esa-sceva/satcom-chunk-collection huggingface dataset:
# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## 4 Proposed Algorithms  
To alleviate the number of active SCs and enhance the PE of the system, a linearly increasing IW-BPSO algorithm for SC on/off switching is proposed in this paper. Moreover, a novel SFR technique using CTs is proposed for SC sub-band allocation. The BPSO algorithm is applied first while the linearly increasing IW enhances the convergence of the algorithm. Then, the sub-bands are allocated to the active SCs using the novel SFR technique. It is worth noting that the new SFR technique is applied after the SC switching to allocate the sub-bands to the active SCs, only aiming to reduce the number of operations needed in the sub-band allocation to the SCs
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 7 Micromegas for Neutron Detection  
Charged particles and photons can be directly detected with Micromegas detectors, as they ionise gas atoms in the conversion region. Neutrons can also be detected with a Micromegas; however, an additional neutron/charged particle converter is necessary to create primary electrons in the gas. There are different ways of creating a neutron/charged particle converter. It can be realized by a solid target in front of the detector or by additions to the operational gas. Different converter materials are necessary depending on the neutron energy, and a combination of materials can be used to widen the neutron energy range. Next to the additional converter, all typical benefits of a Micromegas, such as the low material budget, the high-rate capability, and the precise tracking and energy resolution are conserved.
## 2 Theoretical Modeling  
Figure 1 depicts the model in detail: sea ice is regarded as a multilayered porous medium embedded between the water and air half-spaces. The ice is divided into \(n\) layers whose physical properties are different vertically but the same horizontally [16], and lateral dimensions are assumed to be infinite. When an incident wave propagates at the interface between water half-space and the first layer of ice, it is reflected as an acoustic wave in water and gives rise to three transmitted waves [17]: a fast longitudinal wave L\({}_{\mathrm{f}}\), a transverse wave T and a slow longitudinal wave L\({}_{\mathrm{s}}\). When these three refracted waves impinge on the next boundary, these waves generate three reflected waves and three refracted waves, and this process continues at the following ice interfaces [18]. Finally, when reaching the ice-air interface, the longitudinal wave is transmitted into air half-space.  
Figure 1: Reflection model of plane wave from water into multilayered porous sea ice.  
To better illustrate the inner structure of sea ice, the cross-section of the \(i\)th ice layer is chosen to portray it. It can be seen from Figure 2 that porous sea ice is comprised of an ice frame and tubular pores in which fluid is trapped. These pores are randomly distributed and approximately along the depth direction, so sea ice is regarded as a transversely isotropic fluid-saturated porous medium.
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
###### Abstract  
Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR's axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX-spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12\({}^{\circ}\), which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX-spotlight data.  
SAR; dihedral corner reflector; RCS; SCR; deformation; sentinel +
Footnote †: journal: Applied  
0000-0001-9993-3227][PERSON]  
1 Global Navigation Satellite System (GNSS) Research Centre, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (J.-L.); [EMAIL_ADDRESS] (S.M.-J.); [EMAIL_ADDRESS] (G.N.)
## 2 Methodology
### Fourier Direct Grey Model (the FDGM(2,1) Model)  
The Fourier correction approach is one of the residual correction approaches, and it is used to transform the residual into frequency spectra and then select the low-frequency term [20]. In grey prediction, the Fourier series is commonly used to increase the precision of grey models when the data is fluctuating significantly [12].  
Step 1: Where \(x\) is the actual series of n entries and \(\hat{x}\) is the predicted series obtained from the DGM(2,1) model, the residual series named \(e\) is defined as follows:  
\[e=\{e\left(2\right),e\left(3\right),\ldots,e\left(k\right),\ldots,e\left(n \right)\} \tag{9}\]  
where \(e(k)=x(k)-\hat{x}(k)\quad with\quad(k=2,3,\ldots n)\).  
Step 2: Expressing the \(e^{(0)}(k)\) into the Fourier series, the error residual \(e^{(0)}(k)\) is rewritten as follows:  
\[e^{(0)}\left(k\right)=\frac{1}{2}a_{0}+\sum_{i=1}^{M}\left[a_{i}\text{cos} \left(\frac{2\pi i}{n-1}k\right)+b_{i}\text{sin}\left(\frac{2\pi i}{n-1}k \right)\right] \tag{10}\]  
where \(M=\left[\left(n-1\right)/2-1\right]\) is called the minimum deployment frequency of the Fourier series and there is only a select integer number [21].  
Transfer the Fourier series as:  
\[e=AB \tag{11}\]where  
\[A=\left[\begin{array}{ccc}\frac{1}{2}&\cos\left(\frac{2\pi\times 1}{n-1}\times 2 \right)&\sin\left(\frac{2\pi\times 1}{n-1}\times 2\right)&\cdots&\cos\left(\frac{2\pi \times R}{n-1}\times 2\right)&\sin\left(\frac{2\pi\times R}{n-1}\times 2\right)\\ \frac{1}{2}&\cos\left(\frac{2\pi\times 1}{n-1}\times 3\right)&\sin\left(\frac{2\pi \times 1}{n-1}\times 3\right)&\cdots&\cos\left(\frac{2\pi\times R}{n-1}\times 3 \right)&\sin\left(\frac{2\pi\times R}{n-1}\times 3\right)\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ \frac{1}{2}&\cos\left(\frac{2\pi\times 1}{n-1}\times n\right)&\sin\left(\frac{2\pi \times 1}{n-1}\times n\right)&\cdots&\cos\left(\frac{2\pi\times R}{n-1}\times n \right)&\sin\left(\frac{2\pi\times R}{n-1}\times n\right)\end{array}\right] \tag{12}\]  
The parameters of \(B\) are obtained by using the ordinary least squares method (OLS), which results in the following equation:  
\[B=\left(A^{T}A\right)^{-1}A^{T}\left[e\right]^{T} \tag{13}\]  
From the predicted series \(\hat{x}\) and \(\hat{e}\), the Fourier modified series \(\hat{x}_{f}\) is determined by:  
\[\hat{x}_{f}=\left\{\hat{x}_{f}(1),\hat{x}_{f}(2),\hat{x}_{f}(3),\ldots,\hat{x} _{f}(n)\right\} \tag{14}\]  
where \(\left\{\begin{array}{cc}\hat{x}_{f}(1)=x(1)\\ \hat{x}_{f}(k)=\hat{x}(k)+\hat{e}(k)&(k=2,3,\ldots n)\end{array}\right..\)
## 2 Material and Methods  
### Site Description  
This study was carried out in the Rzecin (POLWET) peatland area (Figure 1) located in western Poland (52 deg45'N, 16 deg18'E, 54 m asl). The whole Rzecin peatland, which is considered a poor fen, covers an area of 114 hectares [39]. According to historical and cartographic data, the peatland is a result of lake shallowing, a decelerated but ongoing process [39; 40]. At the middle of the peatland, there is a station where measurements of greenhouse gas exchange (CO\({}_{2}\), CH\({}_{4}\), H\({}_{2}\)O) have been carried out continuously since 2004 [41; 42; 43; 44; 45]. Geomorphologically, this peatland is located in between the dune areas of the Notec Forest within the drainage basin of the Warta and Notec rivers. The past 200 years of paleoecology history of the peatland is described in detail in [PERSON] et al. [46].  
The entire Rzecin peatland is extremely valuable from a floral and natural conservation perspective, and is included in the Natura 2000 network protected areas covering Europe's most valuable and threatened species and habitats (\"Torfowisko Rzecinskie\" PLH300019 [47]). Different species of brown mosses, _Sphagnum_, and vascular plants (e.g., _Eriophorum angustifolium, Carex lusiocarpa, Carex rostrata, Carex limosa_, and _Oxycoccus plulstris_) make this region eco-biologically unique [46; 48]. In total, 127 species of vascular plants belonging to 43 families, and 34 taxa of mosses belonging to 10 families, were observed in this peatland area [48]. Among mosses, the most abundant moss species belong to _Sphagnaceae_ and _Amblystegiaceae_. Twenty-six of the plant species found in Rzecin peatland are considered as rare and endangered at a regional-scale, while 20 species are locally endangered [48]. The annual mean air temperature of the study area is 8.3 degC, while the average annual precipitation sum is 530 mm (for 2004-2014).  
Figure 1: Location of the Rzecin peatland experimental site in the Wielkopolska region, Poland. An RGB composite map was obtained by combining reflectance bands at 156 nm, 105 nm, and 51 nm for the red, green, and blue bands, respectively—including flight lines of the HyPlant over the site during the Spectrometry of a Wetland and Modelling of Photosynthesis (SWAMP) campaign on 11 July 2015.
# Interfacial Shear Properties of Red Clay and Polyurethane with Different Densities
## 4 Model Validation
### Analysis of Finite Element Simulation Results of Shear Strength  
Figure 12 presents a comparison between the simulation results for shear strength and experimental data under different polyurethane densities and normal stresses. The figure shows that while there is some error between the simulation and test results, it falls within an acceptable range. Specifically, the composite sample with a polyurethane density of 0.5 g/cm\({}^{3}\) exhibits the smallest shear strength error, with a difference ranging from 1 kPa to 28 kPa and an error of less than 8%. The composite sample with a polyurethane density of 0.8 g/cm\({}^{3}\) has the largest shear strength error, with a difference between 1 kPa and 66 kPa and a maximum error of 25%. For the composite sample with a polyurethane density of 0.2 g/cm\({}^{3}\), the shear strength difference ranges from 10 kPa to 39 kPa, with a maximum error of 17%. However, when the polyurethane density is low, the polyurethane's elastic modulus is small, resulting in weak bonding strength between the polyurethane and soil, making it prone to structural failure under normal stress. Conversely, when the polyurethane density is too high, the single mechanical model of polyurethane, as a polymer elastomer, fails to accurately describe its stress-strain relationship, leading to bias when using the elastic modulus alone. This comparison suggests that the bilinear cohesive force model is a feasible approach for simulating the interfacial shear behavior of polyurethane-red clay composite specimens.  
Figure 10: Setting of simulated working conditions for direct shear test.  
Figure 11: The stress-displacement curve test results of different density composite specimens are compared with the finite element simulation results: (**a**) polymer density of 0.2 g/cm\({}^{3}\); (**b**) polymer density of 0.5 g/cm\({}^{3}\); (**c**) polymer density of 0.8 g/cm\({}^{3}\).  
Figure 12: Comparison chart of shear strength test values and numerical simulation values of composite specimens with different densities under different normal stresses.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Design of Reliable Cluster Routing Protocols
#### 2.5.2 Evaluation Indicators  
In order to obtain the optimal number of clusters, the quality of the clustering results was comprehensively assessed by using three evaluation metrics: the Davies-Bouldin Index (DBI), Calinski-Harabasz Index (CHI), and Silhouette Coefficient (SC). Calculations for the DBI, CHI, and SC are shown as Equations (9)-(11).  
\[DBI=\frac{1}{k}\sum_{i=1}^{k}\underset{j\
eq i}{max}\frac{S_{i}+S_{j}}{d_{ij}} \tag{9}\]  
where \(k\) is the total number of clusters, \(S_{i}\) is the average distance between points in the \(i\)th cluster and its center of mass, \(S_{j}\  
The operating platform for the simulation experiments comprised an Intel(R) Core(TM) i7-8550 CPU, 16 GB of RAM, and a Windows 10 system with source code running on MATLAB (R2022b, MathWorks, Natick, MA, USA).
# Research on the Settlement Patterns of Tunnel-Surrounding Rock Under Groundwater Conditions
## 4 Results and Discussion
### Settlement Simulation Analysis
#### 4.2.2 Displacement Curves Analysis  
During the excavation process, settlement data at the crown and ground surface of each monitoring cross-section were collected. Graphs were then generated to visually analyze the settlement patterns. Subsequently, a comparative analysis was performed between the settlement conditions under both scenarios with and without groundwater influence.  
As illustrated in Figure 9, the settlement rates of the vault and ground surface at all monitoring cross-sections demonstrate consistent trend characteristics. After tunnel excavation, the vault rock mass is in an unsupported state, resulting in a substantial increase in vault settlement rates. The ground surface settlement rates are lower than the vault settlement rates due to time-space effects. As the support structure becomes effective, the settlement rates gradually decline. Eventually, with soil stress redistribution and soil consolidation and compaction, the settlements stabilize. The simulation processes in the absence of and under groundwater influence exhibit consistent trend characteristics, as shown in Figure 10. Both simulation processes demonstrate a settlement evolution pattern characterized by \"steep decline--gradual decline--stable\". However, settlement values in the absence of groundwater influence are significantly lower than those under groundwater influence.
## 3 Results and Discussions
### Microorganisms Related to Verticillium Wilt of Horseradish Roots
destructans_ in relation to the underground parts of many plant species [63; 64; 65; 66; 67]. According to [PERSON] [28], _F. acuninatum_ is a constant component of the complex of Verticillium wilt of horseradish roots. Pathogens such as _F. acuninatum_, _R. solani_, and _G. irregulare_ were most frequently isolated from the ChP plot. This proves chemical protection to be ineffective against these pathogens. In particular, _R. solani_ exhibits exceptional resistance to synthetic fungicides [68; 69]. In view of the above information, the use of BCAs and PGPs is revealed as an alternative method that can be used to reduce pathogens and disease severity without adversely affecting the environment. [PERSON] [70] claims that _P. oligandrum_ not only reduces the concentration in soil of _R. solani_, _Fusarium_ spp. and many other significant pathogens, but it also increases the population of antagonists. Ourstudydoes not supportthis conclusion. The antagonistic species of _Trichoderma viride_ with the highest frequency was found in the community of fungi isolated from the infected horseradish roots of the BPEm plot (treated with _E. maxima_ extract). This was also the observation of previously published studies [61; 71]. Antagonism of fungi of the genus _Trichoderma_ against _R. solani_ has been observed repeatedly, and other mechanisms such as competition for food and induction of resistance genes have been pointed out [72; 73; 74]. In the present study, the increase in _T. viride_ frequency was also accompanied by a smaller proportion of _R. solani_. [PERSON] and [PERSON] [75] report that _E. nigrum_ is an antagonist effective in reducing the pathogenic activity of _P. irregulare_. The same reaction was observed in our study. Infected roots from the combination of seedling treatment with _B. subtilis_ and _P. oligandrum_ as well as _E. maxima_ were colonized by _E. nigrum_ with the highest frequency, while the share of _G. irregulare_ decreased. Diversity of interactions between the dominant microorganisms colonizing horseradish roots with symptoms of Verticillium wilt of horseradish roots was demonstrated (Table 6). Eudominants _V. dahliae_ significantly negatively correlated with _I. destructans_ and _T. viride_. It was concluded that _I. destructans_ had different microhabitat preferences than _V.dahliae_. The frequency of this fungus increased in the years with more rainfall, while the development of _V. dahliae_ was favored by drought (Supplementary Table S1). It turned out that _T. viride_ as an antagonist can significantly reduce the population of _V. dahliae_ as well as _G. irregulare_ and _F. acuninatum_. _E. nigrum_ showed very strong antagonism towards _F. acuninatum_ and _F. acuninatum_.The significantly positive strong correlation between _V. dahliae_ and _A. brassicae_, as well as _R. solani_ and _G. irregulare_, indicates the synergism of these pathogens in the Verticillium wilt of horseradish roots complex.
## 5 Results and Discussion
### Frequency Dependence of \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) Ratio  
The \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratio exhibits a clear dependence on frequency, as expected due to the skin and proximity effects. Figure 6 illustrates the increase in the \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratio with frequency for single-segment, three-segment, and five-segment cables in both 2D and 3D models.  
As frequency increases from 0 Hz to 250 Hz, the \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratio rises significantly across all segmentations. This trend is consistent with the theoretical understanding that higher frequencies lead to reduced skin depth (\(\delta\)), confining more current to the conductor's surface and thereby increasing AC resistance. The rate of increase in the \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratio is consistent across different segmentations, indicating that while segmentation can mitigate the effects to some extent, the dominant influence is frequency-dependent.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Segment** & \multicolumn{2}{c}{**Single-Segment**} & \multicolumn{2}{c}{**Three-Segment**} & \multicolumn{2}{c}{**Five-Segment**} \\ \cline{2-7}
**Frequency (Hz)** & **2D** & **3D** & **2D** & **3D** & **2D** & **3D** \\ \hline
0 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
50 & 1.85 & 1.86 & 1.76 & 1.76 & 1.74 & 1.75 \\
150 & 3.04 & 3.07 & 2.91 & 2.92 & 2.88 & 2.91 \\
250 & 3.88 & 3.93 & 3.70 & 3.74 & 3.66 & 3.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratios for 2D and 3D models.  
Figure 6: \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratios for 2D vs. 3D models across different segmentations and frequencies.
## 3 Methodology
### Model BFRS and Analyze Driving Behavior  
In this work, BFRS is defined as situations/road surface conditions, including speed breakers and transverse gutters, potholes, and cracks distributed on the road surface that cause abnormal changes in the recorded data stream, as shown in Figure 2. Taking a speed breaker shown in Figure 2 as an example, vehicle \(A\) is passing the BFRS \(B\) with speed \(v\). The length (between front wheels and rear wheels) of the vehicle and BFRS is \(l_{A}\) and \(l_{B}\), and the accelerometer sampling frequency is \(h\). Hence, the sampling period for object \(B\) can be denoted as (4).  
\[\Delta t=\frac{l_{B}}{v} \tag{4}\]  
However, based on [PERSON]'s theorem, a continuous signal has a maximum frequency of \(f_{max}\), and to fully reconstruct this signal, the sampling frequency \(f_{s}\) must be at least twice the maximum frequency of the signal. Mathematically, it can be represented as \(f_{s}\geq 2f_{max}\). Therefore, the minimum frequency \(H\) to catch the bump feature can be denoted as shown in (5).  
Figure 1: The BFRS detection workflow.  
Figure 2: The BFRS detection process and different kinds of BFRS. (**a**) The BFRS detection process, (**b**) speed breaker, (**c**) transverse gutter, (**d**) pothole, and (**e**) crack.  
\[H=\frac{2}{\Delta t} \tag{5}\]  
It can be observed that the faster the speed \(v\) or the smaller the BFRS length \(l_{B}\), the higher minimum frequency \(H\) is needed. For example, in a vehicle with a length \(l_{A}\) = 2.7 m, a length \(l_{B}\) = 0.6 m, and a driving speed \(v\) ranging between 1 km/h and 60 km/h, the optimal minimum sampling frequency \(H\) should be larger than 0.93 Hz, and the frequency of BFRS ranging between 0.93 Hz and 55.56 Hz, based on (4) and (5).  
Accordingly, in this work, the sampling frequency of the accelerometer and gyroscope is set at 100 Hz. On the other hand, suppose a vehicle is driving at the speed \(v\) = 30 km/h and the length \(l_{B}\) is 0.6 m, then the BFRS recorded frequency is 13.98 Hz. However, the length \(l_{A}\) between front and back wheels is 2.7 m; there can be a feature with a frequency of 3.09 Hz detected from the recordings since two bump features are recorded by front wheels and back wheels, i.e., the BFRS is recorded twice, and a new signal is generated.