esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Particle Size Distribution  
Particle size distribution of microcapsules obtained by SD and SFB is shown in Figure 5. As can been seen in Figure 5, a unimodal and narrow distribution was obtained for the microcapsules obtained by SD with particle size ranging from 1.22 to 46.40 \(\upmu\)m, while the microcapsules by SFB had a unimodal and wide size distribution with particle size ranging from 3.65 to 350 \(\upmu\)m. Similar values were obtained by [PERSON] et al. [17] with particle size between 0.579 and 44 \(\upmu\)m in microcapsules by spray drying of \(S\). _purpurea_ extract, and by [PERSON] et al. [48] with particle size between 0.06 and 600 \(\upmu\)m in microcapsules obtained by spout fluid bed drying of _Euterpe olerca_ extract.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Spray Drying** & **Spout-Fluid Bed Drying** \\ \hline Moisture Content (\%) & 3.95 \(\pm\) 0.10 \({}^{\mathrm{a}}\) & 2.25 \(\pm\) 0.43 \({}^{\mathrm{a}}\) \\ Water Activity (\(a_{\mathrm{w}}\)) & 0.25 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.26 \(\pm\) 0.03 \({}^{\mathrm{a}}\) \\ \hline \hline \end{tabular} Data are expressed as the mean \(\pm\) standard deviation of the analysis in triplicate. Different letters per row indicate significant statistical difference (\(p\leq 0.05\)).  
\end{table}
Table 2: Moisture Content and Water Activity (\(a_{\mathrm{w}}\)) of microcapsules obtained by spray drying and spout-fluid bed drying.  
Figure 5: Particle size distribution of microcapsules obtained by spray drying and spout-fluid bed drying.  
Difference in particle size and particle size distribution between both drying methods was expected due to the different drying mechanism present. In SD, the sprayed droplets are dried by the hot air producing mainly shrunken spheres. For the SFB, the drying solution was sprayed over a bed of inert solids; the fed solution forms a coat on the surface of the inert particles, and this coat is stripped off when dried and brittle because of collisions, friction and shearing between the inert particles and dryer wall. Therefore, the microcapsules by SFB are in the forms of irregular flakes with a larger and varied particle size than microcapsules by SD.  
Sautter diameter (D[3,2]) and equivalent spherical diameter (D[4,3]) were determined. The microcapsules by SD had 11.73 \(\pm\) 2.77 \(\upmu\)m for D[3,2] and 15.77 \(\pm\) 0.27 \(\upmu\)m for D[4,3], while the microcapsules by SFB showed 23.95 \(\pm\) 3.93 \(\upmu\)m and 52.94 \(\pm\) 2.93 \(\upmu\)m, for D[3,2] and D[4,3], respectively.  
The above results show that the drying mechanism had a significant effect in the bioactive compounds and antioxidant capacity retention. As shown by the above-mentioned results, higher retentions were obtained in SFB microcapsules which had a thicker wall than SD microcapsules. Additionally, according to [PERSON] et al. [49], the microcapsules with a larger particle size have a better delayed release.
# Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
## 2 Test Principle and Method
### Test Programme and Steps  
1. Under dry conditions, the uniaxial compressive strength of sandstone was determined to be 48.24 MPa, accompanied by a modulus of elasticity measuring 13.22 GPa and a Poisson's ratio of 0.25. Conversely, when subjected to saturated conditions, the sandstone exhibited a reduced uniaxial compressive strength of 38.76 MPa, coupled with a modulus of elasticity of 15.42 GPa and a Poisson's ratio slightly increased to 0.26.
2. The sandstone underwent triaxial compression tests in both dry and saturated states, along with different gradients of confining pressure, which provided a blank control group for the subsequent hydromechanical tests under saturation conditions. The following four gradients of confining pressure were utilized: 5, 10, 15, and 20 MPa.
3. Hydromechanical tests were carried out on sandstone, encompassing various magnitudes of confining pressure gradients and seepage pressure gradients. Confining pressure gradients of 5, 10, 15, and 20 MPa were established, while seepage pressure gradients of 2, 4, and 8 MPa were implemented, the test programme is shown in Table 2. The ensuing hydromechanical test procedures were executed according to the subsequent steps:  
* Firstly, the sandstone was secured in the triaxial pressure chamber and then the installation of the circumferential and axial sensors began in sequence.
* Secondly, the confining pressure was applied to the sandstone automatically using a control system, gradually ramping it up from 0 MPa to the designated target value at a controlled rate of 1 MPa/min. Following the stabilization of the confining pressure, the seepage pressure was applied from the upper chamber, incrementally increasing from 0 MPa to the intended value at a controlled pace of 1 MPa/min. It is important to note that the seepage pressure within the lower chamber was maintained at atmospheric pressure throughout this process.  
Figure 3: Rock Top 50 HT full-stress multi-field coupling triaxial test system.  
Thirdly, subsequent to achieving the targeted seepage pressure and ensuring its stability, the next step is to proceed to apply the deviatoric stress. This should be executed by applying a loading rate of 0.01 mm/min until the point of specimen failure is reached.
## 4 Results and Discussion
### Lessons Learned  
This study proved to be possible to incorporate multiple training components, specifically, balance, strength, cognition and pelvic floor muscle training, in a single exergame solution, relying on an interaction with multidirectional steps and inertial sensors placed on the feet. Although being simple, the interaction constrained game narrative and the evaluation of full-body movements. As a web-based solution, the exergame allowed cross-platform compatibility, and could communicate with Bluetooth devices without any noticeable delay. A vaginal dynamometer could be integrated to provide feedback to older women wanting to confirm pelvic floor muscle contractions. The connection with a clinician portal allows clinicians to remotely follow patient training at home, as well as prescribe and change interventions. These functionalities may support better access to services, e.g., in the context of a pandemic. The game mechanics allows adaptation to different training requirements, and ensures automatic control of training times as prescribed by the therapist. The game design and mechanics can be adapted to additional interventions.
# Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations
## 2 Related Work  
Typically, scenario vehicle models in open-source autonomous driving simulators are designed to follow certain simple yet hard-coded rules. Their behaviors may exhibit a limited number of predefined driving styles in terms of lane changing, car following, and collision avoidance [7; 8]. Nevertheless, in comparison with the more conservative tracking modes of open-source platforms, commercial software, such as Automotive Artificial Intelligence (AAI) and Cognata, generally uses artificial intelligence to build decision-making systems for scenario vehicles in simulations. As the development and adjustment of such models are expensive, the range of scenarios is extremely limited.  
Since the proposal of the car-following model in the 1950s, the continuous optimization of the microscopic traffic simulation has rendered it highly mature. Several researchers have attempted to create a dynamic driving environment in the aforementioned simulator, with traffic participants provided by microscopic traffic simulations [9; 10; 11]. However, some researchers have argued that directly deploying microscopic traffic simulations may cause significantly biased evaluation results during the testing of autonomous vehicles. Therefore, the parameter values of scenario vehicles are adjusted such that different levels of aggressiveness are produced [12] or their behaviors appear statistically closer to reality [2]. However, the background requirement of traffic flow simulation is significant in traffic engineering, which is used to accurately reproduce the macroscopic characteristics [13]. Although these models have achieved the desired granularity in autonomous driving tests, they are over-simplified in their dynamics [14], implicitly use safe rather than actual behaviors [15], and are unrealistic at the inter-individual level despite calibrations with real-world data [4].  
Recently, certain researchers have attempted to eliminate the conventional framework of microscopic traffic simulation and develop more realistic interactive models. [PERSON] et al. [16] proposed a force-based framework that could replicate the sophisticated behaviors of various road users and their interactions in a unified manner. [PERSON] et al. [17] introduced a cognition-driven traffic simulation method capable of generating virtual drivers with heterogeneous behaviors in unstructured road networks. Additionally, researchers at the University of Michigan have addressed the need for a more realistic and interactive model using the \"level-k\" game theory [18; 19; 20]. Several researchers have also borrowed popular development methods from autonomous driving. [PERSON] et al. [21] trained the policies of scenario vehicles using imitation learning to mimic human-like driving behaviors. However, these new models provide staged results as they can be applied only in specific scenarios.
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## 5 Discussion  
In this work, an SEIRS-D agent-based model to simulate the spreading of malware on wireless sensor networks is introduced. Agents, coefficients and transition rule have structured this model. Each agent has been classified in one of the following compartments: susceptible, infected, exposed, recovered and dead.  
Moreover, the model proposed in this work was based on seven significant coefficients: infection, transmission, detection, recovery, maintenance, energy and malware. Satisfactory results will be obtained if correctly identify such coefficients and then set up the proper transition rules.  
The simulation of the proposed model has been executed in three different scenarios that correspond to the phenomenon of interest agent. In each situation, different global values have been established to simulate a real environment. These parameters have been replicated in the three network topology agents. The analysis of the results has been carried out in two categories: by scenario and by topology.  
Figure 6: View of complex networks in Mesa Framework.  
have started to lose their energy and become dead. In summary, the network can lose data due to intercommunication failures between nodes (see Figure 8).
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 4 Communication Handover
i.e.,  
\[\text{Entropy}\;=-\Sigma(p(x)^{*}\log_{4}(p(x))) \tag{18}\]  
In this analysis, for simplicity, we consider just one indicator, namely the received signal strength (RSS), and with three samples from three base stations. This assumption was made since the study was done for a single satellite. The introduction of new satellites or a constellation of satellites would require inputs of other indicators like the capacity, remaining service time, and satellite load (capacity). The weights for other indicators are not considered, thereby simplifying the weighted entropy method to a basic entropy calculation. The standardized values of RSS are computed as follows:  
\[p_{j}=\frac{RSS_{j}}{\sum_{j=1}^{4}RSS_{j}} \tag{19}\]  
This study aimed to develop a signal handover strategy for drones, focusing solely on RSS. The network conditions before and after handovers are illustrated, along with the corresponding signal strengths. The assumptions for the study include the following:  
* A drone travels in a straight flight path along the X-axis. * The rain and gas attenuation are considered. * A constant UAV altitude and a velocity of 45 m/s are involved. * The satellite is on the XY plane
* The drone has an elevation mask of 60\({}^{\circ}\), as shown in Figure 4.
## 3 System Model
### Physical Layer Model  
Attenuation and absorption have a significant impact on acoustic waves over long distances. To determine transmission power in acoustic modems, an underwater propagation model is presented. Depending on the location of the destination node, the source node modifies its power. Sonar equations and the propagation of underwater acoustic waves are defined in [35], and the acoustic intensity of the transmitted signal is determined as [36].  
\[SL(d,f)_{ij}=A(d,f)_{ij}+N(f)+\gamma(d)_{dB}+DI \tag{1}\]  
where \(SL(d,f)_{ij}\) represents the sound intensity magnitude emitted by node-i at a distance \(d\) (in meters) in relation to the acoustic pressure of sound (\(dB\) ref \(\mu\)\(Pa\)) and is calculated using the acoustic signal frequency \(f\) (in kHz). Path loss of acoustic waves at frequency \(f\) is represented by \(A(d,f)_{ij}\), which increases with node distance \(d\) and is quantified as dB in [37]  
\[A(d,f)_{ij}=10\kappa\log_{10}(d_{ij})+\alpha(f)\times d_{ij}\times 10^{-3} \tag{2}\]  
where \(\kappa\) denotes the spreading factor; the absorption coefficient is expressed as \(\alpha(f)\) given as dB/km in Equation (3) [38]  
\[\alpha(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100}+f^{2}+2.75\times 10^{- 4}f^{2}+0.003 \tag{3}\]  
The ambient noise level is computed in \(dB\) ref \(\mu\)\(Pa\) accounting for the cumulative noise levels induced by water turbulence (i.e., \(N_{t}(f)\)), surface ships (i.e., \(N_{s}(f)\)), thermal activities (i.e., \(N_{th}(f)\)), and breaking waves (i.e., \(N_{w}(f)\)) defined in [37].  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{th}(f)+N_{w}(f) \tag{4}\]  
The bit error rate (BER) is defined for 16-QAM modulation with orthogonal frequency division multiplexing (OFDM) encoding in Equation (5). The signal-to-noise ratio (SNR) can be calculated as follows in order to maintain a BER on a link (i.e., \(10^{-9}\)) [34]  
\[p_{ij}^{b}=\frac{3}{8}erfc\Bigg{(}\sqrt{\frac{4}{10}\frac{B_{N}}{R}\gamma(d)_{ ij}}\Bigg{)} \tag{5}\]where \(B_{N}\) is noise bandwidth (in Hz), \(\gamma(d)_{ij}\) is SNR in linear form, and \(R\) is data rate in bps. The intensity level of sound (\(I_{ij}^{tx}\)) at a distance \(d\) is used to calculate the electrical power needed for transmission [34]  
\[p_{ij}^{tx}=I_{ij}^{tx}\times 2\pi\times 1m\times h_{i} \tag{6}\]  
where \(h_{i}\) represents the sea depth of node-i in meters. The source level (\(SL(d,f)_{ij}\)) is used to calculate the intensity of the acoustic signal (\(I_{ij}^{tx}\)) [34].  
\[I_{ij}^{tx}=10^{\frac{SL(d,f)_{ij}}{10}}\times I_{0} \tag{7}\]  
\(I_{0}\) is the reference intensity at 1 m defined as \(0.67\times 10^{-18}\) (\(\mathrm{Watts/m^{2}}\)) [34]. To satisfy the predetermined BER and SNR threshold, the required transmission power is calculated with respect to the distance between the source and destination. Given electrical transmission power (\(p_{ij}^{tx}\)), the total energy consumption of the node transmitting a single data packet is calculated  
\[E_{ij}^{t}=p_{ij}^{tx}\times\tau_{tx} \tag{8}\]  
where \(\tau_{tx}\) is duration of single packet transmission determined as \(\tau_{tx}=L/R\).
# A Cell Counting Framework Based on Random Forest and Density Map
## 3 Method
### Training Data Preparation
#### 3.1.1 Ground Truth Generation  
As we have analyzed in Section 1, the RF has two drawbacks when predicting the density map. The one is that when training data are imbalanced the output will be inclined to majority data, and the other one is that the output is always limited in the range of input label data. Let's take the most used Gaussian function as an example to explain it. Each cell is labeled by a dot and the ground truth is the convolution result of Gaussian function and the labeled dot map. Figure 2 is the ground truth and the histogram distribution of the labeled region. It can be observed that the data with high density values are in minority, which may result in underestimation. However, if the DT gets deeper to learn the minority data, there is a risk of overfitting. In addition, even the data are balanced, RF still cannot predict the data that are beyond the scope of the training data. In terms of accuracy, it is improper for RF to predict density map directly.  
To mitigate the above problems, we proposed a novel way to generate the ground truth which is defined as the probability map.  
First, we used a blob to label a cell rather than a dot. This can make sure that the distribution of data is more identical after convolution. Second, the limited output of RF impedes the accuracy of density map. Therefore, we chose to count cells by detecting the centroids of cells, rather than by predicting density map directly. The convolution filter is designed as:  
\[f(x,\sigma)=G(x,\sigma)+\delta(x-x_{c})*G(x,\sigma) \tag{1}\]  
where, \(G\) is a Gaussian function, \(\delta\) is a pulse function, \(\sigma\) controls the spread, and \(x_{c}\) is the center of the filter. Note that there is a \(\delta(x)\) function in the convolution filter, which makes it different from the Gaussian function.  
Figure 1: The overview of the proposed cell counting framework (CCF). After the training data preparation, the random forests (RFs) can be trained. The probability maps are predicted first where the centroids of cells are easier to be detected. Take the decisions of all RFs into consideration, the final count result is decided by multiple detection maps rather than a detection map, which can improve accuracy and robustness.  
Figure 3 is the generated probability map. Compare the histograms in Figures 2b and 3b, it can be observed that data in Figure 3a are more equally distributed. In this way, the training data get more balanced without any extra operations. Figure 4a is the probability map generated by Gaussian function and Figure 4b is the probability map we defined. It can be noticed that when cells overlap the centroids of cells in Figure 4a cannot be distinguished anymore but the centroids of cells in Figure 4b still are highlighted which is helpful to locate cells.
## 2 Analytical Framework
### Peak Factor  
Peak factor \(g_{u}\) is defined as the ratio of maximum wind speed fluctuation in a duration \(\tau\) to the standard deviation of the fluctuating wind speed within an observation period of \(T\):  
\[g_{u}(\tau,T)=\frac{\max[u(\tau,T)]}{\sigma_{u}(\tau,T)}\frac{\sigma_{u}(\tau, T)}{\sigma_{u}}. \tag{23}\]For a stationary stochastic process following Gaussian distribution, the peak factor with \(\tau\to 0\) and \(T\geq 3600\)\(s\) could be calculated as [42]:  
\[g_{u}(\tau,T)=\sqrt{2\ln[\
u(\tau,T)T]}+\frac{0.5772}{\sqrt{2\ln[\
u(\tau,T)T]}}, \tag{24}\]  
where \(\
u(\tau,T)\) is the zero up-crossing rate. It can be calculated by [42; 43]:  
\[\upsilon^{2}(\tau,T)=\frac{\int_{0}^{\infty}n^{2}S_{u}(n)\chi^{2}(n,\tau,T) \mathrm{d}n}{\int_{0}^{\infty}S_{u}(n)\chi^{2}(n,\tau,T)\mathrm{d}n}, \tag{25}\]  
in which \(S_{u}(n)\) represents the wind velocity spectrum; \(n\) denotes the frequency in Hertz, and \(\chi^{2}(n,\tau,T)\) is a filter function used to consider the influence of sampling frequency, averaging time and response characteristics of the anemometer. In this study, the von Karman-type spectrum is employed as:  
\[\frac{nS_{u}(n)}{\sigma_{u}^{2}}=\frac{4\left(\frac{nL_{u}^{x}}{U}\right)}{ \left[1+70.8\left(\frac{nL_{u}^{x}}{U}\right)^{2}\right]^{\frac{5}{6}}}, \tag{26}\]  
The filter function is chosen as following for sonic anemometers [44]:  
\[\chi^{2}(n,\tau,T)=\left[\frac{\sin(\pi n\tau)}{\pi n\tau}\right]^{2}-\left[ \frac{\sin(\pi nT)}{\pi nT}\right]^{2}, \tag{27}\]  
For propeller anemometers, the following filter function, which takes the mechanical features of propeller anemometers into consideration, is adopted [43]:  
\[\chi^{2}(n,\tau,T)=\left\{\left[\frac{\sin(\pi n\tau)}{\pi n\tau}\right]^{2}- \left[\frac{\sin(\pi nT)}{\pi nT}\right]^{2}\right\}\frac{1}{1+\left(\frac{2 \pi n\lambda}{U}\right)^{2}}, \tag{28}\]  
where \(\lambda\) is the distance constant of the propeller anemometer.  
Equation (24) is valid for calculating the average of instantaneous peak factor ( \(\tau\to 0\)) from a long enough wind speed record (e.g., \(T\geq 1\) h). With a finite averaging time, \(\tau\), and a finite observation period, \(T\), the estimation of standard deviation in Equation (26) could be biased since the measured spectrum is truncated in both high-frequency and low-frequency regions, and might eventually lead to an inaccurate estimation of the peak factor. In the case that these conditions are not satisfied, the following relation is necessary to be introduced to consider the effects of the variance reduction due to the truncation of the velocity spectrum:  
\[\frac{\sigma_{u}(\tau,T)}{\sigma_{u}}=\frac{\int_{0}^{\infty}S_{u}(n)\chi^{2} (n,\tau,T)\mathrm{d}n}{\int_{0}^{\infty}S_{u}(n)\mathrm{d}n}, \tag{29}\]  
The 3-s peak factor, \([g_{u}]_{s}\), in time scale \([T]_{s}=600\) s in the standard terrain can be estimated by introducing \(U_{s}\) and \(L_{us}^{x}\), which were respectively calculated through Equations (3) or (7) and (16).
# A Single-Launch Deployment Strategy for Lunar Constellations
## 2 Low-Energy Captures in the Sun-Earth-Moon System
### Characterization of Low-Energy Trajectories Crossing the Equilibrium Region
For this class of trajectories, the osculating orbital elements at capture can be characterized, as for the long-term behavior, by their state representation at time \(t_{0}\) when the trajectory is inside the equilibrium region [40]. Equation (7) can be rearranged as follows [43]  
\[\frac{\lambda_{1}}{2}(x_{2}^{2}+y_{2}^{2})+\frac{\lambda_{2}}{2}(x_{3}^{2}+y_{ 3}^{2})=h(1-\varepsilon) \tag{8}\]  
with \(\varepsilon>0\) arbitrarily small. The two constant terms of Equation (8) are named energy fractions and indicated as \(h_{1}=\frac{\lambda_{1}}{2}(x_{2}^{2}+y_{2}^{2})\) and \(h_{2}=\frac{\lambda_{2}}{2}(x_{3}^{2}+y_{3}^{2})\). The value of the energy fractions can be related to those of some osculating orbital elements. For the sake of clarity, the following expressions for the properties of the canonical transformation \(\mathbf{T}_{N}\) shall be recalled:  
\[ \begin{cases}y_{2}=-j\bar{x}_{2}(x,y,v_{x},v_{y})\\ y_{3}=-j\bar{x}_{3}(z,v_{z})\end{cases}  \tag{9}\]  
From the conservation of the angular momentum, the following expression for the inclination \(i\) can be derived [44]  
\[\cos i=\frac{1}{\sqrt{1+\frac{h_{2}}{h_{1}}}} \tag{10}\]  
Therefore, a target inclination at capture can be fixed by selecting any couple \((x_{2}(t_{0}),x_{3}(t_{0}))\) verifying Equation (10). By virtue of Equation (9), Equation (10) represents a constraint for the six state variables in the position space. Moreover, if \(h\) is not fixed (i.e., it can be varied within an admissible range) for a given set of in-plane variables \((x,y,v_{x},v_{y})\), the target inclination can be fixed by selecting only the out-of-plane variables \((z,v_{z})\). Another relationship between the energy fractions can be derived based on Tisserand's parameter  
\[\sqrt{a(1-e^{2})}\cos i\propto const. \tag{11}\]  
where \(a\) and \(e\) are the semimajor axes and the eccentricity of the capture orbit. Introducing Equation (10) into (11) leads to  
\[a(1-e^{2})\propto\frac{h_{1}+h_{2}}{h_{1}}=\frac{h}{h_{1}} \tag{12}\]  
Equations (10) and (12), which were verified using numerical analysis in a previous study by the author [44], indicate that fixing the value of the inclination and a combination of \(a\) and \(e\) (or equivalently, the desired pericenter distance \(r_{p}\) at capture) the energy fractions \(h_{1}\) and \(h_{2}\)-and therefore the values of \(x_{2}(t_{0})\) and \(x_{3}(t_{0})\)-are determined. In Section 3, the above-mentioned constraints are rearranged in terms of position and velocity in \(\mathcal{F}_{r}\) and implemented to design the deployment strategy.