esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cognitive Radio Combined with WBAN
### Tier-2 WBAN Model
#### 4.2.1 Central Controller Architecture  
The IP-BNC and OP-BNC are controlled by the CR central controller, which is divided into three stages: the CR Controller (CRC), the server system, and the BNC, as shown in Figure 4. The server controls the CRC, initiates commands to the CRC, and informs the BNC of the allowable transmission power. CRC plays the role of master, while the BNC acts as slaves. This architecture operates in a licensed channel for the IP-BNC and unlicensed channel OP-BNC. Next, we summarize the components in the architecture. This unit is deployed in the local area where all the IMDs patients are monitored and diagnosed for treatment; thus, a dedicated environment and server devices are allocated.
## 3 Results  
### Distribution of Strains by Origin and Capacity to Reduce Nitrate/Nitrite  
Seventy-five strains were isolated from tallygrass prairie soils after enrichment in nitrate broth (Supplemental Table S2). Out of the 75 strains tested, 48 strains were identified as nitrate reducers, six as nitrite reducers, and 21 strains did not show the ability to reduce nitrate or nitrite under the test conditions (Table 1). Among the 54 that showed a reduction of nitrate/nitrite, 17 were obtained from brine/oil-contaminated soils, nine from oil-contaminated soils, and 28 from uncontaminated prairie soils (Table 1, Figure 1). The highest proportion of nitrate/nitrite reducers isolated from a particular type of site was obtained from brine/oil contaminated soil (85%), next highest from oil-contaminated sites (75%), and the lowest proportion from the uncontaminated sites (65%). \begin{table}
\begin{tabular}{c c c c c} \hline
**Taxonomic Group** & **Genus \({}^{*}\) (Total \# of Strains)** & **Brine/Oil** & **Oil** & **Prairie** \\ \hline \multirow{6}{*}{\(\gamma\)-Proteobacteria} & _Steurotrophomonas_ (27) & 3 NR \({}^{**}\) & 2 NR, 3′′None\({}^{\prime}\) &
\begin{tabular}{c} 11 NR, 1 DN \\ 7 “None\({}^{\prime}\) \\ \end{tabular} \\ \cline{2-5}  & _Pseudomonas_ (9) & 2 NR, 2 DN & 1 NR & 3 NR, 1 DN \\ \cline{2-5}  & _Aeromonas_ (1) & 1 NR & 0 & 0 \\ \cline{2-5}  & _Serratia_ (1) & 0 & 1 NR & 0 \\ \cline{2-5}  & _Enterobacter_ (3) & 0 & 3 NR & 0 \\ \cline{2-5}  & _Acinetobacter_ (5) & 0 & 0 & 2 NR, 3 ”None\({}^{\prime}\) \\ \cline{2-5}  & Total \# Y Proteobacteria (46) & 8 & 10 & 28 \\ \hline \multirow{6}{*}{\(\alpha\)-Proteobacteria} & _Ensifer_ (2) & 1 DN & 0 & 1 DN \\ \cline{2-5}  & _Bosea_ (2) & 2 ”None\({}^{\prime}\) & 0 & 0 \\ \cline{2-5}  & _Phenylobacterium_ (1) & 1 NR & 0 & 0 \\ \cline{2-5}  & Total \# \(\alpha\)-Proteobacteria (5) & 4 & 0 & 1 \\ \cline{2-5}  & _Achromobacter_ (4) & 1 NR & 0 & 3 NR \\ \cline{2-5}  & _Burkholderia_ (1) & 0 & 0 & 1 ”None\({}^{\prime}\) \\ \cline{2-5}  & Total \# \(\beta\)-Proteobacteria (5) & 1 & 0 & 4 \\ \hline \multirow{6}{*}{Actinobacteria} & _Arthobacter_ (1) & 1 ”None\({}^{\prime}\) & 0 & 0 \\ \cline{2-5}  & _Rhodococcus_ (1) & 0 & 0 & 1 ”None\({}^{\prime}\) \\ \cline{1-1} \cline{2-5}  & _Microbacterium_ (1) & 0 & 1 NR & 0 \\ \cline{1-1} \cline{2-5}  & _Kocuria_ (1) & 0 & 0 & 1 NR \\ \cline{1-1} \cline{2-5}  & Total \# Actinobacteria (4) & 1 & 1 & 2 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the 75 strains by genus, origin, and capacity to reduce nitrate/nitrite.
## 4 ESS Controller Parameter Design for Electromechanical Oscillation Suppression  
Based on the above analysis, it can be known that when the power system runs stably, the feedback signal obtained by the ESS is 0, the ESS does not work, and the system inertia and damping parameters are the inherent coefficients of the equivalent SG-based power system. When the power grid is disturbed and the electromechanical oscillation occurs, the PID controller will act accordingly, and the controller parameters will be dynamically modified in real time according to the specific characteristics of the frequency oscillation. The inertia effect and damping ability of the power system are equivalently changed, so that the grid frequency is also changed according to the rules given by (12) and (13), so as to achieve better control performance.  
Figure 6: Grid frequency response curve.  
When the power system is disturbed and begins to oscillate, the ESS will operate immediately. Based on (2) and (11), it can be known that the dynamic process of the equivalent SG system at this time satisfies the following equation:  
\[T_{\rm J}\frac{\mathrm{d}\Delta\omega}{\mathrm{d}t}=-T_{\rm S}\Delta\delta-T_{ \rm D}\Delta\omega, \tag{14}\]  
Because the SG system itself has a strong synchronous operation capability, theoretically, the ESS does not have to provide additional synchronous torque to the power system [19]. That is to say, the ESS controller parameter \(K_{\rm i}\) can be designed to be 0, which is the main reason why I controller-based control strategies are seldom used in the existing literature. In the simulation section of this paper, the parameter of the I controller is set as a value approximating to zero, therefore, the \(T_{\rm S}\) in (14) can be approximated as \(K_{\rm g}\). The research findings in [19] indicate that \(K_{\rm g}\) is a parameter with a value close to 1. Considering that the change value \(\Delta\delta\) of the power angle is usually small, it is approximated to 0 in most literatures. Based on this, the \(T_{\rm S}\Delta\delta\) part in (14) can be ignored for the controller parameters' design in this paper and (14) can be rewritten as:  
\[T_{\rm J}\frac{\mathrm{d}\Delta\omega}{\mathrm{d}t}=-T_{\rm D}\Delta\omega, \tag{15}\]  
Considering the ROCOF constraint given by (13), in the limit condition, (15) can be rewritten as an algebraic equation under limit conditions. To remove the absolute value in (13), it can be divided into two cases; ROCOF greater than 0 and less than 0.
### State of the Art in Proximal Sensing Sensors for SWC
#### 1.1.1 Ground-Penetrating Radar  
Ground-Penetrating Radar (GPR) is a non-invasive technique capable of mapping SM and is commonly used to detect buried objects (pipes, roots), voids, and cracks [10].  
GPR estimates the dielectric permittivity of the soil by transmitting electromagnetic waves and recording the travel time between a RADAR transmitter and receiver. The velocity of the EM wave in a specific medium is directly related to the dielectric permittivity, and therefore, one can estimate the SWC based on the electromagnetic waves' travel time because of the large contrast between the dielectric permittivity of soil's components: water, air and minerals [13].  
GPR devices can be operated on the soil surface or from off-ground (proximal) or airborne platforms (remote) [14].  
GPR can accurately measure soil moisture, reporting frequently a correlation above 0.93 with conventional soil sensors [15; 16]. GPR is one of the few technologies that can map the SM variation in space [12]. The term \"map\" relates to being feasible and practical to achieve a birds-eye 2D representation where each cell (in this case) has SM information. If a GPR-like system is used, each map cell will have a vector of soil moisture readings over depth. Some disadvantages are the time it takes to map the large areas and being prone to errors in saline soils [9; 17].
# Tensegrity Applications to Architecture, Engineering and Robotics: A Review
## 4 Robots and Electromechanical Engineering
### Underwater Robots  
[PERSON] et al. [98] proposed a portable, lightweight underwater robot capable of translating and rotating, necessary for inspecting marine structures such as breakwaters, pipes and quay walls (Figure 32a). With this purpose, they designed a three-strut tensegrity structure connecting three pipes with rubber strings. They described a transformation mechanism for their underwater robot with a deformable tensegrity structure. Similar to this project, but more recent, [PERSON] et al., proposed the U3 DTT, an Underwater 3D Tactile Tensegrity, which structure is more complex, based on the expanded cuboctahedron [99]. It uses soft self-powered triboelectric nanogenerators and deep-learning-assisted data analytics, proving to have several degrees of freedom, a high sensitivity, quick response rates and shape adaptability, with the advantage of not being very expensive.  
Another bio-inspired (like those of next section), fish-like robot based on a tensegrity system was developed by [PERSON] et al. [100]. They built a prototype in the form of a rainbow trout powered by a water-resistant servomotor located in the robot's head (Figure 32b). While the elastic cables of the tensegrity structure simulate the muscles, the rigid elements play the bone/tissue's part. Their authors claim that the system is a class k = 2 tensegrity structure, significantly simpler than others used for underwater robots. When they tested the robot's swimming speed, it reached 0.23 m/s, which is comparable to previous robots of this kind.
# Methods of Analyzing Microsized Plastics in the Environment
## 2 Microscopy Analyses  
### Microscopy  
Light microscopy is widely used to identify microplastics in the range of several hundred micrometers. Microplastics generally have no luster and are identified based on physical reaction properties, such as specific elasticity or hardness [24]. Furthermore, microplastics usually take the form of fibers, fragments, and beads in the environment [39]. About 70% of microplastic samples are transparent [40]. Colored plastics to which dye is added during synthesis can be easily identified with an optical microscope [41]. It is difficult to characterize plastic particles in the size range below 100 um that are colorless or non-obvious in shape. Furthermore, the poor separation of sample particles can interfere with the microscopic identification of microplastics. In addition, sediments and biological materials that are not completely removed by chemical decomposition make it difficult to observe microplastics under a microscope. Previous studies have shown that the rate of microscopic misidentification of plastic-like particles is more than about 20% and more than 70% for transparent particles. Synthetic and natural fibers constitute similar particles with interfering factors, making it difficult to distinguish them under a microscope alone. \"Destructive tests\" have been conducted to identify particles, but cotton fibers are often misidentified as plastic. Studies have used a method to identify such particles to compensate for this; particles are put in contact with a heated needle, and the plastic particles melt under such treatment [42; 43].  
In the plastic synthesis and textile industries, whitening agents are the most used plastic additives. Since whitening agents generally have fluorescence, the detection of plastics can be performed through a fluorescence microscope. However, whitening agents are also added to paper. Detection errors may still occur because there are substances with a characteristic of self-fluorescence among minerals [41].
###### Abstract  
Vegetation exerts profound influences on evapotranspiration (ET) partitioning. Many studies have demonstrated the positive impact of vegetation cover on the ratio of transpiration (T) to ET. Whether it is universally true with regard to different vegetation types and different sites is understudied. In this study, five sites in Northern China with different vegetation types were selected for comparison study. ET partitioning is conducted using an approach based on the concept of the underlying water use efficiency with eddy covariance measurements. The results show various patterns of vegetation's effects over ET partitioning and, when compared with existing studies, also reveal a new relationship between the T/ET ratio and Normalized Difference Vegetation Index (NDVI) at some of the sites. At the alpine meadow site, the T/ET ratio gradually increase when NDVI is low and rapidly increase as NDVI go beyond a certain value, whereas at the arid shrub site, the T/ET ratio rapidly increase when NDVI is low and plateaus at a certain value when NDVI reaches a relatively high value. In deciduous forest, the T/ET ratio becomes unresponsive to NDVI beyond a threshold value. This study also reveals that irrigation schemes play a major role in determining the correlation between the T/ET ratio and NDVI because the T/ET ratio becomes well correlated with NDVI in case of flood irrigation and irrelevant to NDVI in the case of muld drip irrigation. Furthermore, this study helps us to understand ET partitioning under different sites and different human activities such as irrigation. These findings can help policymakers to better understand the connection between vegetation and climate change or human activities and provide significant information for water management policy.  
ET partitioning; vegetation; eddy covariance; underlying water use efficiency (_uWUE_); NDVI; climate change +
Footnote †: journal:  
Article
## 2 Materials and Methods
### Detrended Moving Average Analysis  
The detrending moving average (DMA) method is used to estimate the scaling exponent of long-range correlated series [10], including EEG during cognitive workload [20]. The DMA algorithm is widely applied to evaluate the long- and short-term correlations of random time series, both one- and high-dimensional, in time and spatial domains [21].  
DMA analysis consists of the following steps: First, the time series of each EEG channel are filtered using a 4-th order Butterworth band-pass filter with a pass band corresponding to the required frequency range described in the experiment design. The values of the cutoff frequencies were chosen directly from specified EEG bands: \(\theta_{1}\)[3.5, 5.8], \(\theta_{2}\)[5.9, 7.4], \(\alpha_{1}\)[7.5, 9.4], \(\alpha_{2}\)[9.5, 10.7], \(\alpha_{3}\)[10.8, 13.5], \(\beta_{1}\)[13.6, 25], \(\beta_{2}\)[25.1, 40] Hz. As the second step, the Hilbert transform was applied to each filtered signal to obtain the analytic signal, and then the envelope was estimated as the absolute value of the analytic signal [22].  
Afterwards, we integrated the transformed signal as follows:  
\[y[i]\ =\ \sum_{j=1}^{i}x[j], \tag{1}\]  
In the next step, we calculated the fluctuation function \(F(s)\) of the integrated series:  
\[F(s)\ =\ \sqrt{\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}y[i]-\tilde{y}_{\text{SG}}^{(m,s)} [i]\Bigr{)}^{2}}, \tag{2}\]  
where \(\tilde{y}_{\text{SG}}^{(m,s)}\) represents the \(m\)-th order Savitzky-Golay smoothing filter [23] for \(\{y[i]\}\), and \(s\) is window length of the Savitzky-Golay filter. In our approach, we used the fourth Savitzky-Golay filter that corresponded to the centered DMA [24].  
A linear relationship on a double-logarithmic plot of \(F(s)\) against scales \(s\) indicates the power-law scaling range. In this scaling range, fluctuations can be characterized by a scaling exponent \(\alpha\). Scaling exponent \(\alpha\) was derived from the slope of the linear part of the relation between \(log(F(s))\) against scales \(log(s)\), where the linear part was empirically chosen to fit the aim of the research for all the subjects with different types of stimulus. As a result, the values of scaling exponents were obtained for all channels in the defined frequency bands. Depending on its value, the meaning of the scaling exponent is interpreted as follows:  
1. \(\alpha<0.5\)--long-range anticorrelated signal;
2. \(\alpha\ =\ 0.5\)--uncorrelated signal;
3. \(\alpha>0.5\)--long-range correlated signal;
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation  
In this section, SL_GAS is compared with most related schemes [11; 16; 20], according to security requirements, computation cost, and transmission delay.
## 1 Introduction
We expected this method to result in an improved approach to BPH management for the following reasons:  
* In contrast to previous studies that have only used weather factors from ground stations for pest population forecasting, this study proposed an approach that also uses host-plant phenology data extracted from satellite-based NDVI time series in order to improve the accuracy of forecasting models. * By using a combination of weather and host-plant phenology data integrated with an ANN algorithm, a more accurate and precise forecasting can be provided (in comparison to the traditionally used linear regression models). These findings could be applied to support an integrated pest management (IPM) programme to help farmers reduce pesticide use and minimize crop loss in rice paddy fields, such as in the central plain of Thailand.
# Detection of Household Furniture Storage Space in Depth Images
## 2 Related Research
### Finding Empty Space within Furniture  
In [20], modular furniture is segmented in 2D image into doors, drawers, and shelves. The method returns 2D bounding boxes of the front faces of the closed furniture and does not provide information about volumes and 3D positions of storage space.  
In our previous work [21], an algorithm is proposed for detecting empty spaces in storage furniture based on a voxelized 3D model. This algorithm takes voxelized 3D CAD models of furniture as input and computes 3D bounding boxes of the empty space within these models. We have improved this algorithm for better detection of storage volumes in more complex CAD models. While in [21], the proposed approach was tested only on few synthetic examples, in this study we used the improved method to generate a dataset for training of a neural network which detects storage space in depth images acquired by a real 3D camera.  
There are not many other approaches that focus on empty space detection in storage furniture. In [17], related research is presented that provides information about empty space in furniture. The goal of the presented network, SSCNet, is to predict both occupancy and object category for voxels on the observed and occluded regions. This includes labeling of voxels belonging to different objects and voxels representing empty space, which is not necessarily storage space. Similarly, in [22], real-time semantic mapping for indoor environment was proposed. Labeled voxels represent parts of the room, including furniture and empty space. Storage volumes are, however, not labeled. This approach is evaluated on real-world 3D scenes. In contrast, the main idea of the approach presented in our work was to detect storage space bounding boxes in depth images, independently of the small objects within that space. The presented detection of storage space bounding boxes with supporting horizontal surface can be applied in automatic item placing and retrieval planning.
## 3 Results and Discussion
In the images of \(\mu\)-PL, the behavior as light guides of both structures is also observed. Due to the high refractive index of ZnO (\(\mathrm{n_{ZnO}}\approx 2\)) with respect to that of the surrounding medium (\(\mathrm{n_{air}}\approx 1\)) and the cross-section geometry of the structures, the light can be confined inside by internal reflections. The light beams travel the same optical path within the cavity, producing interference between successive beams. This interference pattern is reflected in the form of modulations in the photoluminescence spectrum, as observed in the spectra of Figure 3:1 To further assess the origin of these modulations, calculations were performed for the structure and spectra are shown in part a of the figure. In this case, the cross-section is a rectangle which allows two types of resonant modes, Fabry-Perot (FP) or Whispering Gallery (WG). However, for this particular structure, since some of the angles  
Figure 3:2 Scheme of the different electronic levels involved in the luminescence transitions of ZnO and Eu\({}^{3+}\). Figure 3:3 \(\mu\)-PL images and spectra recorded on individual structures grown on a sample with 5 wt% Li. In part **(a)**, the selected structure is tape-like and part **(b)** a plate. In both cases, modulations are clearly observed in the near-band edge emissions. The images also show that the structures act as waveguides. are well below the critical angle for internal reflection, which is around 30\({}^{\circ}\), the WG modes cannot be established, and FP modes are more likely to occur. For an FP cavity, the length of the optical path (\(\delta_{op}\)) can be calculated as:  
Figure 3:4  
where \(n\) is the material refractive index, \(\lambda\) is the wavelength and \(\Delta\lambda\) is the separation between resonant modes. This calculation gives an optical path length of 36 \(\upmu\)m for our case, in agreement with the lateral dimension of the structure measured from the optical image. The quality factor (Q) and finesse (F) of the cavity can also be calculated (Table 2). The results are very promising, with a Q value of 1022 for a wavelength of 384 nm, which indicates that these structures can be used in photonics applications in the UV range. The incorporation of Li into the structures improves their crystallinity and shape regularity which drastically enhances their behavior as optical resonators, as already reported in previous works [28; 29; 30].
## 4 Conclusions  
In summary, a dual-frequency wideband LTC polarization conversion based on a reflective metasurface is presented in this article. The proposed converter can convert EM-waves into RHCP and LHCP in a dual non-adjacent frequency band of 16.49-23.54 GHz and 26.44-34.56 GHz, which corresponds to the relative bandwidths 35.23% and 26.62%, respectively, according to the 3 dB axial ratio property. Moreover, a comprehensive theoretical investigation is proposed to get a physical insight into the underlying cause of the LTC polarization converter. It can be related mainly to the second pair of resonance modes, which keep \(\Delta\varphi_{uv}=+90^{\circ}\) or an integral multiple of \(-90^{\circ}\) in a dual and ultrawide-band frequency range of 16.27-23.79 GHz and 27-35.23 GHz. Any reflective converter can be operated as a dual-frequency wideband LTC polarization conversion if the \(\Delta\varphi_{uv}\) between \(r_{uu}\) and \(r_{vv}\) is close to \(\pm 90^{\circ}\) or an odd integral multiple of \(\pm 90^{\circ}\).  
Writing--original draft preparation, A.M.; writing--review and editing, J.Z. and C.W.; methodology, A.M.; software, A.M.; formal analysis, Z.A.A.; validation, M.I.; investigation, C.W.; data curation, S.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (No. 2019 XD-A07), the National Nature Science Foundation of China (Grant No. 62271063), the director Fund of Beijing Key Laboratory of Space-ground Interconnection and Convergence and the National Key Laboratory of Science and Technology on Vacuum Electronics.  
Not applicable.  
The authors declare no conflict of interest.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques
#### 3.2.3 Air Flow Energy  
Harnessing _wind energy_ is another energy-harvesting technique that can be utilized to provide power to sensor nodes in agricultural applications. An adaptive routing protocol, wind energy harvesting, and sleep scheduling were considered in [126] to minimize the power consumption of the ZigBee transceiver and extend the lifetime of the WSN.