esa-sceva/satcom-chunk-collection huggingface dataset:
# A Surrogate Model Based Multi-Objective Optimization Method for Optical Imaging System
## 2 Methodology
### NSGA-III Multi-Objective Optimization Algorithm  
Most multi-objective optimization algorithms using evolutionary optimization methods have demonstrated their efficiency in various practical problems involving mostly two and three objectives. There is a growing need for developing multi-objective optimization algorithms for handling optimization problems with more objectives. The multi-objective optimization algorithm used in this paper is the Non-dominated Sorting Genetic Algorithm III (NSGA-III) [136], which is an upgrade from NSGA-II [134]. NSGA-III is a reference-point-based many-objective evolutionary algorithm that emphasizes population members that are non-dominated, yet close to a set of supplied reference points.  
The framework of NSGA-III is basically the same as that of NSGA-II. The biggest change in NSGA-III is the use of the well-distributed reference points to maintain a good diversity of the population. Therefore, it shows a better diversity and convergence. It also uses the simulated binary crossover (SBX) [145], mutation operator (polynomial mutation), and Pareto sorting in the process and selects population in the key layer L, using a niching algorithm rather than the crowding distance method used in NSGA-II. In order to deal with the constraint problem, the model used here also adopted the penalty method, which means a certain penalty value would be added to the individual for triggering the constraint depending on its adaptability.  
The steps of using the NSGA-III algorithm are as follows:1. Generate the initial population \(P_{o}\), which contains \(N\) randomly generated individuals.
2. Conduct binary competition selection, simulated binary crossover, and mutation operations on individuals in the initial population to generate \(N\) offspring populations \(Q_{o}\).
3. Merge the parent and child populations. The number of individuals in the new population is \(2N\).
4. Apply a fast non-dominated sorting technique on the population to obtain the individuals' order and carry on selecting the next generation population \(P1\).
5. Decide whether the conditions have been reached for terminating the iteration. If it is, output the individuals; otherwise, go to step 2.
# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs
### Fuzzy Control in Air Conditioning Systems
In addition, the inclusion of new control variables (PMV, CO\({}_{2}\) concentration, number of people) has gradually transformed the controller's approach to maintaining conditions that do not affect health. These scenarios make the controllers more and more complex, demonstrating that fuzzy logic can achieve satisfactory results, but they also make their application increasingly complex to implement, which would limit the applicability of this type of controller. These limitations can be overcome with the use of other strategies, such as neural networks, which gradually allow fuzzy logic to handle secondary tasks rather than the main control action.
## 2 System Description
### UIC Cable Modelling and Measurements  
The first step to be performed when modeling a multiconductor transmission line is to obtain its per-unit length parameters. It is worth mentioning at this point that, due to the OFDM frequency range, it is of fundamental importance, in order to correctly evaluate the channel's performance, to characterize the frequency dependence of the parameters. For this reason, an accurate 2D Finite Element Method (FEM) model of the cable section (Figure 1) has been implemented, obtaining the R, L and C matrices. In this case, the frequency range typical of the OFDM protocol is between 2 and 30 MHz, and for this reason, the p.u.l. parameters have been calculated for three frequencies in the above-mentioned range, and the frequency behavior has been modeled as described in [17].  
In order to validate the simulations, a measurement campaign has been carried out on a 6-m UIC cable. In particular, the measurements have been performed on a couple of conductors whose distances were minimum (i.e., 9-11 or 14-16). The p.u.l. behavior in the range 0-30 MHz is shown in Figures 2-5, showing good agreement with the parameters obtained by the FEM simulation.  
Figure 1: Section of the 16-conductor cable.  
The measurement of the p.u.l. was performed using an Agilent Vector Network Analyzer and following the approach described in [18; 19]. In particular, the approach described in [19] introduces a method to normalize the measurement with respect to the length of the cable, and the 6-m cable length is adequate to determine the p.u.l. in the frequency range of interest.  
The relative error between measurements and simulations in the frequency range of interest is below 10%. Some small divergences are caused by the measurement system, but the global frequency behavior of the measured p.u.l. parameters is well reproduced by the FEM model. While the p.u.l. L, R and C have been calculated and measured, the simple FEM model could not allow us to calculate the conductance (mainly because the dispersive properties of the insulating material composing the cable were not known); for this reason, the value of G used in the channel simulations is taken directly from the measurements.  
Figure 4: Measured capacitance.  
Figure 3: Measured and simulated resistance.  
Figure 2: Measured and simulated inductance.
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 2 Model and Methods
### Aerosol Model
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Abbreviation** & \multicolumn{1}{c}{**Distinction of definition**} & **References** \\ \hline \multirow{8}{*}{FMF} & The fine-mode fraction (FMF) is defined optically and calculated by the & \\  & optical spectral deconvolution algorithm (SDA) rather than & \\  & intermediate computations of the aerosol particle size distribution (PSD) & \\  & parameters and refractive indices. & \\ \cline{2-3}  & The FMF is defined optically and retrieved independently by irrelevant & \\  & aerosol model assumptions for total aerosol optical depth (AOD) and & \\  & fine-mode AOD. & \\ \hline \multirow{8}{*}{FMF\({}_{\mathrm{v}}\)/FMF\({}_{\mathrm{o}}\)} & The physical volume fine-mode fraction (FMF\({}_{\mathrm{v}}\))/optical fine-mode fraction (FMF\({}_{\mathrm{o}}\)) is defined based on a physical model in which the fine & \\  & and coarse components follow a unified bimodal aerosol model. & \\ \cline{1-1}  & The subscripts ’v’ and ’o’ denote volume FMF and optical FMF, & \\ \cline{1-1}  & respectively. & \\ \cline{1-1}  & The SMF (Sub Micrometer Fraction) is defined in terms of a & \\ \cline{1-1}  & microphysical cutoff of the associated PSD at some specific radius. Two & \\ \cline{1-1}  & acquisition methods are available: (1) calculating using the cutoff radius & \\ \cline{1-1}  & based on the bimodal aerosol model; and (2) obtaining it by in situ & \\ \cline{1-1}  & measurement. The widely accepted cut-off radius is 0.6\(\mu\)m. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definitions for the proportions of fine aerosols to total aerosols in different studies. FMF\({}_{\rm v}\) is a wavelength-independent physical quantity, while FMF\({}_{\rm o}\) varies with wavelength. The relationship between FMF\({}_{\rm v}\) and FMF\({}_{\rm o}\) is shown in Figure 1. The conversion is performed by Mie code with aerosol parameters listed in Table 2. Those aerosol model parameters are derived from the statistics of multi-year ground-based observation network inversion products [36]. The values of refractive indices in the table correspond to 490, 550, 670, 870, and 1610 nm, respectively. It can be seen from Figure 1 that there is a significantly nonlinear relationship between the two parameters, especially in visible bands. At 550 nm, FMF\({}_{\rm o}\) reaches up to 0.9 when the FMF\({}_{\rm v}\) is equal to 0.5, which means the fine particles produce significant extinction effects in the visible light band. However, for the curve of the NIR band (1610 nm), it is different from the other four curves in visible bands. The FMF\({}_{\rm o}\) grows relatively slowly when FMF\({}_{\rm v}\) is less than 0.6. The reason for this phenomenon is that the extinction effect of coarse mode aerosols improves significantly while the extinction effect of fine mode aerosols decreases in long NIR bands. In general, the relationship between FMF\({}_{\rm v}\) and FMF\({}_{\rm o}\) at 1610 nm reveals the sensitivity of longer NIR wavelengths to coarse particles.
## 6 Conclusions  
This study proposed DP with action reduction and PPO to an inventory management model that considers dynamic pricing and ordering policies. First, we formulated the perishable inventory management problem with price-sensitive demand as an MDP problem. Then, we implemented PPO and DP with action reduction to solve the problem. The proposed DP method can find the optimal policy for this problem without loss of optimality. In addition, the PPO algorithm uses neural networks to approximate the value function and policy. We conducted numerical experiments to compare the performance of DP, ar-DP, and PPO. The experiments revealed that ar-DP reduced computation time by 63.1% on average compared to DP. Additionally, PPO achieved an optimality gap of less than 10% in all cases except one, with an average computation time reduction of 75.6% compared to DP. The sensitivity analysis through numerical experiments, where price sensitivity was varied, shows that when customers are sensitive to shelf lives and prices, costs tend to be higher and sales lower. On the other hand, when price sensitivity is low, sales tend to increase on average, and total profit tends to be higher.  
Several limitations and future research directions of the present study should be noted. First, the demand model used in this study focused on how price sensitivity and shelf life affect customer demand. Future research should explore more complex non-stationary demand models that consider other factors, such as seasonality, promotions, and brand preferences. Also, examining the stability of the proposed method under various demand models is an important research direction. Second, this study focused on stationary costs. Future research should consider time-varying costs, such as holding costs that change with the season or ordering costs that vary with the number of orders. This will allow for a more accurate representation of real-world inventory management problems. Third, this study only uses PPO as a reinforcement learning algorithm. Future research should investigate the performance of other advanced DRL algorithms in solving perishable inventory management problems.  
Conceptualization, Y.N., Z.L. and T.N.; methodology, Y.N., Z.L. and T.N.; software, Y.N.; validation, Y.N., Z.L. and T.N.; formal analysis, Y.N., Z.L. and T.N.; investigation, Y.N., Z.L. and T.N.; resources, Y.N., Z.L. and T.N.; data curation, Y.N. and Z.L.; writing--original draft preparation, Y.N.; writing--review and editing, Y.N., Z.L. and T.N.; visualization, Y.N. and Z.L.; supervision, Z.L. and T.N.; project administration, Z.L. and T.N.; funding acquisition, Z.L. and T.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Numbers JP22H01714, JP23K13514.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Related Work
### Smart Logistics  
The use of communication and IoT technologies has been subject to research since about 2008 and is still ongoing. In most recent research, [PERSON] et al. [8] provide an overview of smart logistics applications and use cases enabled by IoT technologies and includes an overview of the respective requirements. Similarly, [PERSON] et al. [9] point out that Information and Communication Technologies (ICT) are a key enabler for efficient and sustainable logistics. In [16] the authors describe the potential of IoT technologies to measure logistics KPIs and improve the efficiency and quality of logistics processes within a balanced scorecard approach. In the publications [14; 15], [PERSON] and [PERSON] describe the development and advantages of an intelligent container for fruit transportation, which is equipped with localization and communication devices. The requirement of \"real-time response in case of unexpected situations detected during the transportation phase\" is addressed in [10], while the idea of an IoT-based cargo tracking system was published in 2012 [13]. In the same year, [PERSON] et al. [12] described the connection of ITS with multimodal logistics for a sea port location in a simulation-based approach. In 2009, an intelligent freight transportation system based on advanced fleet management, improved city logistics, and e-business was subject to research published in [11].  
In summary, smart logistics have been continuously addressed in research since 2008. However, until today, the implementation of advanced use-cases was very much limited to research prototypes. Limited quality and performance of the existing cellular network are considered to be the main constraints limiting their commercial implementation and roll-out.
# Optical Design of the Goaly3 Multi-Mirror Telescope System with a Wide Field of View
## 2 Design of the Goaly3 Multi-Mirror Telescope System
### The Design of the Corrector Group  
In addition to the spherical aberration from the primary mirror, the system suffers from such severe coma that it is limited to a very narrow FOV. With the increase of the FOV, the off-axis aberrations such as coma and astigmatism of the Golay3 MMT become dominant and the image quality is getting worse. Furthermore, the diameter of the secondary mirror also increases with a given mirror distance \(d\) (the black solid circle in Figure 6), which increases the obstruction of the secondary mirror.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surface** & **Radius (mm)** & **Thickness (mm)** & **Semi-Diameter (mm)** & **Conic** \\ \hline Sub-mirror & \(-2100.0\) & \(-641.6\) & \(46.2\) & \(0\) \\ Secondary mirror & \(-1633.6\) & \(816.8\) & \(58.257\) & \(11.537\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the initial configuration of the Golay3 MMT.  
Figure 4: The initial configuration of the Golay3 MMT.  
Figure 3: The configuration of the two-mirror system.  
The corrector group is designed with three lenses to enlarge the FOV. The radii of the six surfaces are set as variables. The optimization is performed with the ZEMAX program to increase the FOV to 1.5\({}^{\circ}\) gradually.  
During the optimization process, constraints of a 2100 mm focal length and a secondary mirror with a semi-diameter less than 62 mm are established. It is found that the mirror distance \(d\) increases with the increase of the FOV if the diameter of the secondary is fixed (the red solid box in Figure 7).  
Figure 5: The MTF of the initial configuration.  
Figure 6: The relationship between semi-diameter of the secondary mirror and half FOV. The relationship between the mirror distance and half FOV.  
Figure 7: The configuration of the corrector group.  
To reduce the chromatic aberration introduced by the corrector group, the refraction index and Abbe number of the three lenses are also set as variables. All the back surfaces of the three lenses are converted to the even aspheric with up to 8 th-order terms to further improve image quality. The final configuration of the corrector group is shown in Figure 7 and their parameters are listed in Table 3.
## 3 System Model and Optimization Formulation
### Diverse QoS Constraints of Multiple Requests  
In remote S-IoT scenarios, requests of heterogeneous devices have different QoS requirements. For stream transmission services, such as continuous data collection and surveillance services in the forest, QoS requirement is mainly on the available data transmission rates, which should exceed data emerging rates to avoid data discarding. Furthermore, monitoring services, such as disaster identification, agricultural machinery control, and industrial monitoring, may have stringent requirements on delay and reliability. As various QoS requirements on transmission rate, latency, and reliability should be simultaneously guaranteed for multiple types of services in remote S-IoT, we analyze the diverse QoS constraints in this section.
## 2 Analysis Method
### Numerical Model and Parameters  
The ABAQUS software program, based on FEM, was used for simulating the process of tunnel excavation and support structure. The calculation model adopts the ideal elastoplastic model and the Mohr-Coulomb yield criterion, and is processed according to the plane strain problem. The model includes the site rock, support structure, and the reinforcement layer. Solid elements are used for both the surrounding rock and the primary lining, the secondary lining is not simulated, and only the self-weight stress is considered in the initial stress field of the surrounding rock. The finite element model is shown in Figure 3. For the selection range of the model, the upper surface is the ground surface, the bottom is greater than three times the height of the structure from the bottom of the structure, and the horizontal side is greater than three times the span of the structure from the side of the structure. Therefore, the length of the model is 220 m, and the depth is 100 m. The boundary constraints are: the left and right sides constrain the displacement in the x direction, and the bottom boundary constrains the displacement in the x and y directions.  
In the finite element calculation, it can be considered that the auxiliary construction method forms a certain thickness of the reinforcement zone in the surrounding rock of the cavern [37]. Therefore, the simulation of the pre-support effect can be implemented by an equivalent method of improving the surrounding rock parameters within the reinforcement zone. The thickness of the growing reinforcement zone was calculated as follows: Consider the case where the grouting diffusion ranges overlap each other, and first calculate the slurry diffusion radius according to Equation (4):  
\[\mathrm{R}=(0.6\,\sim 0.7)L_{0} \tag{4}\]  
Figure 3: Finite element model.  
where \(L_{0}\) is the center distance of the conduit, which is 0.4 m. Second, calculate the thickness of the grouting reinforcement zone according to Equation (5):  
\[\text{D}=2[R^{2}-(\frac{S}{2})^{2}]^{0.5} \tag{5}\]  
where \(S\) is the spacing between two adjacent grouting holes, valued at 0.2 m. Finally, the thickness, D, of the grouting reinforcement zone is calculated to be 0.5 m. The research results in [38] show that the cohesive force of surrounding rock can be increased 1-2 times after grouting, and the surrounding rock can be improved by one level. Considering the geological conditions of the site, the parameters \(E\) and \(c\) in the equivalent reinforcement zone are increased 1 time in the calculation. According to the geological survey report, the site soil layer is simplified into three layers. From top to bottom are plain fill, medium-weathered granite, micro-weathered granite, and crossing the fault fracture zone. C25 concrete is used for shotcrete. The simplified physical and mechanical parameters of the rock and the initial support parameters of the shotcrete [39] are shown in Table 1.
## 4 Experimental Studies
In this sense, Raman spectra at these positions, which are recognized as the peaks of the sharpness function, are collected by the Raman spectrometry module to verify and calibrate the calculated spatial positions. In this experimental study, all peaks within \(\pm\)15 \(\upmu\)m from the main peak are collected with the corresponding Raman spectral signal. Among these positions, the one with the highest signal-to-noise ratio of the Raman spectrum can be  
Figure 8: 2D images (**a,d,g**), elemental images (EIs) (**b,e,h**) and reconstructed image (**c,f,i**) of three different targets on the second sample in the experimental studies. Figure 7: 2D images (**a,d,g**), elemental images (EIs) (**b,e,h**) and reconstructed images (**c,f,i**) of three different targets on the first sample in the experimental studies. determined as the confirmed spatial position. The time resolution of the proposed ARS system mainly depends on two aspects. Autostereoscopy measurement methodology can finish the data acquisition process through single snapshot and can calculate all image stacks within 1 s, as well as initially identify the target depth range simultaneously. On the other hand, the acquisition of the Raman spectrum can be completed in a few hundred milliseconds to a few seconds. In this sense, the time resolution of the proposed ARS system is within 1 s. To be noticed, the calculated depth positions cross-checked and confirmed by Raman spectral correction of three different types of fibers are compared with the true depth, which is obtained by the working distance and defocusing amount, the average error of 15 repeated measurement process is within 1 \(\upmu\)m. The spectrum collection success rate of this equipment is 98%. Figure 9: Sharpness evaluation function of three different kinds of fibers at different positions and related Raman spectrum acquired at these different positions; (**a**) is the result of the first sample, and (**b**) is the result of the second sample. Figure 9 also shows the Raman spectra of the three types of fibers, the Savizkg-Golay filter is used for smoothing and the adaptive iteratively reweighted Penalized Least Squares algorithm proposed by [PERSON] [32] is used to remove the baseline. Materials from different sources have distinct characteristic peaks that can be clearly distinguished. By building a database of Raman spectra of possible contaminants, we can easily classify contaminants in practical detection.