esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sensitivity Analysis and Discussions
### The Impact Analysis of Step Distance  
With an increasing step distance, the trend and values of the brine discharge concentration closely align with the control group, as shown in Figure 31. The outlet brine concentration first rapidly increases, and then rises steadily to saturation. As the step distance lengthens in each stage, the overall concentration of discharged brine increases, the fluctuations lessen, and it nears saturation. At a 100 m step distance, the brine concentration in the last three stages stabilizes and achieves saturation. Thus, reaching saturation occurs earlier with larger step distances, highlighting the importance of optimizing the step distance in practical leaching processes. As the step distance grows, the increase in cavern volume is not obvious, as shown in Figure 32. While a larger step distance theoretically expands the contact area between the salt wall and brine, potentially enhancing the leaching rate and the cavern volume, in practice, the step distance does not significantly impact the leaching rate or the final cavern volume. Instead, the cavern volume is primarily influenced by the injection flow rate.  
Figure 31: Outlet brine concentration evolution with leaching time under different injection intervals.  
Figure 30: Cavity’s final shapes in vertical section (X = 0 m) under different injection concentrations.  
At a 50 m step distance, the cavern is relatively flat with a uniform height of approximately 12.5 m after the first stage. The height and length of the reflux cavern decrease as step distance increases, as shown in Figure 33, due to more fluid reaching the distal end and less returning to the reflux cavern. The cavern contour tends to be higher in the middle and lower areas of the outlet with an increased step distance, a trend that becomes more pronounced with larger distances, as shown in Figure 34. At certain intervals, the highest part of the cavern is just in front of the injection point, tapering off towards the outlet. A larger step distance results in a greater disparity between the maximum and minimum cavern heights. A step distance of 50 m results in a flat, smooth cavern, while at 100 m, the height difference reaches 4 m, with discrepancies forming during the later dissolution stages.  
Cavern shape consistency across stages is maintained with specific step distances, injection flow rates, brine concentrations, and temperature differences. A shorter step distance under a given flow rate results in more irregular cavern shapes, indicating the minimum necessary step distance. For an injection flow rate of 200 m\({}^{3}\)/h, this minimum is 60 m, below which the cavern shape becomes uneven. The optimal step distance for achieving uniform dissolution lies between 70 and 80 m. Increasing the step distance requires extending the dissolution time of each stage to attain the desired cavern heights. In practice, a comprehensive assessment of various parameters is essential for optimizing the design parameters to maximize the volume and minimize the costs.
## 3 Flexural Capacity Prediction Methods and Applicability Analysis
### Widely Used Methods for Predicting Flexural Capacity
#### 3.1.1 Statistical-Based Method  
The statistical-based method was usually built by the regression analysis of test data or numerical model results. The most representative model was the ACI 318-14's equation [40]. It was initially proposed by [PERSON] et al. [46] based on the statistical analysis of tests of monolithic beams prestressed with un-bonded steel tendons. [PERSON] and [PERSON] [47] subsequently improved the equations by introducing the effects of the span-depth ratio \(L/d_{\mathrm{p}}\).  
\[\begin{split}\Delta\sigma_{p}&=\min\big{\{}70+f_{c} ^{\prime}/(100\rho_{p}),420\big{\}}(\mathrm{MPa}),\ for\ L/d_{\mathrm{p}}\leqslant 35\\ \Delta\sigma_{p}&=\min\big{\{}70+f_{c}^{\prime}/ \big{(}300\rho_{p}),200\big{\}}(\mathrm{MPa}),\ for\ L/d_{\mathrm{p}}>35\end{split} \tag{2}\]
## 4 Simulations
### Simulation Setting  
#### 4.1.1 Simulation Data  
In all our simulations, the following data set was used. (1) Realistic contact trace: We used the datasets which were collected with iMotes in Infocom 2006 [34]. It is publicly available from the CRAWDAD collection. There were 79 students and researchers who had 74,981 contacts in 337,418 s. In our simulation, the data of 61 participants was only used, because other people's information was incomplete. Six social features were extracted from these datasets, including graduated school, topics, nationality, country, city, and affiliation. The first 40 h of data was used to gather the historical data for the network training, and the remaining 53 h of data was used to evaluate the proposed routing performance. (2) Synthetic contact trace: It was formed in the simulator ONE. The parameter of ONE was: The size of the network map was \(12\times 12\) km; the wireless range of the ferry nodes were around 300 m and their movement speed was 20 m/s; the wireless range of the ordinary nodes was around 150 m and their movement speed was 10 m/s; the simulation lasted 26,500 s.
## 2 Related Works
### Object Positioning  
Regarding the outdoor object positioning problem, the multi-view location method is mainstream and has been applied to a variety of scenes, such as with UAVs and handheld devices. With the popularization of UAV applications, using UAVs to achieve target localization has attracted some attention [21; 22]. These types of methods usually require the vision sensor to move along a desired track and take multiple photographs or videos of the target; thereafter, it uses a multi-view method to achieve target localization. [PERSON] et al. [23] studied the yaw angular errors and relative height estimation problems of two flight scenarios (flyover and wandering) and identified 3D target geolocation. Using vehicle platforms to achieve short-range target localization in an outdoor environment [24; 25; 26; 27] is a popular topic. Additionally, [PERSON] et al. [28] introduced an algorithm to estimate the distance from a target in super stereo images using mobile devices, but it was only used for relative target localization. These methods essentially use other means to obtain high-precision camera poses and multi-view methods for target localization, requiring the camera to move in a fixed pattern. Moreover, in urban areas with many buildings, multi-view localization methods are used for target positioning but are limited in practical application because of the occlusion of buildings.
## 3 Criteria for Assessing the Interface Connectivity and the Reconfiguration of the Module
### Module Reconfiguration Using the Criteria  
Several solutions for reconfiguration using the connectivity criteria between module interfaces are explained below:  
1. Reconfiguration of the interface type: Either interface type is to be reconfigured. For the plug interface type, it can be changed by adding structural components that are perpendicular to the pre-connected direction of the module. The socket interface can be changed in two ways. One way is to add pre-connected structural components that are parallel to the pre-connected direction of the module. The other way is to remove the corresponding pre-connectedstructural components. The former may change the total length of the module, while the latter does not.
2. Reconfiguration of the position of the pre-connected structural components: In order to achieve the connectivity between the two interfaces, both node networks need to fully overlap (i.e., the location of the interface pre-connected structural components can meet the matching relationship). In the real situation, some of the pre-connected structural components are adjustable and some are non-adjustable. In the process of translation of the node network, first, the non-adjustable structural components need to fully overlap. Thereafter, the position of the adjustable structural components are moved within a certain range. This proceeds until the ends of the interface node network of the two pre-connected module fully overlap.  
Figure 6: (**a**) The connection relationship between structural component (\(i\)) and structural component (\(k\)); (**b**) The parent and child relationship between matrix \(Q_{ij}\) and matrix.  
Reconfiguration of the connection section of the pre-connected structural components: In order to satisfy the different types of module interfaces and the full overlap of the interface node network, it is also necessary to meet the matching relationship for each pre-connected structural component. This means that the feature points of each of the pre-connected structural components of the plug interface are within the acceptable scope of the socket interface. There are two ways to reconfigure the connection section for pre-connection of structural components. One way is to reconfigure the shape and size of the pre-connected structural components of the plug interface. This changes the relative coordinates of the corresponding feature points. The other way is to reconfigure the shape and size of the socket interface, which changes the accepted range. This is done until the ends of all the connection sections of the pre-connected structural components meet the requirements of the connection-matching relationship.
# A Computational Study on the Aeroacoustics of a Multi-Rotor Unmanned Aerial System
## 2 Materials and Methods  
### CFD Method  
Our in-house code CFDFOAM is used for the simulations [38; 39]. The CFDFOAM solver has been developed around OpenFOAM for hydrodynamic and aerodynamic applications using either absolute or relative inertial coordinate systems with non-deforming and deforming control volumes. It utilizes conventional body-fitted methods as well as immersed boundary methods for moving/morphing boundaries based on the cell-centered finite volume method. In the present work, absolute inertial earth-fixed coordinates are employed with k-\(\omega\) SST turbulence model, where the coupled pressure and velocity are solved using PISO. Second order linear upwind and central-differencing schemes are employed for discretization of convective and diffusion terms of the governing equations, respectively, and the Euler scheme is used for time discretization.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 3 Effects of Modification Coefficients and Load
### Influence of Load for Case 2 Modification Method  
Changes in the transmission performance metrics of an RV reducer are obtained using the case 2 modification method, as shown in Figure 13. It can be seen from Figure 13a that the meshing area under the rated output torque is reduced as the modification amount increases. However, as the load torque is increased to twice and three-times the rated torque, the meshing area increases slightly rather than declining. It can be concluded that the TPM does not always reduce the meshing area of the tooth profile but is also affected by the load conditions. However, as can be seen from Figure 13b, the increase in the TPM causes the transmission efficiency to decrease. This means the TPM will increase the friction torque, and the smaller the load torque is, the faster the transmission efficiency will decrease. In Figure 13c, the torque load significantly increases the maximum value of the contact  
Figure 12: Effect of case 1 modification method on the transmission performance of RV reducer: (**a**) contact ratio, (**b**) transmission efficiency, (**c**) max contact force, (**d**) max friction, (**e**) mean TE value, and (**f**) TE amplitude.  
force and friction. However, the TPM causes the contact force and friction to decrease first, and then increase at a certain value of modification. Moreover, as can be seen from Figure 13c,d, the turning point is related to the load torque.  
Although the TPM increases the mean value of the TE, as shown in Figure 13e, the amplitude of the TE does not always increase with the TPM, particularly in the case of large loads. The results obtained in [9] also show that, if variables for compound modification are appropriately selected, a larger value of modification can still acquire smaller kinematic errors. It can be seen from Figure 13f that when the modification amount is less than 0.125 mm, the TPM has little effect on the amplitude of the TE. When the modification amount is larger than 0.125 mm, the TPM causes the amplitude of the TE to significantly decrease at three-times the rated load. In addition, the amplitude of the TE at the rated torque and at twice the rated torque is slightly increased.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 2 Materials and Methods--Acoustic Spectrum Characterization  
In this study, the spectrum of the acoustic reflection coefficient is defined as the ratio between the energy leaving the soil and the energy entering it in an aqueous environment. This characterization quantifies energy absorption within different soil types, enabling classification based on spectral response. The fundamental assumption is that distinct soils exhibit unique frequency-dependent attenuation patterns, which can be identified by analyzing energy transformation between the incident and reflected acoustic waves.
## 1 Introduction
The subjective assessment process is expensive and takes much time because the reliability of the evaluation results is also dependent on the number of participants. According to [4], there is also no consistency across studies as to what acceptance is and how to measure it. Therefore, it is hard to assess the acceptance of systems in the design phase although everyone knows that it is desirable. If the acceptance can be accurately predicted as early as possible in the design process, different alternatives are not only able to be evaluated but also some obstacles may be identified and overcome. To secure acceptance in the design phase, an objective evaluation method or index is needed. In general, an objective evaluation method has a significant advantage in quantifying some values to be assessed. The effects of the objective evaluation method can be increased for cases where assessment results are conventionally performed by some subjective methods such as the driver acceptance of an advanced driver assistance system. If acceptance is objectively evaluated in design phases, developers may have several advantages. For example, the design targets of a developing system can be clearly defined as driver acceptance based on objective and observable values, and the development period and cost can be substantially reduced. Hence, in this study, the primary objective was the study of an objective evaluation methodology of passenger acceptance for autonomous driving systems with a representative autonomous driving system with an automatic lane change system, which is regarded presently as close to commercialization. The specific research objectives were to determine how objective parameters for describing driver acceptance can be derived and how driver acceptance can be evaluated with the derived objective parameters. It is assumed that the objective parameters must be simply measurable. The suggested objective evaluation method can be used for assessing driver/passenger acceptance of an automatic lane change system. To study the objective acceptance evaluation method, a special database constructed in a previous study was used [15]. In Section 2, driver/passenger acceptance is defined and measurement strategy is discussed. Section 3 describes part of the special passenger-vehicle interaction database used throughout this study. Section 4 presents a novel objective evaluation methodology for passenger acceptance. Section 5 presents the objective parameters and objective evaluation models derived based on the suggested methods. Then, the verification results are presented.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 1 Introduction
[PERSON] and [PERSON] [38] were challenged in achieving the topology, size, and shape optimization of guyed masts using a modification of the binary particle swarm optimization (PSO) and the attractive and repulsive particle swarm optimization. This paper discusses the size optimization of guyed masts using a genetic algorithm (GA) by considering different design scenarios (e.g., [PERSON] et al. [37] and [PERSON] et al. [39]). [PERSON] and [PERSON] [40] noticed that the particle swarm optimization (PSO) is more effective than ACA and the harmony search scheme for optimizing truss structures. However, [PERSON] et al. [41] and [PERSON] and [PERSON] [42] were successful in optimizing tapered masts and transmission towers using modifications of genetic algorithms (GA). Moreover, [PERSON] et al. [27] attempted the topology-sizing optimization problem of the guyed mast as a single-level single-objective global optimization problem using GAs. Therefore, given the numerous successful solutions of guyed masts using GAs, the authors chose to investigate the size optimization of a guyed mast structure using GAs. Following [35], this paper focuses on the size optimization by considering eight possible design scenarios. The purpose of the present paper is two-fold. Firstly, this work aims at achieving a size optimization on a real application case adopting structural verification according to Eurocode 3. During the load evaluation phase, detailed analyses have been conducted, including wind, ice, and seismic actions and the verifications against instabilities. Secondly, the computational intelligence procedure adopted by the authors allowed the investigation of several scenarios simultaneously. As a result, the parameters that mainly affected the design process have been detected to provide preliminary indications to engineers in the practical design of similar structural typologies. Furthermore, the considered case study may represent a benchmark case for validating the reliability and accuracy of alternative numerical approaches. Therefore, the paper is organized as follows. After the case study description and the FE model, the authors introduce the first numerical results and the outcomes of the size and shape optimization.
## 3 Results
### Volatile Compounds in Essential Oils and Recovery Oils
The presence of trace floral and green note compounds, such as _cis_-rose oxide, _trans_-rose oxide, and citronellal, remained consistent across matrices and years but did not exceed 1% in any sample. Minor aldehydes and esters, such as nervl formate, geranyl formate, and citronellyl formate, were sporadically present only in the essential oil, with maximum levels of 0.5% for nervl formate in 2021. The interannual variation in minor constituents, including _trans_-chrysanthemal, borneol, and \(\alpha\)-terpineol, suggests a strong influence of environmental factors, such as temperature and rainfall, on their biosynthesis. The compositional data affirm that while the major volatile signature of Lemon catnip is retained in both essential oil and recovery oil, distinct compound classes are differentially partitioned during the distillation process, with lipophilic compounds enriched in essential oils and more polar volatiles selectively enriched in the hydrate-derived recovery oil. The regression analysis of the volatile compound profiles in Lemon catnip essential oils and recovery oils, presented in Table 2, revealed distinct and compound-specific sensitivities to weather conditions over time. Temperature had the most pronounced positive influence on the accumulation of geraniol (K\({}_{\rm T}\) = 2.86 in essential oil; 4.10 in recovery oil), geranial (K\({}_{\rm T}\) = 0.29 in essential oil; 4.23 in recovery oil), neral (K\({}_{\rm T}\) = \(-\)0.76 in essential oil; 4.03 in recovery oil), and citronellal (K\({}_{\rm T}\) = 2.61 in essential oil; 0.00 in recovery oil), indicating enhanced biosynthesis of these monoterpene aldehydes and alcohols at higher temperatures, particularly in the hydrate fraction. In contrast, nerol exhibited a markedly negative temperature response (K\({}_{\rm T}\) = \(-\)2.58 in essential oil; \(-\)4.41 in recovery oil), suggesting the thermal suppression of its formation. Similarly, linalool displayed strong thermal sensitivity (K\({}_{\rm T}\) = \(-\)1.49 in essential oil; \(-\)2.17 in recovery oil), confirming its preferential formation under cooler conditions. Regarding precipitation, the strongest positive effects were observed for nerol (KP = 0.61 in essential oil; 1.20 in recovery oil) and linalool (KP = 0.24 in essential oil; 0.35 in recovery oil), indicating increased accumulation under wetter conditions, potentially due to higher plant hydration or increased extraction efficiency of the hydrophilic compounds. Conversely, geranial (K\({}_{\rm P}\) = \(-\)0.15 in essential oil; \(-\)0.86 in recovery oil), neral (K\({}_{\rm P}\) = 0.05 in essential oil; \(-\)0.80 in recovery oil), and geraniol (K\({}_{\rm P}\) = \(-\)0.52 in essential oil; \(-\)0.69 in recovery oil) showed negative correlations with precipitation, suggesting possible dilution or hydrolytic degradation effects in more humid environments.
## 3 Results and Discussion
### Power Spectrum  
Figure 13 shows the power spectrum of the local lift coefficient at 0.5 H of models under different \(Re\) values. Due to the strong free end effect, low-aspect-ratio cylinders were found not to have significant vortex shedding, unlike slender cylinders [23; 46; 47]. For M1 in Figure 13a, the flow around the cylinder was in a subcritical laminar separation at \(Re=1.8\times 10^{5}\), and the power spectrum contained energy over a wide frequency band. In the case of \(Re=2.6\times 10^{5}\), the flow had a transition from sub-criticality to criticality, from which a separation bubble appeared on the one side of cylinder, and a peak occurred distinctly at around \(fD/U=0.13\), which was the frequency of vortex shedding \(St\). With a further increase of \(Re=3.1\times 10^{5}\)-\(5.5\times 10^{5}\), two separation bubbles were gradually visible, and the spectral peaks became broad and unstable. It can be found that, despite being affected by the end effect, there was still a Karman vortex street around M1, which was consistent with [PERSON]'s result [34]. As the same time, the broad spectral peaks at higher \(Re\) indicated that vortex shedding occurred under varying flow configurations, adding contributions to the overall spectrum at different Strouhal frequencies, which were not all obtained simultaneously [6]. For M2 in Figure 13b, the power spectrum essentially showed broad spectral peaks in \(Re=2.7\times 10^{5}\)-\(5.7\times 10^{5}\), indicating that the free end effect was predominant, and the broad spectral peaks were associated with vortex shedding around the free end of the cylinder [34]. The influence of wind fields on the power spectrum of the local lift coefficient is given in Figure 14. With the increase in turbulence, it was clearly seen that there were broad spectral peaks in the power spectrum for two cylinders. The greater turbulence can be cited as the other factor that may affect vortex shedding [41; 48].  
Figure 13: Power spectrum of the local lift coefficient at 0.5 H of models under different \(Re\). (**a**) M1; (**b**) M2.