esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement of the Cytotoxicity of the Samples  
The cytotoxicity of the samples was determined by using the 3-(4,5-dimethylhazo-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay [25]. For this experiment, 1.0 \(\times\) 10\({}^{4}\) human fibroblast cells/well (CCD-986 sk, ATCC, Manassas, VA, USA) were inoculated into a 96-well plate and subsequently incubated with DMEM supplemented with 10% (\(v/v\)) fetal bovine serum (FBS) (GIBCO, Bend, OR, USA), 1% gentamycin sulfate (Sigma, St. Louis, MO, USA), and HEPES buffer (Sigma, St. Louis, MO, USA) in a 5% CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C for 24 h. Then, 200 \(\mu\)L of various concentrations of AW and AF were added into the cells and cultured for 24 h. Then, a 5 \(\mu\)g/mL of MTT solution was added to each well, and the supernatant was removed 4 h later. Thereafter, 10 \(\mu\)L of acid-isopropanol (0.04 N HCl in isopropanol) were added to the wells, and the absorbance of the solution was measured using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA) at a wavelength of 565 nm. Cytotoxicity was estimated as the percentage of the ratio of cell growth of the treated samples to that of untreated cells as a control using the following Equation (1):  
\[\text{Cytotoxicity }(\%)=\left(1-\frac{Sample\ group}{Untreated\ group} \right)\times 100 \tag{1}\]  
The effects of the samples on preventing the decrease in mitochondrial reductase activities were also observed after human skin fibroblasts (CCD-986 sk) were exposed to UV\({}_{\text{b}}\) at a wavelength of 312 nm and intensity of 80 mJ/cm\({}^{2}\) using an ultraviolet lamp (model VL-6 M, [PERSON], Marine, France) for 120 s at room temperature. Then, 100 \(\mu\)L of various concentrations of AW and AF and 10 \(\mu\)g/mL quercetin (Sigma, St. Louis, MO, USA) as a positive control were added into the wells and cultured for 24 h. Afterward, 5 \(\mu\)g/mL of MTT solution was added to each well and analyzed using the same MTT assay protocol as described above for the cytotoxicity assay. The increase in the mitochondrial activities of the samples was calculated using the same Equation (1) shown above.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 5 Conclusions  
This paper proposes a cislunar resonant constellation design methodology and applies it to generate 1229 four-satellite resonant navigation constellations using a working orbit database comprising 17 orbital families. Further analyses of the constellation selection for near-Earth and lunar regions using DOP as the evaluation metric revealed the following:  
* For near-Earth regions, constellations combining L2 southern/northern NRHOs with homologous periodic orbits at L4/L5 points exhibit optimal performance.
* Lunar-proximity regions achieve optimal navigation with constellations composed of L1 vertical orbits, L2 southern/northern NRHO orbits, and L4 vertical orbits.
* The combined region shares the optimal configuration with the near-Earth case.  
Additionally, taking the combined region as an example, the relationship between spherical target zone radius and average DOP distribution was investigated. The results demonstrate a nonlinear monotonic increase in average DOP values with a spherical radius for near-Earth regions. For lunar regions, while the DOP values generally show an ascending trend, extremal zones emerge due to the intersections between lunar target spheres and orbital trajectories; nevertheless, the lunar DOP values remain consistently low, confirming the method's high-precision navigation capability for lunar areas. A temporal DOP evolution analysis revealed periodic peaks aligned with baseline orbital periods, where the peak frequency correlates with resonance ratios.  
Conceptualization, J.H. and H.H.; methodology, J.H.; validation, J.H. and Z.Y.; formal analysis, J.H.; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; funding acquisition, [PERSON] and Z.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 124B2049, the Beijing Institute of Technology Research Fund Program for Young Scholars under Grant XSQD-202101012, the open Fund of the National Key Laboratory for Rapid Design and Intelligent Clusters of Micro/Nano Spacecraft under Grant MS02240110, and the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA0350101.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 4 Proposed QL-Driven ABC-Based Solving Approaches  
The ABC algorithm [38] is a competitive bio-inspired algorithm that simulates the behavior of bee colonies when searching for promising food sources associated with higher nectar amounts. In the ABC algorithm, the food source represents a candidate solution to the problem and the nectar amount of a source corresponds to the quality, known as fitness, of the related solution. The ABC search process is conducted by three bee swarms, namely: employed, onlooker, and scout bees. A review of its fundamentals and some applications can be found in [69]. In [70], the ABC algorithm showed effective performances over the differential evolution (DE) algorithm, Particle Swarm Optimization (PSO), and evolutionary algorithm (EA). The main advantages of the ABC algorithm are its ability to balance exploitation and exploration, it having few control parameters, its fast convergence, its strong robustness, and its ease of being combined with other methods [71]. It is also easy to implement compared to GA and Ant Colony Optimization (ACO) [72].  
In an attempt to solve the integrated PFSP with a flexible maintenance problem under learning and deteriorating effects in a reasonable time, in this paper, we propose two efficient ABC algorithms enhanced with the RL mechanism. The first ABC algorithm, named IQABC-LE for Integrated QL-based ABC under Learning Effect, takes into account the learning effect. After generating the initial solutions, the search process of IQABC-LE starts with the employed bees phase, which applies an RL mechanism to drive the neighborhood exploitation of the associated solutions in order to update the search space with optimal solutions. After that, the onlooker bees select the best individuals from the search space to exploit their neighborhood for further optimization. To allow exploration of the search space, the scout bees randomly replace the solutions that have not been updated after a defined number of trials. The search process is iterated until a termination condition is met. The general scheme of the IQABC-LE algorithm is given in Algorithm 1 and its flow chart is indicated in Figure 1. The second ABC algorithm, named IQABC-LDE for Integrated QL-based ABC under Learning and Deteriorating Effects, follows the same steps as IQABC-LE but also takes into account the deteriorating effect. The main features of the proposed algorithms are detailed in the sections below.
## 2 Components for the Testing
### Antennas under Study
#### 2.1.2 Circularly Polarized Microstrip Patch Antenna  
The specifications of the circularly polarized antenna are listed in Table 1. This was a 17.0 \(\times\) 17.0 \(\times\) 4.0 mm\({}^{3}\) microstrip patch centered at 1575.42 MHz. At its zenith, this antenna's gain was \(-\)1.0 dBi. Figure 2 illustrates the 80 mm by 60 mm device's ground plane, with the microstrip patch positioned close to the left corner of the device, being a typical position.  
As for the LNA, this was integrated into the patch antenna. The specifications of this antenna are given in Table 2. Only the ceramic patch was included in this measurement; the actual antenna size was 17.0 \(\times\) 17.0 \(\times\) 6.1 mm\({}^{3}\).  
This was centered at a frequency of 1575.42 MHz and has a gain of 16 dB. The noise value introduced by this LNA was 2.5 dB.
## 4 Discussion
### Uncertainty of the Study  
The definition of forests plays a vital role in the assessment of the forest areas. Tree density and tree height make up the two main aspects of forest definition. However, different forest products have different requirements for these two standards, which could be the source of uncertainty. The main difference in the spatial distribution of forest maps lies in the areas with low forest coverage, which was considered to be the main reason for the definition of forests [1]. In this study, the PALSAR/Landsat forests and the JAXA forests both use the FAO forest definition: forest canopy coverage \(>\) 10% and tree heights \(>\) 5 m with a minimum area \(>\) 0.5 ha, while the GLCF-VCF forest products and the OU-FDL forest map use the tree density (\(>\)10%) and the tree height threshold (5 m). The GlobeLand30 forest maps used the looser forest definition (only tree cover 10%, no criteria for tree height). Forest in the FROM-GLC is defined as having a land cover of \(>\) 15% forest canopy coverage and tree heights \(>\) 3 m. The uncertainty of the forest maps could be also from the varied algorithms. The PL-based forest map was generated based on a robust algorithm which adopted the improved thresholds for the algorithm and the involvement of higher resolution Landsat images.  
The reliability of the unified threshold has been proved in our previous study [26]. The JAXA forest map [22] used a decision tree algorithm, whose HV threshold values are slightly different from that of the PL-based forest map in this study. The object-based analysis after segmentation process may lead to the loss of sparse forest patches [22]. As a result, the JAXA forest maps often tend to miss those areas with low forest coverage. The GlobeLand30 adopted a pixel-object-knowledge-based (POK-based) classification approach which also leads to the loss of some potential information in the sparse forests. The FROM-GLC used automatic classification algorithms and the GLCF-VCF used the supervised classification. Both algorithms require the collection of forest and non-forest training samples through on-site survey and/or very high-resolution images for algorithm calibration. Therefore, the algorithm was affected by the richness, spatial distribution, and spatial representation of training samples.  
Under the GGP, both restoration and afforestation have contributed to the increase of forest area. However, some studies have shown that due to the increased transpiration, afforestation in unsuitable areas can lead to water shortage, decrease of land surface temperature, and deterioration of soil ecosystems [54; 55; 56]. In order to more accurately understanding the ecological and environmental effects of GGP, more studies are needed on the biophysical mechanism and ecosystem services due to the ecological restoration project and climate change in the future.
## 3 Timing Predictability in CPSs
### Timing Predictability as a Prerequisite for Functional Safety  
Based on the discussion in the previous subsection, we make a strong case about timing predictability being a prerequisite for functional safety in time-critical CPSs. That is, if the CPS cannot be verified to be timing predictable at the design time, then it cannot be guaranteed to meet one or more of the timing requirements at run-time. Intuitively, the functional safety of the system cannot be guaranteed. For example, consider the navigation and collision hazard in the autonomous quarry discussed in Section 2. In this example, timely detection of objects is crucial in avoiding the hazard. If the timing predictability verification techniques and tools at the design time conclude that any of the timing requirements corresponding to the object detection functionality cannot be satisfied, the system is deemed not timing predictable. This, in turn, implies that, if such a system is run, then the safety hazard can occur at runtime. Therefore, timing predictability should be considered as a vital prerequisite to functional safety of time-critical CPSs.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 3 Simulation Results
### Size  
The cut-on frequency \(f_{\text{ABH}}\) of Acoustic Black Holes is essential for its efficiency. For smaller ABH radii, the cut-on frequency increases. For this study, three different-sized ABHs were modeled. For the target frequency range and the used panel properties [37], the cut-on frequency \(f_{\text{ABH}}\) for a 2D ABH with radius \(a<0.07\) m was shown to not appear in the target frequency range. For three radii \(a\in 0.07;0.1;0.13\) m, the simulated mean surface velocities \(\overline{v}_{x,y}(f)\) are shown in Figure 12.  
The graph shows that the larger the ABH gets, the bigger its impact is. The even modes, which are activated through the unsymmetrical placement of the ABH, indicate this effect quite well. The small ABH with radius \(a=0.07\) m has a quite high cut-on frequency \(f_{\text{ABH}=7\text{cm}}\approx 380\) Hz so the effects seen in Figure 12 are mostly due to breaking the panel symmetry at those frequencies \(f_{2,1}\approx 140\) Hz and \(f_{1,2}\approx 180\) Hz. On the other hand, the simulations of the two bigger ABHs show a similar normalized mean surface velocity at these modes \(f_{2,1}\approx 140\) Hz and \(f_{1,2}\approx 180\) Hz, which is significantly higher compared to the smaller ABH. For higher frequencies, the velocity of the \(a=0.13\) m ABH is higher. In addition, the ABH circular mode, responsible for the cut-on frequency \(f_{\text{ABH}}\approx 125\) Hz, is in between the fundamental mode \(f_{1,1}\approx 105\) Hz and the second panel mode \(f_{2,1}\approx 140\) Hz. Based on this analysis, it was chosen to manufacture the small ABH with radius \(a=0.07\) m and the big ABH with radius \(a=0.13\) m to verify whether the predictions of the FE model generally hold up in practice.
# Numerical Investigation on the Droplet Dispersion inside a Bus and the Infection Risk Prediction
## 2 Methodology  
As noted above, the distribution of exhaled droplets in a bus is crucial for determining a passenger's infection risk. [PERSON] et al. [22] has compared the performance of Reynolds averaged Navier-Stokes (RANS) turbulence model, detached-eddy simulation [DES], and large-eddy simulation (LES) on predicting indoor airflow. The RANS turbulence model includes a zero-equation model, three two-equation models (the RNG \(k\)-\(\varepsilon\), low Reynolds number \(k\)-\(\varepsilon\), and SST \(k\)-\(\omega\) models), a three-equation model (\(\overline{v^{2}}-f\) model), and the Reynolds-stress model. Studies have shown that the LES model can better capture fluid characteristics, but it takes more computational time. While the RNG \(k\)-\(\varepsilon\) model has good comprehensive performance and requires less computation time. In addition, the RNG \(k\)-\(\varepsilon\) model was used to predict the flow field in the aircraft cabin and passenger car cabin in previous studies [12; 23], which was in good agreement with the experimental data. Therefore, RNG \(k\)-\(\varepsilon\) model was applied in this study. Then, the Lagrangian approach was used to track the droplets, combining Wells-Riley equations to evaluate the infection risk for passengers [24].
# An Uncertainty-Driven Proactive Self-Healing Model for
## 2 Preliminaries and Scenario Description
### Offloading Strategy Based on the Overloading Indicator  
Assume that the outcome of the Fuzzy System, i.e., the \(OI\), indicates that the local queue will be overloaded and offloading actions should take place. We can consider the following cases. **Case A**: the number of tasks in the local queue is greater or equal to \(\theta\) (our mechanism delays detecting the overloading status); **Case B**: the number of tasks in the local queue is less than \(\theta\) (our mechanism proactively detects the overloading status). In Case A, we opt to offload all the upcoming tasks to peers and high-priority tasks will be locally placed if there is space for that. In Case B, we propose the use of an additional mechanism that opts to keep some tasks locally until the threshold \(\theta\) is as follows: high-priority tasks and tasks that exhibit a high demand locally are kept in the queue only if we do not violate \(\theta\).  
A study on how the demand for tasks may affect their management can be found in [28]. Hence, we can benefit from re-using previously calculated results, spending less resources and maximising the node performance. The proposed processing is depicted in Algorithm 1.  
```
while True do  Receive a task \(OI=FuzzySystem(\tau,P_{\delta})\) if Queue is full then  Offload the task else if\(OI>\beta\)then\(\triangleright\)\(\beta\) is a pre-defined threshold ifNumber of local Tasks \(\geq\theta\)then\(\triangleright\) Case A ifTask has high priority then  Place the task in the local queue else  Offload the task endif else ifTask has high priority OR high demand then  Place the task in the local queue else  Offload the task endif endif endif endwhile
```  
**Algorithm 1** Algorithm for Task Offloading based on \(OI\)
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 4 Experiment
### Experiment Results  
#### 4.4.1 Comparison with the State-of-the-Art Method  
In this part, we investigate the advantages of the proposed FDG-Trans method. We have compared the proposed FDG-Trans method over 3 other hybrid methods. The results are reported in Table 2. The results of 4 methods are all trained and calculated based on exactly the same training and validation datasets. Note that the average of \(r^{2}\) means the mean of \(r^{2}\)s of different stocks. Hence, values in the table with symbol \(\pm\) does not indicate the confidence interval but the range of \(r^{2}\) of different stocks. It is shown that FDG-Trans method has higher \(r^{2}\) and less errors. For example, our method achieves 2.88 \(r^{2}\), surpassing DeepLOB [28], DeepAtt [54] and MHF [29] with 0.52, 1.13 and 1.59. This result validate the stronger regression capacity of our proposed method.
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 8 Conclusions  
When exploring specific practical modularity problems, it seems to be useful to stress that the first important task is to select appropriate method(s) to assess this system attribute. For the purpose of this study, the optimal modularity indicator \(M(G)\) has been applied along with the operational complexity indicator (_PCI_) and MLT indicator. The second precondition to deal with this issue is to take adequate process models to carry out experiments. We tried to fulfill these two conditions consistently.  
As the purpose of this study is linked to the research questions presented in the introduction section, we provide the following summarized answers to them, although this was partially completed in Sections 6 and 7:  
* Answer to **RQ 1**: Considering the achieved results presented in Sections 6 and 7, one can state that process structure modularity positively affects the manufacturing lead time in the sense that if manufacturing process modularity increases, then MLT decreases, as was proved in both the testing cases.  
To answer the question regarding to what extent modularity can impact MLT, it is useful to quantify percentage change in MLT caused due to modularity using both the simulation experiments. Then, one can easily find that difference of MLT between less and more modular manufacturing process is: 4.8% in case of theoretical process models and 2% in case of real process models.
* Answer to **RQ 2**: Taking into account the obtained results from the experiments, it can be stated that process structure modularity positively affects the complexity of manufacturing processes, so that if manufacturing process modularity increases, then process complexity decreases. These rigorously obtained results evidence the statement that the concept of modularity is purpose-built to reduce complexity by breaking a system into varying degrees of interdependence [60].  
Finally, potential future research could be directed at exploration of the influence of process modularity on smart mass customization assembly systems with the aim to validate presented findings with a higher degree of generality.  
Conceptualization, V.M.; methodology, V.M. and Z.S.; software, Z.S.; validation, Z.S., formal analysis, Z.S.; writing--original draft preparation, Z.S.; writing--review and editing, V.M.; visualization, Z.S.; supervision, V.M. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the project SME 5.0 with funding received from the European Union's Horizon research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 101086487 and the KEGA project No. 044 TUKE-4/2023 granted by the Ministry of Education of the Slovak Republic.  
Figure 13: (a) Relationship between modularity and MLT; (b) relationship between modularity and process complexity for case study.
# Underwater Optical Path Loss after Passage of a Tropical Storm
###### Abstract  
Underwater wireless optical communications (UWOCs) have attracted considerable attention in recent years as an alternative means for acoustic communication. However, optical path loss of light propagation from attenuation is in large part due to absorption and scattering in various water conditions. Identification of environmental effects, especially tropical storms on underwater optical path loss, is key to the success of using optics for UWOCs. Underwater inherent optical properties (IOPs), such as the beam attenuation coefficient for 470 nm light in the western North Pacific Ocean, were measured from U.S. Naval Oceanographic Office Seagliders deployed after Super Typhoon Guchol's (June 7-20, 2012) passage from June 25 to June 30, 2012 and without any typhoon passage from January 9 to February 28, 2014. The two observed sets (with and without the super typhoon) of IOPs are taken as input for a recently developed radiative transfer equation solver. The simulated normalized received powers for the two durations show a large impact of typhoon passage on UWOCs.  
underwater wireless optical communication (UWOC); radiative transfer equation solver; optical path loss; tropical cyclone +
Footnote †: journal: Applied Sciences
# The Stationary Thermal Field in a Multilayer Elliptic Cylinder
## 2 Physical and Mathematical Models
Ultimately, the following was obtained:  
\[ \begin{array}{ll}v_{i}(\eta,\psi)=&-\frac{\delta i\epsilon^{2}}{8\Lambda_{i}}[ \cosh(2\eta)+\cos(2\psi)]+(A_{i}+B_{i}\eta)(C_{i}+D_{i}\psi)+\\ &+\sum\limits_{n=1}^{\infty}\big{[}A_{ni}\cosh(n\eta)+B_{ni}\sinh(n\eta)\big{]} [C_{ni}\cos(n\psi)+D_{ni}\sin(n\psi)]\\ &\text{for}\ \eta_{i-1}\leq\ \eta\ \leq\ \eta_{i},\ 0\leq\psi\ \leq 2\pi,i=1,\ 2,\ \ldots N \end{array}  \tag{6}\]  
where \(A_{i}\), \(B_{i}\), \(C_{i}\), \(D_{i}\), \(A_{ni}\), \(B_{ni}\), \(C_{ni}\) and \(D_{ni}\) are either constants or coefficients, \(\eta_{0}=0\), whereas in sourceless regions it is necessary to take \(g_{i}=0\). The singular points of solution (6) (i.e., focuses) should always be examined. This leads to the elimination of non-physical solutions. Moreover, if a field exhibits a symmetry or asymmetry relative to the coordinate system, then some constants and coefficients of appropriate terms (even or odd) of series (6) can additionally be zeroed. Such simplified solutions are substituted in the conditions of continuity of temperature and flux in (4) and (5). This leads to a further reduction in the number of unknown coefficients. At the next stage, the reduced series (6) for \(i=N\) should be substituted in Hankel's condition (3). The relationship thus obtained is multiplied by respective trigonometric functions and then integrated with respect to the \(\psi\) coordinate using the orthogonality conditions. As a result, a system of equations is obtained, which ultimately enables the determination of the coefficients and constants in (6) that are sought.