esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis and Simulation Results
### Modelling and Simulation  
Although each question in the questionnaire is aimed at different influencing factors, one answer sheet contains a respondent's psychological threshold preference for multiple influencing factors. Therefore, when this paper adopts the method of generating computer multiagents to describe the behavior preferences of taxi drivers using EVs, an accurate description of their preferences needs to rely on the joint probability information of the psychological threshold of multiple factors in the questionnaire. The extraction process of the joint probability information of the questionnaire is shown as follows in Figure 2.  
The formal expression of range anxiety is shown in the below Equation (1), where \(A\) is the range anxiety value (\(A\in[0,1]\)); the higher the value of \(A\), the higher the range anxiety  
Figure 2: Logic flow and modelling of the joint probability distribution from the survey data.  
about EV; \(T_{a}\) is the daily threshold of charging times; \(T_{b}\) is the threshold of daily driving mileage; \(T_{c}\) is the threshold of expected driving mileage.  
\[A=\frac{T_{a}\cdot T_{c}-T_{b}}{T_{a}\cdot T_{c}}\times 100\% \tag{1}\]  
Following in-depth information extraction via the modelling and logic flow as shown in Figure 2, based on the conditional probability distribution P(c! ab) of the driving mileage and the conditional probability distribution P(e! ab) of the expected nodes in the street network, i.e., potential locations for charging stations, multiagent modelling and simulation was undergone using random samples generated by Monte Carlo algorithm. The statistical distribution of the multiagent population for influencing factors is consistent with the statistical distribution of questionnaire samples in general, and each agent reflects the threshold preference for influencing factors. As a result, the probability distribution of daily mileage is a generated distribution of all agents under different cruise range anxiety conditions, as shown in Figure 3. Different colors in Figure 3 represent a different number of agents. A deeper color indicates a greater number of agents. The simulation results reveal that drivers with low daily driving mileage have more anxiety about the EV cruise range than drivers with high daily driving mileage.  
Furthermore, different agents' preference for expected cruise time to arrive at the closest charging stations is illustrated in Figure 4, when the cruise range anxiety parameter varies. The results indicate that taxi drivers prefer to arrive at the closest charging stations within 15 min of driving time.  
Figure 4: Preference distribution between expected driving time and nodes of closest charging stations considering cruise range anxiety.  
Figure 3: Daily driving mileage distribution of multiagents under different cruise range anxiety.
# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
## 1 Introduction
Moreover, the real-time travel time and travel distance between any two points can be extracted from online maps by calling its web service application programming interface (API) [15; 17]. However, the real-time travel time was rarely used to estimate fire service coverage in previous studies. The main objective of this study is to establish a dynamic estimation method of fire service coverage rate for high-rise buildings based on the big data of POI and real-time travel times extracted from online maps. In this study, the high-rise building data in Changsha, China are gathered and identified as case studies. Furthermore, based on the effective coverage rate of fire service for high-rise buildings in Changsha, that in China was discussed with national fire apparatus data and high-rise building data.
## 5 Liquid Mycelial Cultivation of Macrofungi: Methods and Strategies
The design of the vessel and stirring type can have a big impact on those factors, and this has led to the building of numerous different types of bioreactors for lab and industrial use. Chemical factors are the initial pH and the pH correction during the process, dissolved oxygen, substrate composition, and more specifically: carbon and nitrogen sources and the C/N ratio [177], vitamins, minerals, and other additives such as surfactants and by-product formation [68]. Biological factors are inoculum size, age and type, and the species and strain of the fungus [186]. Lighting is another important factor that may contribute to increased productivity in macrofungi cultivation. Controlling the lighting intensity in _C. militaris_ cultures resulted in increased yield and production of cordycepin, mannitol, and polysaccharides [187]. Another study examined the effect of blue and red LED lights on submerged cultures of _C. militaris_ and concluded that red light enhances biomass production while blue light gives the highest EPS production [94]. Another group of researchers achieved maximum production of cordycepin in submerged cultures when they combined red:blue LED lights and extended the illumination time to 20 h/day [188]. Stimulatory agents such as fatty acids, organic solvents, and surfactants have been employed in submerged cultures of macrofungi to improve the production of biomass and EPS. Tween 80 added at the late stage of the exponential growth phase contributed to a 51.3% increase in biomass and a 41.8% increase in EPS [189]. The addition of vegetable oils to submerged cultures can increase the production of desired compounds. [PERSON] et al. report that the low solubility of vegetable oils in the substrate of _C. militaris_ inhibits the carbon catabolite repression and allows for an increased rate of secondary metabolites biosynthesis [190]. Supplementation of oleic acid in submerged cultivation of _I. obliquus_ doubled the growth of the mycelium, while the use of a fungal elicitor stimulated mycelia growth and increased the betulinic acid accumulation by 429.5% [191]. Elicitors are widely used to boost the production of secondary metabolites in plants grown in vitro [192], but they remain relatively unexplored in the submerged cultivation of macrofungi. Various elicitors have been used for achieving the overproduction of desired triterpen from _G. lucidum_ submerged cultures [193], but not much literature exists on the use of elicitors with other species.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 5 Conclusions  
In this paper, sparse representation-based classification has been applied to the 10-class MSTAR data set. A preprocessing method of the MSTAR images is introduced to unify the sizes of the images, reduce the complexity and improve the performance of the classifiers. Then, the identity of the test sample is inferred by the framework of SRC. Compared with SVM, SRC also achieves good performance of recognition accuracy, and when the dimensions of the feature space surpass a certain threshold, the performances of the classifiers under conventional and unconventional features converge. Additionally, the recognition rate of each target also has been discussed. The performance is badly influenced by the variation of the target configurations and articulations, the different depression angles in the sensor and the aspect angles of the targets, which is an open problem called over-fitting. Simultaneously, the wrong classification of the specific targets BRDM2 and 2S1 is discussed further. The main reason is that the feature extraction methods cannot describe the targets effectively.  
Taking the minor flaws into consideration, on one hand, the future work will focus on improving the robustness of SRC. In other words, a more efficient framework of SRC should be explored. On the other hand, we expect to find more effective features to describe the targets. Although baseline features are studied here, conventional features need to be enriched to describe targets more effectively and so do the unconventional features.  
This work is partially supported by the National Natural Science Foundation of China under Grant 61372163 and 61240058.  
[PERSON] performed the simulations and algorithm analysis, and contributed to a main part of manuscript writing. [PERSON] contributed in conceiving and the structure of the manuscript. All authors contributed to the writing of the paper.  
The authors declare no conflict of interest.
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 1 Introduction  
When the ground motion is severely affected by shear waves propagating vertically from the underlying rock, the soil deposits may undergo cyclic shear deformations. The dynamic properties of soils, including the strain-dependent shear modulus and damping ratio, are the basic input parameters in the analysis of the seismic ground response and site amplification [1]. The soil properties exhibit strong nonlinear responses; the shear modulus decreases, and the damping capacity increases with the amplitude of shear strain. The increasing damping capacity is directly associated with the hysteresis. These macroscopic, hysteretic, nonlinear behaviors of soils are the consequences of complex physics at microscales including inter-grain contact, friction and adhesion, and rearrangement of grain structures under loading-unloading.  
Numerous stress-strain models have been proposed for the analysis of dynamic soil responses [2; 3]. Classical models in terms of empirical fitting parameters are often used for their simplicity [4; 5; 6; 7; 8]. More sophisticated models for cyclic loading require more fitting parameters to better describe the soil hysteresis loops. The hysteresis models have been proposed to capture a closed hysteresis loop characterized by imposed shear strain amplitude, and state of stress [9; 10; 11; 12; 13; 14; 15; 16; 17]. While these models have been successful in expressing the complex strain-dependency for certain soil types under the steady-state cyclic loading, they still have lack of robustness and universality due to physical uncertainty of the input parameters and their uses are quite limited to other soils. Therefore, a simple but robust model is needed to describe inherently hysteretic nonlinear nature of soils.  
The present study is to develop a new theoretical framework for the purpose of characterizing the strain-dependent dynamic shear modulus and damping ratio of soils. This paper extends the previous study by the author of [18] to (1) derive the explicit formula with the second model order;(2) to test damping ratio data obtained from resonant column test (RCT); and (3) to explore the effect of confinement stress on the hysteretic parameters. In particular, the modified procedure of conventional data interpretation is proposed to accurately characterize the hysteretic soil properties and responses from resonant column.
# Enhanced Nondestructive Testing Using Pulsed Eddy Current and Electromagnetic Ultrasonic Techniques
###### Abstract  
Owing to the challenging working conditions aboard ships, equipment may develop various defects that traditional nondestructive testing methods cannot fully detect. This study proposes a combined approach using pulsed eddy current and electromagnetic ultrasonic testing for enhanced probe detection. Utilizing COMSOL 6.2 finite-element software, a two-dimensional model of the probe was developed to optimize the probe's design. Based on these findings, a composite probe was designed and structurally optimized, resulting in improved energy conversion efficiency. An experimental platform was established to evaluate the detection capabilities of the composite detection probes. The results indicate that the composite detection probe, as designed and optimized in this study, is effective in testing materials with various artificial defects and enhances the detection of near-surface defects in materials.  
pulsed eddy current detection; electromagnetic ultrasonic testing; nondestructive testing Article
## 5 Discussion
The method of dry beach monitoring using image processing is relatively efficient and can realize real-time monitoring. A potential solution to address this challenge is to integrate both methods. Image processing can provide real-time monitoring to ensure timeliness, while UAV-based surveys can be conducted periodically to create a 3D point cloud model, offering more comprehensive spatial information.
## 3 First-Order Theory of Beams (TBT)
### Equations of Equilibrium  
The principle of minimum total potential energy for the TBT has the same form as that for CBT, except that one must add the strain energy terms associated with the transverse stress \(\sigma_{xz}\). The curvature in the TBT is given by  
\[\omega_{y}=\frac{1}{2}\bigg{(}\frac{\partial u_{1}}{\partial z}-\frac{ \partial u_{3}}{\partial x}\bigg{)}=\frac{1}{2}\bigg{(}\phi_{x}-\frac{dw}{dx} \bigg{)},\ \ \chi_{xy}=\frac{1}{2}\frac{\partial\omega_{y}}{\partial x}=\frac{1}{4}\bigg{(} \frac{d\phi_{x}}{dx}-\frac{d^{2}w}{dx^{2}}\bigg{)} \tag{41}\]The stress resultants of the TBT are defined as  
\[\begin{split} N_{xx}&=\int_{A}\sigma_{xx}\,dA,\quad N_{ xz}=K_{s}\int_{A}\sigma_{xz}\,dA,\\ M_{xx}&=\int_{A}z\sigma_{xx}\,dA,\quad P_{xy}=\int_ {A}\mathcal{M}_{xy}\,dA.\end{split} \tag{42}\]  
Here \(K_{s}\) denotes the shear correction factor. The Euler equations of the TBT are  
\[-\frac{dN_{xx}}{dx}-f =0 \tag{43}\] \[-\frac{dN_{xz}}{dx}-\tfrac{1}{2}\frac{d^{2}P_{xy}}{dx^{2}}-\frac{ d}{dx}\bigg{(}N_{xx}\frac{dw}{dx}\bigg{)}-q =0\] (44) \[-\frac{dM_{xx}}{dx}-\tfrac{1}{2}\frac{dP_{xy}}{dx}+N_{xz} =0 \tag{45}\]  
The three duality pairs for the TBT are  
\[(u,N_{xx}),\quad(w,V_{\text{eff}}),\quad(\phi_{x},\bar{M}_{xx}). \tag{46}\]  
where the effective shear force and bending moments are  
\[V_{\text{eff}} \equiv N_{xz}+N_{xx}\frac{dw}{dx}+\tfrac{1}{2}\frac{dP_{xy}}{dx} \tag{47a}\] \[\bar{M}_{xx} =M_{xx}+\tfrac{1}{2}P_{xy} \tag{47b}\]  
We note that the effective shear force in the TBT has the modified couple stress term.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 3 Shake Table Test of Six-Story Coupled Wall System with Damper
### Test Setup  
Figure 12 depicts the test specimen assembly, including the rigid frame (blue) mounted at the center of the shake table. Figure 13 illustrates the test specimen for the six-story frame (brown), the coupled shear walls (blue), and the damper attached to the coupled shear walls. Figure 14a shows the elevation view and Figure 14b shows the top view of the damper.  
Figure 11: Shake table experiment procedure for 6-story test specimen.  
Figure 12: Test specimen with rigid frame on shake table test.  
Each story of the frame consists of a main steel plate, a Natural Rubber Bearing (NRB) with a diameter of 200 mm and height of 132 mm placed on the floor center, and four linear motion (LM) guides. The steel floor size is 1500 mm by 300 mm, with a thickness of 25 mm. The story stiffness and restoring force are provided by the NRBs. NRBs contribute to the restoring force through their inherent elasticity, where the rubber layers deform under lateral loads and then rebound, recentering the structure after shaking. The LM guides are installed to restrain the bending deformation of the specimen during excitation.  
Figure 14: Damper used for the shake table test: (**a**) front view, (**b**) top view.  
Figure 13: Test specimen of the 6-story frame with coupled shear walls and damper.  
Table 4 presents a summary of the structural properties of the six-story frame. A damper installed between the fifth and sixth stories introduces an additional mass of approximately 2510 N.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### Supervised Learning for Network Slicing
#### 4.1.1 Support Vector Machine (SVM)  
SVM is one of the supervised learning classification algorithm, it helps to classify data by separating them by finding the decision boundary, called the hyperplane. Support vectors helps to define the hyperplane. Spectrum sensing can be challenging due to heterogeneity of the network, SVM can be helpful in this regard by classifying support vectors and intelligently creating the model to able to sense the spectrum efficiently [27]. Feature selections in network slicing can also be challenging, so features can be selected according to the application by using the SVM [28].
# Assessment of Quality and Safety of Farm Level Produced Cheeses from Sheep and Goat Milk
## 3 Results
### Milk Quality and Safety
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **April** & **May** & **June** & **July** & **August** & **September** & \(p\)**Value** \\ \hline Fat (\%) & 3.82 \(\pm\) 0.61 \({}^{\rm a}\) & 3.37 \(\pm\) 0.54 \({}^{\rm b}\) & 3.32 \(\pm\) 0.58 \({}^{\rm b}\) & 3.60 \(\pm\) 0.85 \({}^{\rm bc}\) & 3.99 \(\pm\) 0.58 \({}^{\rm a}\) & 4.15 \(\pm\) 0.73 \({}^{\rm a}\) & \(<\)0.001 \\ Protein (\%) & 3.66 \(\pm\) 0.49 \({}^{\rm ab}\) & 3.52 \(\pm\) 0.55 \({}^{\rm b}\) & 3.41 \(\pm\) 0.63 \({}^{\rm b}\) & 3.26 \(\pm\) 0.65 \({}^{\rm b}\) & 3.54 \(\pm\) 0.71 \({}^{\rm b}\) & 4.01 \(\pm\) 0.67 \({}^{\rm a}\) & \(<\)0.001 \\ Lactose (\%) & 4.36 \(\pm\) 0.44 \({}^{\rm a}\) & 4.22 \(\pm\) 0.36 \({}^{\rm ab}\) & 4.17 \(\pm\) 0.43 \({}^{\rm ab}\) & 4.17 \(\pm\) 0.53 \({}^{\rm ab}\) & 4.01 \(\pm\) 0.65 \({}^{\rm b}\) & 3.95 \(\pm\) 0.52 \({}^{\rm b}\) & \(<\)0.05 \\ SNF (\%) & 9.41 \(\pm\) 0.35 \({}^{\rm d}\) & 9.50 \(\pm\) 0.29 \({}^{\rm d}\) & 9.74 \(\pm\) 0.39 \({}^{\rm c}\) & 9.93 \(\pm\) 0.72 \({}^{\rm c}\) & 10.35 \(\pm\) 0.51 \({}^{\rm b}\) & 11.10 \(\pm\) 0.74 \({}^{\rm a}\) & \(<\)0.001 \\ Density & 1026.97 \(\pm\) & 1027.49 \(\pm\) & 1027.18 \(\pm\) & 1028.66 \(\pm\) & 1028.52 \(\pm\) & 1027.59 \(\pm\) \\ (g/L) & 0.26 \({}^{\rm c}\) & 0.66 \({}^{\rm d}\) & 0.20 \({}^{\rm db}\) & 0.34 \({}^{\rm a}\) & 0.41 \({}^{\rm b}\) & 0.34 \({}^{\rm d}\) & \(<\)0.001 \\ Ad\({}_{w}\) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & \(>\)0.05 \\ Acidity (\({}^{\rm c}\)SH) & 5.81 \(\pm\) 0.41 \({}^{\rm e}\) & 4.79 \(\pm\) 0.54 \({}^{\rm d}\) & 5.06 \(\pm\) 0.74 \({}^{\rm c}\) & 5.38 \(\pm\) 0.56 \({}^{\rm c}\) & 6.27 \(\pm\) 0.50 \({}^{\rm b}\) & 6.74 \(\pm\) 0.4 protein content than that milk in the mid lactation (\(p<0.05\)). A similar trend was found out also for fat content, with significantly higher values in the early and late stages of lactation compared to mid stages of lactation. Protein content was in range of 3.26 \(\pm\) 0.65% to 4.01 \(\pm\) 0.67% (\(p<0.001\)) (Table 2). Results showed the decreasing of lactose during the month of milking (\(p<0.05\)). The highest level was at the beginning of lactation on April 4.36 \(\pm\) 0.44% and the lowest at the end of lactation period 3.95 \(\pm\) 0.52%. Non-fat solids and density of goat milk significantly rose during the period (\(p<0.001\)). The higher concentration of SNF was examined in September (Table 2). The density of goat milk was unstable during the whole period of time (\(p<0.001\)). As well as in sheep milk, no added water was found in goat's milk. This means that no attempt was made to adulterate the milk. Goat milk acidity changed throughout the year (\(p<0.001\)). The highest acidity of goat milk was detected in September and the lowest value was detected during May. In accordance, because the higher the acidity, the lower the pH, the pH was higher during the spring and lower during autumn. In April, the pH was 6.83 \(\pm\) 0.70 compared to September 6.67 \(\pm\) 0.90.
# Profiling Croatian Whisky Using GC/MS-MS and UHPLC-QTOF
## 3 Results and Discussion
### Aromatic Profile of Croatian Whisky Determined by SPME-GC-MS
#### 3.3.2. Maillard Reactions and Congeners  
In whisky production, some of the sugars can be involved in the Maillard reaction (MR), a complex series of reactions between reducing sugars and amino groups accelerated by thermal energy. This reaction produces N-substituted glycosylamine, which then transforms further to contribute flavor compounds (congeners) [42; 43; 44]. Detected congeners in Croatian whiskies include methylglyoxal, D-erythrose, glyceraldehyde, and 3-deoxyglucosome. Methylglyoxal is a reactive compound that forms in small amounts through the MR and caramelization processes. It adds to the whisky's complexity with its pungent aroma contributing to both color and flavor. D-erythrose, a tetrose sugar, has the potential to take part in MR during the heating stages of whisky production. D-erythrose interactions and breakdown products could potentially add to the overall array of flavors. It may be found in whisky as a result of decomposition of larger carbohydrates during fermentation or aging, though its flavor influence remains minor due to potentially low concentrations [1]. Glyceraldehyde is an intermediate in sugar metabolism, contributing to color and flavor through MR. As a simple sugar, it could also contribute sweetness, and it is an intermediate in several metabolic pathways [31; 34; 37]. 3-Deoxyglucose, a product of the MR, can occur during whisky production and contributes a desirable browned food flavor that also occurs during the cooking or klining phase of whisky making or aging processes in barrels [31; 45].
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 2 Locata Core Technologies
### LocataNet  
A LocataNet is a terrestrial network of synchronised LocataLites that provides positioning signals that support a range of PNT applications. [PERSON] et al. summarise the characteristics of the LocataNet [17; 18]. When building a LocataNet the location of the LocataLites should satisfy the requirements of: (1) be able to receive the signal from at least one other LocataLite; and, (2) the geometry of the network (as indicated by a measure such as the Dilution of Precision--DOP) is suitable for PNT application.  
Figure 1: The Locata technology positioning concept.  
In general, a LocataNet can be established in any environment, and could be optimised for power source, power transmission, DOP, security of the LocataLites, and other factors. In a Locata applied environment, the elevation angles are usually low, which typically makes vertical DOP (VDOP) significantly larger than the horizontal DOP (HDOP) values [19; 20; 21]. LocataLites must be distributed in such a way that there are sufficiently large height differences between pairs of LocataLites to achieve better VDOP. In general, it is considered that the LocataNet geometry (location and number of LocataLites) can be optimised beforehand to satisfy the positioning accuracy and reliability that are required by specific applications. However, optimisation is only guaranteed without any misspecification in the function and as long as the assumptions underlying the mathematical model hold, especially the stochastic model. Previous studies typically assumed a unit weight for all code pseudorange or carrier phase measurements, regardless of the spatial and temporal heterogeneity of the signals [22; 23]. Therefore, the prior quality control of a LocataNet is still problematic, unless the Locata signals' stochastic characteristics are theoretically investigated and accurately specified.
## 3 Challenges in Terahertz-Based FSO Communication
### Manufacturing Challenges in THz Communication  
There are several manufacturing challenges that limit the large-scale deployment of THz communication systems. Unlike microwave and millimeter-wave technologies, THz systems require high-precision fabrication, specialized materials, and advanced device integration to achieve efficient signal generation, transmission, and detection.