esa-sceva/satcom-chunk-collection huggingface dataset:
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 3 Results  
### Optimization of Growing Crabgrass in Multi-Well Plates  
Considering its fast and even germination and compact plant growth, crabgrass was selected as a model plant for this study. Crabgrass seeds were sown in wells of 24- and 96-well plates, and we checked their seedling growth. The well size of the 96-well plate was too small to grow crabgrass, while that of the 24-well plate was large enough to grow crabgrass. Therefore, the 24-well plate was selected for further study. To optimize seed germination and seedling growth, various media, including water, agarose (0.4%, \(w/v\)), and Murashige and Skoog media, were tested, and it was found that that the medium composed solely of agarose (Inclonebiotech Co., Seongnam, Republic of Korea) was appropriate [18]. To optimize growth conditions in the 24-well plate, a number of seeds per well\({}^{-1}\) and the temperature from germination to seedling growth were tested, and it was found that 15 seeds per well\({}^{-1}\) appeared to be appropriate, and the optimum growth conditions in the 24-well plate were 30 \({}^{\circ}\)C/20 \({}^{\circ}\)C (day/night) with a 16 h photoperiod until the 1-leaf stage. At the 1-leaf stage, the crabgrass had a sufficient canopy size to exhibit spectral responses to the herbicides. Therefore, our results demonstrated that crabgrass can be grown in 24-well plates for herbicide bioassays. Crabgrass seeds at 15 seeds per well\({}^{-1}\) need to be sown in wells containing 0.8 mL of a 0.4% agar medium and incubated in a growth chamber that is maintained at 30 \({}^{\circ}\)C/20 \({}^{\circ}\)C (day/night) with a 16 h photoperiod until the 1-leaf stage (5 days). After herbicide treatment, the crabgrass needs to be placed in the growth chamber for image acquisition.
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
###### Abstract  
Hyperspectral images (HSIs) are data cubes containing rich spectral information, making them beneficial to many Earth observation missions. However, due to the limitations of the associated imaging systems and their sensors, such as the swath width and revisit period, hyperspectral imagery over a large coverage area cannot be acquired in a short amount of time. Spectral super-resolution (SSR) is a method that involves learning the relationship between a multispectral image (MSI) and an HSI, based on the overlap region, followed by reconstruction of the HSI by making full use of the large swath width of the MSI, thereby improving its coverage. Much research has been conducted recently to address this issue, but most existing methods mainly learn the prior spectral information from training data, lacking constraints on the resulting spectral fidelity. To address this problem, a novel learning spectral transformer network (LSTNet) is proposed in this paper, utilizing a reference-based learning strategy to transfer the spectral structure knowledge of a reference HSI to create a reasonable reconstruction spectrum. More specifically, a spectral transformer module (STM) and a spectral reconstruction module (SRM) are designed, in order to exploit the prior and reference spectral information. Experimental results demonstrate that the proposed method has the ability to produce high-fidelity reconstructed spectra.  
hyperspectral image; spectral super-resolution; reference-based learning; attention network; convolutional neural network +
Footnote †: journal: Sensors
## 3 Results Analysis
### Bendable Property  
The MMA sheet may be curved due to the external effect during installation [36]. The banding effect on absorption behavior was investigated for both TE and TM modes. Figure 10 shows the absorption curve of banding angles \(\theta\) = \(0^{\circ}\) to \(18^{\circ}\) with \(3^{\circ}\) intervals. In the TE mode, the absorption increases in the lower wavelength region and slightly decreases in the upper wavelength region with the increment in \(\theta\). The same absorption behavior is also shown in the TM mode. A unique absorption property is achieved for both TE and TM modes of various banding angles, which offers a bendable property of the proposed absorber.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Theta (0)**} & \multicolumn{2}{c}{**TE Mode Absorption**} & \multicolumn{2}{c}{**TM Mode Absorption**} \\  & **Average** & **Peak** & **Average** & **Peak** \\ \hline \(0^{\circ}\) & 97.12\% & 99.99\% & 97.10\% & 99.99\% \\ \(10^{\circ}\) & 97.07\% & 99.93\% & 97.07\% & 99.96\% \\ \(20^{\circ}\) & 96.86\% & 99.70\% & 97.29\% & 99.99\% \\ \(30^{\circ}\) & 96.02\% & 99.32\% & 97.51\% & 99.96\% \\ \(40^{\circ}\) & 93.76\% & 98.60\% & 97.54\% & 99.70\% \\ \(50^{\circ}\) & 90.61\% & 96.09\% & 96.81\% & 98.98\% \\ \(60^{\circ}\) & 84.43\% & 89.61\% & 94.32\% & 96.64\% \\ \(70^{\circ}\) & 70\% & 76.25\% & 86.41\% & 89.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Absorption properties of various oblique incident angles.  
Figure 8: Polarization insensitivity of (**a**) TE mode and (**b**) TM mode.  
Figure 9: Oblique incident angle stability of (**a**) TE mode and (**b**) TM mode.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
###### Abstract  
The study of energy, water, and carbon exchanges between ecosystems and the atmosphere is important in understanding the role of vegetation in regional microclimates. However, they are still relatively scarce when it comes to Caatinga vegetation. This study aims to identify differences in the dynamics of critical environmental variables such as net radiation (Rn), evapotranspiration (ET), and carbon fluxes (gross primary production, GPP) in contrasting recovered Caatinga (dense Caatinga, DC) and degraded Caatinga (sparse Caatinga, SC) in the state of Paraiba, northeastern Brazil. Estimates were performed using the Surface Energy Balance Algorithm for Land (SEBAL), and comparisons between estimated and measured data were conducted based on the coefficient of determination (R\({}^{2}\)). The fluxes were measured using the Eddy Covariance (EC) method for comparison with the same variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the Terra satellite. The estimates showed higher Rn values for the DC, indicating that this area should have greater energy availability for physical, biological, and chemical processes.  
The R\({}^{2}\) between daily Rn estimates and observations was 0.93. The ET estimated using the SEBAL showed higher differences in relation to the observed values; however, it presented better spatial discrimination of the surface features. The MOD16A2 algorithm, however, presented ET values closer to the observed data and agreed with the seasonality of the Enhanced Vegetation Index (EVI). The DC generally showed higher ET values than the SC, while the MODIS data (GPP MOD17A2H) presented a temporal behavior closer to the observations. The difference between the two areas was more evident in the rainy season. The R\({}^{2}\) values between GPP and GPP MOD17A2H were 0.76 and 0.65 for DC and SC, respectively. In addition, the R\({}^{2}\) values for GPP Observed and GPP modeled were lower, i.e., 0.28 and 0.12 for the DC and SC, respectively. The capture of CO\({}_{2}\) is more evident for the DC considering the whole year, with the SC showing a notable increase in CO\({}_{2}\) absorption only in the rainy season. The GPP estimated from the MOD17A2H showed a predominant underestimation but evidenced the effects of land use and land cover changes over the two areas for all seasons.  
MODIS; net radiation; energy exchange; evapotranspiration; gross primary production; carbon dioxide; semiarid area; eddy covariance +
Footnote †: journal: Physics Letters B
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Security  
As opposed to RF channels, VLC channels have their own unique traits. Since light cannot pass through walls, its wireless transmission is safe from any outside eavesdroppers in a VLC network, making it a more secure alternative to RF networks. VLC networks must be secured because of their broadcast nature, making signals vulnerable to eavesdropping in public places like libraries and shopping malls, as well as in multi-user indoor environments like conference rooms. Exploration at the physical level is often linked to ensuring reliable signal transmission, whereas the upper layers are typically accountable for protecting the connection by leveraging encryption technology. Moreover, a potential complementary strategy is offered to improve the protection of wireless communication by means of the physical layer [153]. This is because of the necessity for larger data speeds. An alternative to encryption that takes advantage of the properties of the wireless medium is physical layer security (PLS) [154]. By identifying the difference in SNR between the legitimate user and the eavesdropper, PLS mitigates the negative effects of the wireless channel, such as noise, fading, and the use of multiple transmitters (such as cooperative relays or multi-LEDs), thereby allowing for a more secure transmission. PLS is mostly needed to enhance network  
Figure 12: Critical challenges in NOMA-VLC.  
information security. Because of the channel capacity mismatch between both platforms, the results of prior research on PLS in RF cannot be directly transferred to VLC, where the critical challenge of nonlinear distortion on VLC systems and positive real-valued signal are needed. Recently, there has been a lot of interest in PLS research for NOMA-VLC networks employing various strategies, such as artificial noise (AN) and beamforming [142]. Aside from PLS, the inventors of [155] used a two-level chaotic encryption approach in the PD-NOMA-VLC system to guarantee both privacy (among legitimate users) and security (against eavesdroppers).
## 2 Materials and Methods
### Experimental Design  
A statistical design of experiments (DOE) has been carried out with a double objective. On the one hand, the aim is to establish the influence of different operating variables on the dye removal efficiency of nZVI, which has been generated in situ; on the other hand, the goal is to analyze the possible interactions between these variables and to determine an optimum within the study limits. The selected operational variables, which are expected to have a significant effect on the target variables, are polyphenol concentration (PF), expressed as equivalent mg of gallic acid per liter; the concentrations of Fe(II) and Fe(III) in mol\(\cdot\)L\({}^{-1}\); and the concentration of hydrogen peroxide, also expressed in mol\(\cdot\)L\({}^{-1}\). The number of experiments that constitute the matrix is determined by considering the operational variables. For a factorial, central, composite, orthogonal, and rotatable design (FCCORD), the total numbers of experiments (N\({}_{\text{E}}\)) are:  
\[\text{N}_{\text{E}}=2^{\text{k}}+2\text{k}+\text{n} \tag{1}\]  
where k is the number of operational variables (in this case, 4) and n is the number of replications of the central experiment. To ensure both rotatability and orthogonality, twelve replications of the central experiments are required. Thus, the experimental matrix consists of sixteen factorial experiments (i.e., 2k), eight axial experiments (i.e., 2k), and the sixteen replications of the central experiment, as mentioned earlier. Hence, N\({}_{\text{E}}\) equals 36.  
Table S1 (Supplementary Material) shows the operational variables that constitute the experimental design, the study region between the maximum and minimum values taken by these variables, the central value, and the steps. The experimental matrix shown in Table S2 (Supplementary Material) was designed from these values. It is worth noting that the experiments must be carried out in a precise order to ensure randomization in the experimental procedure.  
To adequately apply this experimental methodology, it is of paramount importance to carefully determine the operating intervals. To properly select the working intervals of the experimental design, a series of preliminary experiments were run. The results of these preliminary tests and the design of the experimental matrix are discussed in detail in Section 3.  
Figure 1: Experimental setup for the in situ generation of nZVI and dye removal.
## 2 User Terminal Specifications  
### Link Budget Analysis  
The analysis of the link budget is common practice to estimate the end-to-end performance of wireless links in UL and DL. The technical specifications of ground terminals such as gain and radiation pattern can be lowered by considering suitable satellite constellation geometries and satellite antenna configurations. A sample uplink budget for the n78-band is detailed in Table 1. We consider very low Earth- orbit (VLEO) constellations with orbital heights of d = 400... 500 km and multi-beam antenna arrays with sufficient gains at the edge of the coverage area. Given a 3-dB beamwidth \(\Theta_{\text{sat}}=2^{\circ}\), a satellite antenna edge gain of \(\text{G}_{\text{sat}}=38.3\) dBi was calculated at the center frequency f = 3.5 GHz [13]. A moderate UE antenna gain of \(\text{G}_{\text{UE}}=10\) dBi has been considered to keep the terminal simple, and cost-efficient. The n78-band is less prone to atmospheric losses than at higher frequencies, represented by \(\alpha_{\text{atm}}=3.5\) dB [2]. According to these data, we anticipate an uplink data rate of DR = 21 Mbit/s at f = 3.5 GHz, which can be achieved even with a moderate gain of \(\text{G}_{\text{UE}}=10\) dBi. For the higher n257- band, a sample link budget was presented by the authors in [12], where an uplink data rate of DR = 7 Mbit/s at f = 28.5 GHz was anticipated for a moderate gain of \(\text{G}_{\text{UE}}=15\) dBi and hence low complexity of the UE antenna.  
Data rates of this order can be meaningfully utilized for several automotive use cases [14]. For instance, \"sharing and collection of high-definition maps\" demand uplink DR = 4 Mbit/s and downlink DR = 16 Mbit/s with a link latency of up to L = 100 ms. The \"obstructed view assist\" provides a host vehicle with an alternate view of obstructed road segment, requiring DR = 5 Mbit/s and L = 50 ms. Patient transport vehicles sharing vital patient telemetry data, images, voice, and video between paramedics and patient monitoring personnel necessitate DR = 8.1 Mbit/s with service latency L = 150 ms. The firm-ware update of reconfigurable radio systems to comply with regional requirements necessitates a data rate DR = 0.3... 3.3 Mbit/s without any critical requirement of latency.
## 2 Materials and Methods
### Bacterial Cell Survival Assay  
Overnight cultures of each bacterial strain (\(\sim\)10\({}^{7}\) CFU) were used to inoculate autoclaved distilled water to make a water suspension. Samples from this water suspension were then exposed to plasma for different times starting from 15 to 45 s, with a step of 15 s. Each sample was then serially diluted and spot plated on TCBS agar plates; and then, these plates were incubated at 37 \({}^{\circ}\)C for 24 h. The number of colonies on each plate was counted, and the colony-forming units per ml (CFU/mL) were calculated using the following formula.  
\[\text{Colony forming unit(CFU/mL)}=\frac{\text{No. of colonies }\times\text{ Dilution factor}}{\text{Volume of culture plated}} \tag{1}\]  
The killing % overall inactivation efficiency of the plasma was calculated using the following formula:  
\[\text{Killing }\%=\left(1-\frac{\text{No. ofCFU/mlin treated samples}}{\text{No. ofCFU/mlin untreated control samples}}\right)\times 100 \tag{2}\]  
An untreated water suspension and un-inoculated water were used as the positive and negative controls.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 5 Performance Evaluation
### Clustering Scheme Comparison  
In this section, we compared different clustering algorithms in terms of the node life cycle, the energy consumption, and the transmission success ratio under different UAV nodes.  
Figure 9 shows the energy consumption of different clustering schemes within a certain amount of time under different numbers of UAV nodes. It can be seen that the energy consumption of the DTPCH-C scheme is lower than the other two counterparts. This is because the cluster head can flexibly adjust its transmitting power according to the distance with different cluster members, avoiding redundant energy consumption and saving resources.  
Figure 8: Lifetime of FANET in different numbers of UAV nodes versus the number of intervals \(M\).  
Figure 9: Energy consumption of different clustering schemes versus the number of UAV nodes.  
Figure 10 compares the performances of the proposed DTPCH-C scheme with conventional DSWCA and GWO algorithms in terms of lifetime of FANET by varying the number of UAVs. We can see from Figure 10 that the network lifetime in all three schemes decreases with the number of UAVs increases. However, because the DTPCH-C scheme optimizes the cluster head selection and cluster head transmission power, the energy consumption of cluster head nodes in the network is reduced and the election of cluster head is more reasonable. As a result, the network survival time of the proposed method is much higher than that of DSWCA and GWO.  
Figure 11 shows that packet delivery ratio of three schemes grows with the increasing number of UAV nodes. This is because as the number of UAV nodes increases, the average distance between UAVs decreases. As a result, the nodes become more dense, and the reliability of communication increases. It can also be seen that under the condition of the same number of nodes, the DTPCH-C scheme outperforms the other two candidates. The reason for this is that the DTPCH-C scheme optimizes the cluster head election. The elected cluster head is more stable in that it can reduce the probability of link disconnection, thereby improving the packet delivery ratio.
## 3 Results of the Experimental Validation
### Raw Data Signals from IMU Sensors  
3D accelerometer, 3D gyroscope, and 3D magnetometer chips were housed in a package called the MPU-9250, which provides a range of functions. All the sensors are analogue, each equipped with a 16-bit AD converter. In addition to sampling the raw  
Figure 7: Top plot shows the measured packet loss for the IMU units attached to the pelvis, right/left thigh, and right/left lower leg. The middle plot shows the RSSI signal levels for various 2.4-GHz Wi-Fi band channels. Bottom diagram shows the RSSI signal levels for various 5-GHz Wi-Fi band channels.  
signals from the sensors, the data in the chip can be low-pass filtered and passed to a digital motion processor located in the same chip hardware. Detailed descriptions of the options can be found in the original specifications. In our case, this option is skipped in order to fully manage the data transmitted from the IMU units placed at different points on the body to the central motion controller.  
To verify the acquired data and to verify the complete software bits from sampling to the master unit drivers, the example measurement results for the IMU accelerometers, gyroscopes and magnetometers are shown in Figure 8. In this particular case, the IMU sensors were not calibrated, the subject ran on the treadmill for 600 s while samples were taken every 10 ms. The subject was equipped with a complete set of WSA with five IMU units, as shown in Figure 6. The laptop PC was in the backpack as before, and the acquisition software and data post-processing were the same as above.  
The first column in Figure 8 presents the 3D gyroscope signals for the IMUs attached to the pelvis in the top diagram and to the right/left thigh and right/left lower leg in the bottom diagrams of the same first column. In the same way, the second column lists the 3D accelerometer signals. The third column contains the 3D magnetometer signals for the same IMUs. It is important to note that in each of the 15 graphs, the left side shows the traces for the first minute of walking and the right side shows the traces for the last minute  
Figure 8: First column shows the gyroscope signals from the IMUs attached to the pelvis, right/left thigh, and right/left lower leg. Second column shows the accelerometer signals, and third column shows the magnetometer signals for the IMUs attached to the pelvis, right/left thigh and right/left lower leg. The subject walked on the treadmill for 10 min. Left side of each graph shows the curves for the first minute of walking, and right side of each graph shows the curves for the last minute of walking. Blue, orange, and yellow curves represent the \(x\)-axis, \(y\)-axis, and \(z\)-axis signals, respectively.  
of walking. The colours blue, orange, and yellow represent the \(x\)-axis, \(y\)-axis, and \(z\)-axis signals, respectively.
The cause is the presence of Permanent Polarimetric Scatterers (PePS) that have a high Radar Cross Section (RCS) and a well-defined scattering pattern, which remains constant within a large solid angle [7; 8]. The spatial and polarimetric distribution of PePS has appeared to be particular for each vessel model, characterizing its macro-scale features. This has allowed the definition of a _feature set_ from which a new quantitative manner for ship discrimination is possible [7]. The feature set has been exploited to propose a new Vessel Classification Algorithm (VCA) that takes profit of the information diversity provided by POLInSAR and the specifications of the new generation of orbital sensors. A _similarity parameter_ (S) is used to evaluate the correlation among the feature set estimated from SAR images and the reference ones defined from simulated imagery. In comparison to other classification approaches [9; 10], the decision rule is simpler and more robust as _only_ one condition has to be evaluated, the value of similarity. In the paper, all the details explaining the complete SAR simulation chain built with GRECOSAR and the resulting ship classification studies are presented. The paper compiles and extends the main conclusions obtained giving a comprehensive overview of what is disseminated in different papers. The main goal is to present how a complete numerical tool can help to make improvements in SAR image post-processing and, particularly, in ship classification.
## 6 Challenges  
### Low Power Output  
While hybrid systems hold promise, the energy produced by piezoelectric transducers remains relatively low. On its own, the power from piezoelectric harvesters is not enough for high-energy applications. Likewise, the output from a single piezoelectric tile is minimal, so powering larger or more energy-intensive devices would require significantly more tiles or additional energy sources [5]. This situation is comparable to the proposals by [PERSON] et al. [3] and the \"magic mat\" of [PERSON] et al. [87]. Meanwhile, the TENG cells in [PERSON] et al. [97] depend on mechanical contact to create energy; although sufficient for tasks such as fall detection, they are too limited for broader energy-harvesting purposes.