esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Elite Selection Mechanism
#### 2.3.3 IDCMGA  
IDCMGA introduces dynamic crossover and mutation rate mechanisms based on MPEG to enhance the algorithm's adaptability at different evolutionary stages and optimize performance. When the population's fitness is high, a lower crossover and mutation rate is used to protect good genes; when the population's fitness is low, the rates are increased to boost diversity and explore new solutions, preventing the algorithm from falling  
Figure 4: Crossover operation.  
into local optima. This dynamic adjustment helps improve the algorithm's global search ability, convergence speed, and stability in complex problems.  
The dynamic crossover rate enhances the algorithm's adaptability by adjusting the crossover probability during the evolutionary process. In the early stages, a suitable crossover rate is set to increase population diversity and explore new solution spaces, helping to search the solution space extensively and avoid premature convergence to a local optimum. During each generation's evolution, the crossover rate is adjusted based on the current fitness distribution. When the fitness value of the fittest individual decreases to a certain range, the crossover rate is increased to ensure population diversity and stability at different stages.  
Similar to the dynamic crossover rate, the dynamic mutation rate is designed to improve the algorithm's global search ability and local optimization ability. In the early stage of evolution, setting an appropriate mutation rate helps increase population diversity and explore a wider solution space. The mutation rate is dynamically adjusted by monitoring changes in the population's fitness. When the fitness value of the fittest individual decreases to a certain range, the mutation rate is increased to adaptively optimize the population structure at different evolutionary stages.  
The improved genetic algorithm shows significant advantages in the path optimization problem. By improving several aspects of the classical genetic algorithm (SGA), the IDCMGA algorithm achieves superior results in optimization performance, convergence speed, and the ability to solve complex problems.
## 2 Methodology
### Determination of Electromagnetic Parameters of Composite Materials  
The dielectric properties of the materials were tested using the waveguide method. This assessment was conducted on an MS4644A Vector Network Analyzer (VNA) produced by Anritsu, Atsugi, Japan. The waveguide method, commonly applied within the X-band frequency range of 8.2 GHz to 12.4 GHz, necessitates sample planes measuring 22.86 mm by 10.16 mm. To evaluate the dielectric properties across different frequency bands, varying sample sizes are required, the material surfaces were meticulously polished, cleaned, and dried to ensure accurate and reliable measurement results.  
Upon the completion of this calibration phase, a coaxial transmission line was adjoined to an apparatus containing the sample material, facilitating the transmission of a microwave signal from the source. This configuration allowed for the signal to be conveyed directly to the sample via the coaxial line. Upon interacting with the sample, the signal was then rerouted back through the transmission line to the receiver. It was at this juncture that the VNA played a pivotal role, meticulously tracking, processing, and cataloguing the signals and their fluctuations, culminating in the visualization of the data on a computer system. This process effectively completed the acquisition of scattering parameter (S-parameter) data, a critical step in our analysis.  
The gathered S-parameters were then rigorously applied to Equations (1) and (2), facilitating the computation of the complex permittivity and permeability of the nickel-plated carbon fiber. This methodical approach not only ensured the precision of our measurements but also provided a comprehensive understanding of the material's electromagnetic characteristics. Through this sophisticated analytical procedure, we were able to derive invaluable insights into the dielectric properties of the nickel-plated carbon fiber materials, thereby contributing significantly to our knowledge base in the field of electromagnetic material science.  
\[\epsilon=\frac{\frac{\frac{1}{kd}\cos^{-1}\left[\frac{1}{2S_{21}}(1-S_{11}^{2}+S_{2 1}^{2})+2\pi m\right]}{\sqrt{\frac{\left(1-S_{11}\right)^{2}-S_{21}^{2}}{\left(1 +S_{11}\right)^{2}-S_{21}^{2}}}} \tag{1}\]  
\[\mu=\frac{1}{kd}\cos^{-1}\left[\frac{1}{2S_{21}}(1-S_{11}^{2}+S_{21}^{2})+2\pi m \right]\sqrt{\frac{\left(1+S_{11}\right)^{2}-S_{21}^{2}}{\left(1-S_{11}\right) ^{2}-S_{21}^{2}}} \tag{2}\]
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 2 Literature Review
### On Hybrid Theory-Based Forecasting Models  
Even if the single theory-based forecasting models can achieve great closeness to the actual short-term situation, many flaws still exist in those models. Reliable prediction results can only be assured under specific circumstances for statistics-based models, which means that they have poor adaptabilities in dealing with nonlinear or irregular time series data; and for the AI-based models,most of them are a bit too sensitive in parameter selection and are not capable of properly dealingwith the problem of data dimensionality. In this context, hybrid theory-based models have been proposed based on the combination of different basic theories in not only short-term traffic flow forecasts, but forecasts in other industries as well. During recent years, [PERSON] et al. (2010) have proposed an approach based on the genetic algorithm (GA), which has been coupled with ANN for short-term traffic flow prediction; [PERSON] and [PERSON] (2015) have proposed a hybrid layered system, which has been made up of KARIMA with several neural networks; and [PERSON] et al. (2016) have also proposed a global solar radiation prediction method based on multiscale decomposition methods. Considering the advantages of EMD which have been already discussed in Section 1.2, many hybrid approaches have been proposed with EMD as an irreplaceable choice, and have achieved relatively high accuracy in the forecast results. [PERSON] et al. (2017) have employed an EMD-ANN approach in forecasting wind speed, whereas [PERSON] and [PERSON] (2018) have also used such a fusion model of EMD and ANN to make predictions on metro passenger flow. EMD-ARIMA is also a popular hybrid theory-based model, which has been used by [PERSON] and [PERSON] (2019) as well as [PERSON] et al. (2013) to forecast the peak load in a power supply system and traffic speed on a certain road section, respectively. [PERSON] et al. (2010) have even made an improvement on EMD and proposed an ensemble EMD (EEMD) data analysis approach, and have employed EEMD-SVM to a short-term air passenger flow forecast issue.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 4 Results
### Multiple Target Tracking  
This subsection shows the results of target tracking based on the detections in Figure 9. The sampling time in Equations (2) and (3) is 0.1 s since every third frame is processed. The track association and the directional track association are compared in the case of IMM-CV and IMM-CA. The details of IMM-CV can be found in [22]. The parameters for target tracking are shown in Table 1; \(\theta_{g}\) is required only for the directional track association.  
In the case of the IMM-CV, a total of 61 and 65 valid tracks are generated when the track association and the directional track association were used, respectively. They are shown in Figure 10a,b. A total of 84 and 64 valid tracks are generated in the case of IMM-CA as shown in Figure 11a,b, respectively. Four supplementary multimedia files (MP4 format) for Figures 10 and 11 are available online. The first is the IMM-CV with the track association (Supplementary Material Video S1), and the second is the IMM-CV with the directional track association (Supplementary Material Video S2). The third is the IMM-CA with the track association (Supplementary Material Video S3), and the last one is the IMM-CA with the directional track association (Supplementary Material Video S4). The black squares and the numbers in MP4 files are position estimates and track numbers in the order they were initialized. The blue dots are the measured positions including false alarms. Every third frame was processed but the movie was recorded at 30 fps, thus the movies are three times faster than the original video.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **IMM-CV** & & **IMM-CA** \\ \hline Sampling time & & 0.1 s & \\ Max. target speed for initialization, \(V_{max}\) & & 30 m/s & \\ Process noise variance & \(\sigma_{1x}=\sigma_{1y}\) & 1 m/s\({}^{2}\) & & 0.01 m/s\({}^{2}\) \\  & \(\sigma_{2x}=\sigma_{2y}\) & 10 m/s\({}^{2}\) & & 0.1 m/s\({}^{2}\) \\  & Mode transition probability \(p_{ij}\) & & \(\left[\begin{array}{c}0.8&0.2\\ 0.3&0.7\end{array}\right]\) & \\ Measurement noise variance, \(r_{x}=r_{y}\) & & 1.5 m & \\  & Gate threshold, \(\gamma_{f}\) & 8 & \\ Measurement association & Max. target speed, \(S_{max}\) & & 35 m/s & \\  & Gate threshold, \(\gamma_{g}\) & & 70 & \\ Track association & Angle threshold, \(\theta_{g}\) & & \(\frac{\pi}{9}\) rad & \\  & Max. searching number & & 20 frames (=2 s) & \\ Track termination & Min. target speed & & 1 m/s & \\ Min. track life length for track validity & & 20 frames (=2 s) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for target tracking.  
Figure 9: Moving object detection for all frames.  
Table 2 shows the overall tracking performance, number of tracks, average TTL and MTL, number of targets with broken tracks, and number of missing targets. No false tracks were generated for all cases. The IMM-CA with the directional track association outperforms other methods showing that the average TTL is 0.917 and the average MTL is 0.842.
## Methods
### Data Collection  
Motion capture data, which are marker data, underwent their own post-processing in order to create and gather the marker names and coordinates. All disconnected frames of marker positions needed to be corrected and smoothed to eliminate noise. Furthermore, force plate data were also filtered with a 2 nd Butterworth filter with a cut off of 6 Hz to eliminate noise, especially from the power line and movement. Each data point was then resampled to a sampling frequency of 200 Hz. The coordinated data from motion capture were collected as raw data for the calculation process. The COM was calculated from the eight-segment model. The total body COM position was obtained by the weighted summation of the individual segment COM position, as mentioned in Equation (1). Constant value for each segment was shown as in Table 1[22]. The ground reaction force (\(F_{p}\)) was obtained by summing up all the vertical forces from the strange gauge (i.e., \(f_{z1}\), \(f_{z2}\), \(f_{z3}\), and\(f_{z4}\)) of the force plate, as shown in Figure 1. The force plate moment in the x-axis (\(M_{x}\)) was generated by these vertical forces, as shown in Equation (2). The joint movement coordinates (\(x\), \(y\), \(z\)) obtained from motion analysis systems with a 1 kHz sample rate were used to measure joint angle displacement (\(\theta_{antkle}\) and \(\theta_{hip}\)) and body segment length (\(h_{antkle}\), \(h_{hip}\), and \(h_{seg}\)) for segmental COM location. The COP displacement was determined from Equation (3) below where \(d_{z}\) was the distance from the surface to the platform origin. The ankle and hip angle displacement were calculated with Equation (4) by using 2 vectors, as shown in Figure 2. The average of head peak-to-peak amplitude for each frequency was also calculated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Segment** & **Segment Weight/Total Body Weight** \\ \hline Head, arm, trunk (HAT) & 0.536 \\ Pelvis & 0.142 \\ Left/Right Thigh & 0.1 \\ Left/Right Leg & 0.0465 \\ Left/Right Foot & 0.0145 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constant value for each segment [22].  
The COM and COP displacement in the AP direction was as follows:  
\[COM_{AP}[mm]\ =\ \sum\Bigl{(}COM_{each\ seg}\times constant\ value_{each\ seg}\ \Bigr{)} \tag{1}\]  
where the constant value is in the table below.  
\[M_{x}\ =\ a(f_{z1}+f_{z2}+f_{z3}+f_{z4}) \tag{2}\]  
\[COP_{AP}[\text{mm}]\ =\ \frac{M_{x}-\Bigl{(}F_{y}\cdot d_{z}\Bigr{)}}{F_{v}} \tag{3}\]  
where \(a\) is the sensor offset value and \(F_{y}\) is the horizontal component in \(y\)-direction force.  
The ankle and hip angle displacement in the AP direction were as follows:  
\[\cos\theta(deg)\ =\ \frac{\overset{\rightarrow}{A\cdot\overset{\rightarrow}{B}} }{|A||B|} \tag{4}\]
## 7 Conclusions and Future Work  
In this paper, we proposed the CCBE algorithm to extend the network lifetime and to reduce the energy consumption in end-to-end packet transmission. The simulation results are compared with the well-known clustering algorithms, LEACH and HEED. The proposed CCBE algorithm divides all of the sensor nodes in the network into different hexagonal clusters. The side length of the hexagonal structure is derived, and clusters are formed. Then, the CMs elect a CH in the cluster. The CM (within the cluster) that minimizes the value of \([\alpha\frac{r^{i}}{2r}-(1-\alpha)\frac{E_{res}}{E_{cap}}]\) is elected as the CH. Furthermore, this CH is the node that has the best residual energy and requires the minimum energy to be reached by the CMs. In addition, weight parameter \(\alpha\) decides the relative importance placed on these two parameters. The MAC scheme is based on TDMA. Sink nodes allocate the time slots to the neighboring six CHs. Then, the CH allocates time slots to all of the CMs based on the residual energy. The CMs change to the sleep state after the transmission and wake up only during the listening state (CH transmission to CMs). The CH collects data from three other CHs behind the current CH and forwards to the next CH or the sink. Before forwarding the data to the next CH, the CH aggregates the data based on the residual energy level and chooses the appropriate aggregation level. The results from simulations show that the CCBE algorithm has the best efficiency in terms of both data packets received by the sink node and the network lifetime.  
CCBE creates the additional overhead of control packets during the end-to-end packet transmission and unbalanced utilization of nodes near the sink. Our next step is to compare the single-hop transmission from the CH to the sink and multi-hop transmission from the CHs to the sink node; additionally, to  
Figure 11: Precision of the data received.  
compare the energy consumed during data aggregation and to compare it without data aggregation; furthermore, to find the optimal location of the sink node based on the deployment of the sensor nodes; then, to adaptively increase and decrease the size of the hexagonal structure based on the number of alive nodes; moreover, to implement a dynamic traffic scenario with an adjustable hexagonal structure based on the cluster size.
## 4 Discussion
In particular, areas with subtle topographical variations or complex morphological configurations (such as partially urbanized floodplains or slopes with local fractures) can introduce uncertainties in the predictor values, which in turn may negatively affect the model's classification accuracy. Moreover, areas with dense vegetation or recent anthropogenic changes not well represented in the available datasets may also lead to errors in classification. These methodological developments are closely linked to the practical goals of this study. The proposed framework is designed to support spatial planning, emergency response, and territorial risk management. In this context, future work will aim to integrate the resulting susceptibility maps into early warning systems and urban planning protocols, making the model a useful tool for operational decision making. In addition, comparative studies involving other multi-hazard-prone regions in Italy and abroad are planned to test the scalability and generalizability of the approach. Finally, building on the current findings, future applications will explore how key variables, such as drainage density and land use, respond to specific hydro-geological mitigation strategies.
## 2 Results
### Circular Dichroism  
To understand how the new peptide interacts with membranes, Pln149-PEP20 was characterized biophysically. The synchrotron radiation circular dichroism (SRCD) spectroscopy spectrum of Pln149-PEP20 was obtained in an aqueous solution (Figure 5a). Pln149-PEP20 in aqueous solution showed peaks attributed to the disordered peptide content [22]. However, a disordered-to-helix transition in the Pln149-PEP20 secondary structure was observed in the presence of 50% trifluoroethanol (TFE) or when the peptide formed a partially dehydrated film (Appendix B, Figure A1) due to the favored intramolecular hydrogen bonds [10]. The induced alpha helix in the Pln149-PEP20 structure shows off its amphipathic character (Appendix B, Figure A1) to interact at lipid interfaces.  
Figure 5: (**a**) SRCD spectra of Pln149-PEP20 in 10 mM sodium phosphate buffer pH 7 (black), 50% TFE (red), in the presence of the surfactants sodium dodecyl sulfate (SDS, olive) or N\(-\)hexadecyl\(-\)N\(-\)N\({}^{\prime}\) dimethyl\(-\)3\(-\)ammonia\(-1-\)propane\(-\)sulfonate (HPS, blue), or vesicles of POPC (gray), POPE (magenta), POPS (green), and POPG (orange), at a 1/50 peptide-to-lipid molar ratio. (**b**) o-SRCD spectra of PEP20 in lipid bilayers of POPS (black) and POPG (red).  
Distinct model membranes were employed to investigate the binding of peptide Pln149-PEP20 to lipid systems in aqueous solutions and oriented lipid bilayers. The peptide assumed a stable \(\alpha\)-helical structure in the presence of negatively charged or zwitterionic surfactants. In an aqueous solution, no conformational changes in Pln149-PEP20 were observed in the presence of zwitterionic large unilamellar vesicles (LUVs) of 1-palmitoyl-2-oleoyl-phosphocholine (POPC) and 1-palmitoyl-2-oleoyl-phosphoethanolamine (POPE) (Figure 5a). However, the two minima at 222 nm and 208 nm and a positive maximum at 192 nm were observed in the peptide SRCD spectra in the presence of the vesicles of 1-palmitoyl-2-oleoyl-phosphoglycerol (POPG) and 1-palmitoyl-2-oleoyl-phospho-L-serine (POPS). This observation revealed the partition of the peptide to the negatively charged vesicles.  
o-SRCD spectroscopy was used to characterize the alignment of the \(\alpha\)-helix induced in Pln149-PEP20 in the macroscopically oriented lipid bilayers of POPG and POPS. The o-SRCD spectra of the peptide in both negatively charged bilayers (Figure 5b) exhibited two minima at 208 nm and 220 nm, with a more pronounced negative value at 208 nm. This spectral line shape is typical of peptides assuming a surface-bound state, S-state [23], and aligned parallel to the plane of the lipid bilayers.
## 2 Material and Methods
### Magnetic Resonance (MR) Imaging Protocol  
Examinations were performed on a 3T MRI (Magnetom Skyra, Siemens, Erlangen, Germany) using a combined 12-element head and a 6-element neck coil. Initially, the sequence time-of-flight (TOF) covering the carotid arteries was conducted to identify the exact location of the left and right carotid bifurcations. The scan parameters were as follows: ratio of repetition time (TR) to time to echo (TE) = 20/3.4 ms; flip angle = 20\({}^{\circ}\); slice thickness = 0.8 mm; sensitivity encoding (SENSE) factor = 2.5; field of view = 200 \(\times\) 200 mm; echo train length = 1; number of average (NEX) = 1. Image quality per artery was rated on a 4-point scale (1 = poor, 4 = excellent). Images with a quality \(<\)3 point were excluded from the study. The predominantly axial volume of 3D imaging was angulated to include the common carotid artery (CCA), the carotid bifurcation, the internal carotid artery (ICA), and the external carotid artery (ECA) based on the TOF-MRI data. The bifurcation in the left or right carotid arteries was positioned in the center of the 3D volume. The 4D flow MRI data were acquired with a non-contrast segmented 3D time-resolved RF-spoiled phase-contrast gradient-echo sequence with 3 directional velocity encoding. In combination with a k-space segmentation factor of 2 for each cardiac time frame, interleaved velocity encoding, i.e., consecutive measurement of the 1 reference and 3 velocity-sensitive scans, resulted in a temporal resolution (TR) = 47.5 milliseconds (msec) per time frame. The study parameters were as follows: dimensions were \(176\times 176\times 52\) millimeter (mm)\({}^{3}\), spatial resolution was \(1.1\times 1.1\times 1.1\) mm\({}^{3}\), number of frames were 14, and velocity sensitivity was 100 centimeter/second (cm/s) in all 3 directions. Data acquisition was prospectively gated to the cardiac cycle.
## 2 Materials and Methods  
### Materials  
2,6,6-Tetramethylpiperidine 1-oxyl (TEMPO), 4-Hydroxy-2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPOL) spin probes (Figure 1), Fluorescein isothiamate (FITC), 4-Amino-2,2,6,6-tetramethylpiperidine 1-oxyl (4-amino-TEMPO), and L-Ascorbic acid (Vitamin C) were purchased from Sigma-Aldrich. The powders of spin probes and vitamin C were diluted in distilled water (25 mg in 50 mL) and added appropriately to the cell culture.  
A fluorescent spin-probe reagent (TEMPO-Fluorescein, Figure 1) was obtained using the following synthesis procedure. Two methylene chloride solutions (10 mL) of FITC (7.72 mg) and 4-amino-TEMPO (3.48 mg) were prepared separately. Afterward, the solutions were combined and magnetically stirred for 12 h at room temperature. Then, the solvent was evaporated, and the product TEMPO-Fluorescein was obtained. The structure was confirmed using UV-VIS spectroscopy and mass spectrometry.  
Bakery yeast (Saccharomyces cerevisiae) (1 g) was diluted in distilled water (50 mL) with the addition of sucrose (0.3 g) and incubated at 37 \({}^{\circ}\)C for one hour. Vitamin C was used as a model antioxidant to study its effect on the process of TEMPO entering the cells and its subsequent disappearance. After 1 h, the solutions of the spin probe (3.2 mM TEMPO/2.9 mM TEMPOL) and vitamin C were added to yeast cells, and the incubation was continued. At specific time intervals (15 min), aliquots of the solution were withdrawn for ESR measurements.  
The cancer cell research was carried out using the human acute promyelocytic leukemia cell line (HL-60, ATCC CCL-240). Cells were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% (\(\
u/\
u\)) fetal bovine serum (FBS), 1% (\(\
u/\
u\)) penicillin (100 U/mL)/streptomycinycin (100 mg/mL), and 1% (\(\
u/\
u\)) glutamine (200 mM) at 37 \({}^{\circ}\)C in a humidified atmosphere consisting of 5% CO\({}_{2}\). All reagents for cell culture were obtained from Sigma Aldrich (St. Louis, MO, USA).  
Cells were counted using a hemocytometer, and the viability of cells was assessed by trypan blue exclusion assay. Aliquots of 9 \(\times\) 10\({}^{6}\) cells were washed with phosphate buffer saline (PBS), centrifuged, and resuspended in 10 mL of PBS. Then, 10 mL of 3.2 mM TEMPO (2.9 mM TEMPOL) solution was added. The molar concentration of the spin probes did not affect cell multiplication. The cells were kept in an incubator (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)), and the aliquots (1 mL containing 2.4 \(\times\) 10\({}^{5}\) cells) were taken every 10 min and immediately frozen in liquid nitrogen.