esa-sceva/satcom-chunk-collection huggingface dataset:
# Technological Processes for Steel Applications in Nuclear Fusion
## 4 Fusion Welding
### Defects  
Prior to specimen fabrication, the weld microstructures were investigated. All TIG and the 12 mm EB welds show coarse grain formation, which is typical for solidification microstructures that form during the welding cycles. Both 5 mm beam welds do not show this severe grain coarsening. Additionally, typical for TIG welds is the observation of soft regions in the HAZ. The lateral extension of the beam welds is significantly smaller and a distinct softening in the HAZ is not observed. Hardness in the fusion zones of all welds ranges from 400 to 450 HV1 (base material varies around 220 HV1). Only the 10 mm and 12 mm TIG welds suffer from softening in the HAZs on both sides, as is clearly recognizable in Figure 12. The 5 mm TIG weld shows only slight softening, while no effect can be seen in the hardness profiles of the beam welds.  
All TIG welds show a very low defect density. In very rare cases formation of delta-ferrite could be detected, which is consistent to the theory and other studies [24; 25; 26; 27; 28] in the 5 mm weld, and some inclusions have been found in the 10 mm weld. With a carbon content of 0.1% in the fusion zone of TIG welds, de-carbonization of the filler material was not observed. All beam welds (EB and laser), however, revealed more or less extended bubbles. Their size and distribution depend primarily on the beam intensity and purity of the weld surfaces. While the 12 mm EB weld showed even small cavities, in the 5 mm EB weld gas pores with diameters of 20-40 um have formed. In the 5 mm laser weld the pores are even smaller.  
Figure 11: Cross-section of a 40 mm electron beam (EB) weld with an underlay.  
Figure 12: (a) Hardness profiles of 5 mm TIG, EB, and laser welds, and of the 10 mm TIG weld. The hardness level of the base material is shaded grey; (b) hardness profile of the 12 mm TIG weld from the top and middle cross-sectional part; (c) hardness profile of the 12 mm EB weld from the top and lower cross-sectional part.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 4 Experiments
### Parameters
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Values** & **Descriptions** \\ \hline \multicolumn{3}{c}{Planar primitive detection parameters} \\ \(\theta_{th}\) & \(10^{\circ}\) & The threshold for the region-growing. \\ \(k\) & 20 & The searching radius of neighborhood. \\ \(\xi\) & 0.01 & The threshold to adjust the weight \(w_{pq}\). \\ \hline \multicolumn{3}{c}{Contour extraction parameters} \\ \(d_{b}\) & \(1/2^{*}b_{h}\)-\(1^{*}b_{h}\) & The maximum distance to extract the boundary point in convex hull. \\ \(b_{h}\) & 0.05 m & The bin size of histogram (equals to the distance between the vertical slice). \\ \(s_{r}\) & \({}^{*}\) & The searching radius of the parallel inner walls. \\ \hline \multicolumn{3}{c}{Building reconstruction parameters} \\ \(d_{m}\) & 0.05 m & The merging distance for different contours. \\ \(\theta_{m}\) & \(5^{\circ}\) & The merging angle for different contours. \\ \hline \multicolumn{3}{c}{The symbol “” represents the parameters determined by the datasets.} \\ \end{tabular}
\end{table} 
Table 3: Parameters of proposed layer-wise method. Figure 21: The experimental results for different values of search radius \(s_{r}\): (**a**) Over-segmentation; (**b,c**) well-segmented; (**d**) under-segmentation. Parameters of building reconstruction: Our strategy of building reconstruction was to generate 3D building models by assembling the contours of different representative layers. \(d_{m}\) and \(\theta_{m}\) control the level-of-detail in the final models. In practice, we mainly adjusted the model's level-of-detail by \(d_{m}\) due to the observation that the inconsistency of distances regarding linear primitives was more significant than the inconsistency of angles. A large value of \(d_{m}\) discards the details of models as indicated by the blue rectangles in Figure 23c,d. However, if we assembled the contour without mergence, the output model would be jagging and zig-zagging between layers. Figure 23a is the initial model without mergence, and the boundaries of the model are jagging. In general, the inconsistency of boundaries was less than 0.2 m.  
Figure 23: The effects of the merging parameter \(d_{m}\) to the final model: From 0 cm to 15 cm. (**a**) zig-zagging model without mergence; (**b**) proper merging parameter keeps the details of building; (**c**,**d**) details discard because of the over-mergence. Figure 22: The experimental results for different values of search radius \(s_{r}\): (**a**) Under-partition, and (**b**) over-partition. Red circle: Detected parallel structures; Blue circle: Over-partition situation; Dashed line: Partition boundaries.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### The Mechanisms of Enhancement in Nanofluids
#### 2.3.3 Micro-Convection  
The convective mass transfer process is strongly influenced by the Brownian motions and the resultant micro-advection near the particles that scatter a considerable fraction of the measured species molecules [80]. The solute diffusion can also be enhanced by micro-convection that is promoted by Brownian movements, this enhancement will also improve the diffusion coefficient. There is also a synergistic effect between the mass and heat transfer. When heat transfer is enhanced, changes in the gas-liquid phase temperature result in an enhancement in the potential for absorption [81]. The governing mechanism is that the micro-convection is enhanced by the fluid disturbance, the liquid disturbance is caused by random particle movement called Brownian motion [82]. The determination of the optimal nanoparticle concentration in a liquid depends on the Brownian motion that creates micro-convection. The mentioned three mechanisms play key roles in the enhancement of CO\({}_{2}\) mass transfer [83].
###### Abstract
Unlike the methods that have been aforementioned in the literature, the methodology proposed in this study offers researchers a comparative analysis by outlining the feature utilization procedure in classical machine learning, regression, tree, ensembling, and deep learning approaches. Therefore, it facilitates studies on which method will yield more accurate and faster results. The improvements made to the model parameters and architectural structure are discussed in detail. Comparative results of the LSTM-based hybrid bidirectional long short-term memory (Bi-LSTM) methods are included in this study to demonstrate the superiority of the proposed methods. To compare the observed and predicted discharge capacities, evaluation metrics such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), and R-squared have been applied through proposed methods [19]. Neural network fitting methods are applied, and their effects on model success are explained in detail. This study compares traditional machine learning methods and deep learning techniques with classical models and discusses in detail the procedure of utilizing features to determine the state of health of LIBs and evaluate the remaining useful life (RUL). In this context, it offers a comparative analysis of the performance of various methods, including regression, decision trees, ensemble models, and deep learning algorithms. Additionally, details of the improvements made to the LSTM and Bi-LSTM methods for estimating the health status of LIBs and assessing the remaining useful life are explained. In order to evaluate the performance of the proposed methods, the differences between observed and predicted discharge capacities are examined by using criteria such as MSE, MAE, RMSE and R-squared. These investigations provide a valuable knowledge base for the development of technologies based on new models for EV management interfaces and systems, predict the future performance of LIBs used in EVs, and contribute to their more efficient use. The study carried out in this context has the potential to contribute to technological developments in the electric vehicle industry by making predictions to increase the performance of LIBs. The rest of the study was designed as follows: In the second section, the materials and methods used were given. In the third section, LIB data are first prepared and presented for the study. The data were then analyzed from different perspectives. In the fourth chapter, based on the data analysis, various models for a LIB's SoH were proposed with machine learning and ANN techniques. In the following sections, the presented ML and ANN models were evaluated and compared in detail. Finally, the study was concluded by giving results from the literature and providing recommendations. Additionally, the boundaries and trends of this study are included in the research. Thus, researchers are given predictions for future studies.
###### Abstract  
This study investigates slope stability under rainfall infiltration using numerical modeling in Plaxis 2D, comparing poorly graded sand (6.5% fines) and well-graded sand (11.9% fines) under high-intensity rainfall of 30 mm/h for durations of 8, 12, 18, and 24 h. The results indicate that, as rainfall duration increases, soil saturation rises, leading to reduced suction, lower shear strength, and decreased safety factors (S.F.s). Poorly graded sand shows minimal sensitivity to infiltration, with the S.F. dropping by only 4.3% after 24 h, maintaining values close to the initial 1.126. Conversely, well-graded sand demonstrates significant sensitivity, with its S.F. decreasing by 25.4% after 8 h and 73.7% after 24 h, due to higher water retention capacity and suction. This highlights the significant contrast in stability behavior between the two soil types. The findings emphasize the critical role of soil hydro-mechanical properties in assessing slope stability, especially in regions with intense rainfall. This study establishes a methodology for correlating safety factor variations with rainfall duration and soil type, offering valuable insights for modeling and mitigating landslide risks in rainy climates, considering the hydraulic and mechanical parameters of the soil.  
s +
Footnote †: journal: Applied Sciences  
article
## 3 Results and Discussion
### Compressive Strength Development
The trend suggests that while FGCBA does contribute to strength development over time, it does so at a reduced rate compared to FA. Figure 8: Compressive strength trends with FGCBA as a cement substitute at W/B ratios of (**a**) 0.4; (**b**) 0.45; (**c**) 0.5. Increasing the water-binder ratio to 0.5 offers a broader view of the materials' performance. The control mix without replacements exhibits an initial strength of 20.78 MPa, ascending to 49.15 MPa over 180 days. When 20% of the cement is replaced with FA, the 7-day strength is lower at 18.92 MPa but undergoes a significant enhancement, reaching 57.19 MPa at 180 days. This robust long-term strength amplifies the efficacy of FA in high W/B ratio mixes, potentially offering an optimized solution for applications requiring a balance between workability and durability. For FA, 60% replacement at a 0.5 W/B ratio starts with a 7-day strength of 11.00 MPa, which substantially increases to 40.75 MPa by 180 days. Though the initial strength is lower than the control, the significant gain by 180 days demonstrates the potential of high-volume FA mixes in structural applications in which long-term performance is emphasized. For FGCBA replacement at a 0.5 W/B ratio, 20% substitution results in a 7-day strength of 24.27 MPa, with a 180-day strength of 44.10 MPa. At 60% replacement, the initial strength is 9.49 MPa, showing a considerable rise to 33.02 MPa at 180 days. Although the increase is notable, the overall strength development with FGCBA is less pronounced compared to FA, suggesting its application may be more suitable for scenarios in which moderate strength gains are acceptable. Such results were also found by [PERSON] et al. in their research on high-volume fly ash concrete [35]. In summary, the contrasting roles of FA and FGCBA in the evolution of concrete's compressive strength can be elucidated from the data. FA exhibits a pronounced impact on long-term strength across all W/B ratios, particularly at lower replacement levels. Despite early strength reductions with higher volumes of FA, the ultimate strength is substantially enhanced, advocating for its use in scenarios in which durability and strength over an extended period are paramount. FGCBA, while beneficial for long-term strength development, shows a more subdued and gradual increase, which may necessitate careful consideration when optimizing for specific performance objectives.
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 5 Future Development and Prospects  
In summary, researchers over the world have a certain level of understanding of the mechanisms of using ultrasonic excitation for rock breakage and fragmentation. Laboratory tests were conducted using high power ultrasonic waves to reveal the specific characteristics and microscopic performance of the ultrasonic excitation rock damage. It is foreseeable that in the future using ultrasonic excitation for rock breakage will be one of the important aspects in rock engineering, significant to rapid construction in underground engineering, such as tunneling in hard rock.  
However, the current researches mainly focus on the small scale, low power, and light weight crushing and processing of small size materials. The understanding of the rock breaking mechanism under the action of ultrasonic excitation and the associated rock resonance and mechanical impact still needs to be improved. The feasibility of using the technique for in-situ rock breaking at large geological bodies in underground engineering needs to be further explored. Under the in-situ condition, the coupled response of \"ultrasonic wave-rock mass-fracture flow\" needs to be further improved. The structural parameters of ultrasonic rock breaking equipment and their impact on the overall rock breaking effect still need to be improved.  
In the present study, 3D-XRM scanning and SEM scanning are applied to study the microcracking process and failure characteristics of rock specimens. In a future study, NMR will be adopted to investigate the distributions of pore structures of rock specimens after ultrasonic excitation; such investigation will further optimise the 3D presentation of the fracture morphology.  
With the continuous increase of coal mining depth and the continuous improvement of technical requirements for underground engineering, a single rock breaking method cannot fully meet the requirements of high efficiency, energy conservation, and maintenance of environmentally friendly conditions in mines. Ultrasonic technology has significant advantages in improving the current rock breakage methods for rapid and efficient underground structure development.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(E\)/GPa & \(\mu\) & \(R\)/MPa & \(R_{\text{m}}\)/MPa & \(\theta\)/\({}^{\circ}\) & \(C\)/MPa \\ \hline
70 & 0.25 & 53.6 & 6.01 & 42.38 & 18.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and mechanical parameters of the specimens.  
Figure 8: Excitation time versus compression strength curve.
## 5 Discussion
### Efficiency-Accuracy Trade-Off and Linear Complexity Advantage  
The DRFAN achieves competitive or superior PSNR and SSIM performance on multiple benchmarks with 1057K parameters and 65.1G FLOPs. Compared to other lightweight models, it demonstrates a strong capability to reconstruct high-frequency details while maintaining a lightweight footprint. The DRFAN leverages the MLLA module to achieve linear computational complexity \(O(N)\). This contrasts with subquadratic Transformer variants such as LongViT and BigBird, which attempt to lower the attention cost to \(O(NlogN)\) or \(O\Big{(}N\sqrt{N}\Big{)}\) through window partitioning, local/global token sparsification, or memory token compression. While effective in reducing computational load, these designs often rely on heuristics that may degrade fine-grained texture recovery--especially critical for pixel-level tasks like super-resolution.  
The DRFAN's use of MLLA enables efficient global feature modeling with minimal loss in local detail precision. This design offers a practical balance between computational tractability and visual quality, though we acknowledge that further empirical comparisons with subquadratic Transformers are warranted to more precisely quantify this trade-off under diverse workloads.
## 2 Full DC Microgrid System Modeling
### Diesel Generator and Supercapacitor  
DG is a backup source that can provide long-term support for the microgrid. However, the DG start-up stage presents slow dynamic behavior. Therefore, during the period of the DG starting up, anSC has been suggested to compensate for the power balance because of its fast response and high-power density [19; 20]. In addition, it is assumed that the DG works at duty cycle mode as in [21], where the duty cycle mode is proved to be better than a load-following mode, and one-hour is proposed as a good trade-off between fuel consumption and start-up frequency. The DG power \(p_{DG}\) is limited by the maximal DG supply power \(P_{DG,MAX}\), as given in Equation (9). Due to the slow dynamic behavior of the DG start-up stage, the DG cannot support the microgrid until it satisfies the conditions expressed by Equation (10), which can also prevent the converter from being broken by the peak power of the DG start-up stage. During the period between DG start-up and DG stable state given by Equation (10), the SC compensates the microgrid to keep its power balance.  
\[0\leq p_{DG}(t)\leq P_{DG\_MAX} \tag{9}\]  
\[\left\{\begin{array}{c}310~{}V<v<340~{}V\\ 48~{}\mathrm{Hz}<f_{DG}<52~{}\mathrm{Hz}\end{array}\right. \tag{10}\]  
The SC power \(p_{SC}\) is limited to its maximal SC charging power \(P_{SC\_MAX}\) and maximal SC discharging power \(-P_{SC\_MAX}\), as in Equation (11). The energy of SC \(E_{SC}\) is calculated according to SC capacitance \(C_{SC}\) and SC voltage \(v_{SC}\) as expressed by Equation (12). The \(E_{SC}(t)\) and \(E_{SC\_Rated}\) provide the \(soc_{SC}(t)\), which can be simplified as in Equation (13).  
\[-P_{SC\_MAX}\leq p_{SC}(t)\leq P_{SC\_MAX} \tag{11}\]  
\[E_{SC}=\frac{C_{SC}\cdot p_{SC}^{2}}{2} \tag{12}\]  
\[soc_{SC}(t)=\frac{E_{SC}(t)}{E_{SC\_Rated}}=\frac{\frac{C_{SC}\tau_{SC}^{2}(t )}{2}}{\frac{C_{SC}\tau_{SC}^{2}\cdot Rated}{2}}=\frac{v_{SC}(t)}{v_{SC\_Rated }}100\% \tag{13}\]  
As to the SC model given in [19], the SC is naturally self-discharging. It should be recharged at a certain time to keep its lowest energy for DG start-up compensation, and thus, it is necessary to define the SC recharge period. Therefore, the following SC state of charge \(soc_{SC}\) limitations are introduced: \(SOC_{SC\_MIN\_MIN}\), \(SOC_{SC\_MIN\_MAX}\), \(SOC_{SC\_MAX\_MIN}\), and \(SOC_{SC\_MAX\_MAX}\). When PV power is enough for load power demand, the recharging start-time and recharging end-time are the time when \(soc_{SC}\) reaches \(SOC_{SC\_MAX\_MIN}\) and \(SOC_{SC\_MAX\_MAX}\), respectively. The \(soc_{SC}\) minimal limitation for the SC is \(SOC_{SC\_MIN\_MIN}\) and the \(soc_{SC}\) minimal limitation for DG start-up compensation is \(SOC_{SC\_MIN\_MAX}\). When PV power is insufficient for load demand power, the recharging start-time is the time when \(soc_{SC}\) reaches \(SOC_{SC\_MIN\_MAX}\), which can keep the SC energy supporting the DG start-up.
###### Abstract  
Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.  
multipath TCP; software-defined networking; energy consumption; neural networks; congestion control; wireless; multihoming; multiconnectivity +
Footnote †: journal: Sensors
## 4 Pipeline System Modeling  
A basalt fiber reinforced polymer (BFRP) composite pipeline finite element model is established as shown in Figure 3.  
Figure 1: Convolution operation.  
Figure 2: Max pooling operation.  
The ANSYS software version is R14.5 [41; 42; 43], and both tetrahedron meshing and free meshing are applied, the minimum element size is set 0.005 m. The element type of the composite pipeline is SHELL 181. The composite pipeline is 1 m long, the distribution of which is [\(-\)45, 45, \(-\)45, 45, \(-\)45]. The density of the model is 2.8 g/cm\({}^{3}\). The internal radius is 0.04 m, and the external radius is 0.043 m. The boundaries are fixed for both two ends of the pipeline. Random excitation load is perpendicular to the wall of the pipe. Damage is modeled by reducing part of the stiffness of the pipeline. An enlarged view of damaged areas (marked in purple) in the pipeline is depicted in Figure 4.  
Figure 4: Damaged areas in the pipeline.  
Figure 3: Pipeline modeling.  
Table 1 shows a detailed parameter list of structural property of BFRP composite pipeline.  
Three damage levels are introduced, i.e., location: D1: 0.42-0.48 m, D2: 0.52-0.58 m, D3: 0.42-0.48 m + 0.52-0.58 m, all damages went across 180-360\({}^{\circ}\), and are assumed to occur at the internal surface. The characteristics of the damages are:  
* D1, D2: Same damage extent but different locations
* D3: Larger damage extent than D1 and D2 (D1 + D2)  
Random excitation (Rand1) (-400-400 kN) is shown in Figure 5, and Figure 6 shows power spectral density (log) of random excitation. Sampling frequency is 10
###### Abstract  
Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen input information in a public place. Our approach carefully exploits WiFi channel interference to introduce noise to attacker's CSI measurements to reduce the success rate of CSI-based attacks. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI-based attacks from 92-42% for text-based passwords and from 82-22% for pattern lock.  
CSI-based attack; channel interference; sensitive information protection +
Footnote †: _Appl. Sci._**2018**, \(8\), 515; doi:10.3390/app8040515