esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Effectively preventing hanger bending damage during the configuration transformation of the spatial main cables in suspension bridges is a critical challenge, particularly under the influence of construction errors. This study proposes an active fault-tolerant control method that integrates real-time data feedback, tolerance interval inversion technology, and a suspended lateral bracing (SLB) system to mitigate the risk of hanger bending damage in real time. The method establishes a dynamic inversion mechanism, utilizing data feedback, constraint function reconstruction, and secondary optimization to compensate for construction errors. This ensures that hangers remain undamaged throughout the transformation process. Construction errors are quantified as intervals, with the lower bound of the reliability interval used to account for extreme disturbances. This transforms the inversion process into a multi-objective optimization problem constrained by the worst reliability conditions. By integrating finite element analysis (FEA), reliability analysis, surrogate modeling, and interval analysis, the proposed approach establishes a direct relationship between design variables and the lower bound of the reliability interval. Case study results demonstrate that the proposed method not only ensures structural performance and hanger safety, but also significantly enhances the constructability of the configuration transformation. Additionally, it provides a larger fault tolerance margin, thereby improving the overall efficiency and safety of the construction process.  
self-anchored suspension bridge; configuration transformation; hanger bending damage; active fault-tolerant control; suspended lateral bracing system; multi-objective optimization
## 5 Discussion
### Limitations  
First, the sample size was small and geographically concentrated in Fujian Province, China, which may limit the generalizability of this study's findings. Cultural and regional differences could significantly influence driving behaviors and preferences for voice assistant interactions. For example, users' expectations regarding tone of voice, interaction frequency, or emotional expression may vary based on cultural backgrounds. Therefore, the findings of this study should be interpreted with caution, taking participants' cultural contexts into account. Future research should aim to expand the geographical and cultural diversity of the sample to enhance the external validity of the results. In addition, although the voice interaction strategies were validated in a driving simulation environment, the experimental scale was limited, and the personalization mechanisms remain at an exploratory stage. Further empirical validation is needed in future work, ideally incorporating physiological monitoring in real-world driving contexts to optimize differentiated interaction strategies and strengthen the scientific rigor and practical applicability of the proposed dialogue-awakening mechanism.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 2 Theoretical Background
### eXtreme Gradient Boosting (XGBoost) Classifier Design  
For the traditional radient Boosting Decision Tree (GBDT) algorithm, only the first order derivative information is used. Moreover, due to the dependency between weak learners, it is difficult for GBDT to train data in parallel [39]. XGBoost takes the Taylor expansion of the loss function up to the second order and adds a regularization term to find the optimal solution, which is used to balance the decline of the objective function and the complexity of the model to avoid overfitting [40]. The XGBoost model is  
\[\hat{y}_{i}=\sum\limits_{k=1}^{K}f_{k}(x_{i}),f_{k}\in F \tag{3}\]  
where \(K\) is the number of decision-tree, \(f_{k}(x_{i})\) is the function of input in the \(k\)-th decision-tree, \(\hat{y}_{i}\) is the predicted value, and \(F\) is the set of all possible CART. The objective function of XGBoost includes two parts: training error and regularization, which is\[X_{obj}=\sum\limits_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum\limits_{k=1}^{K}\Omega(f_{ k}) \tag{4}\]  
where \(\sum\limits_{i=1}^{n}l(y,\hat{y})\) is used to measure the difference between the predicted value and the real value of the loss function. \(\sum\limits_{k=1}^{K}\Omega(f_{k})\) is the regularization term and \(\Omega(f_{k})=\gamma T+\frac{1}{2}\lambda\|w\|^{2}\).  
T is the number of leaf node, \(w\) is the scores of leaf node, \(\gamma\) is the leaf penalty coefficient, and \(\lambda\) ensures that the scores of leaf node is not too large.  
The XGBoost algorithm uses the gradient boosting strategy, adds one new tree at a time instead of getting all the trees at once, and continuously repairs the previous test results by fitting the residuals of the last prediction:  
\[\left\{\begin{array}{l}\dot{y}_{i}^{(0)}=0\\ \dot{y}_{i}^{(1)}=f_{1}(x_{i})=\hat{y}_{i}^{(0)}+f_{1}(x_{i})\\ \dot{y}_{i}^{(2)}=f_{1}(x_{i})+f_{2}(x_{i})=\hat{y}_{i}^{(1)}+f_{2}(x_{i})\\......\\ y_{i}^{(K)}=\sum\limits_{k=1}^{K}f_{k}(x_{i})=\hat{y}_{i}^{(K-1)}+f_{K}(x_{i}) \end{array}\right. \tag{5}\]  
Combined with Formulas (4) and (5), for the \(t\)-th decision tree, the objective function can be updated to  
\[L^{(K)}=\sum\limits_{i=1}^{n}l(y_{i},\hat{y}_{i}^{(K-1)}+f_{K}(X_{i}))+\Omega( f_{k}) \tag{6}\]  
In order to find the objective function which can be minimized, taking the Taylor expansion of the loss function up to the second order. Then, the objective function is approximately as:  
\[L^{(K)}  
When \(K\) trees are obtained after training, the features of prediction samples will have a corresponding leaf node in each tree, and each leaf node corresponds to a score. Finally, the corresponding scores of each tree are added up to obtain the recognition prediction value of the sample [30]. The flow chart of XGBoost is shown in Figure 3.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
###### Abstract  
Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2 Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry.  
digital printing; color prediction; Pix2 Pix; color management; silk fabric; color differences +
Footnote †: journal: Applied sciences
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 5 Simulations and Numerical Results
### Throughput  
Figure 9 illustrates the throughput derived in (17). The proposed scheme is compared with three schemes; i.e., Random, Social-, and Physical-TOPSIS. The Random scheme selects the D2D peer randomly without considering social or physical parameters. The Social-TOPSIS scheme only performs peer selection based on social parameters using unmodified TOPSIS algorithm. The Physical-TOPSIS uses the same algorithm for peer selection taking into account only the physical parameters. Hence, the two methods use \(\delta_{s}\) and \(\delta_{p}\) to compute \(\mathfrak{I}\) instead of \(\Delta\) in (17), respectively. Finally, the proposed scheme selects the best D2D peer based on both the social and physical attributes (i.e., \(\Delta\)) using the modified TOPSIS algorithm.  
It is clear from the graph that the D2D throughput for the proposed scheme improves significantly, as it combines both the social and physical attributes of UEs and applies the modified TOPSIS algorithm to perform peer selection. Moreover, when the number of UEs is increased, the distance between UEs decreases and the number of friendly UEs increases. Both these parameters affect the \(\gamma\), \(R\), and ultimately the \(\mathfrak{I}\) as obtained using (15) to (17). The Random scheme gives very low throughput, as it selects D2D peer randomly without any manipulation of social and physical attributes. The Social- and Physical-TOPSIS schemes are better than the random scheme as these two methods select the D2D peer based on one type of attribute. The Physical-TOPSIS is slightly better than its counterpart, as  
Figure 8: Selection of the UE with best \(\Delta\) values.  
it considers the minimum value of \(D\) for the computation of \(\gamma\). However, both methods have lower throughput compared to the proposed scheme, which reveals that only these types of attributes are not sufficient for D2D peer selection.
# Recent Advances in Lithiophilic Porous Framework toward Dendrite-Free Lithium Metal Anode
## 3 Non-Metal-Based Li Host
### Graphene and Graphene Oxide  
The graphene-based electrode gives rise to stable SEI due to their higher flexibility, lower-dimensional change, and a relatively larger surface area than 2D current collectors [77]. The infusion of Li inside the layered rGO films with uniform nanogaps can retain capacity up to 3390 mAh g\({}^{-1}\) of capacity with a low overpotential of 80 mV at 3 mA cm\({}^{-2}\)[78]. This can be attributed to the fast and uniform Li intake due to the lithiophilic nature of sparked rGO and the capillary force produced by the nanogaps. The uniformly spread lithium on layered rGO regulates the initial Li nucleation due to the good lithiophilicity of rGO and the low Li nucleation barrier. The functional group such as carbonyl (3.080 eV) and alkoxy (2.974 eV) on rGO have higher binding energy (BE) to Li than the bare graphene counterpart (1.983 eV), responsible for Li affinity.  
Besides, the edge and high coordination sites in graphene and nitrogen-doped graphene with a functional group enhance the lithiophilic property [89; 90; 91; 92]. [PERSON] et al. employed crumpled paper ball-like ultrafine rGO particles with a specific surface area (SSA) of 382 m\({}^{2}\) g\({}^{-1}\) and pore volume 1.8 cm\({}^{3}\) g\({}^{-1}\) to accommodate high Li loading up to 10 mAh cm\({}^{-2}\)[81]. The improvement can be attributed to the lithiophilic rGO due to a sufficient amount of surface oxygen functional groups. This eliminates the need for additional lithiophilic coating/seed growth.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 2 Basic Theory of Transient Stability Analysis
### Introduction of Transient Stability  
Power system transient stability analysis could be generally described with high dimensional differential algebraic, such as Equations (1) [20].  
\[\left\{\begin{array}{c}X^{\prime}=F(X,Y,t)\\ 0=G(X,Y,t)\end{array}\right. \tag{1}\]  
where \(X\) and \(Y\) are the state and algebraic variables, respectively. The upper part of Equation (1) describes the state variables of dynamic components during the transient period, while the lower part of Equation (1) is the power network coupling in terms of nodal voltages and currents. The most important dynamic component for transient stability is for generators, including the conventional generator as detailed in the following and the wind power generator which will be elaborately presented in Section 3.
# Value-Added Use of Waste PET in Rubberized Asphalt Materials for Sustainable Payement
## 3 Results and Discussion
### Fatigue Properties of CRMA Mixtures Incorporating PET Additives  
To better evaluate the fatigue lives of the CRMA mixtures incorporating PET additives, ITFT tests were carried out with target stress control of 250 kPa, 300 kPa, 350 kPa, 400 kPa, and 450 kPa at 20 \({}^{\circ}\)C. Equation (10) below is typically adopted to describe the relationship between the cycles to fatigue failure and loading stress [36]:  
\[N_{f}=k_{1}(\sigma)^{k_{2}} \tag{10}\]  
where \(\sigma\) refers to the loading stress; \(N_{f}\) refers to the cycles to fatigue failure; and \(k_{1}\) and \(k_{2}\) are the coefficients of the fatigue equation.  
To further obtain the values of the coefficients, Equation (6) was transferred to the following linear expression (see Equation (11)) by taking the logarithm (base 10) of both sides of the equation:  
\[logN_{f}=logk_{1}+k_{2}log\sigma \tag{11}\]  
where \(logk_{1}\) and \(k_{2}\) are the y-axis intercept and the slope of the fitted fatigue curve, respectively, and higher values of \(k_{1}\) and \(|k_{2}|\), indicate better fatigue resistance.  
Table 8 presents the ITFT results of CRMA mixtures incorporating PET additives at 20 \({}^{\circ}\)C. As expected, the \(N_{f}\) value dramatically decreases as the loading stress increases, but the two PET additives have different effects on the fatigue resistance of the 18 CRMA mixture. It is worth noting that the \(N_{f}\) value of the 18 CRMA mixture is significantly increased when 2 wt.% PET-TETA is used, indicating that PET-TETA has a strong ability to improve the fatigue resistance of the 18 CRMA mixture. By contrast, the \(N_{f}\) value of the 18 CRMA2 PET-EA mixture is very close to that of the 18 CRMA mixture, implying that incorporating 2 wt.% PET-EA has an insignificant effect on the resistance of the 18 CRMA mixture. This is because PET-TETA can be thermally melted and evenly dispersed in the rubberized binder at the processing temperature and cause the binder to be produced with a powerful electrostatic force on aggregates due to the attachment of amino and amide-based structural characteristics, while PET-EA acts as a functional filler dispersed into the binder in association with less amine-derived groups. This explanation can be made by the fact that the thermal behavior and amine-based characteristic groups of both PET additives are totally different.  
Figure 7: Rate of rut development of CRMA mixtures containing PET additives with elapsing time at 60 \({}^{\circ}\)C.
## 4 Conclusions  
In conclusion, the basal plane catalytic activity for the HER was investigated with three metallic \(T\)MTe\({}_{2}\) (_TM_: Mo, W, and V). We measured the electrochemical measurements, LSVs, EISs, and CVs, and obtained the overpotential, Tafel slope, exchange current, \(\alpha\) site density, TOF, C\({}_{\rm DL}\), and R\({}_{\rm CT}\). Among the three \(T\)MTe\({}_{2}\), VTe\({}_{2}\) had the best HER performance with an overpotential of \(-\)441 mV and  
Figure 4: Hydrogen adsorption energies at \(\alpha\) and \(\beta\) sites of \(T\)MTe\({}_{2}\) with or without Te vacancies.  
Figure 3: (**a**) Cyclic voltammetry (CV) curves at different scan rates and (**b**) capacitive current as a function of scan rate of VTe\({}_{2}\), MoTe\({}_{2}\), and WTe\({}_{2}\).  
a Tafel slope of 70 mV/dec. Even though VTe\({}_{2}\) had the lowest exchange current, the \(\alpha\) site density and TOF among the three _TMTe\({}_{2}\)_, the lowest RCT of VTe\({}_{2}\) seems to be critical to achieve the best HER performance. Our study suggests that the inherent HER performance of metallic TMDs can be enhanced by enhancing the charge transfer process, increasing the electrochemical active surface area or by creating Te vacancies.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/9/3087/s1](http://www.mdpi.com/2076-3417/10/9/3087/s1)]([http://www.mdpi.com/2076-3417/10/9/3087/s1](http://www.mdpi.com/2076-3417/10/9/3087/s1)), Figure S1: Optical image and SEM image of WTe\({}_{2}\) single crystal for the electrochemical measurements, Figure S2: Residual resistance ratio (RRR) of three TMTe\({}_{2}\) single crystals.  
H.K.: Data curation, Investigation, Writing --Original draft, Writing--review & editing. D.B.: Data curation, Investigation, Writing--Original draft. H.J.: Data curation, Investigation. B.J.: Investigation, D.W.: Investigation. J.-H.L.: Formal analysis. Y.-W.S.: Formal analysis. H.Y.: Conceptualization, Supervision. S.C.: Conceptualization, Supervision, Writing--Original draft, Writing - review & editing. All authors have read and agreed to the published version of the manuscript.  
**Acknowledgments: S.C.: H.K. and D.B. were supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (2020R1A2C2003377). H.K. and H. J. were supported by the National Research Foundation of Korea (NRF) and Center for Women In Science, Engineering and Technology (WSIET) grant funded by the Ministry of Science and ICT (MSIT) under the team research program for female engineering students. H.Y. acknowledges support from the National Research Foundation of Korea (NRF) under Grant No. NRF-2020R1A2B5B02002548. Y.-W.S. was supported by the NRF of Korea (Grant No. 2017R1A5A1014862, SRC program: vdWMRC Center). We thank the Korea Institute for Advanced Study for providing computing resources (KIAS Center for Advanced Computation Linux Cluster System) for this work.**  
The authors declare no competing financial interests.
# Semantic 3D Mapping from Deep Image Segmentation
## 6 Conclusions  
We have presented our contributions to 3D segmentation from 2D segmented images acquired with a 3D camera in this work. Our approach does not limit the direct correspondence of image pixels with their 3D coordinates. It solves the problem at the limits of 2D segmentation using a novel spatial expansion algorithm. In the first step of the algorithm, it isolates the part of the point cloud that belongs in the raw to each detection. Selecting a position in space, in the second step, it recursively applies an expansion to close connected points. This process eliminates false positives that may appear in the point cloud.  
To validate our approach, we have carried out two experiments. In the first one, we have measured our approach's effectiveness to eliminate false positives, comparing it with  
Figure 11: Mapping of a real domestic environment.  
the direct approach and with one that performs a previous erosion in the segmentation in the image. It also showed how our method overperforms in speed compared to other state-of-the-art approaches.  
In the second experiment, we have shown how a real environment can be semantically mapped using a mobile robot's location. This method takes into account the uncertainty in estimating the position of a robot. The final map is composed of an octomap where the cells represent the space occupied by the detected object. The color of the cell indicates its class, as well as a probability associated with it.  
As future works, we propose to use the semantic map to locate the robot in case of not knowing the initial position of the robot. Another possible extension is to carry out a semantic nevaging, sending the robot positions relative to objects on the stage, such as \"near the oven\" or \"in the kitchen\", instead of metric positions.  
Conceptualization, F.M.; methodology, F.M. and J.M.G.; software, F.M. and F.G.; validation, J.M.G. and F.G.; formal analysis, M.F.; writing--review and editing, J.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the EU-funded projects RobMoSYS ITP MROS under Grant Agreement No. 732410.  
Not applicable.  
The authors declare no conflict of interest.
## 9 Discussion of the Test and Obtained Results
To keep this disturbance at a minimum level, it requires an experienced operator to stop and move forward the test at the right moment--not too early (causing underestimation of peak strength) and not too late (causing too much disturbance to the sample leading to underestimation of the rock strength). In this research, Iddefjord granite was used to demonstrate the method for estimating UCS based on triaxial test Type II but the method can be used for other rock types. From a technical point of view, the limitation of using this method is the same as for performing a triaxial test Type II. The rock type must show tendencies in the stress-strain development so that peak stress values can be determined pre-failure/weakening. Some rock types may not be suitable because of too-brittle behaviour and the subsequent non-zero-confinement steps cannot be carried out. After having a successful triaxial test Type II, depending on the indication of the test results, a linear or non-linear strength envelope can be selected for appropriate estimation of the UCS. As mentioned earlier, the radial strain rate was larger than the axial strain rate at every near-peak point of the tests. Thus, it is essential that the speed of applying load during these peak periods should be controlled by the radial strain rate rather than the axial strain rate as it is normally performed. The adjustment of loading speed is necessary to avoid too-quick loading that may lead to a sudden failure of the rock samples, especially for brittle rock samples.
# Toward More Efficient Large-Scale Green Hydrogen Systems via Waste Heat Recovery and ORC
## 3 Results
### Maximizing ORC Efficiency  
The ORC is being maximized by increasing the electricity output from the turbine and reducing pump work while maintaining energy conservation and mass balance. Solving Equation (6) resulted in the electricity power output reaching 555.88 kW and the pump power reaching 23.47 kW. Therefore, the electricity power output increased by 555.88 kW from 169.97 kW. In order to achieve maximum efficiency, the mass flow rate of working fluid, cooling water, and hot water are also optimized. Based on the optimized value, the working fluid and cooling water mass flow rates are 61,200 kg/hr and 1,033,092 kg/hr, respectively. Additionally, this optimization improves the total system efficiency (\(\eta\)), calculated using Equation (4), from 72.95 to 74.80, and increases the ORC system's total efficiency from 3.06 to 10.13 based on Equation (5). Table 10 compares the results before and after optimizing the ORC.  
Figure 7 illustrates the pressure enthalpy diagram for the ORC. In this optimization, the efficiency of the turbine and pump is assumed to be 70%, which is a conservative assumption.  
\begin{table}
\begin{tabular}{l c c} \hline
**Variable** & **Before** & **After** \\ \hline Working fluid R245 fa mass flow rate kg/hr & 65,000 & 61,200 \\ Cooling water flow rate kg/hr & 2,000,002 & 1,033,092 \\ Power output kW & 169.97 & 555.88 \\ Net power kW & 165.96 & 532.41 \\ \hline \end{tabular}
\end{table}
Table 10: ORC variables before and after optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Stream** & **Stream 2 *** & **Rec Water *** & **R245 fd-IN *** & **R245 fa-OUT *** \\ \hline Temperature (\({}^{\circ}\)C) & 76.47 & 24.8 & 21.44 & 30.97 \\ Pressure (bar) & 0.98 & 0.88 & 1.3 & 1.3 \\ Mass density (kg/m\({}^{3}\)) & 942.80 & 994.12 & 1350.42 & 7.17 \\ Average energy MW & 18.02 & 18.02 & 134.04 & 134.04 \\ Mass flows (kg/hr) & 86,000 & 86,000 & 65,000 & 65,000 \\ Enthalpy flow (W) & \(-3.75\times 10^{8}\) & \(-3.81\times 10^{8}\) & \(-1.62\times 10^{8}\) & \(-1.58\times 10^{8}\) \\ \hline \end{tabular}  
* Refer to Figure 3.  
\end{table}
Table 8: ORC module stream results.  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Values** \\ \hline Electricity from ORC & 169.97 kW \\ Exit water temperature from condenser & 22 \({}^{\circ}\)C \\ Total waste heat recovered & 27.32\% \\ Required electricity & 2.7 kW \\ Exit water for plant cooling & 25 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 9: ORC results for 86,000 kg/hr water at 76 \({}^{\circ}\)C.