatio}\big{(}n_{j}\big{)}\big{)} \tag{14}\]
## 2 Related Work  
### Semantic Segmentation Methods  
Semantic segmentation is a fundamental task in computer vision, aiming to assign a precise class label to each pixel in an image. Traditional approaches primarily relied on handcrafted features and graphical models such as Conditional Random Fields (CRFs) [9] and Markov Random Fields (MRFs) [10]. While effective in regular structures or simplified scenes, these methods perform poorly in complex backgrounds, non-rigid structures, and fine-grained categories. With the rise of deep learning, Fully Convolutional Networks (FCNs) [11] marked the beginning of end-to-end pixel-wise classification. Subsequently, U-Net [12] employed a symmetric encoder-decoder architecture to efficiently fuse low-level and high-level features, achieving remarkable success in medical image segmentation. The DeepLab series [13] further enhanced multi-scale context modeling through atrous convolution, Atrous Spatial Pyramid Pooling (ASPP), and encoder-decoder designs. PSPNet [14] introduced pyramid pooling modules to aggregate global contextual information, thereby strengthening semantic representation.  
In addition, to capture larger receptive fields and model long-range dependencies, models such as OCRNet [15] and SegFormer [16] introduced contextual reconstruction mechanisms, attention modules, and Transformer architectures, significantly improving segmentation accuracy. However, these general-purpose models are often designed for large-scale object segmentation in urban scenes or natural images, and they may underperform in domains like facial parsing, which require highly detailed and compact semantic structure modeling.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 3 NewLib Code
### Encoding
#### 3.1.2 The Aligned Request  
We take a request as an example to illustrate this situation. Suppose a request has an offset of 0 and a data size of 2 k.  
From Figure 4, we can see that the EC policy is 4:2, and each strip data block size is 512 bytes. This task is divided into six requests after encoding, and each request is written to the corresponding node. If it is a write operation, all nodes will complete the task in cooperation. If it is a read operation, the data read from each node need to be transferred to the buffer of the user task in the reverse direction of the arrow. The data chunk and the parity chunk are not indicated in the figure, but there is an encoding process in the actual process.  
Figure 3: The erasure matrix in Liberation when k is 7 and w is 7.  
3.1.3. The Non-Aligned Request  
A non-aligned read request is different from an aligned read request. For non-aligned write requests, there may be an offset that is not aligned or the data size may not be aligned. The other data in the strip need to be read first. Then, they will be re-encoded and written back to the disk. The following is an example of offset non-aligned and data size non-aligned.  
As shown in Figure 5, the non-aligned strip needs to be read and encoded together. A non-aligned write operation requires a partial read operation, which affects the performance of the system to some extent.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 2 Methodology
### The Technology of ACO-OFDM  
A significant problem is multipath interference during the channel transmission by the indoor visible light signal. The orthogonal frequency division multiplexing (OFDM) technology is widely studied and applied and has the benefits of high-frequency utilization and strong resistance to multipath interference. In order to satisfy the requirement of non-negative real signals and improve the power efficiency of the system at the same time in the situation of introducing OFDM into visible optical communication, researchers came up with a single-polarity real-valued scheme called the ACO-OFDM technique [15].  
As shown above, in Figure 1, the data input at the transmitter side and the data output at the receiver side are both binary serial data streams. Due to the limitation that light signals can only propagate in a positive direction, the limiting operation is performed to output a positive real number signal, and the cyclic prefix (CP) is added to eliminate inter-code crosstalk during the channel transmission.
## 5 Performance Evaluation
When the number of nodes exceed 200, APD decreases because the probability of selecting a PFN with minimum distance to sink increases which reduces APD per delivered packet. Figure 7 shows that the performance of WDFAD-DBR is better than Intar and Re-Intar because it selects a PFN with least depth as next forwarder. These two protocols bypass the shortest path due to avoidance of channel interference which increases their APD. Re-Intar shows better performance than Intar because Re-Intar reduces propagation distance of the packet by using depth of PFN. Relatively, Re-Intar is approximately 8.82% more efficient than Intar and 8.36% less efficient than WDFAD-DBR in APD. The APD of both Hydrocast and H2-DAB schemes is higher than Intar. Re-Intar has higher APD than existing schemes from node number 200 to 450, while it shows slightly lower APD than H2-DAB at 500 node number. The Hydrocast outperforms Re-Intar in APD throughout the network operations but still lacks the effectiveness as compared to Intar scheme. This is because in proposed work only one backward hop is traversed, whereas Hydrocast traverses back until the eligible forwarder is nominated to resume the network operations. Figure 6: End-to-end delay of the existing and proposed protocols with varying number of nodes.
## Results
### Effect of Dealcoholization on the Physicochemical Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Chemical Parameter**} & \multicolumn{3}{c}{**White Wine**} & \multicolumn{3}{c}{**Rosé Wine**} & \multicolumn{3}{c}{**Red Wine**} \\ \cline{2-9}  & **CK** & **RO** & **VD** & **CK** & **RO** & **VD** & **CK** & **RO** & **VD** \\ \hline Alcohol (\% \(v/v\)) & 13.4 \({}^{\text{a}}\) & 0.7 \({}^{\text{b}}\) & 0.7 \({}^{\text{b}}\) & 12.2 \({}^{\text{a}}\) & 0.7 \({}^{\text{b}}\) & 13.9 \({}^{\text{a}}\)  
In addition, CIELab measurements such as lightness (\(L^{*}\)), the intensity of red and green hues (\(a^{*}\)), and the intensity of blue and yellow hues (\(b^{*}\)) revealed significant differences between all original wines and their corresponding dealcoholized fractions obtained by RO and VD. Dealcoholization by VD significantly decreased the lightness (\(L^{*}\)) of white, rose, and red wines by 1.6%, 11.1% and 6.3%, respectively, while red and green hues (\(a^{*}\)) increased by 22.6%, 23.5% and 16.26%, respectively. In contrast, RO did not affect the lightness of the rose wine compared to the original wine but significantly decreased the lightness in dealcoholized white and red wine fractions by 1.3% and 3.3%, respectively. Compared to the original wines, RO resulted in a more significant reduction in color intensities (\(a^{*}\) and \(b^{*}\)) for all wines except red wine. According to [PERSON] et al. (2019), color changes are expected to be perceptible to the human eye when \(\Delta E^{*}\geq 3.0\). Dealcoholization by RO and VD had a significant effect on the color of dealcoholized red wines (\(\Delta E^{*}=3.14\) and 4.59, respectively, Table 1), which was detectable to the human eye. In white wine, RO also visibly affected the color (\(\Delta E^{*}=3.23\)), which was perceptible to the human eye, while in rose wine the color difference was only noticeable in VD dealcoholized fractions (\(\Delta E^{*}=7.14\)). In a study of partial dealcoholization of Shiraz Cabernet Sauvignon and Shiraz wines by RO-EP, a perceptible color difference (\(\Delta E^{*}=5.3\) and 3.0, respectively) was observed ([PERSON], 2019). Likely, the changes in wine color characteristics observed in this study between treatments are due to more than just the concentration of ethanol remaining after dealcoholization by the two methods, but the dealcoholization process could have affected the co-pigmentation content and/or the formation of derived pigments (such as anthocyanins) as a result of oxygen uptake, sulfur dioxide loss, and/or adsorption on the membrane surface, as suggested by [PERSON] et al. (2019).
## 3 Results and Discussion  
### Hydrotreater of Oil Refinery Plant  
#### 3.1.1 SHM-System Location  
Overheating caused by a run-away of the process induced the formation of defects at the hydrotreater at the oil refinery plant. During periodic AE testing, significant AE sources were detected and it was assumed that it is the destruction of the cladding layer. In December 2006, SHM-system was put into operation, the task of which was to ensure the safe operation of the hydrotreater with an extended lifespan until it is possible to replace it.  
Structurally, the hydrotreater is a thick-walled vertical steel vessel. The inner surface of the hydrotreater is protected from the influence of an aggressive medium by a cladding layer. The high operating temperatures of the hydrotreater (from 320 \({}^{\circ}\)C to 425 \({}^{\circ}\)C), the aggressive environment and high operating pressure are factors that can lead to the generation and catastrophically rapid flaw growth, especially when the cladding layer is destroyed, which can induce hydrogen embrittlement and a decrease in the strength of the material.  
The main diagnostic method of SHM-system was the method of AE testing. The AE handling part was represented by 18 AE sensors GT200 UB (130-200 kHz), grouped into 6 belts, 4 on the hydrotreater shell and 1 belt on each lid. Sensors were mounted on waveguides to ensure the temperature allowed for the sensor is not exceeded. Temperature, vibration and pressure sensors were also installed. A threshold data acquisition algorithm was used. The threshold value was about 40 dB. The analog-to-digital converter (ADC) sampling rate was equal to 2 MHz.  
By May 2007, the state of the hydrotreater had deteriorated. It was found to have 9 active zones, corresponding to AE sources with varying degrees of danger (Figure 2). The activity of some zones increased significantly, which indicated a progressive flaw growth and the possibility of the hydrotreater destruction. The source found in zone #5 and characterized by 370 events, was considered to be critically active.  
Figure 2: Location map. Color of points mean quantity of location events. Blue points: 1–2 events, yellow: 3–10 events, orange points: 11–25 events, red points: 26–100 events, brown points: more than 100 events.  
In order to resolve the issue of the time of decommissioning, it was decided to conduct additional analysis of monitored AE data.
## 2 Modeling
### Nonlinear Dynamic Model  
The arrangement of pinions on the circumference of the face gear and the position of the face gear in space is illustrated in Figures 9 and 10, respectively. Figures 9 and 10 show the local coordinate system representing the direction of the DOFs of each gear. The \(x\)- and \(y\)-axis of the pinion are radial, the \(z\)-axis is axial, and the \(y\)-axis points to the upper face gear. The \(y\)-axis of the face gear is parallel to the axis of Idler 1 and points in the negative direction of \(z_{3}\). The \(z\)-axis of the upper face gear is in the same direction as the \(z\)-axis of the lower face gear. According to the principle of the meshing transmission between the orthogonal face gear and pinion, the meshing force can be decomposed into the radial and circumferential forces on the pinion and the axial and circumferential forces on the face gear. The pinion is not subjected to axial component force, which simplifies the support structure of the system and reduces the degrees of freedom of vibration. Accordingly, two radial degrees of freedom, \(x\) and \(y\), and a torsional degree of freedom around the axis of the pinion were considered in this study. In the CFGSTTS, the face gear simultaneously meshes with five pinions. The nonuniform distribution of the pinions on the circumference of the face gear and the difference in the support stiffness of each gear result in the meshing force transmitted by each pinion being different. As a result, the face gears are subjected to time-varying overturning moments. Therefore, the face gear model considers six degrees of freedom. The dynamics model of the concentric face gear split-torque transmission system is shown in Figure 11.  
Figure 11: Dynamics model of concentric face gear split-torque transmission system. Figure 10: Distribution of face gears. Figure 9: Distribution of pinions around circumference of face gear. Under the action of external driving torque and internal excitation, the meshing point of the face gear and pinion deforms and vibrates along the meshing line. The relative displacement \(\delta_{16}\) of the meshing point between Input Gear 1 and the lower face gear pair in the direction of the meshing line is expressed as:  
\[\delta_{16}=\left(r_{1}\theta_{1}-r_{f}\theta_{6}-X_{1}+X_{6}\cos\beta_{1}-Y_{6} \sin\beta_{1}\right)\cos\alpha+\left(Z_{6}-Y_{1}\right)\sin\alpha+\Delta e_{16} -e_{16} \tag{2}\]  
where \(\alpha\) is the pressure angle; \(\beta_{1}\) is the pinion distribution angle; \(r_{1}\) and \(r_{f}\) are the distances from the meshing point to the pinion and face gear axes, respectively; and \(e_{16}\) is the normal static transmission error of the gear pair, which is obtained from Equation (3). \[e(t)=e_{0}+e_{r}\cos(\omega_{h}t+\varphi_{r}) \tag{3}\]  
where \(e_{0}\) is the constant value of the comprehensive transmission error; \(e_{r}\) is the amplitude; \(\omega_{h}\) is the meshing angular frequency of the gear pair; and \(\varphi_{r}\) is the initial phase.
## 3 Coordinate Transformation of Structural Plane Information  
To accurately reconstruct structural planes in 3D space, it is essential to convert their two-dimensional image information into physical spatial coordinates. This process involves three key steps: coordinate system definition, image preprocessing, and coordinate sampling, which together establish a geometric foundation for subsequent surface modeling.
# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 3 Feature Aggregation Module
### Feature Aggregation  
By utilizing the proposed lightweight HT-Block and multi-input attention mechanisms, we design an efficient feature aggregation module (FAM) with cross-layer shortcut connections to efficiently capture video frame features through multi-scale feature fusion. As shown in Figure 5, the FAM module adopts encoder-decoder architecture to achieve multi-scale feature extraction through dense cross-layer shortcut connections for video frame reconstruction and utilizes a series of HT-Blocks to extract features of low-resolution video frames. Then the features from the low-resolution and reconstructed video frames, along with the raw video frames, are all fed into multi-input attention for further multi-scale feature aggregation. Note that different from traditional paradigms of deepening the network layers (like ResNet [26]) or broadening the architecture (like Inception [27]) to boost performance, FAM leverages dense cross-layer short-cut connections and feature reuse to learn optical flow features from video frames while significantly reducing the number of parameters, so as to simplify the training process and enable more efficient feature learning.  
Figure 4: Architecture of multi-input attention.  
Figure 5: Architecture of feature aggregation network.  
Specifically, denote the input to the FAM as:  
\[input=concat(LR,\ LR\ flow\ next,\ LR\ flow\ prev) \tag{12}\]  
Then the output of the \(i\) layer encoder can be represented as:  
\[encoder_{i}=\varphi(concat(encoder_{i-1},\ input)) \tag{13}\]  
where \(\varphi\) represents a series of convolutional operation, \(concat\) is the tensor concatenation operation, \(encoder_{i-1}\) is the output of the \(i-1\) layer encoder, and \(input\) are the collection of bi-directional optical flow information and low-resolution video frames. The output of the \(i\) layer decoder can be represented as:  
\[decoder_{i}=\varphi(concat(encoder_{1:n},decoder_{i-1},\ input)) \tag{14}\]  
where \(encoder_{1:n}\) are the output of all encoders, \(decoder_{i-1}\) is the output of the \(i-1\) layer decoder. For the \(j\) layer HT-Block, its output \(HT_{j}\) is represented as follows:  
\[HT_{j}=Conv(GELU(\varphi(HT_{j-1})))+HT_{j-1} \tag{15}\]  
where \(\phi\) represents the head-tail convolution operation. Finally, we integrate the outputs from the input layer, decoders, and HT-Blocks into the multi-input attention mechanism as follows:  
\[Output=Att(decoder_{1:n},\ input,\ HT_{m}) \tag{16}\]  
where \(m\) represents the total number of HT layers, \(decoder_{1:n}\) are the output of all decoders. This integration leverages both shallow features that capture fine-grained texture details and deep features that encode high-level structural and semantic information to allow the multi-input attention mechanism to dynamically weigh the contributions from each component, achieving efficient fusion of shallow and deep features.
# Viability of Cupola Slag as an Alternative Eco-Binder and Filler in Concrete and Mortars
## 2 Materials and Methods
### Characterization of the Materials
#### 2.1.1 Valorization and Properties of the Cupola Slag
A Nicolet Nexus diffractometer from the Thermo brand with a microscope attached to the bench was used, a service performed by the SERCAMAT of the University of Cantabria (Spain). For the analysis, the sample was combined in mortar with potassium bromide in a 1:20 ratio. The mixture was subsequently compacted in a press to form a compressed capsule. The spectrum obtained is shown in Figure 5.  
The identification of the crystalline compounds present was carried out by means of the Fourier transform. These results confirm the analyses performed by X-ray diffraction and the spectrum obtained enabled the following compounds to be detected:  
* Mainly silicates and aluminosilicates, with vibration bands in range 800-1100 cm\({}^{-1}\) and 500 cm\({}^{-1}\). * Calcium oxide due to its peaks at frequencies around 1400-1500 cm\({}^{-1}\) and at 910 cm\({}^{-1}\). * Iron oxides, peaks at 2400 cm\({}^{-1}\), 1400-1500 cm\({}^{-1}\), and 300-600 cm\({}^{-1}\). SEM images were also obtained using a Zeiss SEM, model EVO MA15. In Figure 6, the appearance of the material is observed at two different scales, observing some microporosity on the surface of the particles (Figure 6a) and also an irregular shape of the particles when viewed together (Figure 6b). Figure 4: Diffractogram of the cupola slag. The analysis of the leachate from the cupola slag was carried out in accordance with the provisions of the European standard EN 12457-4 [23]. For this, 2 kg of sample were used without being subjected to a previous treatment. The results obtained were compared with the limits defined in Annex I of Decree 104/2006 of the Community of Cantabria (Spain) [24], which is one of the most restrictive regions in Europe. The results are compiled in Table 1.  
Cupola slag is a material that does not pose any environmental risk; the concentrations of dangerous elements in the leachate test are even significantly lower than with electric furnace slag, comparing the values with those obtained in other studies [25].