esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Current Model and Numerical Algorithm
### Numerical Method  
The PhyFire code used here incorporates a new numerical scheme based on the use of _P1 finite-element_ approximation on a regular mesh for spatial discretization, as in the previous schemes. However, a new _predictor-corrector finite difference scheme_ was developed for time discretization. The predictor step is an Euler semi-implicit scheme, and the corrector step is a modified Crank-Nicolson scheme. The computational cost was reduced by defining the set of _Active Nodes_, as we explain at the end of Section 2.5, solving the equations only in the neighborhood of the fire front and adapting the numerical scheme and the corresponding code to parallel computing [32].  
Given the initial values \(u^{0}\) and \(c^{0}\) defined by the initial conditions, we set the value of the initial enthalpy for each node \(i\) of the spatial discretization depending on the initial nondimensional temperature \(u^{0}\) as follows:  
\[e_{i}^{0}=\left\{\begin{array}{ll}u_{i}^{0}&\text{if}&u_{i}^{0}\leq u_{ \text{\tiny{$\
abla$}}},\\ u_{i}^{0}+\lambda_{\text{\tiny{$\
abla$}}}&\text{if}&u_{i}^{0}>u_{\text{\tiny{$ \
abla$}}},\end{array}\right. \tag{40}\]  
where, as we have mentioned before, \(u_{\text{\tiny{$\
abla$}}}\) is the non-dimensional water evaporation temperature and \(\lambda_{\text{\tiny{$\
abla$}}}\) is the dimensionless evaporation heat. As we explained in the description of the multivalued operator \(G\), we assumed that the maximum value that the solid fuel temperature reaches is the pyrolysis temperature, because the gaseous phase is parameterized, so the only phase change in the temperature of the solid fuel corresponds to the water evaporation.  
Given the values of the unknowns \(u^{n}\), \(c^{n}\), and \(e^{n}\) at time step \(n\), we computed \(u^{n+1}\), \(c^{n+1}\), and \(e^{n+1}\) by means of the following steps:  
1. Build the set of _Active Nodes_.
2. Compute the _Radiation Heat_.
3. _Prediction step_: semi-implicit Euler method.
4. Update the set of _Active Nodes_.
5. Update the _Radiation Heat_.
6. _Correction step_: modified Crank-Nicolson method.  
Lets us remember that a node \(i\) is considered an _Active Node_ if \(u_{i}>0\) and \(c_{i}\geq 0.1\) or if it belongs to the _Radiation Molecule_ associated with a node fulfilling the previous condition. The number of nodes defining the _Radiation Molecule_ was modified in order to keep its real size independent of the spatial discretization size. Specifically, a number of nodes of the radius of the round radiation molecule was fixed for each mesh size. The mesh size of the spatial discretization varied depending on the precision level, which currently varied from precision level 0 corresponding to 50 m cell size, to simulate large fires, to Precision Level 5, corresponding to a 2.5 m cell size, to simulate small fires. We also contemplated much thinner meshes, on the order of centimeters, to simulate laboratory experiments. In any case, the input raster files were adapted to the FEM precision level.
# Special Issue on Small Satellites Missions and Applications
###### Abstract  
We present a new approach to the study of the effect of  
Brazil), whose main payload is a software-defined radio module. AlfaCrux is also an example of an educational project that trained university students on all the stages of the mission development [5], i.e., design, integration, and operation of satellite systems, all. Most of the small satellites for communications are placed in low Earth orbit (LEO). Their link performance and channel characterization are directly related to the elevation angle. Ref. [6] presents an innovative method to calculate the statistics of the random variable that characterizes the elevation angle distribution, overcoming the difficulties related to its direct analytical expression.  
Earth observation is the third field of application for small satellites investigated in this Special Issue. Once again, what is lost in terms of available resources on board a small satellite (power, weight, etc.) is made up for by the greater number of spacecraft dedicated to the task. Small satellites offer the potential for high-frequency imagery of the Earth, with near real-time global Earth observation reconnaissance of all sunlit cloud-free locations. One of the issues faced by this application are the aerosol effects that degrade image interpretability. Ref. [7] describes an algorithm for atmospheric correction that was specifically developed for small satellites application.  
The great number of spacecraft needed to perform all of these missions means that the launch and reentry phases are a critical aspect for the small satellites ecosystem. As a matter of fact, new companies have been created to target this sector of the market, and innovative launcher concepts have been proposed to allow launching on specific orbits at a low cost. The study of an analytic method to define multistage launcher trajectories is introduced in [8]. This method allows one to predict the payload mass that can be inserted into different orbits. The investigation of the attitude motion of the TNS-0 #2 nanosatellite during reentry [9] gives some insight into the magnetic and aerodynamic stabilization using real telemetry data from the spacecraft.
## 2 Data Collection  
The China Seismo-Electromagnetic Satellite (CSES) was successfully launched on 2 February 2018. It is China's first dedicated space science satellite for studying seismic electromagnetics [23; 24; 25; 26; 27]. The CSES satellite is designed to investigate electromagnetic phenomena and related physical mechanisms in the field of geophysics before and after seismic events. CSES is equipped with a series of scientific instruments, including a fluxgate magnetometer (FGM), electric field detector (EFD), plasma analyzer package (PAP), Langmuir probe (LP), low-energy charged particle analyzer (LECPA), high-energy particle detector (HEPD), seismo-electromagnetic (SEM) field detector, etc. [28; 29; 30; 31]. These instruments work together to study various phenomena in space, including electromagnetic waves, plasma dynamics, magnetic field variations, and their interactions with seismic activities on Earth. Since the launch of the CSES satellite, many studies and experiments have been carried out, including space weather research, ionosphere and electromagnetic wave research, earthquake prediction research, and Earth observation and environmental research [32; 33; 34; 35; 36; 37].  
In this experiment, we processed VLF waveform data collected by the CSES EFD over the period 2019-2020. By performing short-time Fourier transform, we converted the waveform data into a time-frequency spectrogram, as shown in Figure 1. The spectrogram illustrates the temporal and frequency variations of an 8 s signal. In the spectrogram, the \(x\)-axis represents time with a length of 8 s, the \(y\)-axis represents frequency, and the color bar represents signal intensity. The time-frequency spectrogram obtained by short-time Fourier transform provides an intuitive way to simultaneously display the time domain and frequency domain information of the signal. By observing changes in the spectrogram, we can analyze the temporal and frequency characteristics of the signal's evolution, thus gaining deeper insights into the behavior of very-low-frequency waveforms.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Overall Delay Analysis
#### 3.3.2 Processing on Directly Connected HAP Drones  
In addition to the parts processed locally, terrestrial IoT devices will offload other parts to the HAP drone directly connected to it, and then forward them to the served usingthe HAP drone, another HAP drone, or a LEO connected to it for processing. The amount of data sent by the terrestrial IoT device \(j\) is \((y_{j}+z_{j}+w_{j})D_{j}\); therefore, the transmission delay can be expressed as follows:  
\[t_{j}^{n,trans} =\frac{a_{j}^{n}(y_{j}+z_{j}+w_{j})D_{j}}{R_{j}^{n}},\] \[=\frac{a_{j}^{n}(y_{j}+z_{j}+w_{j})D_{j}}{b_{j}^{n}\log_{2}(1+ \frac{p_{j}s_{j}^{n}}{\text{N}_{0}b_{j}^{n}})}, \tag{6}\]  
where \(a_{j}^{n}\in\{0,1\}\) represents the connection relationship between the IoT \(j\) and the HAP drone \(n\). If the IoT \(j\) is connected to the HAP drone \(n\), \(a_{j}^{n}=1\); otherwise, \(\alpha_{j}^{n}=0\).  
The processing delay of the IoT \(j\)'s task in the HAP drone \(n\) is as follows:  
\[t_{j}^{n,dirc}=\frac{a_{j}^{n}y_{j}D_{j}c_{j}}{f_{j}^{n}}. \tag{7}\]  
Therefore, the total processing of the IoT \(j\)'s tasks on the directly connected HAP drone can be expressed as follows:  
\[t_{j}^{HAP,dirc} =\sum_{n\in\mathcal{N}}(t_{j}^{n,trans}+t_{j}^{n,dirc}),\] \[=\sum_{n\in\mathcal{N}}\alpha_{j}^{n}(\frac{(y_{j}+z_{j}+w_{j})D_ {j}}{b_{j}^{n}\log_{2}(1+\frac{p_{j}s_{j}^{n}}{\text{N}_{0}b_{j}^{n}})}+\frac{ y_{j}D_{j}c_{j}}{f_{j}^{n}}). \tag{8}\]
# A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants' Classification
## 2 Literature Review
In [22], the authors demonstrated the presence of COVID-19 in the CT scan of a 44-year-old patient. The lesion in the lungs can be found in the virus-affected CT scan. Similarly, in [23], the authors illustrated the detection of the novel coronavirus in a CT scan by detecting the presence of non-invasive fluid. The authors of [24] presented recent advances and emerging techniques for the detection of the virus. From the literature, it is evident that DCNN is efficient in the detection of COVID-19. Motivated by this, we propose a novel algorithm to detect COVID-19 from chest X-rays and CT scans and then classify it into variants. The following are the major contribution of this article. * Detection of COVID-19 from CT scans and chest X-rays;
* Classification of COVID-19 variants based on a unique vocabulary of features (VoF) technique;
* Comparison of the proposed method with state-of-the art techniques. The following section explains the proposed methodology in detail.
## 3 Results and Discussion
### Changes in Fatty Acids  
The changes in fatty acid composition of the sous vide-cooked horse loin based on cooking duration and temperature are presented in Table 7. This study found no significant differences in the proportions of all analyzed fatty acids between the control and treatment groups. This suggests that the sous-vide cooking process, regardless of the specific parameters used, does not significantly alter the fatty acid composition of horse loin. Interestingly, our study revealed that linoleic acid was the most abundant fatty acid in the sous vide-cooked horse loin. This contrasts with the findings of [PERSON] et al. (2018), who reported that, in the loin cut of native Jeju horses, oleic acid was the most abundant, accounting for 32.78% of total fatty acids, followed by palmitic acid (31.16%) and then linoleic acid (16.42%). This discrepancy could be attributed to differences in the breed of horse, diet, or other factors that can influence the fatty acid composition of meat. Furthermore, our study found that the ratios of monounsaturated fatty acids to saturated fatty acids (MUFAs/SFAs) and polyunsaturated fatty acids to saturated fatty acids (PUFAs/SFAs) did not change significantly after sous-vide cooking. This was consistent across all treatment groups, indicating that these ratios are not affected by the sous-vide cooking process. According to the diversity in the fatty acid content of triglyceride molecules among different livestock species, pig fat and horse fat are the most sensitive to oxidative breakdown, with sheep tallow being somewhat less susceptible and cow tallow displaying the highest resilience ([PERSON] _et al._, 2018). Horse muscle contains a high concentration of unsaturated fatty acids (60.49-63.04%), with monounsaturated acids (38-55%) and palmitoleic (3-10%) being the most prevalent, accounting for up to 45.16% of all fatty acids ([PERSON] _et al._, 2018). This resilience in the ratios indicates that the sous-vide method may preserve the relative proportions of these fatty acids, contributing to the maintenance of the nutritional quality of the cooked horse loin. This is an important finding, as these ratios are often considered in nutritional analyses of meat.
###### Abstract  
The automatic identification of emotions from speech holds significance in facilitating interactions between humans and machines. To improve the recognition accuracy of speech emotion, we extract mel-frequency cepstral coefficients (MFCCs) and pitch features from raw signals, and an improved differential evolution (DE) algorithm is utilized for feature selection based on K-nearest neighbor (KNN) and random forest (RF) classifiers. The proposed multivariate DE (MDE) adopts three mutation strategies to solve the slow convergence of the classical DE and maintain population diversity, and employs a jumping method to avoid falling into local traps. The simulations are conducted on four public English speech emotion datasets: eNTERFACE05, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAEE), and Toronto Emotional Speech Set (TESS), and they cover a diverse range of emotions. The MDE algorithm is compared with PSO-assisted biogeography-based optimization (BBO_PSO), DE, and the sine cosine algorithm (SCA) on emotion recognition error, number of selected features, and running time. From the results obtained, MDE obtains the errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS based on the KNN classifier, and the errors of 0.4721, 0.4264, 0.3283 and 0.0114 based on the RF classifier. The proposed algorithm demonstrates excellent performance in emotion recognition accuracy, and it finds meaningful acoustic features from MFCCs and pitch.  
speech emotion recognition; feature selection; differential evolution; mutation
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 2 Background and Related Work
### Background  
Solar-powered UAVs usually fly at the height of near space and can take full advantages of near-space's flight condition. They have already been researched for various applications, such as monitoring tasks or relay-forwarding tasks. Since the appearance of \"sunrise-1\" solar-powered UAV in November 1974, many countries carried out studies on solar-powered UAVs [6; 7; 8]. Moreover, using large wingspans is becoming an important development direction of solar-powered UAVs due to the fact that the lift-drag ratio of aircraft increases with the increase in wing aspect ratios. For example, the \"Odysseus\" solar-powered UAV has a wingspan of 74 m, and the \"Eagle\" solar-powered UAV has a wingspan of 121.9 m. However, the large wingspan also creates other problems, such as an increase in structural complexity and the weight proportion of the structure. These problems could be tackled effectively by removing a small amount of the wingspan and by combining wingtips in the air for combined flight applications and separating the wingtips before landing.  
Currently, the take-off processes of solar-powered UAVs are either from runways or balloon-borne processes [6]. The mode of taking off from a runway is usually applied to UAVs with large or small wingspans. These solar-powered UAVs need to cross strong winds in jet-stream areas during the climbing phase, which impacts flight safety. The other taking-off mode uses balloon-borne processes, which is widely used in the aircraft field. Many high-altitude balloon-launching tests are reported at home and abroad [9; 10; 11; 12; 13; 14; 15; 16; 17], as shown in the Table 1.  
The specific taking-off process of balloon-borne-based UAVs consists of four phases, including carrying high-altitude balloons, launching, cruising in the air, and landing via a runway, as shown in Figure 1. Such forms of taking-off modes have already been successfully applied to UAVs with wingspans that are quite small, and data show that 1090 balloon-borne launch tests in the stratosphere have been conducted since 2015 [18]. For UAVs with large wingspans, performing pull-up control processes is difficult and challenging.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 4 Implementing and Evaluating DaaG
### Empirical Evaluation
#### 4.4.1 Observing Empirical Results  
Figure 10 shows the demonstration of the actual experiment conducted. Our video [40] shows the whole experimentation procedure and result. The detailed procedure and result of the experiment are as follows:  
* First, based on the starting position of Drone #1 and the location of the sensing and actuating area, the expected path of Drone #1 is in Figure 10a for both the DaaG and nearest-based planned path drone.
* Since the light intensity value of Sensor #1 is lower than the threshold in our scenario, Drone #1 runs Algorithm 3, then Sensor #1 is registered in PSL, and \(corr_{s1,a1}\) will be the highest.
* After that, Drone #1 changes the path to visit Actuator #1 rather than Actuator #2, and Figure 10b shows the changed path after running Algorithm 3.
* When Drone #1 has arrived at Actuator #1, it sends an actuation message, which rotates the servo motor to focus on Sensor #1, to Actuator #1, as Figure 10c shows.  
In the case of the nearest-based planned path drone, the path for traveling in the sensing area was the same as that of the DaaG and the planned path. However, the drone visits Actuator #2 first, following the planned path, which is different from the path DaaG chooses. The drone sends the order message to Actuator #2, then it goes to Actuator #1.  
We can see from the experiment that the separated network infrastructure is connected immediately. Before using DaaG, the sensor network can only sense the light intensity, and the actuator network can only film some places. However, after using DaaG, these networks can act as a surveillance system for the brightness problem. This system can create a new value, which the existing network cannot provide.
## 3 Results
### Reconfiguration  
The software demonstrated a high capacity for autonomous reconfiguration when errors were detected. During the tests, various types of faults were introduced into the code, such as syntax errors, logical errors, and unhandled exceptions. In all cases, the software was able to identify and correct the errors efficiently, ensuring the continuous execution of pre-programmed tasks, and our results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Files with the Results of Our Work** & **Who Use the File** & **Description** \\ \hline ActionSummary.Release & The self-adaptative software proposal & Output information on performed actions, detected errors, and the current state of the software. \\ \hline AdaptationLog.outN & The self-adaptative software proposal & Information about the modifications made by the software to prevent errors. \\ \hline errorLogTestSolve.txt & The self-adaptative software proposal & Information about errors detected by the software. \\ \hline SystemOperateNeuronalLog.outN & The self-adaptative software proposal & Information about the actions performed by the software (adaptation, detection, and repair). \\ \hline trainingResultsNN\_log.txt & Neuronal network trained without reinforcement algorithm & Information of the neural network training over 50 epochs. \\ \hline rl\_weights.weights.h5 & Neuronal network trained with reinforcement algorithm & Weights of the neural network trained by reinforcement learning. \\ \hline trained\_rl\_model.h5 & Neuronal network trained with reinforcement algorithm & Model trained by reinforcement learning. \\ \hline nn\_weights.weights.h5 & Neuronal network trained without reinforcement algorithm & Weights of the neural network trained without reinforcement learning algorithm. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Files with the information on the results of the proposed self-adaptive software.  
Figure 16: Failure probability and solution effectiveness during a 100-day trial period.
# A Model for Probiotic Fermented Food Production
## 2 Material and Method
### Acidification Ability of the Commercial Probiotics L. rhamnus Strains in Milk  
The ability of the three _Lacticaseibacillus rhamnus_ probiotic strains to grow in and acidify milk was investigated with impedometric analysis using a Bactrac 4300\({}^{\circledR}\) (Sylab, Generon, San Prospero, MO, Italy) [66]. The BacTrac 4300\({}^{\circledR}\) system measures two specific impedance values: the _E_-value, which refers to the impedance change at the electrode surface, and the _M_-Value, which is the change in conductivity in the medium. This variation in the electrical conductivity of milk is proportional to the change in the number of microorganisms and their metabolic activity. Therefore, the microbial growth in milk can be measured using the corresponding M-Value [67]. The commercial probiotic strains tested were diluted after being revitalized to a final level of 6 Log CFU/mL in sterile Ringer solution and used to inoculate 36 mL of pasteurized milk. The total amount was equally divided into three sterilized BacTrac 4300\({}^{\circledR}\) vials and incubated. The impedance measurement was performed at two incubation temperatures: 37 \({}^{\circ}\)C for 72 h or 52 \({}^{\circ}\)C for 30 min, followed by 37 \({}^{\circ}\)C for 72 h. The ability to acidify was quantified using M-values, which represent the overall impedance variation of the media in the vials. The M-value was recorded every 10 min for 72 h and shown as M%. This value is automatically calculated by the instrument as a relative electrical change compared to a starting value. The pH of the milk was measured electometrically at the end of the 72 h of incubations with a Beckman \(\phi^{\mathrm{TM}}\) 300 series pH meter (Beckman Instruments, Inc. 4300 N. Harbor Blvd., Fullerton, CA, USA). All the analyses were carried out in triplicates. Negative samples, consisting of non-inoculated milk, were also incubated for each temperature tested.
## 5 Requirements of UWSNs
### Security and Privacy  
UWSNs is correlated with security and privacy factors which related to sensor nodes connectivity, synchronization, and data transaction tasks. The dynamic features of underwater environment and its environment expose the network to various treats and malicious attacks. It is required for the networks to create trust before all the nodes can securely connect to the network to allow communication for information exchange. It is essential to study what level of security due to the increased computational load and the amount of transmitted data, yet consuming more energy within the network.