esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
Figure 4 shows some details of the experimental setup. All tests were performed under quasi-static loading conditions, imposing a ramped displacement at a run rate of 0.05 mm/min, according to the (ASTM E8/E8M-04) standard. An external extensometer was used, with a base length of 25 mm. Crosshead displacement, axial load and specimen gauge section elongation coming from the extensometer were recorded during runs. The cross-section of each specimen, uniform and rectangular, was measured, using a Mitutoyo micrometer, by taking three couples of width and thickness at different positions along the axial direction of the gauge section. A very low scatter, in the range of +- 0.02 mm, was observed. At the end, the averages of the measurements were considered as the actual values of the cross-section dimensions. Conversely, to measure the cross-section reduction and consequently have a qualitative estimation about the ductility, some fractured samples were digitized, using a structured light 3D scanner. A GOM Atos Core 300 was employed, being capable of accuracy up to 30 \(\upmu\)m, and having a working volume of 300 x 230 x 400 mm\({}^{3}\) and a blue light fringes projector, making the acquisition process almost insensitive to ambient-light interference. Figure 4: Experimental setup: (**a**) universal testing machine; (**b**) necked specimen just before fracture. Each half of the barrel-finished samples, after the tensile test, was measured with the scanner, and the resulting point cloud was processed in three subsequent stages. First, the overall 3D geometry was reconstructed (Figure 5a), and then multiple cross-sections were isolated along the specimen axis, at fixed steps of 0.5 mm, using the software GOM Inspect. Afterward, the sections' perimeters were identified and exported as closed loops (see Figure 5b), and finally postprocessed by using a MATLAB custom-made routine to compute the area enclosed by the loops. Due to the irregular shape of the fracture, the section closest to the fracture (red) was discarded, and the next section (green) was considered for surface computation, as schematically shown in Figure 5b. The area of the last acceptable deformed section was then compared to the corresponding undeformed one, measured with the micrometer and reported in Table 3, to compute the percentage of area reduction at fracture, presented in the following Section 3.
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 3 Results
### Field Example  
In this section, we adopt a field dataset to test the deghosting effect. The dataset contains a total of 193 shot gathers, 128 offset gathers, 1000 sampling points, and a sampling interval of 4 ms. The sink depth of the geophone is 7 m. The maximum spacing is 3175 m, and the track spacing is 25 m. Figures 10-12 show the original data, the conventional 2D method, and the deghosting result of the algorithm, respectively.  
As a result, there are three main differences: frequency extension, event continuity, and signal-to-noise ratio (SNR). In terms of frequency extension, since the source depth of the data is 7 m, the first non-zero notch frequency is relatively large. The influence of ghost on the seismic records is mainly manifested in the suppression of the low frequency (2-10 Hz) and high frequency (60-80 Hz). The two methods have similar effects on low-frequency energy compensation, but the proposed method has better effects on high-frequency energy compensation. The continuity of the events (Figures 11c and 12c) also proves the effectiveness of the proposed method. Because noise is unavoidable in the field datasets, the continuity of the event becomes worse. The proposed method uses the sparse representation of the seismic signal and retains the main components for reconstruction to improve the event continuity. The SNR difference can be clearly observed in the _f-k_ spectrum, which means the sparse inversion method has a stronger anti-noise performance. The noise reduces the image quality of the _f-k_ spectrum, and the _f-k_ spectrum of the sparse inversion result is smoother, indicating that the redundant information (noise) is attenuated during the inversion process. In summary, the deghosting method in this paper has more advantages in frequency extension. In addition, this method is more suitable for field data processing, which can simultaneously improve the SNR in the process of deghosting.  
Figure 10: The field data before deghosting. (**a**) 3D data cube including 193 CSGs and 128 COGs. (**b**) _f-k_ spectrum in which the black oval marks the effect of ghost. (**c**) COG wherein the events are similar to the underground interface.  
Figure 11: The 2D deghosting results. (**a**) 3D data cube including 193 CSGs and 128 COGs. (**b**) \(f\)-\(k\) spectrum in which the black oval marks the effect of ghost. (**c**) COG wherein the events are similar to the underground interface.  
Figure 12: The sparse inversion-based deghosting results. (**a**) 3D data cube including 193 CSGs and 128 COGs. (**b**) \(f\)-\(k\) spectrum in which the black oval marks the effect of ghost. (**c**) COG wherein the events are similar to the underground interface.
# Research on the Influence of Anxiety Psychology on Unsafe Behavior Among Construction Workers
## 3 Methodology
### Data Analysis Tools  
This study utilized SPSS 22.0 and AMOS 24.0 for data analysis and model validation. SPSS 22.0 was employed to conduct reliability testing and [PERSON] and Bartlett's sphericity tests to ensure data quality. Correlation and regression analyses within SPSS 22.0 were performed to elucidate the association mechanisms between anxiety psychology (and its dimensions) and unsafe behaviors. AMOS 24.0 was applied to develop structural equation modeling (SEM) and perform variance testing to validate the theoretical model's robustness. SEM, a multivariate analytical method, is widely employed in behavioral research due to its capacity to elucidate latent variable structures and quantify structural relationships among observed variables [51]. This methodology enables the systematic examination of complex causal pathways while statistically controlling measurement errors, thereby providing robust insights into the interplay between psychological constructs and behavioral outcomes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Item** & **Frequency** & **Percentage** \\ \hline \multirow{2}{*}{Gender} & Male & 186 & 70.7\% \\  & Female & 77 & 29.3\% \\ \hline \multirow{4}{*}{Age} & 18–27 & 33 & 4.2\% \\  & 27–35 & 42 & 16.0\% \\  & 35–50 & 156 & 67.7\% \\  & \(\geq\)50 & 32 & 12.2\% \\ \hline \multirow{4}{*}{Educational background} & Junior high school or below & 134 & 73.8\% \\  & Higher school & 54 & 20.5\% \\  & College degree & 43 & 4.9\% \\  & Bachelor’s or above & 32 & 0.8\% \\ \hline \multirow{4}{*}{Experience} & \(\leq\)5 & 62 & 23.6\% \\  & 5–10 & 81 & 31.9\% \\  & 10–20 & 88 & 33.5\% \\  & \(\geq\)20 & 32 & 11.0\% \\ \hline \multirow{4}{*}{Level of anxiety} & Normal & 35 & 13.3\% \\  & Mild & 96 & 36.5\% \\ \cline{1-1}  & Moderate & 88 & 33.5\% \\ \cline{1-1}  & Severe & 44 & 16.7\% \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive statistical table of sample selection.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
###### Abstract  
Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.  
smart grid cybersecurity; false data injection; power system operation; power system protection; cybersecurity attacks; intruder detection; cybersecurity for scada systems +
Footnote †: journal: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
## 5 Juno Trajectory Geometry
### Trajectory Components
Because the spacecraft used an Earth gravity assist a little over two years after launch, the Juno reference trajectory is referred to as a \"2+ dV-EGA\" trajectory. Here, \"2+ dV-EGA\" is the official designation given to Juno's path to reach Jupiter. The abbreviation EGA refers to \"Earth Gravity Assist\", and the number \"2+\"  
Figure 2: Representation of the Juno spacecraft’s trajectory around JOI between SOI: 30 June 2016 and EOI 5 July 2016.  
indicates that the flyby took place a bit over two years into the mission. The term dV suggests that the route chosen by Juno leverages hyperbolic surplus velocity.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 3 Description of Data
### Temperature Data  
The meteorological data available for our study site describe the air and soil temperatures. The hourly mean air temperature at 5 cm above ground, soil temperature at 5 cm depth, and 0 cm ground surface temperature were used. The continuous temperature series between 1 November 2016 and 1 March 2017 shows three periods with frozen soil conditions, namely 30 November 2016-9 December 2016, 30 December 2016-9 January 2017, and 16 January 2017-31 January 2017, with temperatures that dropped to \(-5\,^{\circ}\)C (Figure 2).  
Figure 1: Location of the study site.
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 3 Problem and Algorithm Description
### DC-KH Algorithm Description
#### 2.2.1 Dual-Cluster Head Selection  
To select the master cluster heads and vice cluster heads, Krill Herd optimization algorithm is improved. Given that the node deployment environment is three-dimensional, underwater, its location is determined using three components in x, y, and z on the coordinate axis. Therefore, Equation (23) is represented by the group of Equations from (33) to (35):  
\[X_{ix}^{(n+1)}=X_{ix}^{(n)}+\left[F_{i}^{new}+N_{i}^{new}+D_{i}^{new}\right] \cdot t \tag{33}\]  
\[X_{iy}^{(n+1)}=X_{iy}^{(n)}+\left[F_{i}^{new}+N_{i}^{new}+D_{i}^{new}\right] \cdot t \tag{34}\]  
\[X_{iz}^{(n+1)}=X_{iz}^{(n)}+\left[F_{i}^{new}+N_{i}^{new}+D_{i}^{new}\right] \cdot t \tag{35}\]  
Considering that the nodes in the water are distributed discretely, the calculated value of the above formula cannot be mapped directly onto the location of the actual node. Thus, the following adjustments are made to the cluster node location:  
\[p_{i}=\sqrt{\left(p_{ix}\right)^{2}+\left(p_{iy}\right)^{2}+\left(p_{iy}\right) ^{2}} \tag{36}\]  
\[p_{k}=\min(p_{1},p_{2},\ldots,p_{n-1},p_{n}) \tag{37}\]  
\[x_{id}(n)\approx p_{k} \tag{38}\]  
where \(p_{ix}\), \(p_{iy}\), and \(p_{iz}\)are the absolute values of the components of \(x\), \(y\), and \(z\) in the cluster, respectively, \(p_{k}\) is the position that mostly fits the actual situation, and \(x_{i}(n)\) is the adjusted node position.  
Having reached this point, we can confirm that a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs is accomplished. These steps are enumerated as follows:  
1. Initialization of krill. Each individual krill random location in 3D space should be determined, followed by the adjustment of the position and its mapping onto the node distribution in water. This step is accomplished with Equations (36)-(38).
2. Calculation of the fitness value. The current position of krill is calculated within the clusters krill individual extremum and the maximum adaptation values. The krill location is the krill swarm global extremum. Equations (27)-(29) are used for this step.  
* Update and adjust the position. Equations (36)-(38) are employed to adjust the existing position of krill.
* The updated adaptation value is calculated, and the global and local extremums are updated with Equations (27)-(29).
* Steps 3 and 4 should be repeated prior to reaching the maximum number of iterations.
* The global extremum is selected as the master cluster head.
* Using the vice cluster head, the value function Equation (30) is fitted to Equation (32). The preceding step is repeated to remove the vice-cluster head.
###### Abstract  
In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integral algorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.  
extended Kalman filter algorithm; biogeography-based optimization algorithm; BP neural network; state of charge estimate
# HyperDE: An Adaptive Hyper-Heuristic for Global Optimization
## 4 Genetic Algorithm Overview  
Genetic algorithm (GA) is a well established population-based optimization method inspired by the theory of evolution [39]. The approach is based on the Darwinian theory of survival of fittest in nature, adopting various biological-inspired operators such as crossover, mutation and fitness selection, operators that manipulate a set of chromosome representations. Algorithm 2 shows the basic GA structure.  
```
\(G\leftarrow\) the maximum number of iterations \(P_{0}\leftarrow\) initial random population of individuals \(t\gets 0\) while\(t<G\)do  Compute the fitness values of \(P_{t}\) \(P_{p}\leftarrow\) Select the parent population from \(P_{t}\) \(P_{c}\leftarrow\) Crossover individuals from parent population \(P_{p}\) \(P_{c}\leftarrow\) Mutate individuals from offspring population \(P_{c}\)  Compute the fitness values of \(P_{c}\) \(P_{t+1}\leftarrow\) Obtain the next generation's population from \(P_{t}\) and \(P_{c}\) \(t\gets t+1\) endwhile return\(P_{G}\)
```  
**Algorithm 2** Genetic Algorithm  
The algorithm starts by generating a random population of solutions \(P_{0}\). The solutions are evaluated via obtaining the corresponding fitnesses. Based on the fitnesses, a subset of the current population \(P_{t}\) is selected as the parent population \(P_{p}\). The crossover operator is applied on the individuals from \(P_{p}\), obtaining a new set of solutions \(P_{c}\) that may also mutate and are evaluated. At the end of the generation, the population of the next generation is obtained based on \(P_{t}\) and \(P_{c}\). This process is repeated for a number of \(G\) iterations, resulting in \(P_{G}\), the final set of solutions to the problem.  
In the HyperDE context, the chromosome associated with a DE instance is given by the hyper-parameters F, CR and S; for HyperSSA the genes are ST, PD and SD; while for HyperBES we have as genes of the chromosome \(a\), \(\alpha\), \(R\), \(c1\) and \(c2\). We denote \(n_{p}\) as the number of hyper-parameters. The implementations of the basic GA operators are as follows. Single-point crossover was used, meaning that the first \(c\) values of the hyper-parameters (in the order given above) are taken from the first parent and the remaining \(n_{p}-c\) from the second parent, where \(c\) is a random integer between 0 and the number of hyper-parameters. Mutation is implemented as the simple choice of a uniformly random real number (orinteger, for S) in the interval of definition of each hyper-parameter. The fitness function will be described in the next section.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 4 Optimization Methods
### Non-Dominated Sorting Genetic Algorithm-II (NSGA-II)
The process randomly selects two points in both solutions as starting and ending points for exchanging portions and recombines these portions to create two new solutions as shown in Figure 4. Although, this crossover process may introduce solutions with redundant placements, due to fitness score calculation (e.g., redundant locations lead to lower coverage) those solutions will be deemed as low quality and hence be eliminated in the next generation of selection. This reduces the execution time of the algorithm. **Mutation:** The uniform mutation operates a replacement coming from the pool of all vehicle locations defined by street node IDs. Figure 5 illustrates the mutation operator where \(Sample\) is a function to randomly pick one location from the pool and \(1,2,3,\ldots,n\) denotes all street node IDs. However, if the replacement already exists in the current solution, the process is repeated until a valid replacement is found. As mentioned before in crossover, redundant solutions will be eliminated by the process. Figure 4: Two-point crossover process. Figure 3: Example of a chromosome representing a possible solution. Street nodes are represented by IDs (numbers) and a solution is consisted of these IDs. It is worth noting that evolutionary operators inherit known shortcomings such as local optimal and plateau. Evolutionary operators in this work are no exception. Several combinations of crossover and mutations have been experimented and none resulted in guaranteed superiority over all others. This work focuses on the implementation of the FPP model rather than finding suitable parameters and operators for the metaheuristic algorithm (i.e., NSGA-II) under consideration. Therefore, it may be possible that adjustment and tuning of evolutionary operators for their suitability may be necessary for its application in some instances that share too few characteristics with instances in this work.
## 2 Materials and Methodology
### Geotechnical Tests
#### 2.4.3 Hydraulic Conductivity  
The falling head hydraulic conductivity tests were conducted according to the methodology defined in [27] to investigate the hydraulic conductivity of the untreated and nanoparticles-treated residual soils. The soil samples were first prepared at their respective optimum moisture content determined from the standard Proctor test and cured for 24 h. Then, the cured samples were compacted in a rigid wall compaction mold and mounted to the falling head permeameter connected to a standpipe. The samples were allowed to saturate for a minimum period of one week by allowing water to flow from the reservoir to the remolded samples in the compaction molds. Full saturation was confirmed by the water outflow coming through the outlets of the compaction permeameters. This long saturation process was applied to ensure the complete saturation of the specimens prior to taking the first set of readings. During saturation, the specimens were restrained vertically from swelling. This means that all samples maintained their initial dry unit weight throughout the saturation process. Permeation of the samples for the hydraulic conductivity test continued until flow stabilization, and the termination criteria specified in [27] were met (i.e., when there was no significant change in hydraulic conductivity over time). The hydraulic conductivity measurements were taken consecutively for seven weeks to observe the long-term hydraulic performance of the untreated and treated soil samples. Equation (3) was used to determine the hydraulic conductivity of the soil samples:  
\[k=\frac{aL}{At}ln\frac{h_{1}}{h_{2}} \tag{3}\]where \(k\) = hydraulic conductivity (cm/s); \(a\) = cross-sectional area of the standpipe (cm\({}^{2}\)); \(L\) = length of specimen (cm); \(A\) = cross-sectional area of the mounted sample; \(h_{1}\) = head loss across the soil specimen t\({}_{1}\) (cm); \(h_{2}\) = head loss across the soil specimen t\({}_{2}\) (cm); and \(t\) = time period between \(h_{1}\) and \(h_{2}\).
# Web Unevenness Due to Thermal Deformation in the Roll-to-Roll Manufacturing Process
## 4 Conclusions  
In this study, we analyzed the effects of thermally deformed web unevenness on the coating quality of a brittle electrolyte layer. First, the effects of conduction and convection heat from the dryer on the deformation of the tensioned web were analyzed according to drying temperature using FEA. Web unevenness could be generated by thermal deformation due to conduction and convection heat from the dryer, but the amplitudes and positions of the peaks and valleys of the uneven wave-form changed when the web moved in the dryer. The amplitude of web unevenness rose with increasing drying temperature, and web unevenness in the dryer eventually spread to the coating section. The YSZ layers were coated according to drying temperature to clearly show the effect of web unevenness on the coating defects. Results indicated that web unevenness at the coating section could generate a coating defect in the x direction, which could damage the SOFC. This study suggested that coating defects generated by web unevenness because of convection and conduction heat from the dryer should be considered in the fabrication of brittle electrolytes using the R2R process. Results of this study could be useful when designing the R2R process for the mass production of large-area SOFCs.  
Conceptualization, M.J. and C.L.; data curation, S.K.; formal analysis, M.J., G.C., T.-M.L., and S.K.; original-draft preparation, M.J. and C.L.; supervision, J.L. and C.L.; project administration, C.L.; writing--review and editing, J.L. and C.L.; funding acquisition, C.L. All authors read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST; nos. 2020R1A2C1012428 and 2020R1A5A1019649).  
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.