esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 2 Laser Irradiation and Material Addition Mechanisms
### Powder Flow
[70] showed that the average powder velocity increases with increasing the shielding gas flow rate. Moreover, [PERSON], et al. [67] showed a wide velocity distribution of powder particles, as depicted in Figure 10. This wide velocity distribution was attributed to the complexity of the phenomena governing the gas-powder dynamics and the dispersion of the powder size. Figure 9: Effect of substrate position on particle concentration (reproduced with permission from [63]; Copyright © 2010 Elsevier). The relationship between the powder stream process parameters and the corresponding flow is another important aspect to consider in the optimization of the powder flow. [PERSON], et al. [67] suggested that the optimal working area, where the powder capture coefficient is maximized, is around the focal point of the powder stream. Hence, the importance of defining the powder focus plane is evident. The relationships between powder flow characteristics, such as powder distribution and powder focus plane distance, and process parameters were investigated by [PERSON], et al. [71] using a numerical model based on gas-solid flow. The peak of the powder stream focal point and its position were significantly affected by the velocity of the inner gas flow. Specifically, by increasing the inner gas velocity, a reduction in the peak and an increase in the axial distance of the focal point were observed (Figure 11a). Moreover, it was observed that an excessive shielding gas flow leads to defocusing of the powder flow and, as a consequence, a reduction in process efficiency [72]. The peak of powder concentration can be increased by increasing the powder flow rate [71]. It can be observed from Figure 11b that the axial distance of the focal point was unaffected by the powder flow rate. Figure 12 shows the powder flow obtained with the observation method by varying the powder flow rate. Figure 11: Variation of particle concentration with (**a**) inner shielding gas velocity and (**b**) powder flow rate [71]. Figure 4:0 Distribution of particles velocities at a distance of 30 mm below the nozzle exit (reproduced with permission from [67]; Copyright © 2010 Springer Nature). In addition, the powder focus plane is also influenced by the density of the material [66]. Indeed, [PERSON], et al. [73] analyzed the particle trajectory of two different materials, 316L stainless steel and Ti6 Al4V alloy. As depicted in Figure 13, both the analyzed materials exhibited a convergent trajectory of the powder stream. However, the position of the focus plane with respect to the deposition head was farther in the case of Ti6 Al4V. This is because the axial effect of the gas flow is less important for heavier particles, and consequently, heavier powder particles reach the axis of the deposition head closer to the deposition head exit.
## 2 Materials and Methods
### Methods
#### 2.2.4 Pooled OLS and Panel VAR  
In this article, we will also employ regression analysis to further explore the connections among DfG, ECI, and economic development. Considering that our data may be considered panel data, we will employ two regression models--pooled Ordinary LeastSquare (OLS) and panel Vector Autoregression (VAR) models. The general econometric model for panel data is as follows [43; 44]:  
\[Y_{it}=\alpha_{i}+\vec{\beta}_{i}\cdot\vec{X}_{it}+\mu_{it}, \tag{8}\]  
where, \(i=1,2,\ldots,N\), \(t=1,2,\ldots,T\), and \(N\) and \(T\) are the number of individual countries and total time (in year), respectively. \(Y_{it}\) is the dependent variable, \(\vec{X}_{it}\) is the independent variables (column vector), \(\alpha_{i}\) and \(\vec{\beta}_{i}\) are parameters (the latter a row vector with dimension matched to the column vector \(\vec{X}_{it}\) so that the inner product is defined), and \(\mu_{it}\) is the error term. As our purpose in this research is to find (and design) effective measures for quantifying economic complexity, we first assume that \(\alpha_{i}\) and \(\vec{\beta}_{i}\) are constant for all countries and time. This scenario is called the pooled OLS model, which is equivalent to the simple OLS model performed on panel data. The concrete equation used here is as follows:  
\[\ln\text{GDP}_{it}=\alpha+\beta_{1}\text{DfG}_{it}+\beta_{2}\text{ECI}_{it}+ \mu_{it}, \tag{9}\]  
We also use a panel-data VAR methodology. This technique combines the traditional VAR approach, which treats all the variables in the system as endogenous, with the panel-data approach, which allows for unobserved individual heterogeneity [45; 46]. We employ a first-order panel VAR model as follows:  
\[z_{i,t}=\Gamma_{0}+\Gamma_{1}z_{i,t-1}+\mu_{t}, \tag{10}\]  
where \(i\) represents the country in the panel-data, \(z_{i,t}\) is a three-variable vector \((\ln\text{GDP},\text{DfG},\text{ECI})\), \(\Gamma_{1}\) is a 3 \(\times\) 3 matrix of coefficients, \(\Gamma_{0}\) is a vector of individual effects. The stationarity of the three variables will be examined by using the LLC test [47] before we employ the PVAR model. Moreover, we can explore the statistical causality between the three variables based on the PVAR model.
## 4 Test Results  
### Material Tests  
Tensile tests were performed to evaluate the mechanical properties of steel components used for test specimens, that is, the SWM-R steel with diameters of 8, 10, and 13 mm used for upper and lower rebars, the SWM-R steel with diameters of 5 and 6 mm used for lattice steel wires, and the SGC 570 steel with a thickness of 0.5 mm used for deck plate. Results are presented in Table 1, where mean values for three specimens for each material are used.  
KATS [18] specifies the nominal tensile strength greater than 540 MPa for SWM-P and the nominal yield and tensile strengths greater than 440 MPa and 540 MPa, respectively, for SWM-R. It has been confirmed that the SWM-P and SWM-R steels satisfy the standard, as shown in Table 3. For SGC 560Y (SGC 570), KATS [19] specifies its yield and tensile strengths of 560 and 570, respectively, but the tensile tests somewhat underpredicted both the yield and tensile strengths. It was thus observed in the structural tests if this would affect the overall structural performance.  
Figure 10 shows stress-strain curves obtained from tensile tests for SWM-R with the 10 mm diameter and SGC 570 with the 0.5 mm diameter used for deck plate. Results for other steel materials are provided in detail in Lee [20]. The material test data given in Table 3 were used in the numerical simulations and the results are discussed based on the test results for the proposed tr  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Coupon**} & \multirow{2}{*}{**Diameter/Thickness**} & \multirow{2}{*}{**Steel Type**} & \multicolumn{2}{c}{**Yield Strength**} & \multirow{2}{*}{**Yield Strain**} & \multirow{2}{*}{**Tensile Strength**} & \multirow{2}{*}{**Elongation (\%)**} \\  & & & **(MPa)** & & & \\ \hline Upper/Lower rebar & 13 & SWM-R & 548 & 2330 & 693 & 12.8 \\ Upper rebar & 10 & SWM-R & 628 & 2700 & 713 & 12.0 \\ Lower rebar & 8 & SWM-R & 542 & 2680 & 582 & 13.3 \\ Lattice steel wire & 5 & SWM-P & 532 & 2560 & 582 & 22.7 \\
6 & SWM-P & 519 & 2560 & 624 & 17.8 \\ Steel sheet & 0.5 & SGC 570 & 464 & 2320 & 546 & 24.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of material tests.  
Figure 9: Strain-gauge plan. (**a**) Side view. (**b**) Top view.
## 1 Introduction
Another in vitro study showed that Er:YAG, CO\({}_{2}\) and diode lasers could achieve a high percentage or complete elimination of surface bacteria on contaminated titanium surfaces [19]. Other investigations showed that CO\({}_{2}\) and diode lasers did not cause surface alterations following irradiation [20,21]. Conversely, Nd:YAG laser irradiation caused extensive surface melting of titanium discs irrespective of output energy [22]. Studies investigating temperature changes of implant surfaces after laser irradiation showed that diode lasers caused a temperature increase above the critical threshold of 10 \({}^{\circ}\)C after only 10 s and was ineffective at removing calcified deposits [16,23]. Conversely, use of theEr:YAG laser irradiation for implant treatment was demonstrated without altering the titanium surface if proper settings were applied [20; 21]. Nevertheless, complete implant surface decontamination using chemical and mechanical procedures has been proven unsuccessful for the following reasons: (1) limited access to implant microstructures, (2) the presence of resistant bacterial strains, (3) ineffective drug dosages and (4) inadequate bactericidal effect [24]. Several other factors, including the equipment utilized, the macro- and micro-texture of the implant and local anatomy and access, may influence the effectiveness of surface decontamination of biofilm-contaminated implants. In addition, biofilms adhere more strongly to rough implant surfaces than to smooth ones [25]. Previous in vitro investigations mostly applied laser irradiation to titanium discs with smooth or rough surfaces. These studies were not able to evaluate the effects of anatomical access limitations caused by peri-implant intra-bony defects and macro-structures of implants on the effectiveness of implant surface decontamination in clinical scenarios. Therefore, the aims of the present study were to integrate computer-aided design (CAD), reverse engineering and 3D-printing techniques to establish an experimental model mimicking various peri-implant intra-bony defects of root-form implants with rough surface texture between teeth. We evaluated the influence of access limitation and the effectiveness of biofilm decontamination by an Er:YAG laser in these clinically relevant models. Herein, we demonstrate effective sterilization using Er:YAG laser irradiation in both shallow and deep peri-implant intra-bony defects at different positions and diameters of dental implants.
# Service for Deploying Digital Twins of QKD Networks
## 3 Functional Overview
### Service Capabilities  
In addressing shortcomings in the access and implementation of secure QKD networks, we have developed a digital twin service that offers significant advantages. This service allows users to construct virtual replicas of such networks by simply providing a description of the desired deployment. Additionally, it provides a working environment aligned with a standardized API, meaning that the emulated network adheres to the same guidelines as a real QKD network.  
Figure 3: Functional architecture of the QKD network digital twin sites. Components along with their interfaces are shown.  
Users can address QKD-related issues without the need to access the physical infrastructure of a real QKD network, resulting in significant savings of resources, both in terms of time and funds, which would otherwise be required to build a real network. Leveraging virtualization and NFV technologies, our service can easily scale both horizontally (in terms of the number of QKD nodes) and vertically (in terms of the processing capacity of each QKD node). Notably, to the best of our knowledge, no other service allows for the automatic deployment of distributed emulated QKD nodes, as is the case with the service we are presenting.  
Furthermore, the service also solves the typical problems of incompatibility of devices from different manufacturers, i.e., it offers much more flexibility in testing diverse network designs, as all elements communicate with the same ETSI API. The modular Python design we have employed facilitates implementation and integration of new developments, standards, protocols, and more.  
Our approach not only enables the emulation of QKD nodes but also opens up multiple possibilities regarding its utilization. These include but are not limited to conducting research into securing novel Internet applications through QKD, testing new protocols and APIs for the development of standards in the QKD domain, experimenting with large multi-operator QKD networks in a realistic manner, and working on integrating independent islands of QKD nodes offered by different service providers.
###### Abstract  
High-efficiency permanent-magnet synchronous machine (PMSM) drive systems need not only optimally designed motors but also efficiency-oriented control strategies. However, the existing control strategies only focus on partial loss optimization. This paper proposes a novel analytic loss model of PMSM in either sine-wave pulse-width modulation (SPWM) or space vector pulse width modulation (SVPWM) which can take into account both the fundamental loss and harmonic loss. The fundamental loss is divided into fundamental copper loss and fundamental iron loss which is estimated by the average flux density in the stator tooth and yoke. In addition, the harmonic loss is obtained from the Bertotti iron loss formula by the harmonic voltages of the three-phase inverter in either SPWM or SVPWM which are calculated by double Fourier integral analysis. Based on the analytic loss model, this paper proposes a maximum efficiency per torque (MEPT) control strategy which can minimize the electromagnetic loss of PMSM in the whole operation range. As the loss model of PMSM is too complicated to obtain the analytical solution of optimal loss, a golden section method is applied to achieve the optimal operation point accurately, which can make PMSM work at maximum efficiency. The optimized results between SPWM and SVPWM show that the MEPT in SVPWM has a better effect on the optimization performance. Both the theory analysis and experiment results show that the MEPT control can significantly improve the efficiency performance of the PMSM in each operation condition with a satisfied dynamic performance.  
maximum efficiency per torque control; efficiency improvement; PMSM; fundamental loss; harmonic loss; copper loss; iron loss; double Fourier integral analysis Article
## 2 Methodology
### Global Feature Extraction Module  
The global feature extraction module is primarily used to extract global semantic features from large-format input images. In this paper, we first downscale large-format images into low-resolution small-size images and also downscale labels corresponding to large-format images to small-size labels. Then, we select the appropriate semantic segmentation model to extract global semantic features. In this paper, we have chosen the TransUNet [30], a classic semantic segmentation network, which is primarily composed of three components: an encoder, an attention module, and a decoder. The encoder utilizes the ResNet-50 network to generate multi-scale feature maps. The height and width of these multi-scale feature maps are 1/2, 1/4, 1/8, and 1/16 of the small-size image's dimensions, respectively. The attention module consists of 12 transformer units connected in series to process the encoder's final output feature map, which has a height and width that is 1/16 of the small-size image. This process yields an enhanced feature map that is rich in contextual information. The encoder consists of a series of convolutional upsampling and skip connection units. It employs a U-Net-like strategy to progressively restore the feature map layer by layer. The final output of the TransUNet is a pixel-by-pixel semantic feature map that matches the size of the low-resolution small-size image.  
Subsequently, to fuse with the local features output by the subsequent feature coding module, we divided the global semantic features into 64 blocks, which we referred to as a partition operation. Finally, to fuse with the highest-level features output by the feature encoding module, we further resized each block of the global semantic features to the same size as the highest-level features output by the feature encoding module, e.g., 1/16 of the small-size image's dimensions.  
The equation for the global feature extraction module is summarized as follows:  
\[x_{g}=Resize(Partition(TransUNet(Resize(x)))), \tag{1}\]  
where \(x\) denotes the large-format input image, Resize represents the operation of downscaling the image, \(TransUnet\) represents the network structure used by the global feature extraction module in this paper, \(Partition\) stands for partition operation, and \(x_{g}\) represents the global feature of the final output.  
It should be noted that to better utilize the global semantic information extracted by the semantic information extraction module, high-dimensional semantic features are used as input to the subsequent feature aggregation module, instead of the semantic segmentation results. To alleviate the computational load and GPU memory usage during the training of large-format image semantic segmentation, we use the downsampling image and corresponding label to train the model in advance. During the training of the entire proposed network, the parameters of the global feature extraction module are fixed and do not participate in the backpropagation and gradient update, greatly reducing the GPU memory consumption by the network.
## 1 Introduction
(2018), proposed an open-pit mine truck scheduling system based on dynamic ore selection decisions. A multi-stage approach was proposed, consisting of the following three sequential stages: (a) fleet size selection, (b) fleet allocation, and (c) real-time truck scheduling. The research on scheduling and transportation optimization provides valuable reference schemes for vehicle transportation scheduling in the new transportation mode of battery-swapping trucks in open-pit mines. However, previous studies have primarily focused on traditional fuel trucks and new energy electric trucks. The unique battery-swapping transportation mode of these trucks differs significantly from the transportation modes of previous scheduling objects. Therefore, it is necessary to consider scenario factors, battery-swapping characteristics, and scheduling objectives specific to the new transportation mode of battery-swapping trucks in open-pit mines in order to construct a scheduling model for battery-swapping truck transportation planning and achieve efficient scheduling. This study, based on the new transportation mode of battery-swapping trucks in open-pit coal mines, analyzes the production and operational conditions of the mine and the transportation characteristics of battery-swapping trucks, and constructs a transportation scheduling model for battery-swapping trucks suitable for open-pit mines. The model is solved using a multi-objective basic genetic algorithm and an adaptive genetic algorithm, resulting in an intelligent scheduling plan for the new transportation mode of battery-swapping trucks in open-pit coal mines. This plan addresses the issue of the lack of scheduling plans under the new transportation mode, optimizes scheduling, significantly reduces transportation costs, and improves transportation efficiency. The structure of this paper is as follows: Section 2 presents the transportation scheduling model for battery-swapping trucks in open-pit mines, including model assumptions, objective functions, and constraints. The multi-objective optimization algorithm used to solve the scheduling model is also introduced. Section 3 provides a case study, including case model, algorithm parameter configuration, and mine scenario parameter configuration. Section 4 presents simulation experiments, including comparative experiments on multi-objective weight coefficient optimization and comparative experiments on multi-algorithm solutions. Section 5 presents the conclusions.
###### Abstract  
To meet the demand for cooperative signal control at oversaturated heterogeneous traffic flow intersections containing CAVs and HVs, cooperative control including dedicated CAV lanes has been explored to improve intersection safety capacity and reduce vehicle delays while avoiding uncertain HV driving behaviour. However, this approach does not fully exploit CAV network connectivity advantages and intersection spatial and temporal resources. Here, an oversaturated heterogeneous traffic flow signal control model based on a variable virtual waiting zone with a dedicated CAV lane is proposed. Within the model, CAVs going straight or left share a dedicated CAV lane, a CAV variable virtual waiting zone is within the intersection ahead of the dedicated CAV lane, and CAVs and HVs share the straight-through lane. The model framework has three layers. The upper layer optimizes the barrier time using a rolling time domain scheme. The middle layer optimizes the phase duration and variable virtual waiting zone switching time based on the fixed phase sequence, returning the vehicle delay to the upper optimization model. The lower layer performs CAV grouping and trajectory planning in the dedicated CAV lane based on signal timing and variable virtual waiting zone duration, returning the CAV delays to the middle level.  
variable virtual waiting zone; dedicated CAV lane; heterogeneous traffic flow; oversaturation; cooperative signal control
# Coupling and Trapping of Light in Thin-Film Solar Cells Using Modulated Interface Textures
###### Abstract  
Increasing the efficiency of solar cells relies on light management. This becomes increasingly important for thin-film technologies, but it is also relevant for poorly absorbing semiconductors like silicon. Exemplarily, the performance of a-Si:H/\(\omega\)-Si:H tandem solar cells strongly depends on the texture of the front and rear contact surfaces. The rear contact interface texture usually results from the front surface texture and the subsequent absorber growth. A well-textured front contact facilitates light-coupling to the solar cell and light-trapping within the device. A variety of differently textured ZnO:Al front contacts were sputter deposited and subsequently texture etched. The optical performance of a-Si:H/\(\omega\)-Si:H tandem solar cells were evaluated regarding the two effects: light-coupling and light-trapping. A connection between the front contact texture and the two optical effects is demonstrated, specifically, it is shown that both are induced by different texture properties. These findings can be transferred to any solar cell technologies, like copper indium gallium selenide (CIGS) or perovskites, where light management and modifications of surface textures by subsequent film growth have to be considered. A modulated surface texture of the ZnO:Al front contact was realized using two etching steps. Improved light-coupling and light-trapping in silicon thin-film solar cells lead to 12.5% efficiency.  
surface texture; light-trapping; light-coupling; light-scattering; thin-film solar cell; front contact; ZnO:Al +
Footnote †: journal: Applied Sciences