esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The inverter-to-rectifier phase shift angle is set to \(\delta=90^{\circ}+\Delta\delta\) with \(\Delta\delta\) calculated analytically from the discharging current of the power electronic switch, the resonant circuit parameters, and the input/output DC voltage to maximize the ZVS operation range of the inverter and rectifier bridges. The robustness performance of this TPS control method is questionable because it is challenging to obtain accurate circuit parameters for the inverter and rectifier phase shift angle online calculation in a practical wireless charging system. In [17], a phase-lock-loop (PLL) based double phase shift control scheme is proposed for a DAB S-S resonant converter wireless charging system. The inverter phase shift angle controls battery charging voltage/current. The rectifier phase shift angle controls the effective AC load resistance of the resonant circuit. A PLL algorithm was proposed to adjust the rectifier voltage window based on the zero-crossing detection of the receiver winding current to achieve closer to zero reactive power operation of the rectifier AC circuit. The PLL timing synchronization control algorithm implicitly adjusts the inverter-to-rectifier phase shift angle. The inverter ZPA can be realized with rectifier-side PLL timing synchronization control. It is hard to understand rectifier-side PLL auto-tuning results concerning inverter AC voltage and current because the inverter-to-rectifier phase shift angle for this DAB S-S resonant converter needs to be analyzed and evaluated. An explanation needs to be given about how the PLL algorithm starts to work in DC/DC converter power-on the startup process when there is current in the resonant circuit. This paper presents a detailed TPS closed-loop control implementation method on DC/DC LCC resonant converter for the wireless charging system. The battery charging current/voltage feedback control generates the inverter phase shift angle. The rectifier phase shift angle is generated by LCC resonant circuit AC load resistance feedback control to improve the efficiency performance in CV charging operation. The theoretical DAB inverter-to-rectifier phase shift angle, which satisfies the LCC resonant compensation circuit ZPA design constraint for unity power factor operation of the inverter, is derived from the equivalent LCC resonant circuit model. The inverter-to-rectifier phase shift angle is set to \(\delta=90^{\circ}\) for unity power factor operation of the DAB inverter and rectifier. The possible methods for TPS control implementation in decoupled inverter and rectifier control hardware are explained for future engineering development in wireless charging products. The simulation and measurement study results are discussed in the conclusion section.
# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 2 Materials, Processes and Methods
### Influence of Shielding Gases  
The influence of different shielding gases on welding processes is commonly known. The occurring effects of different shielding gases can be easily adopted to wire arc additive manufacturing, but the purpose might differ. While deep penetration of the weld metal can be beneficial for joint welding and can be supported by a shielding gas, in additive manufacturing, for shallow penetration, a higher build up can be desired, and the shielding gas needs to be chosen for this purpose. [PERSON] et al., for example, studied the influence of different CO\({}_{2}\) contents on the additive manufacturing of low-alloy steel [94]. They found that an increased CO\({}_{2}\) content led to a lowered layer height and higher melt pool surface temperatures, but it had little influence on the strength and toughness of the used material [94].  
[PERSON] et al. described the influence of O\({}_{2}\) on arc wandering in WAAM of aluminum alloys. They found that the addition of up to 200 ppm of oxygen had no impact on the arc behavior, and the addition of up to 20.000 ppm of oxygen led to the significant formation of oxides on the weld metal surface [95].
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 3 Results and Discussions
### Sensitivity Study of BVI Noise
#### 3.3.2 Thrust Coefficient  
In this section, by holding the advance ratio, tip-path-plane angle, and tip Mach number constant, we can examine the impact of the thrust coefficient from 0.0047 to 0.0080 on the BVI noise characteristics.  
The thrust coefficient impacts the wake-induced downwash but has less impact on the interaction angle. Figure 14 shows the predicted sound pressure at different thrust coefficients at #7, #3, and #9.  
Figure 12: Contour plots of SPL on the sphere surface for different tip-path-plane angles. (**a**) \(\alpha_{tpp}=2^{\circ}\); (**b**) \(\alpha_{tpp}=-2^{\circ}\).  
Figure 13: Max BVISPL variance with the tip-path-plane angle.  
The sound pressure waveforms at the three observation locations are seen to be less sensitive to the thrust coefficient due to the double effects of vortex strength and miss-distance. On the one hand, the BVI noise increases with the vortex strength, which is proportional to the thrust coefficient. On the other hand, the miss-distance increases with the thrust coefficient, producing reductions in BVI noise.  
The contour plots of SPL at two thrust coefficients are compared in Figure 15. At \(C_{T}=0.0065\), two peak noise levels appear in space, respectively, from the parallel interaction #A3 and the oblique interaction #A2, which radiates to 180\({}^{\circ}\)-240\({}^{\circ}\) in azimuth down the rotor. With \(C_{T}\) increasing to 0.0080, interaction #A2 becomes stronger than #A3, and finally dominates the BVI noise propagation. Meanwhile, as the vortex strength influences more strongly than the miss-distance, the BVI noise at \(C_{T}=0.0080\) is stronger than that at \(C_{T}=0.0065\).  
In the above analysis, \(\alpha_{tpp}\) is kept constant so the miss-distance and vortex strength both make contributions when the thrust coefficient increases. Therefore, another sample trimming \(\alpha_{tpp}\) to keep the miss-distance constant is provided to analyze the isolated effect of the vortex strength. Figure 16 accordingly presents the predicted sound pressure varying with \(C_{T}\) at #3. Compared with the results in Figure 14, the relative strength of the sound pressure from each interaction is seen to be similar and the amplitude changes proportionately with the thrust coefficient.  
Figure 14: Predicted acoustic pressure at #7, #3, and #9 variance with the thrust coefficients.  
Figure 15: Contour plots of SPL on the sphere surface for different thrust coefficients. (**a**) \(C_{T}=0.0065\); (**b**) \(C_{T}=0.0080\).  
Figure 17 shows the peak BVI noise level varying with the thrust coefficient in different states. If we only consider the impact of vortex strength, the peak noise amplitude is linear to \(C_{T}\), while if the effects of both vortex strength and miss-distance are considered, the peak noise amplitude seems to be less sensitive to \(C_{T}\), which means that variation in the thrust coefficient changes the noise propagation, but has little effect on the noise intensity.
# Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
## 5 Results Analysis Using Synthetic Models  
### Varying the Number of Hosts  
The buffer size was fixed at 10 MB and message lifetime was set to 120 min. The number of hosts was varied and the effect of this variation on the message delivery ratio, average latency, number of dead nodes, and overhead ratio, were investigated. The results are given in Figures 2-5 respectively.  
In Figure 3, it is observed that the average latency decreases as the number of hosts increases. This is due to the fact that, as the number of node increases, the movement and density of the nodes increases the chance of faster delivery. Increases in the number of deliveries ensure that the average latency decreases. It is also observed that in terms of average latency, EECSG outperforms both CGOPP and GSAF.  
In Figure 4, it can be observed that the number of dead nodes increases as the number of hosts increases. EECSG produced the lowest number of dead nodes compared to CGOPP and GSAF. This is due to the fact that a node consumes its energy in three ways (sending, receiving, and scanning neighbors). As the number of nodes increases, their process of scanning, sending, and receiving increases automatically, which yields more dead nodes.  
Figure 3: Average latency vs. number of hosts.  
Figure 2: Delivery ratio vs. number of hosts.  
In Figure 5, it can be observed that the overhead ratio increases as the number of hosts increases and EECSG yields the lowest overhead ratio compared to CGOPP and GSAF. This might be due to the intrinsic Check-and-Spray mechanism used in EECSG to control the flooding of the messages within the geocast region.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 4 Band Expansion through Porous Materials  
### Porous Materials Arranged in Parallel  
To further expand the band to high-frequency region, a composite structure has been designed, in which the melamine foams, as porous cells, are filled into the groove of the HR structure. When the foams are arranged in parallel with HR structures, the whole absorption band can be significantly broadened without any increase in structure thickness. Nevertheless, as a single cell in the broadband structure, the sound absorption performance of the foam undergoes considerable variations because the area of the foam is smaller than the incident area of the whole structure. The area ratio of the porous cell to the structure is defined as \(\eta=S_{c}/S_{0}\) with \(S_{c}=l\times w\), and the specific relative resistance of the porous cell is, therefore, \(x_{s}=x_{s0}/\eta\), where \(x_{s0}\) is the inherent resistance of the melamine foam. Figure 5a demonstrates the absorption performance of the porous cell with different area ratios. The foam thickness is set as \(h=20\) mm, while the key parameters include the porosity \(\phi=0.95\), tortuous factor \(\alpha_{so}=1.07\), flow resistance \(\sigma=11,\)660 Pa\(\cdot\)s/m\({}^{2}\), viscous characteristic length \(\Lambda=78.2\) um, and the thermal characteristic length \(\Lambda^{\prime}=155.3\) um. It can be seen that as the area ratio gradually decreases, the peak of the porous cell becomes narrow. Especially when the area ratio is \(\eta=1/9\) and \(1/12\), its absorption performance closely resembles that of resonant structures, nearly losing its broadband characteristics. On the other hand, the decreased area ratio will result in an increase in the cell's specific resistance. When the specific resistance increases to be matchable with that of air, the peak can approximately achieve 100% absorption with the area ratio of \(\eta=1/4\). As the area ratio continues to decrease, the peak gradually diminishes due to the increased resistance.  
Therefore, the foam requires a lower flow resistance for excellent absorption with a decreased area ratio. Based on the above analysis, the thicknesses of the two porous cells have been chosen to be \(h_{1}=20\) mm and \(h_{2}=12\) mm, respectively. Figure 5b shows the absorption coefficients of the composite structure, HR structure and the porous cells. It can  
Figure 5: Sound absorption coefficients of (a) the melamine foam with different area ratio \(\eta\), and (b) the composite structure, porous cells and HR structure.  
be seen that the two cells exhibit two peaks near 3500 Hz and 5500 Hz, with absorption coefficients approximating 71% and 82%, respectively. By incorporating porous cells, the absorption band is significantly broadened from 3000 Hz to 6000 Hz, while the absorption performance of the original HR structure remains almost unaffected. This remarkable ultra-broadband absorption performance alleviates the stringent requirements for the upper porous layer, so its thickness can be reduced as much as possible.
## 4 Data-Driven CIM Construction
### Data-Driven CIM Construction Process
#### 4.2.2 Data Processing  
Data processing constitutes a pivotal phase in the construction of CIM, encompassing the cleaning, transformation, analysis, and mining of collected urban data to extract actionable insights that underpin the subsequent decision making and applications. The initial step in data processing is data cleaning, which involves tasks such as imputing missing values, detecting outliers, and standardizing formats to ensure that the data quality meets the prerequisites for advanced analysis [62]. Subsequently, the data undergo transformation, including procedures such as normalization and feature selection, to prepare them for modeling and analysis.  
During this process, it is crucial to identify the patterns and trends in the data through advanced techniques such as spatial analysis, statistical analysis, machine learning, and data mining. GIS technology is employed for spatial analysis, while machine learning methods, including decision trees and clustering algorithms, are utilized to uncover hidden patterns and correlations. Furthermore, spatiotemporal analysis is applied to reveal the temporal dynamics and spatial distribution characteristics within the data, providing a robust scientific foundation for urban management and planning.  
As data processing progresses, CIM systems, supported by efficient data infrastructure such as data lakes and data warehouses, are capable of managing the storage and processing of large-scale, heterogeneous datasets. This ensures that data flow seamlessly and reliably across various application processes, thereby enhancing the operational effectiveness of CIM in urban management.
# A Study on the Seismic Performance of Steel H-Column and T-Beam-Bolted Joints
## 3 Seismic Performance Analysis
### Hysteretic Curve and Skeleton Curve  
Obviously from Figure 9, the hysteretic curve of the BASE node is fusiform in shape, relatively full overall, and exhibits no obvious pinching effect. The specimen was within the elastic range, and the hysteretic curve presented a linear form. During the loading and unloading processes, the curve essentially passed through the origin, and there was almost no residual deformation after unloading. As the loading progresses, the node transitions from the elastic stage to the elastoplastic stage. The slope of the hysteretic curve gradually diminishes, the stiffness deteriorates, and the bearing capacity increases. At this point, the node experiences a certain degree of residual deformation. Once the ultimate bearing capacity is reached, the joint enters the failure stage, the bearing capacity declines, and the hysteretic curve displays a distinct descending section.  
The data of characteristic points during the loading process are presented in Table 3. As is clear from Figure 10, when the BASE node is in the elastic range, the skeleton curve is a first-order function that passes through the origin. As the loading displacement increases, the node enters the elastic-plastic range. Its stiffness gradually deteriorates, and the load reaches its peak value. Subsequently, the skeleton curve exhibits a distinct descending stage.  
Regarding the failure transition in the descending stage, as the load decreased, the structure gradually shifted from a state of stable load bearing with minor in-elastic deformation to a state of approaching failure with significant degradation. This transition was accompanied by visible damage, such as the buckling of steel components. The softening rate, which reflects the rate of load reduction with respect to displacement increase in the descending stage, was calculated as follows: the slope of the linear-fitting line in the descending part of the load-displacement curve was approximately \(-5\) kN/mm. A relatively steep softening rate indicates a rapid degradation of the structure's load-carrying capacity once it passes the peak load, which has implications for seismic design. Structures with a high softening rate may experience sudden and severe failure during strong earthquakes if not properly designed to accommodate the large displacement demands.
## 1 Introduction
These studies confirm the theoretical advantages of conical geometries: better load dispersion, improved settlement control, and enhanced resistance to overturning. However, conical foundations also present specific challenges--most notably, the risk of soil extrusion beneath sloped edges, particularly when the embedment depth is shallow. To overcome these limitations and enhance both vertical and lateral stability, this study proposes a cone-top pile foundation, combining a conical base (with the apex pointing downward) and a centrally embedded pile shaft. This hybrid configuration improves resistance to soil deformation and allows for deeper load transfer. The proposed foundation system also draws conceptual parallels with micropile-supported systems, such as those examined by [PERSON] et al. [23], which demonstrated high performance in layered soils under static and dynamic loads. While conical geometry addresses vertical load efficiency, incorporating a central pile addresses lateral force demands more effectively. In this regard, the findings of [PERSON] and [PERSON] [24], who studied lateral pile behavior in sloped clayey soils, further support the need for such composite foundation systems. The integration of a central pile with a conical head leverages the strengths of both geometries, offering a robust solution in conditions requiring strain isolation, improved settlement control, and resistance to multi-directional loading. [PERSON] et al. [25] investigated the failure mechanisms and combined bearing behavior of a monopile-friction wheel hybrid foundation in sand-over-clay deposits, demonstrating improved load capacity and deformation control through hybrid foundation configurations. The main objective of this study was to build upon the findings of the previous studies, by developing a hybrid conical foundation with an integrated pile stand. This new design aims to retain the advantageous geometry of the conical shape foundation, known for its effective response under tensile stress and enhanced stability in zones susceptible to horizontal displacements within soil massifs adjacent to underground mine workings. In addition, the incorporation of a pile stand is expected to significantly improve bearing capacity, thereby enhancing the overall load-bearing performance and reducing settlement effects.
# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 2 Literature Review
### Contribution of the Study
#### 2.1.2 Decision-Making Technique  
This research contributes to the literature by integrating a hybrid fuzzy MDCM technique (CINFU AHP and CINFU CODAS) under lean principles specifically for machine selection in inventory tracking to answer predefined research questions. It presents the optimized decision-making process under uncertainty by conducting a case study demonstrating the practical implementation and benefits of the proposed framework in real-world scenarios. Moreover, it applies sensitivity analysis to decrease subjectivity and test the what-if conditions.  
While the individual methods utilized in this study, CINFU AHP and CINFU CODAS, are not entirely novel, their customized integration into a specific industrial challenge, machine selection for inventory tracking, contributes to MCDM. In contrast to prior applications, the criteria and decision model developed in this research reflect the priorities of lean manufacturing and digital transformation. Including comprehensive sensitivity analysis further differentiates this work, providing a valuable tool for decision-makers. By validating the framework through a real-world case study, this research effectively bridges the gap between theoretical models and industrial practice, offering a replicable approach for tackling similar challenges in other manufacturing contexts.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 4 Implementation and Application of Overlapping-Grid-Sewing-Machine Library
### Internal Work-Flow of OGSM Library
The coverage relationship is judged based on ADT, and the _inactive_ query nodes are excluded. Note that the properties of the nodes to be queried are screened in parallel. 8. Classify global volume cells. The hole-cutting boundaries are determined and the global volume cells are classified into _flow-field_ cells, _overlapping_ cells and _in-hole_ cells. 9. Query the optimal donor cells for overlapping cells. The tri-linear interpolation coefficients are calculated in parallel. For _defective overlapping_ cells, the recursive-box method is adopted to quickly search for the optimal donor cells, and the other overlapping cells are paired to their donor cells by applying cross-process searching on ADT structures and the Newton-Raphson iterative method. 10. Treat the assembly information in parallel. The mapping \((ip,id)\rightarrow(jp,id,fs)\) holds for any overlapping cell in each process. Hereby, \(ip\) and \(id\) represent the process index and local cell index of the current overlapping cell on the source grid. Meanwhile, \(jp\), \(jd\), and \(fs\) represent the process index, the local cell index and the interpolation factors of the corresponding donor cell on the target grid, respectively. Then, the target processes prepares the data for interpolation based on the mapping. 11. Count the total number of donor cells in the current process. Strictly speaking, this value is equal to the total number of interpolation operations in the current process. For an unsteady flow field with multi-body relative motion, the assembly state of the overlapping grids changes at each physical time step. Meanwhile, the total number of donor cells on each process varies. OGSM provides the solver with this critical parameter, which enables it to dynamically manage the memory space and perform the interpolation of the physical field. 12. Export the information of overlapping interpolation and cell classification. First, the current process outputs the overlapping relations; then the solver interpolates the physical field using the tri-linear factors and exchanges the data across processes for the donor cells. Finally, the type identifiers of volume cells are outputted as an _iBlank_ value (0: in-hole, 1: overlapping, 2: flow-field). OGSM consists of three modules, i.e., the topology generation module of overlapping grids (steps \(1\) to \(3\)), the parallel hole-cutting module (steps \(4\) to \(7\)) and the parallel assembly module for overlapping cells (steps \(8\) to \(11\)).