esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study  
### Influence of Varying Face Sheets Thickness on RCSPs' Face Sheets Deformations  
To investigate the effect of face sheets thickness variation on the front- and back-plate deflections of RCSPs, a parametric study was performed. The TZRC sandwich structure was selected to run this study. The purpose of the parametric study is to find an effective way to enhance the blast resistance of RCSP when the equivalent density increases. The investigation of the influence of face sheets thickness variation run through two scenarios. The first one is the influence of varying the front-plate thickness on the performance of the RCSP, a constant rear-plate thickness with six different front-plate thicknesses: 0.6, 1, 1.4, 1.8, 2.2 and 2.6 mm. Figure 10 shows the effect of changing the front-plate thickness on the front and rear plates' deflections. From Figure 10, it can be noticed that the front-layer deflection decreases upon increasing the front sheet thickness, also the back-layer deflection decreases when increasing the front-layer thickness. The results indicate that when using a front sheet with 2.6 mm thickness, the deflection decreases by an average of 75% for the front-face sheet and by an average of 70% for the rear-face sheet. It can be concluded that the rear-plate deflection decreases with increasing front-plate thickness due to the increased stiffness. In the second scenario, the effect of altering the thickness of the rear face sheet on the behavior of the RCSP is investigated by maintaining a constant front-face sheet thickness with six different rear-plate thicknesses: 0.6, 1, 1.4, 1.8, 2.2 and 2.6 mm. Figure 8 shows the effect of changing the rear-plate thickness on the deflection of the front-face sheet. From Figure 8, it can be seen that changing the rear faceplate thickness has a negligible effect on the front-layer deflection; however, the rear face sheet deflection decreases upon increasing the rear-layer thickness. Comparing the deflection when using a back layer with a 2.6-mm thickness with the deflection when using a back layer with a 0.6-mm thickness, the deflection decreases by an average of 90.6% for the rear face sheet and by an average of 19.36% for the front-face sheet. The core compression is the difference of transverse deflections between the front plate and the rear plate. It can be noticed that, by increasing the rear plate thickness, more core compression occurs due to increasing the stiffness of the core backing. So, the rear-plate thickness has a significant impact on the core compression rather than the front plate. More core compression means more efficiency for utilizing the core to get the maximum energy dissipated by the core.  
Figure 9: Residual midspan deflections for the facing sheets of sandwich structures with different core configurations under the impact of detonation of 55 g TNT at _SoD_ 100 mm.
### Positioning System  
Airport personnel are always equipped with communication systems, such as Tetra transceivers. Each voice or text-based communication device may also have a GPS module installed, which is very useful for locating objects. The advantage of such a system is that employees do not need to carry another device.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & & **Key-Points** \\ \hline Video surveillance with automatic motion detection and tracking & \(\bullet\) & Intrusion and staff location and limited identification \\  & \(\bullet\) & Operator attendance still required \\  & \(\bullet\) & Sensitive to environmental conditions \\  & & and obstacles \\ \hline Short-range microwave radars & \(\bullet\) & Intruders and staff accurate automatic location and tracking \\  & \(\bullet\) & Sensitive to obstacles (beam shadows \\  & & and reflections) \\  & \(\bullet\) & Direct identification impossible! \\ \hline  & \(\bullet\) & Automatic staff location and identification \\ Positioning based on satellite navigation, GNSS (e.g., GPS) & \(\bullet\) & Low positioning accuracy \\  & \(\bullet\) & Less sensitive to obstacles \\  & \(\bullet\) & Not applicable for intruders! \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Unfortunately, solutions based on the GPS system have disadvantages:  
GPS module working time. If permanent operation is required, the battery life is limited;  
Third-party vendors have limited ability to change or update firmware. Communication protocol does not consider system requirements;  
GPS location accuracy is not sufficient for many objects in close proximity (i.e., a few meters).  
The next section presents the airport and industry requirements along with the proposed solution.
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 4 Examples and Application
### Bed Aggradation  
The benchmark experiments conducted by [PERSON] et al. [53] were selected to demonstrate the bed aggradation process. The experiment was designed to investigate longitudinal deposition formed by feeding poorly sorted sediment from upstream. The flume was 45 m long and 0.3 m wide with a slope of 2%. The input-sediment particle size varied from 0.125 mm (fine sand) to 64 mm (coarse gravel) with a median size of 6 mm. Figure 4 shows a sketch of this flume experiment.  
In the experiment, there were three runs, and run two was selected in this study. For run two, at the inlet, a constant flow discharge of 0.049 m\({}^{3}\)/s and a constant sediment discharge of 0.0942 kg/s were imposed, while at the outlet, the water level was 0.45 m. Table 2 lists the input sediment mixture consisting of 10 size classes. The sediment transport model ran for 32.4 h on a 5 \(\times\) 200 rectangle mesh with the bed roughness of 0.025 m\({}^{-1/3}\)  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Size (mm)** & **0.25** & **0.35** & **0.5** & **0.7** & **0.9** & **1.25** & **1.75** & **2.5** & **3.5** & **5** & **7** & **9** \\ \hline Lower Bound & 0.2 & 0.3 & 0.4 & 0.6 & 0.8 & 1 & 1.5 & 2 & 3 & 4 & 6 & 8 \\ Upper Bound & 0.3 & 0.4 & 0.6 & 0.8 & 1 & 1.5 & 2 & 3 & 4 & 6 & 8 & 10 \\ Fraction & 0.075 & 0.125 & 0.165 & 0.035 & 0.035 & 0.065 & 0.04 & 0.09 & 0.1 & 0.195 & 0.05 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Size classes for degradation experiment ([PERSON] and Michiue [3]).  
Figure 2: Erosion development profiles with time.  
and a time step of 1 s. The adaptation coefficient of the suspended load, \(\alpha\), was calibrated as 0.3, while the adaptation length of the bed load, \(L_{b}\), was 0.3 m (the channel width).  
Due to the constant feeding of coarse sediments, sediment deposition developed in this flume. Figure 5 compares the simulated bed profiles at different times to the measured ones. The shape of the bed profiles, including the ending slopes, was well captured by the model, though a small phase shift at 22 h was observed, resulting in an over-estimated deposition. However, the simulation results generally agreed well with the measurements.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Size** & **0.125** & **0.25** & **0.5** & **1** & **2** & **4** & **8** & **16** & **32** & **64** \\ \hline Fraction & 0.022 & 0.104 & 0.102 & 0.07 & 0.04 & 0.172 & 0.12 & 0.168 & 0.124 & 0.078 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input sediment mixture for the bed aggradation experiment ([PERSON] et al. [32]).  
Figure 4: Sideview of the layout of the bed-agradation experiment by [PERSON] et al. [51].  
Figure 3: Bed compositions at \(x=10\) m.
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 4 Experiment and Analysis
### Comparison of Different Detection Methods  
To further substantiate the merits of the proposed method, we conduct a comparative analysis, pitting the machine anomaly detection performance of our method against other mainstream approaches. The comparative results are detailed in Table 2.  
As illustrated in Table 2, Ref. [13] represents the official baseline model provided by DCASE2020 Task 2, while [15; 17; 28] are the detection methods with x-vectors, the log-Mel spectrogram, and spectral-temporal information fusion as input features, respectively. Additionally, other researchers have proposed machine anomalous sound detection networks [19; 20; 24; 32]. Table 2 reveals that our proposed method achieves the best detection results in terms of both the average AUC and the average pAUC. Furthermore, our method outperforms other approaches for the specific machine types of Pump, ToyCar, and ToyConveyor. However, it should be noted that even the method with the highest average AUC and average pAUC fails to attain the best detection performance across all machine types. Notably, the ToyConveyor machine presents a challenge in detecting an effective distribution boundary due to the high similarity of normal acoustic features, resulting in its detection accuracy being far lower than the other five machine types. Therefore, how to better calculate the distribution boundary of the ToyConveyor machine is the key to further improving the comprehensive performance of the system.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 3 Template Matching Based on PCA-LMN
### Learnable Matching Network
#### 3.2.3 Loss Function  
Each training pair includes a template and a base image. Suppose the ground truth transformation between them is \(H_{gt}\), and the transformation estimated between the sub-areas of this training pair is \(\hat{H}_{k}\), the loss function is calculated by the following:  
\[loss=\sum_{k=1}^{4}\sum_{i=1}^{N}||H_{gt}(x_{i},y_{i})-\left(\hat{H}_{k}(x_{i} -t_{x},y_{i}-t_{y})\right)+\left(t_{x},t_{y}\right)||^{2} \tag{8}\]  
where \(N\) is the number of grid points, and \(\left(t_{x},t_{y}\right)\) is the translation between the sub-area and the template, as shown in Figure 7. The loss function is based on the transformed grid loss [24], which minimizes the discrepancy between the estimating transformation and the ground truth transformation.  
The partition approach increases the resolution of the input image and the resolution of the feature extraction, which helps improve the matching precision. In the partition approach, the four subarea pairs share the regression network, which means the number of the parameters of the network is not increased. This facilitates the retraining processing and the deployment of the network, which will eventually enhance the matching performance with a small training dataset.  
The other way of enhancing the precision is inputting image with high resolution, but that may introduce an enormous increase of parameters to the network, which makes the retraining process (for cross-modal images) difficult and eventually lower the performance of the inference.
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
## 3 Results and Discussion  
### Laser Ablation in a Single Location  
Figure 2 shows the relationship between the ablation depth of the micro-craters and the pulse number. As the figure indicates, the mean depth increases almost linearly from 0.13 \(\upmu\)m to 1.24 \(\upmu\)m with the pulse number increasing from 2 to 20. The deviation of the measured depth is in the range of 15%, representing the high stability during picosecond LST process. It is important to note that the total ablation depth after multi-pulses irradiation is almost the accumulation of multiple independent pulses. This is consistent with prior estimation done in modeling of LST. In this study, the depths for 2 and 20 pulses were used as baseline values to determine the value of \(D_{\text{max}}(N)\) from Equations (3) and (4) by linear fitting, and a slope of 0.062 and an intercept of 0.007 were obtained.  
Using the experimental profile from 20 pulses, the scale factor \(k\) was estimated via Gaussian fitting, and a value of 1.5 was obtained. Based on the calculated \(D_{\text{max}}(N)\) and \(k\), the predicted results using Equation (3) compared with the experimental data, as illustrated in Figure 3. The length and width of the computational domain were both chosen as 20 \(\upmu\)m, and the mesh sizes in both directions were set as 50 nm. The simulation was performed via Matlab. The experimentally measured surface topology and the predicted 3D micro-crater are shown on the left and right, respectively, while the comparison between the predicted and experimental cross-sectional profiles are plotted on the center. As shown, the predicted profile agrees very well with the experimental data.  
Figure 3: The comparison between experimental and predicted micro-crater for 20 pulses.  
Figure 2: Effect of pulse number on machined depth of micro-craters.  
Furthermore, the depths of 2, 5, 10 and 15 pulses were validated using the developed model. The calculated \(k\) of 1.5 was used, while the \(D_{\text{max}}(N)\) was calculated according to the above linear fitting and the number of pulses, \(N\). The comparison between the predicted and the experimental results is demonstrated in Figure 4. As it can be seen, the model provides a very good prediction of the ablation depth for a varying number of pulses in a single location. The ablation depth increases quickly with the increasing pulses.
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 2 Methodology
### Benefit-Cost Ratio (BCR) and Return on Investment (ROI)  
The comparative analysis of BCR and ROI for grout joints, bolted flange joints, and PIP slip joints, as depicted in the bar chart, underscores the superior economic performance of PIP slip joints in offshore wind applications (Figure 7). With a BCR of 2.413 and an ROI of 141.29%, the PIP slip joint surpasses the grout joint (BCR: 2.300, ROI: 130.04%) and bolted flange joint (BCR: 2.324, ROI: 132.38%) by 4.91% and 3.83% in BCR, and 8.65% and 6.73% in ROI, respectively, highlighting its ability to deliver the highest economic benefits and returns per euro invested. This advantage stems from the PIP slip joint's higher NPV (EUR 39.559 M) and lower initial investment (EUR 28 M), which enhance its profitability and cost-efficiency. The marginal differences between grout joints and bolted flange joints (1.04% in BCR and 1.80% in ROI) indicate similar economic viability, though the bolted flange joint performs slightly better. These findings, consistent with PIP slip joint's leadership in LCOE, IRR, and payback period, position it as the optimal choice for offshore wind projects, balancing economic feasibility with high financial returns, thereby supporting sustainable investment decisions in renewable energy infrastructure.  
Figure 6: Net present value for different joint types.
###### Abstract
The above studies on the construction of low-carbon economic scheduling model of power system or IPGES based on the background of carbon trading mainly increase the carbon trading cost on the objective function, and do not study the setting of carbon price level for implementing carbon trading in IPGES. The implementation of carbon trading should ensure that it will not bring too high financial burden to the participating sources [29]. If the carbon trading price is too high, it will affect the enthusiasm of power generation enterprises and excessively increase the carbon trading cost of the energy system. If the carbon trading price is too low, it cannot meet the corresponding total carbon emission control requirements. Therefore, in the context of carbon trading, it is necessary to study the optimal tiered carbon price level of IPGES under the requirement of total carbon emission control, so as to provide theoretical guidance for decision makers. On the basis of the above research, the contributions of this paper are as follows:  
1. A reward and punishment ladder-type carbon trading model is constructed, and comparatively analyzed the impact of three carbon price mechanisms of carbon trading on the carbon emission sources in the power system;
2. Comprehensively considering the power system constraints, natural gas system constraints, and coupling element operation constraints, a stochastic optimal dispatching model of IPGES that takes into account the uncertain wind power and carbon trading cost is established. 3. In order to explore the optimal carbon price of IPGES under carbon trading, a bi-level optimization model has been established while taking into account the economics of dispatching and the requirements of carbon emission reduction, which takes the optimal carbon price solution of carbon trading as the outer layer model, and the stochastic optimal dispatching of IPGES as the inner layer model. 4. The solution method of the bi-level optimization model in this paper is proposed. The paper is organized in seven sections. In the second section, the reward and punishment ladder-type carbon trading model is constructed. In the third section, a bi-level optimization model of IPGES is proposed. The processing and solving methods of the bi-level optimization model in this paper are introduced in the fourth section. The simulation case study is discussed in the fifth section, and the expansion of the model and method is discussed and explained in the sixth section. The overall analysis and conclusions are drawn in the last section. The nomenclature and formulae section is given at the end of the article.
## 2 Theoretical Background  
During the last decade, urban energy systems have undergone a significant change, evolving from centralized and hierarchically structured systems to distributed systems characterized by an increase of complexity in both terms of the number and type of resources and of the different actors involved in the energy supply chain.  
Different concepts and innovative approaches have been proposed in the scientific literature. The main research streams on the matter have been recently discussed in [1], which presented a comprehensive analysis of the current definition and evolution trends of UES concepts by describing the relationships between individual entities and the related interoperability requirements. According to [1], the nature of emerging concepts in present UESs can be described by the Unified Modeling Language (UML) class diagram depicted in Figure 1, which summarizes the current understanding of electrical energy resources in UESs based on the almost 500 sources reviewed by the considered study.  
As shown by the diagram of Figure 1, VPPs are expected to play a central role in future UESs by providing improved automation and control capabilities in smart grids through the aggregation and virtualization of dispersed DERs, including power generators, storage units, and active loads. Several works in the literature demonstrated the highly beneficial contribution of the VPP concept for the optimal control of heterogeneous DERs in UESs [19; 20; 21; 22; 23; 24] by also emphasizing the key role of DSM schemes for the successful implementation of VPP management strategies. However, as we also discuss in the following subsections, research to date typically considers VPPs as a generalized abstraction of DERs owned by different stakeholders, and the proposed control and DSM strategies are still designed according to the centralized control paradigm [1]. In this perspective, the distribution system is still granted the role of central controller rather than that of supervisor and coordinator of the different transactive actions among the involved stakeholders.  
Starting from this critical analysis, in this paper we propose a conceptual VPP architecture in which the control perspective is moved from the system (or supervisory) level to the single prosumer's level, who participates in advanced DSM schemes by offering flexible power profiles through the aggregation of owned DERs, including generators, storage units, and controllable loads.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 4 Discussion
### Protocols Designed for Free Scale Networks  
A free scale network consists of a number of peers and super peers (a super peer is a peer with major processing power and bandwidth). The peers are divided into two groups based on their connection types. Some of the peers are only connected to a super peer. Other peers are interconnected between them and super peers [28]. While we surveyed the state of the art, no protocols were found specifically designed for this type of networks. However, protocols designed for plain networks can be adapted to be used in a free scale network architecture. However, these protocols do not use information about network topology and treat peers in different groups in the same manner. Thus, the overhead grows with the number of peers in the system.
## 2 Materials and Methods
### Data Management and Analysis:  
The data analysis procedure involved several steps to examine the collected data systematically. Initially, descriptive statistics were calculated to summarize the characteristics of the variables, including frequencies, percentages, means, and standard deviations. Normality testing, employing the Shapiro-Wilk test, was conducted to assess the distribution of the variables. Following this, group comparisons were performed using the Mann-Whitney U test for the comparison between thermocycling and non-thernocycling groups, as well as the Kruskal-Wallis test for comparing different curing technique groups. The chi-square test was employed to assess the association between two categorical variables: the presence or absence of certain characteristics (e.g., vertical, horizontal, or oblique position) and the groups (thermocycling vs. non-thernocycling) for each curing technique. Post hoc tests, Tukey, were carried out to determine specific group differences when significant differences were detected. _p_-value (<0.05) was adopted as the level of significance. Finally, the statistical package for the social sciences (SPSS) software (SPSS, V.23, IBM Corp, Armonk, NY, USA) was utilized for data analysis.  
Figure 3: The microcracks formed after the metal orthodontic brackets debonding under the stereomicroscope.