esa-sceva/satcom-chunk-collection huggingface dataset:
# (R-ISSUES) Rural Interoperable System of Systems for Unified Environmental Stewardship
## 1 Introduction
### Forest Fire Risk for Mineral Water Sector  
Multiple causes of wildfires exist, from human to natural ones [5]. Several starting and propagation mechanisms exist, such as easily burning trees, dry residues, hot metal particles [6; 7], and many others studied by researchers for decades. Digital enablers like artificial intelligence (AI) are also being used to expand the governing physics to the real world, where complex geometries and multiple causes may coexist [8].  
Mineral and spring water activities are typically conducted in wild forests. They have been officially regulated in Spain by the Real Order 1798/2010 and updated to fit the European regulations [9]. They establish the collection, handling, circulation, and marketing rules. This regulation considers the risk of contamination and the existence of a protection perimeter, where certain activities that may negatively influence the resource are prohibited or conditioned to safeguard it. Some companies in the sector collaborate with the regional forest management plans with the aim that the forests under which the aquifers are located continue to be the best natural protectors, preserving the native vegetation, in addition to collaborating with the cleaning of the diverse and multiple forests so that they are biodiverse [10].  
According to the last decennial (2006-2015) report on forest fires in Spain [11], around 13,111 fire events affect 32,028 hectares (ha) of wood yearly on average and 100,796 ha of forest. The number of events with less than 1 ha was 8651 (4460 of \(>\)1 ha). In 1996, the Spanish inventory of the total forestalled surface of Spain was 50,596,014 ha, 10,625,698 ha of wood [12]. This measures the yearly probability of having a forest fire in woody and forest areas, which is 0.06% and 0.95%, respectively, and, of course, there are regions with more risk than others. There can be several consequences of having a fire in forest areas next to the watershed protection area. A known result is a potential reduction in rainwater infiltration because of the ground compaction. The absence of vegetation may increase the risk of flooding but also have other effects [13]. These consequences could last for years. The Alliance for Water Stewardship (AWS) developed the AWS Standard [14], a globally applicable framework engaging water-using sites to address a site's water risks and opportunities. The AWS Standard certification requests a continuous dialogue with the site's stakeholders to evaluate risks and demonstrate responsibility and resilience. Some digital enablers may help to achieve resilience to forest fire, from new Unmanned AerialVehicles (UAVs) equipped with specific sensors with a square meter resolution for risk assessment and rescue operations [15] to satellites providing fire alerts in minutes at the hectare resolution [16].
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 5 Factors Affecting Fermentation Process of Pharmaceutical Terpenoids
### Inoculum Size  
The size of inoculum used for terpenoid production is generally applied between 2.5 % and 14% of medium volume or initial optical density (OD\({}_{600}\)) of 0.05 to 0.5 is used instead (Table 3). For instance, in a study for optimization of \(\beta\)-amyrin formation by engineered yeast in fed-batch fermentation, the most favorable inoculum size was reported as initial OD\({}_{600}\) of 0.3 [109]. Moreover, in another study, inoculum size of 5.0 and 5.2% (\(p/v\)) with _S. cerevisiae_ strain BY4741 and 8.1 and 2.6% (\(v/v\)) for strain EGY48 were favorable for squalene production in semi-anaerobic shake flask fermentations [110]. According to these results, optimum inoculum size was strain dependent. The difference between the productivity of these two strains at high inoculum size might be related with the synthesis of higher ergosterol by strain EGY48, which enhances tolerance to ethanol, which is produced during the early stages of fermentation, and which could be used as a substratefor squalene production under glucose depletion at the late stage of fermentation [110]. A reasonable high volume of inoculum is required to minimize the length of lag phase, increase specific growth rate, and accumulate the maximum biomass in the production fermenter in a shorter time as possible, which helps to achieve highest productivity. A study for inoculum size effect on metabolic regulation and stress response of _S. cerevisiae_ in high cell density fermentation showed that as inoculum size (40 g/L initial biomass) increased, stress protectants (glycerol and proline) in glycerol biosynthesis and amino acid metabolism improved. However, citric acid cycle (TCA) intermediates are depressed and metabolites (myo-inositol and ethanolamine) associated with membrane structure and function decreased [111]. In the same study, growth rate, glucose consumption rate and ethanol productivity increased in high inoculum size, which is convenient for industrial ethanol fermentation. However, very high inoculum size will cause the rapid depletion of nutrients and oxygen, which results in microaerobic condition and increased oxygen demand. High ethanol production in anaerobic condition might have toxic effects on yeast cells and be the reason for the decrease in terpenoid yield. Low inoculum size causes longer fermentation phases, lower productivity parameters, and high risk of contamination. However, optimum inoculum size can help the culture to grow at extreme conditions, such as high salt concentrations and pH ranges [112; 113].
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 3 Proposed Secure Communication, Multi-User Cooperation, and User Clustering Strategy in PD-NOMA
### Proposed Solution for User Clustering Strategy
Lastly, step three of the proposed algorithm chooses the best cluster based on the rule that each group must have users who are separated by a maximum distance, such as:  
\[d(R_{i})-d(R_{j})>\text{minimum}(d)\qquad\forall\quad i,j=1,2...N,\quad i\
eq j \tag{12}\]  
where \(d\) is the minimum distance separation between two users. In Cluster Types 2 and 3, the minimum distance separation equals \(d\), whereas in Cluster Type 1, it is \(2d\). Due to maximum distance separation among the users of a cluster, it achieves the best performance. So, based on steps 1, 2, and 3, Cluster Type 1 is the best possible cluster for the proposed PD-NOMA system. Once the users are distributed among the \(C_{i}\) clusters, the power allocation is then carried out, which is based on the distance separation to the gNodeB as in [19] and given as follows. \[p_{i}^{{}^{\prime}}=\frac{d(R_{i})}{d_{max}}. \tag{13}\]  
Here \(p_{i}^{{}^{\prime}}\) and \(d(R_{i})\) represents the \(i\)th user absolute power factor and the distance separation, respectively. All the absolute power factors are normalized to get the actual power factors (\(P_{i}\)) for each cluster, which is defined mathematically by  
\[P_{i}=\frac{p_{i}^{\prime}}{\sum_{i=1}^{N/2}p_{i}^{{}^{\prime}}}. \tag{14}\]  
Figure 5: Different types of user clustering for four user PD-NOMA (\(R_{1}\) is near and \(R_{4}\) is far to gNodeB).
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 2 Materials and Methods  
### Platform Architecture  
Our generic platform is designed to support the entire process of field geographic information collection. As shown in Figure 1, it consists of collection software and a management system, which communicate via network transmission. The platform was developed using C++ 17, Java 8, ObjectC 2.0, and JavaScript ECMAScript 2018, customized to particular field deployments using reusable-definition XML-based configuration files.  
The collection software leverages a custom XML toolkit and dynamic page technology to provide tailored functions for different application scenarios. To maximize compatibility across various environments, it integrates technologies such as mobile databases, runtime scripts, and GPS positioning. We offer professional applications for iOS, Android, and Web to accommodate different systems used by collectors. For volunteer geographic projects, we provide mini programs that allow volunteers to quickly collect simple spatial data.  
The management system also uses a custom XML toolkit for task configuration, configuration files, and database operations. Utilizing Geohash, spatial indexing technology, and OpenLayer v6, our management system enables managers to allocate tasks, schedule personnel, and rapidly visualize heterogeneous data. Building on these functionalities, we implemented a comprehensive field geographic information collection workflow in the management system and designed multiple work modes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Software** & **This Platform** & **QField** & **ArcGIS Field Map** & **ODK** \\ \hline Positioning & ✓ & ✓ & ✓ & ✓ \\ Trajectory Recording & ✓ & ✓ & ✓ & ✓ \\ Geometric Feature Collection & ✓ & ✓ & ✓ & ✓ \\ Control Selection & ✓ & ✓ & ✓ & ✓ \\ OfflineData Collection & ✓ & ✓ & ✓ & ✓ \\ Data Upload & ✓ & ✓ & ✓ & ✓ \\ Shapefile Support & ✓ & ✓ & ✓ & ✓ \\ Local Database Support on Mobile & ✓ & ✓ & ✓ & ✓ \\ Phone & & ✓ & ✓ & ✓ \\ Remote Database Support & ✓ & ✓ & ✓ & ✓ \\ Support Multiple Databases for Local & ✓ & & & \\ Central Server & ✓ & & & \\ Navigation & ✓ & ✓ & ✓ & \\ WMS, WTMS, WFS & ✓ & ✓ & ✓ & ✓ \\ Local Central Server & ✓ & & & ✓ \\ Remote Central Server & ✓ & ✓ & ✓ & ✓ \\ Statistical Analysis & ✓ & ✓ & ✓ & ✓ \\ Task Allocation & ✓ & & ✓ & ✓ \\ Nested Tables & ✓ & & & \\ Script Operations & ✓ & ✓ & ✓ & ✓ \\ basemapConfiguration & ✓ & ✓ & ✓ & ✓ \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of existing software.
## 3 Network Model Construction  
### Residual Network  
Neural networks can continuously and automatically extract image features from local to global scales, enabling functions such as image recognition [18]. Deep learning-based networks can capture richer and more complex features and often adapt well to new tasks. However, deeper networks are more challenging to train due to the vanishing gradient problem. Therefore, this study employs the residual network (ResNet), which addresses this issue by introducing shortcut connections. This innovation allows the network to significantly increase its depth, thereby enhancing accuracy. A schematic of the residual network structure is shown in Figure 2. In this research, the ResNet18 architecture was utilized to extract feature vector sequences.  
To prevent accuracy degradation in deeper layers, the input \(x\) is directly incorporated into the output via the residual connection \(H(x)=F(x)+x\), where \(F(x)=0\) represents the identity mapping. This architecture enables efficient feature propagation without increasing the number of network parameters or computational complexity. Leveraging this advantage, the ResNet18 model was selected for its simplicity and modularity, which align well with the characteristics of the seismic waveform dataset. Residual networks are widely used in deep learning and have demonstrated strong performance in image processing tasks.  
Figure 1: Example of microseismic waveform sample.  
The pretrained ResNet18 model, initialized with weights from ImageNet (a dataset containing 12 million images across 1000 categories), was fine-tuned for this study. During transfer learning, the weights and biases of all layers except the final classification layer were frozen. For the microseismic dataset, only the neurons in the last layer were re-initialized and trained to adapt to the new classification task. This approach allows the model to retain general visual features while quickly adapting to domain-specific patterns, thereby ensuring optimal classification performance.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Parametric Investigations
#### 3.1.2 SRR Loading with Reduced Ground  
Initially, the designed parent antenna (substrate size = 47.92 \(\times\) 46.15 mm\({}^{2}\)) with the full bottom surface ground and with 50% ground without an FSS yield no beneficial results below \(-\)10 dB. After this, the antenna ground length was reduced to a value equal to the radius of the fundamental circular patch, i.e., 11.98 mm. This would yield quad-narrow bands below \(-\)10 dB. The antenna achieves a gain value equal to 5.05 dBi at 3.5 GHz and a peak gain of 14.24 dBi at a resonating frequency of 10.82 GHz. The reduced ground length of 10.98 mm (a\({}_{\rm e}-1\)) results in quad-bands with a lower reflection coefficient, but it will yield an excellent peak gain of 12.36 dBi at the design frequency of 3.5 GHz and achieves a peak gain of 12.89 dBi at 3.26 GHz. Further, the ground length is reduced to a quarter guided wavelength (10.21 mm). This will achieve a lower resonance frequency at 3.26 GHz, much closer to the design frequency of 3.5 GHz. This also results in tri-wide bands. The lower band covers the main n77, n78, and partial n79 bands of the 5G sub-6 GHz range. This improves the fractional bandwidth and achieves a peak gain of 13.58 dBi at the lowest first band frequency of 3.20 GHz. Further, the length of the antenna is reduced to 10.1 mm. This will yield an improved bandwidth in the lower and upper bands and achieve a slightly reduced peak gain at a frequency of 3.20 GHz. A split ring resonator (SRR) designed at 3.5 GHz is placed at the bottom ground near the monopole ground with a length of 10.1 mm. This results in degraded values in the gain, as well as in the bandwidth. The only benefit of introducing an SRR is that it shifts the fixed resonating frequency towards 3.5 GHz and results in a resonating frequency tuned at 3.26 GHz and a gain reduced to 10.08 dBi at 3.5 GHz. The gain is further improved by reducing the ground length to 9.7 mm, without disturbing the position of the SRR. The gain is significantly improved to 15.11 dBi at 3.5 GHz, while the peak value of the gain of 16.66 dBi is achieved at 3.26 GHz. This also helps to improve the reduced value of the bandwidth in all tri-bands. The ground length reduction, parametric analysis, and reflection coefficients are shown in Figure 9, and the antenna parameters for each case are recorded in Table 3.  
Figure 9: Effects of reduced ground and SRR loading.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 3 Image Tampering Techniques
### Resampling  
Digital images can be visualized as a grid of evenly spaced pixels. Each pixel can be taken as a subject's sample or amount of light. Resampling is how a tampered picture version can be created with a different height and width in pixels. In upsampling, the size of the image is increased; on the other hand, the reverse is performed in downsampling, where the size is reduced. Image rotation is also achieved through resampling. Figure 8 exemplifies how rotating an image at arbitrary angles transforms a picture.
# Biological Decontamination by Microplasma
###### Abstract  
Dielectric-barrier-discharge microplasma has various applications such as flow control, surface treatment, air treatment, or biomedical applications. Microplasma was used for the inactivation of _Escherichia coli_, _Staphylococcus aureus_, and _Pseudomonas aeruginosa_. Bacterial strains spread on Petri dishes containing Nutrient Agar were treated with microplasma and, after incubation, inhibition zones were observed. By comparison, the experiments carried out with the already-grown bacteria on the Petri dish did not show any inhibitory response. Environmental air was used as discharge gas. The reactive oxygen and nitrogen species mainly carry out the inactivation process. A negative pulse voltage energized the microplasma electrodes. The microplasma treatment was the most potent against _S. aureus_, followed by _E. coli_, and _P. aeruginosa_, which was the least susceptible bacteria from the tested strains. An increase in the inhibitory efficiency was observed with the increase in discharge voltage from \(-1.5\) kV to \(-1.7\) kV. This research proved the efficiency of microplasma in biological decontamination and provides valuable insights of the inactivation of bacteria carried out with a technology that is suitable for easy integration and portability.  
dielectric barrier discharge; microplasma; _Escherichia coli_; _Staphylococcus aureus_; _Pseudomonas aeruginosa_
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 6 Security Analysis  
The main security concerns for the disaster surveillance UAV communication networks are the integrity and availability of drones' readings.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 2 System Model  
In this paper, the LSAS is set as an OFDM based cellular mobile communication network. In this kind of cellular network, the cells within a group use different OFDM orthogonal sub-channels, and the number of cells in the group is set to L [7]. The cell represents a regular hexagonal cell network. Each cell is equipped with a multi-antenna base station, and the base station can provide services to multiple users in the cell. The number of antennas of the base station is set to M, and each antenna is an omnidirectional antenna. The number of users in the cell is set to K, and each user terminal is equipped with an omnidirectional antenna. Accordingly, a cell constitutes a multi-user MIMO system, where K independent users can form a virtual antenna array with number of antennas K. The configuration of the base station and the user end constitutes a MIMO downlink system with the size of KM, or a MIMO uplink system with the size of MK. In the traditional single-user MIMO systems, the total rate capacity of the uplink and downlink channels of a multi-user MIMO system is proportional to min(M, K).  
In the OFDM-based cellular communication network, due to frequency reuse, the channel spectrum utilization is very high and the system communication performance is greatly improved [11]. We further assume that Orthogonal Frequency-Division Multiplexing (OFDM) is used and that in a given subcarrier we have a flat-fading channel [7]. In order to simplify the presentation of the multi-user MIMO system and reduce complex settings, we set that all the subcarriers in the communication frequency band can be used for communication between base stations and users.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 4 Quantitative Analysis of Fluctuating Wind Properties
### Turbulence Integral Scale
#### 4.6.2 Cross-Spectrum  
There is a limited number of studies on the cross-spectrum of fluctuating wind velocity in the field of studying wind characteristic parameters. [PERSON] et al. [32] used normal wind experimental data to derive the expressions for the fluctuating wind speed cross-spectrum:  
\[\frac{-nC_{uw}(n)}{u^{*2}}=\frac{14f}{\left(1+9.6f\right)^{2.4}} \tag{21}\]  
where \(C_{uw}\) represents the cross-spectrum, \(u^{*2}\) represents friction velocity, and \(f\) is frequency.  
After trial calculation, it was observed that there exists a distinct discrepancy between the recorded cross-spectrum of fluctuating wind velocity and the spectrum value derived using [PERSON]'s empirical spectrum. To enhance the precision of the fluctuating wind velocity cross-spectrum, the Kaimal empirical spectrum was modified. Therefore, this study proposes the following improvement to Equation (21):  
\[\frac{-nC_{uw}(n)}{\sigma_{u}^{2}}=\frac{af}{\left(1+bf\right)^{c}} \tag{22}\]  
where \(\sigma_{u}\) is about \(2.5U_{0}^{*}\) and variables \(a\), \(b\), and \(c\) are sample fitting parameters.  
This study employs 10 min sample data to fit the three parameters, \(a\), \(b\), and \(c\), at different heights and derives the cross-spectrum expressions of longitudinal and transverse fluctuating wind velocity at heights of 15, 53, and 82 m. The outcomes are presented in Table 7.  
Figure 17: Longitudinal power spectra: (**a**) 15 m, (**b**) 53 m, and (**c**) 82 m.  
Figure 18 shows the cross-spectrum and modified Kaimal spectrum function curves of wind velocity at heights of 15, 53, and 82 m. From the figure, it can be seen that the measured fluctuating wind speed cross-spectrum and the modified Kaimal spectrum agree well in all frequency bands, indicating the modified Kaimal spectrum may accurately represent the fluctuating wind cross-spectrum of Typhoon Soulik.
###### Abstract
These relationships are generally established by means of mathematical models based on the functional relation of microstructure parameters to thermomechanical parameters [40; 41; 42; 43], as well as using physically based models [35; 44; 45; 46; 47; 48; 49] and computational models, including models based on the cellular automata method [34; 50; 51; 52; 53; 54] and molecular dynamics [55]. Neural network models for describing microstructure formation under high-temperature deformation have not found wide application due to the need to obtain a large amount of experimental data which is time-consuming and leads to the inexpediency of using neural networks. The problem of the scanty experimental data is solved by simulating microstructure formation using cellular automata or embedding functions into finite element programs [56; 57; 58; 59]. Simulation enables computational experiments to be conducted, thus making it possible to reduce the time spent on forming a sample of the necessary size for training neural networks. Nevertheless, this approach is rather time-consuming as it requires first developing or applying a model that adequately describes the microstructure formation in the material and then using the results of calculations based on the model to train neural networks. This paper proposes a new and relatively simple method for processing scarce experimental data relating microstructure parameters to the parameters of thermomechanical action on a metal material for subsequent neural network training. The neural networks constructed in this study are used to identify the softening mechanisms in an AlMg6/10% SiC metal matrix composite at temperatures from 300 to 500 \({}^{\circ}\)C and strain rates ranging between 0.1 and 4 s\({}^{-1}\), as well as to relate the microstructure parameters of the metal matrix composite to the thermomechanical parameters of deformation.