esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Our Construction
### The Proposed Scheme
#### 3.1.4 Authentication
* Secure Mutual Authentication:  
In the Group Joining phase, a group manager will share a long-term secret \(\mathbf{\omega_{B_{i}}}\) with user \(i\), which is also a trapdoor to function SampleD. In the Authentication phase, the group manager first sends \(t_{i}\) to user \(i\) as a challenge, then user \(i\) returns with \(a_{i}=E_{k_{i}}\big{(}\mathbf{d_{B_{i}}},\ r_{i}\big{)}\), where \(k_{i}=H\big{(}\mathbf{\omega_{B_{i}}}\big{)}\). Note that it is necessary to compute \(\mathbf{d_{B_{i}}}\) using SampleD with \(\mathbf{\omega_{B_{i}}}\). Therefore, if the check in Step 4 passes, user \(i\) is authenticated. On the other hand, if the check in Case 1 (Case 2) passes, then we can assure that the group manager decrypts the ciphertext \(a_{i}\) with \(k_{i}=H\big{(}\mathbf{\omega_{B_{i}}}\big{)}\) to obtain the correct \(r_{i}\) for user \(i\). By the security of the underlying symmetric encryption scheme, we are sure that the group manager is authenticated. If the two entities are both authenticated by each other, then we know that the session key \(sk\) can be securely established by the security of SampleD and the underlying symmetric encryption scheme.
## 6 Conclusions  
A triaxial AE experiment was carried out on the Beishan high-level radioactive waste disposal repository, and methods such as fractal theory and machine learning were utilized to analyze the AE characteristics during rock failure. The evolution of fracture clusters within the rock was studied, and the AE characteristics of different fracture clusters were analyzed. The conclusions are as follows:  
1. As the confining pressure increases, categories of fracture reduce, the proportion of major and non-major fractures decrease, and the proportion of outlier fractures increases;
2. During the initial phase, the proportion of major fractures AE significantly fluctuates, while during the active phase, proportion of major fracture AE generally increases. The proportion of major fracture AE remains relatively constant during the calm phase, and slightly decreases in the destructive phase;
3. The variation in the \(b\)-value of major fractures during the process of rock failure can be divided into three stages--increase, decrease, and secondary increase--which indicates that microcracks in rocks also has the self-similarity aspect of the seismic process;
4. A rock major fracture identification model was established based on a BP neural network, and the model's accuracy rate of major fracture identification was 87.22%.  
Conceptualization, C.W.; methodology, C.W. and H.W.; validation, W.R. and J.M.; formal analysis, J.M.; investigation, H.W. and J.M.; resources, C.W. and W.R.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, H.W.; visualization, C.W.; supervision, C.W.; project administration, J.M.; funding acquisition, C.W. and W.R. All authors have read and agreed to the published version of the manuscript.  
This article was funded by the National Natural Science Foundation of China Youth Foundation Project (grant number 52104082), the National Natural Science Foundation of China Key Foundation Project (Regional Joint Fund) (grant number U20A20266) and the Science and Technology Development Fund Project of China Coal Research Institute (grant number 2021 CX-II-12).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Multi-Sensor Fusion
### Tight Coupling of Dual Lidar and IMU  
Although the fusion of dual lidar makes up for the shortcomings of lidar's narrow FOV, the localization accuracy is improved. However, lidars mounted on moving vehicles suffer from motion distortion, which directly affects the localization accuracy. IMU can accurately measure the three-axis acceleration and three-axis angular velocity of moving vehicles, and provide a priori information for lidar odometry. However, in the case of long or short distances, the IMU error will continue to accumulate, which directly affects positioning accuracy. In this paper, the tight coupling of dual lidar and IMU is proposed to solve the above problems. The process is divided into two parts: (1) external parameter calibration of horizontal lidar and IMU and time synchronization, and (2) joint optimization, which are described as follows:
## 3 Results and Discussion
### Transport Study of Cr(VI) through Aliquat 336/PolyHIPE Membrane  
To demonstrate the feasibility of PolyHIPE membrane, transport of Cr(VI) through Aliquat 336/PolyHIPE membrane was investigated as a model system. The transport of Cr(VI) by the carrier across the support liquid membrane is known to involve five consecutive steps [35].  
1. Diffusion of Cr(VI) from feeding phase to the interface.
2. Formation of Cr(VI)-extractant complex at the feed phase-membrane interface.
3. Diffusion of Cr(VI)-extractant complex through the membrane.
4. Dissociation of Cr(VI)-extractant complex at the stripping phase-membrane interface, to release the Cr(VI) into the stripping phase.
5. Diffusion of the Cr(VI) from the interface to the stripping phase.  
Steps (1) and (5) are related to the mixing condition of the bulk phase, which can be presumed to be a fast process, if both bulk phases are effectively stirred. For Steps (2) and (4), the rate is related to the formation and dissociation of the complex at the interfaces. For Step (3), the characteristics of membrane pore structure is the critical factor affecting the diffusion of Cr(VI)-extractant complex through the membrane.
## 3 Results
### Fourier Transform Infrared Spectroscopy (FTIR)  
FTIR was conducted to examine the molecular structure of the native and spray-dried potato starch powders. Figure 2 shows the FTIR spectra of native starch and of spray-dried powders at concentrations of 3% [SD_3%] and 5% [SD_5%] in 3800-300 cm\({}^{-1}\) region and in 1200-850 cm\({}^{-1}\) region. The FTIR spectra of native potato starch and spray-dried starch powders revealed the presence of characteristic peaks associated with specific molecular vibrations and interactions. In detail, the absorbance peak in the range of 3600-3200 cm\({}^{-1}\) is attributed to the stretching vibration of OH groups, indicating the hydrogen bonding between starch and water molecules [40; 41]. In the FTIR spectra of native potato starch, the broad OH- stretching band shows the highest intensity, reflecting the presence of stronger  
Figure 1: Diffractograms (**a**) and DSC thermograms (**b**) of native potato starch and spray-dried potato starch powders at concentrations of 3% [SD_3%] and 5% [SD_5%].  
hydrogen bonds, compared to the other samples. A similar observation was reported by [PERSON] et al. [42]. The peak at 2928 cm\({}^{-1}\) is attributed to the asymmetric C-H vibration of the methyl group [43], while the peak at 1645 cm\({}^{-1}\) corresponds to the water molecules within the starch [44]. The peaks in the 820-1280 cm\({}^{-1}\) region are associated with the C-O and C-C stretching vibration [44]. The peaks at 1045 cm\({}^{-1}\) and 995 cm\({}^{-1}\) are characteristic of the crystalline structure of starch, while the peak at 1022 cm\({}^{-1}\) represents the amorphous region of starch [45]. As shown in Figure 2, these peaks are distinct in the FTIR spectra of all powders. However, the intensity of the peak at 1022 cm\({}^{-1}\) is higher in spray-dried starch powders compared to native potato starch. These differences could be attributed to the gelatinization and drying process, which likely resulted in the disruption of the intermolecular bonds within the starch granules leading to a decrease in the crystalline state and an increase in the amorphous structure. [PERSON] et al. [46] also reported a similar observation using synchrotron FTIR spectroscopy.
## 5 Conclusions  
In this paper, a control algorithm to manage SAPF's operation in a three-phase four-wire system by generating reference current signal was successfully demonstrated. The newly proposed algorithm was named as enhanced-ADALINE, where it was formed by merging three single-phase ADALINE modules, and was further enhanced by integrating an HSF synchronizer module and averaging function. Simulation tests and analyses were thoroughly performed to evaluate the performance of enhanced-ADALINE under the conditions where the grid was non-ideal (three non-ideal scenarios were considered) and the load was unbalanced (two unbalanced loads were considered). The performance demonstrated by the enhanced-ADALINE algorithm was compared to the existing STF-dq0 algorithm to gauge its capability. According to the presented findings, the enhanced-ADALINE algorithm was revealed to perform effectively despite distortion and/or unbalanced source voltage, and unbalanced connected loads. More importantly, the enhanced-ADALINE algorithm revealed to be more reliable than the existing STF-dq0 algorithm where it was able to provide a lower THD performance (0.88-2.63%) and a more synchronized operation (indicated by a lower phase difference between source voltage and current, i.e., in the range of 0.10\({}^{\circ}\)-1.20\({}^{\circ}\)). As a result, almost unity power factor was achieved. Last but not least, the enhanced-ADALINE algorithm also revealed to be effective in removing excessive neutral current, and this feature is mandatory for the harmonics extraction algorithm applied in a three-phase four-wire system. For the next research step, a laboratory prototype will be developed to validate the performance of the enhanced-ADALINE algorithm in a practical environment.  
[PERSON] developed the simulation model, conducted the tests, analyzed all the important findings, and wrote the manuscript; M.A.M.R. verified the simulation study and written manuscript; [PERSON] revised and finalized the drafted manuscript.  
This research received no external funding.  
The authors declare no potential conflict of interest.
## 5 Improved NSGA-II Algorithm
### Parental Selection Based on Linear Ranking  
The conventional NSGA-II algorithm typically uses tournament selection to identify superior adapted parent individuals from the population and incorporate them into the offspring population. This process is repeated until the new population size aligns with the initial population size [38]. As a consequence of the fact that individuals are released back into the original population each time they participate in the race, the random sampling of put-back trials results in the repeated selection of individuals from the Pareto frontier. This has the effect of reducing population diversity and raising the risk of premature convergence of the algorithm [39]. The implementation of a linear ranking-based parent selection method to ascertain the selection probability through the ranking of individuals [40] markedly diminishes the probability of the NSGA-II algorithm entering into local optimization rather than relying directly on fitness and enables efficient selection even in the presence of considerable differences in fitness. This approach mitigates the risk of premature convergence due to significant discrepancies in fitness while maintaining population diversity.  
In linear ranking selection, the population of individuals is sorted from smallest to largest fitness. When applied to the NSGA-II algorithm, this is followed by a descending order of Pareto rank and followed by an ascending order of congestion distance. In the event that the population size is \(N\) and \(x_{1}\) represents the highest ranked individual and \(x_{N}\) is the lowest ranked individual, the probability that individual \(x_{i}\) is as follows:  
\[P(i)=\frac{1}{N}\bigg{(}n^{-}+\big{(}n^{+}-n^{-}\big{)}\frac{i-1}{N-1}\bigg{)} \tag{30}\]  
Figure 5: Comparison of two population initialization methods.  
where \(\frac{n^{-}}{N}\) represents the probability that the least optimal individual is selected, whereas \(\frac{n^{+}}{N}\) denotes the probability that the most optimal individual is selected.  
As illustrated in Figure 6, there is a notable discrepancy between the tournament selection and the linear ranking selection with respect to the distribution of individual selections. The density curve of the linear ranking selection is more concentrated with higher peaks, indicating that its selection pressure is higher and that individuals are more inclined to gather in high-fitness regions. This may facilitate rapid convergence of the algorithm. In contrast, the density profile of the tournament selection is relatively smooth, with a wider distribution and lower selection pressure, which helps to maintain population diversity. This discrepancy may influence the algorithm's global search capability and rate of convergence. The linear ranking selection is more inclined to achieve rapid convergence, whereas the tournament selection is more conducive to comprehensive exploration of the solution space, increasing the probability of identifying a globally optimal solution.
# Project Robust Scheduling Based on the Scattered Buffer Technology
## 5 Experimental Analysis
### Experimental Design  
#### 5.1.1 Parameter Setting  
The activity duration variability and the duration distribution in instances are known in advance through past experience. In the test, suppose that the uncertain activity duration is in lognormal distribution, and the expected value adopts the activity duration given in the instance, to distribute randomly the duration to activities according to this probability. To analyze the impact of uncertainty on schedule robustness, it is necessary to set up three levels of uncertainty, low, medium and high, and the corresponding standard deviation (\(\sigma\)) of activity duration is 0.3, 0.6, and 0.9.  
The weight (\(w_{j}\)) of the activity is the cost caused by the activity failure to start according to the predicted time. It is assumed that it fits the distribution of the discrete triangular probability, which is calculated according to \(P(w=z)=(21-2\times z)\%\), \(z=(1,2,\cdots,10)\).  
The predicted due date has a direct impact on the size and distribution of the buffer area and the possibility of completion according to schedule. Here, according to the deadline coefficient of the shortest duration \(C_{min}\) in the baseline schedule to set up the size of predicted due date (\(Duet\)), the due date coefficient is 1.05, 1.10, and 1.20, namely, \(Duet=1.05\times C_{\min}\)\(Duct=1.10\times C_{\min}\) and \(Duet=1.20\times C_{\min}\). Different robust schedules are obtained from different due dates (\(Duet\)), and then \(Duet=1.05\times C_{\min}\) is carried out in the simulation analysis to evaluate the effectiveness of the algorithm.  
The baseline schedule generated by tabu algorithm is used to test the instances: the number of iterations in tabu algorithm, \(Gen=100\); the number of neighborhood solutions, \(PopSize=30\); and the length of tabu table, \(Len=3\). When the robust schedule generated by these three algorithms are in simulation analysis, the simulation times is 1000 times (\(simul=1000\)).
## 3 Numerical Simulations and Comparisons
### Theoretical Predictions  
For Cases 1 and 2, the critical Poisson's ratio \(\
u_{\mathrm{c}}\) is 0.25 based on Equation (6a) with \(\varphi^{\prime}\) = 30\({}^{\circ}\); the critical friction angle \(\varphi^{\prime}{}_{\mathrm{c}}\) is 36.9\({}^{\circ}\) for \(\
u\) = 0.2 according to Equation (6b). In these two cases, \(\
u\leq\
u_{\mathrm{c}}\) or \(\varphi^{\prime}<\varphi^{\prime}{}_{\mathrm{c}}\), so the backfill along the VCL of the openings can be expected to yield and reach an active state with \(K\) close to \(K_{\mathrm{a}}\) = 0.33 (Equation (2) with \(\varphi^{\prime}\) = 30\({}^{\circ}\)).  
For Cases 3 with \(\varphi^{\prime}\leq\varphi^{\prime}{}_{\mathrm{c}}\) = 36.9\({}^{\circ}\) (from Equation (6b) with \(\
u\) = 0.2), an active state is anticipated within the yielding fill along the VCL (i.e., \(K\) near \(K_{\mathrm{a}}\) defined by Equation (2)). For \(\varphi^{\prime}>\varphi^{\prime}{}_{\mathrm{c}}\) = 36.9\({}^{\circ}\), an at-rest state can be expected, and the \(K\) value should then approach \((K_{0})_{\
u}\) = 0.25 (Equation (4) with \(\
u\) = 0.2).  
For Cases 4 with \(\
u\leq\
u_{\mathrm{c}}\) = 0.25 (Equation (6a) with \(\varphi^{\prime}\) = 30\({}^{\circ}\)), an active state is also expected along the VCL of the backfilled opening (i.e., \(K\) is close to \(K_{\mathrm{a}}\) = 0.33, with \(\varphi^{\prime}\) = 30\({}^{\circ}\)). For \(\
u>\
u_{\mathrm{c}}\) = 0.25, a \(K\) value close to \((K_{0})_{\
u}\) (Equation (4)) is anticipated within the fill.  
For Cases 5, values of the backfill Poisson's ratio \(\
u\) and internal friction angle \(\varphi^{\prime}\) are related according to Equation (7). The theoretical analysis presented above shows that the backfill state should be at-rest, with \(K=(K_{0})_{\
u}=(K_{0})_{\varphi^{\prime}}\).
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 3 Results and Discussion
### Discussion  
The comparison and validation results showed that the method proposed by this study could improve the image quality AOD estimations and provide an alternate method for retrieving AODs from remote sensing data without SWIR bands, such as HJ-1 A/B CCD data. The main issues and research prospects of this method are summarized as follows:  
(1) AOD retrieval accuracy could be significantly improved once the measurements of an AERONET site were used as prior knowledge [30; 31; 32]. The main reason for this phenomenon was that the estimation of offset step \(i\) of the regression intercept could be seriously affected by issues of sensor calibration. Thus, in situ-measured AOD could be used as a calibration dataset for various satellite imageries, getting the optimal estimation of offset step \(i\) directly and improving the estimation accuracy of AODs derived from satellite observations;  
(2) In this study, to avoid the effects of sensor noises, the HJ-1 A/B CCD data were first resampled to 300 m, and then the AOD was retrieved from the low spatial resolution data. We have to admit that this processing procedure reduced the real spatial resolution of the retrieved AOD dataset. However, the image quality of HJ-1 A/B CCD AOD was still superior to that of the MODIS 3 km aerosol product;  
(3) The results of the sensitivity analysis showed that the interband regression coefficients varied by solar/view angle and land cover type. If the interband regression coefficient was assumed as a constant or not well considered, a large estimation error could occur. Therefore, establishing an interband regression coefficient lookup table is critically important for accurately retrieving the AOD from satellite observations;  
(4) The method proposed by this study can be applied to various remote sensing sensors and application scenarios. Once the sensor has visible and NIR bands, the AOD can be retrieval. It can be applied to commonly used satellite data, such as Landsat TM/ETM/ETM+, MODIS, POLDER, etc., and also has a great advantage in retrieving AODs from the sensors without SWIR bands, i.e., Quickbird CCD, Chinese Gaofen series satellite sensors, and low-cost cameras onboard unmanned aerial vehicles (UAVs).
## 2 Materials and Methods
### IKOS: The Framework  
As this article deals with implementing a new optimization algorithm into an existing optimization framework, the following paragraph briefly introduces the present implementation. The basic framework and approach are described in [18]. In addition, an extension of the method and more detailed optimizations are performed in [20]. The general framework is a Python 3-based implementation for the multi-objective structural shape and size optimization of linear and non-linear load cases for structural components. In particular, the framework currently uses SFE CONCEPT as a CAD-modeling tool and as a pre-processor. The solver OptiStruct calculates the linear load cases, and RADIOSS deals with the non-linear calculation of the structural components. Both load cases are evaluated individually or as multidisciplinary optimization. The framework uses the multi-objective NSGA-II as an optimization algorithm [19]. Again, one can formulate the optimization problem as a black-box function, wherein the calculation time for the non-linear evaluations is high and hence costly. The NSGA-II optimized the parts but showed slow convergence ratios and many function evaluations. Still, the NSGA-II is a robust algorithm for optimizing a b-pillar under the given geometrical constraint [20]. Furthermore, the framework allows for the parallelization of the calculations and server support. Hence, the user can surveil the optimization routine locally while the algorithm performs costly calculations on a server of choice, as shown in Figure 1.  
Figure 2 shows the basic feasibility detection method. The geometrical intersection is evaluated in ANSA and processed to the framework. The point \"Assess Feasibility\" summarizes the unique novelty in the framework, which is the optimization of structural components under the geometrical constraint.  
The infeasible designs are penalized following the general approaches presented in the articles mentioned above, which the following rule can summarize:  
\[f(x)_{m}=\left\{\begin{array}{cc}f(x)_{m},&penalty=0\\ f(x)_{m}\times penaltyfactor(penalty),&1>penalty>0\end{array}\right. \tag{3}\]If the design \(x\) intersects with the boundary surface, the intersecting surface area is calculated to the penalty factor. Accordingly, the infeasibility factor penalty varies between zero and one. A design is fully infeasible (\(penalty=1\)) if the hull surface penetrates every element of the proposed design. The factor \(penalty\) turns 0 if the check finds no penetrations. For every mth objective function, the objective function value is penalized.