esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Collection and Processing
### Data Processing
#### 2.2.2 Normalization Process  
In the blast furnace melting process, each key parameter variable has special characteristics, and there are certain differences in the magnitudes and values between each key parameter, which will inevitably affect the output results of the blast furnace permeability prediction. For example, the collected blast furnace data have an oxygen enrichment parameter in the range of [1; 10] and a blast kinetic energy parameter in the range of [8000, 14,000]. As such, the input parameter variables need to be normalized to ensure that some of the data are transformed to the same scale, so that comparisons can be made between the data. In this paper, the data are normalized using the disparity normalization method.  
For the data set \(X(i)=(x_{1},x_{2},\ldots,x_{n})\), this method employs linear transformation using the maximum and minimum values of the dataset, ensuring that the resulting values are normalized within the range of [0, 1], which is calculated as shown in Equation (4):  
\[y_{i}=\frac{x_{i}-min(x)}{max(x)-min(x)}\,i=1,2,\ldots,n \tag{4}\]  
where: \(max(x)\), \(min(x)\) are the maximum and minimum values in the data set, respectively.
## 5 Frequency Hopping Techniques
### Channel-Ignorant Techniques  
In Random Frequency Hopping (RFH) [27], the available frequency band is divided into \(K\) narrow sub-bands, and transmission is carried out by transmitting short bursts of data on one sub-band at a time, hopping from sub-band to sub-band in a pseudo-random way.  
With channel ignorant techniques such as RFH, over a sufficiently large amount of time, all sub-bands will be used a roughly equal number of times, and so, transmission over bad sub-bands is inevitable. This will cause system performance degradation resulting in high packet error rates. To solve this problem, channel-aware frequency hopping techniques are proposed, where the hopping pattern is selected after determining the channel characteristics. Channel-aware or adaptive frequency hopping algorithms are able to provide better throughput against static interferences, dynamic interferences or both static and dynamic interferences, but they might be more vulnerable to jamming attacks as they decrease the frequency diversity.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 2 Protoplanetary Disc Dynamics in Alpha Centauri  
We ran a 3D hydrodynamical simulation to study the circumstellar discs in a binary system using the Phantom smoothed particle hydrodynamics (SPH) code [15]. We modeled the gaseous discs without any dust components for computational costs. Therefore, we assumed that the dust particles are perfectly coupled to the gas and that the total dust mass is around 1% of the gas mass (as is for typical Class II protoplanetary discs). In this section, we neglect the gravitational perturbations caused by Alpha Cen C given the large semimajor axis and that we are primarily interested in the circumstellar discs around stars A and B.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 2 Power Quality Issues of WPP Integration  
### Harmonic Issues of WPP Integration  
In electrical power systems, the alternating current waveform is ideally pure sinusoidal waves with constant magnitude and frequency to meet the expectations of the end-users connected to the grid. Harmonic distortion is a periodical deviation from the ideal condition, with a period of one cycle of the frequency of the grid. IEEE standard 519-2014 provides information about the THD and acceptable levels of voltage and current distortion for each harmonic order. It is important to determine the harmonic content at the PCC of the WPPs to ensure acceptable harmonic voltages at this point [14].  
There are four distinct classifications of WTGs, known as Type 1 through Type 4 [19]. Type 1 and Type 2 WTGs utilize soft starters to reduce inrush currents and voltage fluctuations. The soft starters are power electronic devices that are responsible for producing harmonic currents of very low magnitude. As the soft starters operate only for a very short duration of time, therefore, the harmonic currents produced are negligible. Harmonic currents can also occur in Type 1 and Type 2 WTGs during the energization of transformers. These are low order harmonics that appear along with the DC component. Type 3 WTGs, also known as DFIGs and Type 4 WTGs have a power electronic interface between the turbine and the grid which can produce harmonics in the grid [20].  
Harmonic emissions that originate from WPPs could combine with the harmonic distortion already present in the grid and amplify harmonic distortion around the resonant frequency [20]. Harmonic resonance is caused by the interaction of inductive and capacitive elements in the network. WPPs have several elements such as capacitor banks, transformers, and cables that can interact with each other leading to resonance. Parallel and series resonance can affect the power system by causing harmonic overvoltages or harmonic overcurrents. Comparatively, Type 3 WTGs have a high content of low order harmonics in them as presented in [20]. Harmonics of higher order are reported to be associated with PWM-based Type 3 WTGs [20].
# A SAR-Based Index for Landscape Changes in African Savannas
## Materials and Methods
### Data and Pre-Processing  
#### 2.2.1 Satellite Imagery  
As the investigated ecosystem shows strong seasonal dynamics, the data to be used should be selected carefully. Therefore, all investigated data were acquired during the dry season which extends from November until March where rainfall below 5 mm is expected. These conditions are favorable because SAR backscatter strongly varies with changing soil moisture. This could cause temporally misleading signatures during the rainy season, e.g., high values at smooth and non-vegetated bare soil.  
Figure 1: Location and landscape characteristics of the study area.  
Additionally, overall vegetation dynamics are smaller during the dry season which increases the inter-annual comparability of the images despite smaller temporal differences.  
Table 1 lists all satellite images used in this study. Considering its temporal coverage, wavelength, and high spatial resolution, ALOS PALSAR has been chosen as a main input. Landsat data was additionally used for the collection of reference data. For each year analyzed, a pair of ALOS and Landsat imagery has been found with a temporal difference \(\Delta\)t between 0 and 30 days. There is a gap of observation between 2012 and 2015 because the ALOS PALSAR archive only features data between 2006 and 2011 and ALOS-2 (Daichi) was launched in May 2014. Landsat ETM+ data experienced a failure of its Scan Line Corrector mechanism in May 2003 causing striped gaps in the data (SLC-off) [84]. However, our study area lies in the center of the scene where this effect is minimal, since 2015 data from Landsat 8 (OLI/TIRS) could be used instead.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 2 Background
### Related Work
We integrate in-flight services to handle unexpected conflicts that may occur while the UAS are flying, and we showcase the performance of our system through heterogeneous use cases. Finally, regarding the implementation of particular in-flight services, there are different approaches for conflict resolution and emergency management. Many works [28; 29; 30] have focused on flight planning and scheduling at a strategic level, i.e., in the pre-flight phase; though in-flight automated decision-making has not been properly covered in UTM systems. In general, given the massive search space to find optimal resolutions for conflicts in _Very Low-Level_ (VLL) airspace scenarios, approximate solutions based on heuristic solvers [28] or lane maneuvers [30] predominate over optimal deconfliction approaches. In [31], a probabilistic framework is proposed to formulate the risk involved in UAS operations. That methodology could be integrated for automated, real-time data analysis in an emergency management solution. We take methodological ideas from these previous works, in order to implement conflict resolution and emergency management in our system considering the specifics of UAS operations in a civil airspace. However, the focus of this paper is more on the architecture design and integration, rather than on the particular algorithms for conflict resolution.
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
## 3 Power Stability Analysis of Hybrid Dual-Infeed HVDC Systems
### The Influence of VSC-HVDC Transmission Power on the Power Stability of the System  
In this section, the influence of VSC-HVDC transmission power on the power stability of the system is studied. Keep the AC power grid parameters unchanged. Considering the two control modes of VSC-HVDC, the maximum power curves of LCC-HVDC under different VSC-HVDC rated transmission power are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Mode** & **LCC** & **LCC + VSC (Q)** & **LCC + VSC (V)** \\ \hline PSI & 0.606 & 0.577 & 0.698 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power stability indexes of LCC-HVDC at the rated point under different control modes.  
Figure 3: Maximum power curves of LCC-HVDC under different control modes.  
According to Figure 4, when the VSC-HVDC is in constant reactive power control mode, the greater the rated transmission power \(P_{\text{dN2}}\) is, the worse the power stability of the system is. When the VSC-HVDC is in constant AC voltage control mode, the maximum power curves under different transmission power are coincident, which indicates that the transmission power of VSC-HVDC has almost no effect on the power stability of the system under this control mode.
## 1 Introduction  
Infections caused by fungi, are the major problem of hospital infections, mainly due to the emergence of new infections and the generalized resistance to antibiotics [1]. Due to the rapid resistance acquired by microorganisms the search for natural products with antimicrobial activity hasbeen constant in recent years and drugs derived from plants have contributed largely to human health, historically representing a source for the creation of new drugs [2].  
One way of combating microbial resistance is the investigation of substances that can act as resistance modifiers by combining common antimicrobial drugs with some phytochemicals, and in some cases observed synergism [3,4].  
Yeasts of the genus _Candida_ are found as commensals in the human organism, however, because of factors that disturb the dynamics of the host can favor the growth of these fungi, from superficial infections to systemic infections [5]. Among the yeasts of this genus, _Candida albicans_ stands out as the main cause of infections, being a species with great ability to change its morphology, a necessary factor for its virulence and pathogenicity [6,7].  
For the treatment of infectious diseases, populations without access to medicines, especially those from underdeveloped and developing countries, use medicinal plants to combat disease. Such use of these vegetables is due to a number of advantages, such as their availability, low purchasing power and being in popular knowledge for several generations [2,8]. Among the species of the Brazilian flora, the genus _Piper_ L. is one of the largest in the Piperaceae family, with 290 species and 45 varieties occurring in Brazil [9]. Some species of the genus are used as a flavoring of food and treatment of diseases [10]. A scientific study evidenced the antimicrobial potential of four species of the genus against _Staphylococcus aureus_ and three strains of _Candida_, including C. _albicans_[11]. Among the species of the genus, _Piper caldense_ C. DC., is popularly known in Brazil as \"pimenta d'agua\" or \"pimenta d'arda\", being commonly used for the treatment of snake bites, sedative and stomach problems [10]. Research using _P. caldense_ essential oil has revealed promising antimicrobial potential [12,13].  
In view of the above problem, this study aimed to evaluate the antibiotic and modulator potential of volatile terpen of _P. caldense_ against _Candida albicans_, as well as to determine if the natural product is capable of reducing pleomorphism. Finally, it was evaluated by gas chromatography which terpen were present in the essential oil.
# Security Enhancement for Data Migration in the Cloud
## 2 Background Preliminaries
### Cryptographic Algorithms
#### 2.1.3 Hashing Algorithms, Also Called One-Way Encryption  
Hashing algorithms, also called one-way encryption, are algorithms that, in some sense, use no key. It is a vital information security tool and is used to authenticate messages, digital signatures and documents. A hash function accepts a variable-length block of data as input and produces a fixed-length hash value called message digest. Having a message digest, it is impossible to recover or find the original string. Some examples of hashing algorithms are: MD5, SHA-1 SHA-2, and SHA-3.  
The primary purpose of encryption is to ensure the confidentiality of the data stored on a specific device or transmitted via the internet. While hash function ensures data integrity, any change in the original message, however small, must cause a change in the digest, and, for any given file and digest, it must be infeasible for a forger to create a different file with the same digest [3].
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 5 Results
### Efficiency Comparison of Different Fourier Transform Methods  
Based on the 3D numerical simulation algorithm of a magnetic anomaly in the space-wavenumber domain [37], the model was designed and simulated by the standard-FFT, Gauss-FFT [24], and AS-FT method proposed in this paper.  
The background magnetic field \(\mathbf{H}_{0}\) is 50,000 nT, the magnetic inclination is 58.3\({}^{\circ}\), and the magnetic declination is 45\({}^{\circ}\). The model is shown in Figure 5.  
Figure 5: Prism model. The calculation area is 1000 m \(\times\) 1000 m \(\times\) 1000 m, with ranges 6f \(-\)500–500 m in the \(x\) direction, \(-\)500–500 m in the \(y\) direction, and 0–1000 m in the \(z\) direction. The prism anomaly is a cube with a side length of 400 m, and the prism’s center coincides with the model’s center. The magnetic susceptibility of the prism is 0.01 SI.  
Conditions for comparison of three Fourier transform methods:  
* \(K_{e}\) is the expansion coefficient of standard-FFT, and the expression is as follows: \[K_{e}=\frac{S}{d},\] (26) where \(S\) refers to the outward expansion distance from the boundary of the simulation area, and \(d\) refers to the burial depth of the abnormal body. \(K_{e}\) is taken as 2, 5, and 8, respectively.
* The Gauss points of Gauss-FFT are 2, 3, and 4, respectively.
* The sampling points in the spatial and wavenumber domains of AS-FT are the same and uniformly sampled.  
Gauss-FFT, standard-FFT, and AS-FT algorithms had 101 sampling nodes in the z direction, and the sampling was uniform.  
The number of nodes was changed in the horizontal direction, and the efficiency of the three methods and the Rrms error of the ground field value (\(z=0\) m) were compared, as shown in Table 2.  
According to Table 2, the number of sampling points of Gauss-FFT in the horizontal direction of the spatial domain is 201 \(\times\) 201; when the Gaussian point in the wavenumber domain is 4, the Rrms of the \(B_{ax},B_{ay},B_{az}\) reach 0.08%, 0.08%, 0.24%, but it takes 30.35 s. When \(K_{e}=8\), the accuracy of standard-FFT is the same as that of Gauss-FFT with 4 points, but it takes 39.51 s. When the sample points of spatial and wavenumber domain are 201 \(\times\) 201, the AS-FT algorithm takes only 2.53 s, and the Rrms of \(B_{ax},B_{ay},B_{az}\) are 0.02%, 0.02%, and 0.11%, respectively.  
Therefore, the efficiency and accuracy of the AS-FT algorithm proposed in this paper are better than those of Gauss-FFT and standard-FFT.
# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 2 Measurement Tools and Experimental Methods
### Key Texture Parameters for Friction Coefficient Prediction  
After processing the correlations between the friction coefficient and all evaluated texture parameters, key parameters were selected. These key parameters underwent cross-validation to determine the optimal combination of texture parameters for predicting the friction coefficient. Analysis revealed that these key texture parameters were valley material portion (functional), dale void volume (functional volume), and arithmetic mean peak curvature (feature).  
Functional texture parameters are measures that describe the performance-related aspects of a surface's texture, focusing on how the texture affects fluid retention, wear resistance, and other properties that influence the interaction between the road surface and tires. Road surface functional characteristics can be calculated from the peak and valley regions using the Abbott-Firestone curve, which represents the cumulative distribution of surface heights (Figure 5).  
Valley material portion \(S_{mr2}\) [%] quantifies the percentage of the measured area comprising deeper valley structures, which correlate with the reduced valley depth (\(S_{vk}\)). \(S_{mr2}\) represents the areal material ratio distinguishing the reduced valleys from the core surface. \(S_{vk}\) denotes the arithmetical mean depth of these reduced valleys, derived from the areal material ratio curve.  
Figure 4: 3D Surface model (**Top Left**), height map (**Top Right**), and surface profile (**Bottom**).  
Dale void volume (\(V_{vv}\)) is a functional volume surface texture parameter that quantifies the volume of the valleys or voids within a surface texture profile. This parameter is significant for understanding the surface's capacity to retain fluids, such as water, which is critical for applications involving friction and wear. This parameter is also derived from the Abbott-Firestone curve, which represents the bearing area curve of the surface.  
Arithmetic mean peak curvature \(S_{pc}\) (1/mm) is a feature parameter that quantifies the average curvature of the peaks within a given surface area. This metric is crucial for understanding the sharpness and distribution of surface asperities, which directly affect the interaction between road surface and tire. Higher \(S_{pc}\) values indicate sharper peaks, which can lead to increased friction. \(S_{pc}\) is determined according to the following formula:  
\[S_{pc}=-\frac{1}{2}\frac{1}{n}\sum_{k=1}^{n}\left(\frac{\partial_{z}^{2}(x,y) }{\partial_{x}^{2}}+\frac{\partial_{z}^{2}(x,y)}{\partial_{y}^{2}}\right) \tag{1}\]  
where \(n\) is the number of peaks, \(z\) (\(x,y\)) is ordinate values, and (\(x,y\)) is used to identify the position of the calculation point.
## Theory and Model
### Finite Element Model  
#### 2.2.1 The Development Model  
Honeycomb sandwich composite consists of glass fiber reinforced polymer (the four layer structure of the honeycomb sandwich composite skin in this paper is \([0^{\circ}/90^{\circ}/0^{\circ}/90^{\circ}]\)), aluminum honeycomb core and epoxy resin adhesive layer, as shown in Figure 1. The real model of honeycomb sandwich composite is three-dimensional case. In the actual finite element analysis, there are some problems such as difficulty in modeling, mesh division, high computational complexity, etc. In order to overcome these problems, by analyzing the structural characteristics of honeycomb sandwich composite and the propagation path of acoustic wave in honeycomb sandwich composite [23; 24], three-dimensional problem can be simplified into a two-dimensional problem, as shown in Figure 2. Figure 2b is partial enlargement of the honeycomb composite material of Figure 2a. Figure 2c is a cross section of the honeycomb core of Figure 2b. After observing the structural rule of honeycomb core in Figure 2c, it can be found that when vertically dividing along the honeycomb core, there is mainly a case where the dividing is performed in the form of A-A (The simplified two-dimensional model is shown in Figure 3. The spacing between honeycomb core walls in model will be different.  
Here, taking only vertically dividing at 1/2 honeycomb core wall as example). Therefore, based on this two-dimensional model, it is reasonable to study the propagation rule of acoustic wave inside the honeycomb sandwich composite.  
The transient two-dimensional honeycomb sandwich composite model established in this paper is based on Comsol Multiphysics software. A transmitter is arranged in the upper part of air domain in the FEM. In order to maximize the energy of sound source coupled into honeycomb sandwich composite, the distance between honeycomb sandwich composite and sound source is set as the focal length of sound source. A receiver is arranged in the lower part of air domain. The received signal of receiver is synthesized by sound pressure P integral of the discrete points on the line source. The two-dimensional model includes the air domain, honeycomb sandwich composite sample, and radiation sound source, as shown in Figure 3.  
Figure 1: The diagram of honeycomb sandwich composite.  
Figure 2: The honeycomb sandwich composite model simplification process. (**b**) is the partial enlargement of the honeycomb composite material of (**a**); (**c**) is a cross section of the honeycomb core of (**b**).  
In order to investigate the effect and response of ultrasonic wave to debonding defects of honeycomb sandwich composite, air layer is embedded in the middle of the epoxy resin adhesive layer to simulate the presence of debonding defect in the real adhesive layer, as shown in Figure 4.
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
###### Abstract
In addition, the AO algorithm applies mobility-predictive adaptive multi-hop routing to eliminate link failures and rerouting. The AO is a global search algorithm motivated by the hunting behavior of the aquila eagle, which covers large search spaces efficiently to find optimal solutions. It focuses on local optimization to stabilize clusters in dynamic networks, inspired by the agility of gazelles. HMAO using dual optimization has also been further improved by an innovative density-aware clustering mechanism that can be adjusted in real time to cluster size and density along with UAV mobility. This function is set to reduce the number of re-clustering events and, consequently, the routing overhead and stability of communication links, especially in highly dynamic scenarios with fast node movement.