esa-sceva/satcom-chunk-collection huggingface dataset:
# Phtyoremediation and Bioremediation of Pesticide-Contaminated Soil
## 5 Soils Contaminated with Pesticides  
Good productive soil has a vital role in agriculture, and it is a key component of terrestrial ecosystems. Pesticide-contaminated soil is a global concern because of spills, cleaning of spray equipment and empty containers, and land-use changes from agriculture to residential living. Erosion and flood waters may move pesticides from both treated land and storage sites to soils down-gradient and in the flood plain. Where high concentrations of active ingredients are released, both plants and microorganisms may be killed. There may be health risks when animals and people come to contaminated sites because of toxicity to non-target organisms [41; 48; 66; 67; 118; 161; 170]. Inappropriate pesticides may be taken up into garden vegetables and fruit. Pesticides in soil may contaminate ground water that is used as drinking water. Pesticides in soil from prior use may affect the fate of, or contaminate a different crop that is planted.  
[PERSON] et al. have reported results for 76 pesticide residues in topsoil for 11 European countries for 6 cropping systems. Many soils had more than one pesticide present; the maximum total pesticide concentration reported was 2.87 mg/kg [282]. Detection levels were 0.05 mg/kg for glyphosate, 0.01 mg/kg for LC-MS/MS detection, and 0.005 mg/kg for GC-HRMS detection [282]. No pesticide residues were reported in 17% of the fields. A single pesticide was identified in 25% of the fields, while more than one pesticide residue was found in 58% of the sites [282].  
[PERSON] et al. have reviewed the literature on organochlorine pesticides in soil and reported values for 4 cities in Nepal [293]. A range of values is reported for data from China, India, Mexico, Uganda, Kenya, Pakistan, Central Europe, and Nepal for DDT, hexachlorocyclohexane, and endosulfans [293]. The largest value reported is 2.179 mg/kg = 2179 ng/g for DDT and metabolites of DDT [293]. Most values for DDT and its metabolites are greater than 0.1 ng/g and smaller than 100 ng/g [293]. The values reported by [PERSON] et al. for China are in a similar range [294].  
Historically, excess or obsolete pesticides were buried in trenches, placed in dumps, or deposited in unlined landfills in many countries [30; 84; 246]. Disposing of pesticides in burial sites and landfills is not recommended in most countries, because of the risk of contaminating ground water. Remediation processes have been implemented to remove, contain, or degrade buried pesticides at a number of sites [84]. Monitoring of concentrations in ground water near landfills and pesticide burial sites has enabled communities to be informed of contamination issues and take appropriate action. It is important to collect information on burial sites and locations where ground water is contaminated in order to inform the public and prevent exposure to pesticide contaminants. Information on locations and contents of burial sites can help prevent using these sites for new construction [117; 246].
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Seasonal Variation
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Northern Hemisphere** & **Southern Hemisphere** \\ \hline December solstice & \(60.59\pm 4.05\) & \(-55.8\pm 6.02\) \\ Equinoxes & \(60.23\pm 4.14\) & \(-55.72\pm 6.21\) \\ June solstice & \(59.08\pm 4.09\) & \(-56.8\pm 5.97\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean position of the trough minimum during December solstice, June solstice, and the equinoxes. The corresponding standard deviations are given after the \(\pm\) sign. Figure 10: Ellipse distribution for the northern (**a–c**) and southern (**d–f**) hemispheres of the mean position of the trough minimum in three seasons: December solstice (blue color), June solstice (pink color), and equinoxes (green color). Red points in each hemisphere represents the detected trough minimum location. The dark red star shows the locations of the geomagnetic pole for each hemisphere. by the poleward extension of the nighttime ionosphere in winter due to the axial rotation tilt of Earth [18]. The left plot shows the variation in the monthly mean trough minimum position as a function of months. The northern hemisphere plot illustrates that the trough minimum shifts poleward during winter months and equatorward during summer months. In the southern hemisphere, the trough variations exhibit opposite behavior. These results are in agreement with the results presented in Table 2.
## 6 Numerical Investigation and Validation
### Three-Point Bending Beam
The response of the structure in terms of the reaction force vs. CMOD (crack mouth opening displacement) is compared with the experimental data included in [59], as illustrated in Figure 11. By considering both full implicit and ITE tests, Figure 12b exhibits the evolution of principal stress over two different integration points, identified in Figure 12a, in the band where the damage propagates. A detailed view of damage propagation is provided in Figure 13; specifically, the matrix is shown in Figure 13a, the fiber in Figure 13c, and tensile damages can be recognized, while Figure 13b shows shear 1-2 damage progression. Finally, Figure 14 compares the final distribution of tensile matrix damage using full implicit material integration and the ITE algorithm. The numerical assessment correctly simulates the experimental campaign, even though some differences in the softening region can be detected. In particular, the numerical model contains an average value of matrix fracture energy in contrast to the bilinear behavior. Furthermore, the reduced integration results in a smaller characteristic length. In addition, small inconsistencies between experimental and numerical boundary conditions could induce some variabilities. The principal stress and strain obtained from the full implicit case and ITE are coincident, as shown in Figure 12b. Therefore, the ITE algorithm simulates the mechanical response of the beam with the same accuracy as the full implicit analysis, with the advantage of requiring significantly less computation time. The matrix damage over integration point 2 evolves more rapidly than point 1. However, by looking at Figure 13c, there is an increase in fiber damage that causes the shear damage at point 1 to exceed that at point 2 from the cross point, as highlighted in Figure 13b. Figure 11: Vertical load vs. CMOD for implicit and ITE for the three-point bending beam. Figure 12: Identification of integration points 1 and 2 (**a**) and distribution of principal stress vs. principal strain (**b**) for the three-point bending beam. Figure 14: Final tensile matrix damage distribution for (**a**) implicit and (**b**)ITE for the three-point bending test; (**c**) matrix damage scale. Figure 13: Tensile matrix damage vs. principal strain (**a**), shear damage vs. principal strain (**b**), and tensile fiber damage (**c**) vs. principal strain over integration points 1 and 2 withITE algorithm for the three-point bending beam.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 3 Function Finding Algorithm by Using Gene Expression Programming for Wireless Sensor Networks Data
### Description of Function Finding Algorithm Using Gene Expression Programming (FF-GEP)  
GEP has strong global searching ability. Therefore, it has definite potential in getting sufficiently good solutions to function model finding problems for wireless sensor network data. The core of FF-GEP focuses on putting adaptive population generation strategy into population evolution. The steps of FF-GEP are shown as follows:  
```
Input: popSize, maxGen, maxFitness, \(P_{s}\), \(P_{m}\), \(P_{t}\), \(P_{r}\); Output: Best Function Expression; Begin {
1. double \(fitness=0.0\); int \(i=0\);
2. \(Pop\gets InitPop(popSize)\);
3. \(EvalueFitness(Pop)\);
4. while ((\(fitness\leq maxFitness\)) or (\(i\leq maxGen\))) {
5. \(Pop\gets Select(P_{s},Pop,popSize)\);
6. \(Pop\gets Mutate(P_{m},Pop)\);
7. \(Pop\gets ISTransposition(P_{t},Pop)\);
8. \(Pop\gets RISTransposition(P_{t},Pop)\);
9. \(Pop\leftarrow GeneTransposition(P_{t},Pop)\);
10. \(Pop\leftarrow OnePointRecombination(P_{r},Pop)\);
11. \(Pop\gets TwoPointRecombination(P_{r},Pop)\);
12. \(Pop\leftarrow GeneRecombination(P_{r},Pop)\);
13. \(Pop\gets APGS-SPAE(Pop)\);
14. \(EvalueFitness(Pop)\);
15. return BestFunctionExpression;
```  
**Algorithm 2. FF-GEP**
## 3 The Model of IKDC
### Determining Cluster Centers and Noise Points  
When the dimensionality of data is high, it can be proved by the theorem of large numbers that the sum of independent random variables converges to a fixed value when the size of the variables is large, and the distances between any high-dimensional samples tend to be equal. At this time, density peak points cannot be found using the traditional distance formula, resulting in poor clustering results. Therefore, to solve the above problem, this paper introduces the isolation kernel and improves it. The isolation kernel can increase the similarity of samples in the sparse domain and reduce the similarity of samples in the dense domain, effectively solving the problem of consistent similarity between high-dimensional samples. From this, it can be seen that compared with the distance calculation metric used by the DPC, the isolation kernel is more suitable for calculating the distance between high-dimensional samples. In this paper, the space segmentation mechanism of the isolation forest is used to construct the isolation kernel, and function \(K_{\psi}\) is used to replace the distance calculation metric in the DPC. The optimizing distance based on the isolation kernel is further obtained, as shown in Equation (6).  
**Definition 1** (distance based on isolation kernel).: _Given a data space \(X^{D}\), the distance \(\tilde{d}_{ij}\) based on the isolation kernel between points \(x_{i}\) and \(x_{j}\) is given by Equation (6)._  
\[\tilde{d}_{ij}=\frac{1}{K_{\psi}(x_{i},x_{j}|D)+\frac{1}{t}} \tag{6}\]  
_where \(t\) is the number of total trees in the isolation forest._  
IKDC uses the distance based on the isolation kernel instead of the traditional distance and then follows the idea of DPC to find cluster centers and noise. The cluster centers and noise are put into the core domains and noise domain \(\Omega\), respectively, and the noise domain is removed from the global dataset to put the effect of noise on the clustering results to the minimum.  
To overcome the problem that the KNN algorithm fails in high-dimensional space, the improved KNN is obtained by using the distance based on the isolation kernel instead of the distance formula in the KNN algorithm.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### IRSs for Cellular Networks
#### 2.2.1 Enhancing Secrecy Rates  
In cellular communication systems, the security of transmitted data is paramount, especially in environments prone to eavesdropping or signal interception. An active IRS, with its ability to dynamically alter the reflection pattern, can be employed to enhance secrecy rates by directing signals away from potential eavesdroppers [99]. For instance, IRSs can leverage advanced beamforming techniques, where reflected signals are concentrated toward legitimate receivers while nullifying the signal in directions of potential eavesdroppers. By carefully adjusting the phase shifts of each IRS element, active surfaces can ensure that only the intended recipient receives the signal at full strength, while the signal remains weak or incoherent elsewhere.
## 2 Materials and Methods
### The Probabilistic Approach  
As portrayed in Figure 1, the probabilistic modelling should normally include the following:  
* Ground motion variations;
* Variations in the dynamic soil characteristics and nonlinear soil behavior curves;
* Structural behavior variations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Coefficient** & **Value/Formula** \\ \hline \multirow{6}{*}{Horizontal} & \(a_{1}\) & 1.647 \\  & \(a_{2}\) & 1.01 \\  & \(a_{3}\) & 0.40 \\  & \(n_{1}\) & 7.02 \\  & \(n_{2}(\xi)\) & \(5.1-0.51\) ln \((\xi+10)\) \\  & \(f_{c}(\xi)\) & \(-1.886+2.221\) ln \(\left(\frac{400\pi}{\xi+1}+15\right)\) \\  & \(s\) & 0.00025–0.0005 s/m \\ \hline \multirow{6}{*}{Vertical} & \(a_{1}\) & 3.15 \\  & \(a_{2}\) & 1.00 \\  & \(a_{3}\) & 0.40 \\ \cline{1-1}  & \(n_{1}\) & 4.95 \\ \cline{1-1}  & \(n_{2}(\xi)\) & 1.685 \\ \cline{1-1}  & \(f_{c}(\xi)\) & \(\exp\left(2.43-0.025\ln(\xi+1)-0.048\left[\ln(\xi+1)\right]^{2}\right)\) \\ \cline{1-1}  & \(s\) & 0.00025–0.0005 s/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of coherency function coefficients [59].  
Figure 10: Plane-wave coherency models—Horizontal and vertical components [59].  
The probabilistic simulations use the Latin Hypercube method for a number of 30 simulations.
## 4 Results
### Tool Accuracy  
The location of synthetic events from the Synthetic Power Quality Disturbances database using the radar chart tool with the decision tree algorithm has yielded 83.33% to 100% satisfactory results, which are higher than the detection percentages of boxplots, as shown in Table 14.  
The detection percentages for the radar chart + decision tree system were obtained based on the quantity of event correctly classified by the decision tree. An event is considered satisfactorily detected by the boxplot when the limits established in Table 13 are exceeded.  
As a first observation when treating real sag signals, it can be said that the alteration with respect to the ideal pattern of the radar allows us to conclude in 96.15% of the cases that a sag electrical event in the presence of distortion has occurred. For real impulsive transient signals, the detection percentage is 83.33% compared to 99% for synthetic impulsive events. In both cases, there is a strong correlation between an altered radar pattern and the existence of a disturbance in the time domain. The detection capability of the radar + decision tree system significantly surpasses that of boxplots by up to 50% for notches, oscillatory transients, spikes, and synthetic and real impulsive transients.  
The error percentages of the radar + decision tree system may be due to Gaussian noise affecting the indicators beyond the alteration caused by the event itself or to the observation window not being completely reliable. In that case, the error could be reduced by analyzing the same signal multiple times while varying the starting point of the cycles; however, this approach would risk saturating the system memory.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems
#### 3.6.4 Antenna Feeding  
Feeding the proposed designs is essential for performance optimization. Hor-fed Reflectors (ref. [118]) utilize a symmetrical metasurface for precise phase compensation. Microstrip antennas utilize arrow-shaped and T-shaped stubs to tune the lower and upper band frequencies for compact and dual-band designs. Waveguide-based feeds (ref. [123]) reduce the mutual coupling between elements and maintain low loss. Finally, combining cylindrical wave excitation (ref. [131]) with metallic printed patch arrays can result in designing a scalable element implementing complex radiation patterns.
# Modeling and Resolving Uncertainty in DIKWP Model
## 1 Introduction
**Addressing uncertainty in HFT regulations:** We analyzed the inherent uncertainty in regulations affecting HFT practices during the early stages of the Dodd-Frank Act. By dissecting issues related to vague definitions, regulatory requirements, interpretational differences, and lack of detailed descriptions, this research offers clear insights for more effectively managing regulatory compliance;
3. **Elaboration on Concept, Cognitive, and Semantic Spaces:** We provide detailed explanations and a set of definitions and analytical methods for Concept Space, Cognitive Space, and Semantic Space within the context of the DIKWP model. This enhances understanding of how HFT companies interpret, adapt to regulatory requirements, and formulate strategies around regulatory demands, thereby leveraging these spaces to improve operational coordination and decision-making.
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
## 4 Discussion
### Effect of the Stirrup Rate  
The variation in ultimate shear stress (\(v_{\text{u}}\)) with the stirrup rate for normal RC deep beams and dune sand RC deep beams is shown in Figure 8c. All the specimens of NRC deep beams in Figure 8c are shown with concrete strength as the variable, including: 25 sets from [PERSON] and [PERSON] [52], 28 sets from [PERSON] and [PERSON] [53], 6 sets from [PERSON] and [PERSON] [54], 30 sets from [PERSON] and [PERSON] [55]. The pattern of variation in the stirrup rate was similar for DSRC deep beam specimens and NRC deep beam specimens; the ultimate shear stress increased continuously with an increase in the stirrup rate. This indicated that the addition of DS had no effect on the shear contribution of the stirrup. The ultimate shear stress of the specimens increased by 9.37% and 19.72% when the stirrup rates increased from 0% to 0.168% and 0.252%, respectively, according to a comparison of test data from three specimens with different stirrup rates. This change occurred because as the stirrup rate increased, the stirrup could bear greater tensile stress, slowing down the rate of damage to the concrete strut. This inhibited the expansion of cracks and increased the area of the tensile region, thus, increasing the ultimate shear stress.
# Polarization Influence on Algorithms of TFBG Sensors Data Analysis for Bending Application
## 5 Conclusions  
The practical application of the given technology requires evaluation of all its aspects. The great popularity of fiber optic sensors does not always go hand in hand with the correct assessment of their parameters. In the case of bending sensors, the missing aspect is the algorithmic analysis of optical spectra measurements. The missing link is the assessment of measurement accuracy and resolution. Only such an assessment allows for determination of the possible scope of applications of a given measurement technology. Another overlooked aspect is the role of quantities influencing the measurement. In the case of bending measurements, this quantity is the polarization of the introduced radiation. In this article, we analyzed the resolution and accuracy of two groups of demodulation methods for TFBG grating spectra. We assessed the impact of polarization on individual demodulation methods. We have shown that an important step in optical spectrum analysis is signal preprocessing. The appropriate preparation of spectra allows for expansion of the set of possible algorithms to use. We proposed the use of a PLS algorithm trained using a dataset built for measurements from different polarizations. This solution is characterized by much greater uniformity of error values over the entire measurement range. In our opinion, the research carried out allows to increase the possibilities of using TFBG sensors in practice.  
[PERSON] and [PERSON] contributed to the idea of this work. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] performed data analysis and developed demodulation algorithms. The content of the article was written mainly by [PERSON] The description of the measuring part and the drawings regarding the experiment were made by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Lublin University of Technology (grant number: FD-20/EE-2/301).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Related Works
### Symmetric and Asymmetric Cryptography  
Public-key cryptography has been deemed computationally expensive for small sensor nodes, and traditional public-key algorithms (such as RSA) require extensive computation. As a result, public-key cryptography is not considered feasible for small sensors [23]. However, recent progress in Elliptic Curve Cryptography (ECC) [24] provides new opportunities to utilize public-key cryptography in sensor networks. ECC offers security equivalent to that of public-key cryptography using much smaller key sizes. ECC is especially attractive for constrained wireless devices because the smaller keys result in memory, bandwidth and computational savings. NIST [25] has listed the equivalent key sizes for symmetric and asymmetric cryptography, as Table 1 shows:  
As we known, the security of ECC rests on the difficulty of the elliptic curve discrete logarithm problem. Recently, [PERSON] and [PERSON] [23] proposed a private key agreement and secure communication for heterogeneous sensor networks which is based on pairing-based cryptography over an elliptic curve. Using this protocol, any two nodes that need to communicate can independently compute the same secret key by using pairing and identity-based encryption properties.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Radio Access Network with Distributed RRH  
In the distributed RAN architecture, the base station is separated into two units called BBU and RRH. The RRH is placed on the top of a tower with the antenna, and the BBU is placed in a convenient location that is easily accessible from the RRH. These two units are interconnected by a common public radio interface (CPRI), also known as fronthaul link, as shown in Figure 2a. The RRH functions as a  
Figure 1: Organization of this survey.  
transceiver for the mobile users, and the BBU processes calls and forwards traffic to the mobile core network via carrier Ethernet backhaul, also called backhaul link. The RRH provides the interface to the fiber and carries out digital processing, digital to analog conversion, analog to digital conversion, power amplification, and filtering [26]. The distance between the BBU and RRH can be extended up to 40 km, although this would result in processing limitation and propagation delay [5]. This RAN architecture is developed for 3G and 4G mobile networks.