esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 3 Results
### Comparing UAV and Satellite Imagery  
Changes in de-trended vegetation indices were compared following imputation of missing values in the experimental dataset (Figure 5). The values extracted from the UAV and satellite imagery were very similar up to 20 days following treatment for all three spectral indices examined. Although indices followed a similar general trend after this point, reductions in indices were greater for the data from the UAV than the satellite sensor. This divergence in indices was most pronounced for NDVI.  
Figure 5: The change in the de-trended vegetation indices and field measured discolouration plotted against days after treatment. The solid line shows the mean at each time step and the shaded area shows the confidence interval. For each spectral index the values relate to the mean value of pixels within a cluster. For the ground measured discolouration the values represent observations of the field technician.  
The relationship between satellite and UAV sensors was investigated using the imputed dataset (Figure 6) where missing observations had been imputed using missForest. For both NDVI and GNDVI, linear models described the relationship most accurately. For RENDVI the best model was a second order polynomial. All developed models had high precision and model strength was greatest for RENDVI (R2 = 0.92) followed by GNDVI (R2 = 0.84), and then NDVI (R2 = 0.82).  
A RF regression model was used to investigate the ability of both data sources to predict field measured discolouration as reflected by visible discolouration. A single RF model (RMSE = 15.1, bias = \(-\)0.02) was used to examine the relationship and this was able to account for 82.3% of the  
Figure 6: The relationship between UAV and satellite-derived vegetation indices. Each datum represents the mean results extracted from a single time step for a tree cluster and the shaded area shows the confidence interval.  
variability in the response. The conditional importance scores revealed that the NDVI index collected from the UAV imagery was the most important variable for predicting discolouration (Figure 7). The second most important predictor was the satellite RENDVI index, while the subsequent metrics from both sensors were less important. RF regression models were also fitted using subsets of predictors from only one data source to compare with the initial model fitted with all predictors. The model using only UAV data was found to be more precise than the model that used spectral indices from the satellite data (R\({}^{2}\) = 0.84, RMSE = 15.04, bias = 0.13 vs. R\({}^{2}\) = 0.73, RMSE = 18.56, bias = \(-\)0.03).
## 4 Energy Management Strategies
### Energy Management Protocols
#### 4.3.3 Load Balancing Protocols  
* **Objective:** Distribute energy consumption evenly across the network to prevent early depletion of energy in specific nodes.
* **Examples:** Load-balancing mechanisms that dynamically adjust node roles based on energy levels [82, 83].
## 7 Summary, Conclusions, and Discussion
### Conclusions
#### 7.1.1 Conclusions on the Use of Spatial Side Information  
The performance of the proposed pattern-matching methods was measured in terms of the error distance, \(e_{\mathcal{F}}\), given by Equation (1). From the results obtained using the four pattern-matching methods considered, as discussed in Section 6.6, we make the following observation. For cases using the smallest 2D sensor array and cases using the best number of sub-tiles that minimizes the error distance, all of the methods implementing spatial side information perform much better than the method that does not use side information, 1-SingFingIn-PosOut. This observation is summarized in the CDFs of the error distances shown in Figures 21 and 22. In addition, the same observation can be made by comparing the results distributed in Section 6. Looking at the error distances for each one of the tile arrangements considered (combination of the parameters \(S_{S}\) and \(N_{SS}\)), shown in Figures 17-20, it is observed that the pattern-matching methods that make use of spatial side information always perform better than the method that does not use spatial side information. From this observation it is concluded that for the system and assumptions considered, the use of spatial side information helps to minimize the positioning error.
## 1 Introduction  
Microwave radiation (300 MHz-300 GHz--correctly referred to as radiofrequency electromagnetic fields) originates from various transmitters including broadcast (radio and television) transmitters, base stations for mobile networks, and radars positioned on buildings and towers, as well as from wireless telephones, wireless computer networks (Wi-Fi, Bluetooth), amateur radio transmitters, etc., situated in buildings and residential areas. Microwave radiation leaves its source in a straight line. When the direct wave encounters a solid obstacle, one or more of the following situations can occur simultaneously: penetration, reflection, and absorption. A significant portion of electrically conductive materials can reflect microwaves (similarly to how a mirror reflects light), while electrically non-conductive materials (dielectrics) have minimal influence on microwaves, allowing for penetration. For materials that can convert microwaves into heat faster than others,we speak of absorption. The best absorbers include seawater, slate, clays, and graphite. A portion of the microwaves are also absorbed into the ground, while some are reflected, so the nature of exposure in an outdoor environment greatly depends on the geography of the area [1; 2; 3; 4; 5].  
Building structures alter the territorial profile. Only a small amount of radiation is absorbed directly by building materials; some is reflected (metal roofs and walls, aluminum foils, thermal insulating fillers of modern plastic and aluminum windows, reinforced concrete constructions, steel structures, etc.), and the rest passes through the material without change or with minimal attenuation. For example, classic glass (not that in plastic windows) or drywall allows virtually free passage of microwaves. A small part of building materials (concrete, brick) has higher attenuation, but, for instance, wood and various building blocks have minimal attenuation. Wherever electrically conductive materials (e.g., steel constructions and beams) are used in building construction, there is an influence on microwave fields. The same applies to electrically conductive objects inside buildings (elevators, handrails, door frames, handles, mirrors, iron objects, electrical wiring, switchboards, sockets, lamps, etc.). The intensity of microwave radiation to which individuals inside a building are exposed can, thus, be quite complicated, if not impossible, to calculate [1; 2; 6; 7; 8].
## 2 Materials and Methods
### Hyperspectral Image Acquisition  
The samples fumigated by different amounts of sulfur were used for hyperspectral image acquisition. The hyperspectral image acquisition was conducted on an assembled HSI system. The system acquires hyperspectral images in the spectral range of 874-1734 nm with spectral resolution of 5 nm for 256 wavebands. An imaging spectrograph (ImSpector N17E; Spectral Imaging Ltd., Oulu, Finland) coupled with a 320 \(\times\) 256 camera (Xeva 992; Xenics Infrared Solutions, Leuven, Belgium) was used for hyperspectral images. The light illumination was provided by two symmetrically-placed 150 W tungsten halogen lamps (Fiber-Lite DC950 Illuminator; Dolan Jenner Industries Inc., Boxborough, MA, USA). The HSI system conducted linear scanning with the movement of the samples driven by a linear lead-screw drive stepper motor (Isuzu Optics Corp., Zhubei, Taiwan). For image acquisition, the height between the sample and the lens, the moving speed of the sample, and the exposure time of the camera were adjusted before the image acquisition to 320 nm, 22.8 mm/s and 4 ms to acquire clear and non-deformable images.  
The acquired hyperspectral images by the system were raw images, and should be corrected to reflectance images by the following equation:  
\[I_{R}=\frac{I_{raw}-I_{dark}}{I_{white}-I_{dark}}, \tag{1}\]  
where \(I_{R}\) was the corrected reflectance image, \(I_{white}\) was the white reference image with nearly 100% reflectance. \(I_{dark}\) was the dark reference image with nearly 0% reflectance.  
To extract spectral data, the entire sample region of each sample was defined as the region of interest (ROI), and the average spectrum of each ROI was extracted as the sample spectrum. It was impossible to measure SO\({}_{2}\) residuals of each pixel, and the average value of SO\({}_{2}\) residuals and the corresponding spectrum of each sample were used for calibration.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 2 Research Approach and Numerical Simulation  
### Model Description  
This study focuses on a flexible pavement consisting of four main layers: the surface course, base layer, sub-base layer, and subgrade. The surface course comprises an asphalt concrete (AC) and a binder course containing dense bituminous macadam (DBM). To ensure proper stress distribution, an embankment is placed beneath the subgrade layer. Figure 1 illustrates a schematic representation of the pavement, including the thicknesses of each layer [15]. The geometry and dimensions of the geogrid are also depicted in Figure 1. The properties of each layer are sourced from prior studies [16] and are mentioned in Table 1. Although asphalt is visco-elastic, the majority of researchers and the standard code consider asphalt concrete and binder course as elastic materials. However, the present study majorly focuses on the effect of geogrid on the subgrade layer and hence the material property of the subgrade becomes more important. Here, base, sub-base, subgrade, and embankment layers are modelled using the Mohr-Coulomb failure criterion to simulate the plastic nature of the materials. This Mohr-Coulomb failure behaviour allows the material to harden isotopically using a smooth flow potential with hyperbolic shape in the meridional stress plane and an elliptic shape in the deviatoric stress plane. The Mohr-Coulomb yield surface can be represented as  
\[F=R_{mc}q-p\tan\varnothing-C=0 \tag{1}\]  
where \(\varnothing\) is the friction angle in the meridional stress plane, \(C\) represents the cohesion of the material, \(p\) and \(q\) are the stress invariants, and \(R_{mc}\) is the Mohr-Coulomb deviatoric stress, which is further described as  
\[R_{mc}=\frac{1}{\sqrt{3}\text{cos}\varnothing}\sin\left(\theta+\frac{\pi}{3} \right)+\frac{1}{3}\text{cos}\left(\theta+\frac{\pi}{3}\right)\tan\varnothing\]  
Here, \(\theta\) is the deviatoric polar angle. The geogrid is considered purely elastic following [PERSON]'s law.
## 4 System Implementation and Experimental Results
### Shoulder Joint Abduction-Adduction Exercise
The mean absolute error (MAE), the maximum error (MaxError) and root mean square error (RMSE) for the elbow, wrist and tip of the hand (end-effector) trajectories, between the two sensor nodes, as extracted using Equations (17), are summarized in Table 5.  
Figure 11: Elbow, wrist and hand trajectories during shoulder joint abduction-adduction exercise – Comparison with Shimmer3 sensor nodes.
## 3 Results and Discussion
### Caveats and Future Work  
It is important to note two caveats. First, the CHI was derived for a single poellopopoid coral species. Models will fundamentally differ for species with different environmental physiologies. Secondly, although the CHI was designed to accommodate a wealth of information on the biology of _P. acuta_[41; 42; 43; 44], extensive model ground-truthing was not conducted due to the difficulty in accessing the study sites at multiple time points. Return trips, as well as continued engagement with our in-country colleagues and collaborators, will be critical for further model training and validation. For instance, during the warmest times of the year, we will advise these researchers to survey those sites with low predicted CHI values to ensure that the models are indeed accurate in situ using future data. If a bleaching event is expected based on seawater temperature trends, these models also provide managers and future researchers clues as to where to go to find resilient corals likely to survive such events [45]. Specifically, they would want to explore deeper areas (25-30 m) of fringing fore reefs in the country's far east. The extent and magnitude (severity) of bleaching of corals of differing CHI values during marine heat waves could also be compared to levels projected by temperature-exclusive models, such as NOAA's Coral Reef Watch, though it is important to note that the approach herein aims to make predictions at the colony-to-reef scale, while Coral Reef Watch predicts at a multi-km scale.  
The ML models may have utility not only in bioprospecting for resilient corals [46; 47; 48; 49], but also simply for estimating the CHI in areas currently under study. This is achieved by inserting data into the \"simulator\" (Figure 6). In the example presented in Figure 6, the depth was changed to 40 m, and the CHI subsequently rose from the mean of 2.5 to 2.7. This tool, which is freely accessible at coralreediagnostics.com/environmental-consultation-and-evaluation, could be useful for outputting the optimal environmental parameters [50] for the establishment of a new coral nursery in the Solomon Islands. However, it is important to note that, unless constraints are incorporated, particularly with respect to temperature, the ML-derived desirability analyses tethered to these models may inherently yield environmental conditions that may no longer exist. Even if, for instance, the \"optimal temperature\" is lower than what will be experienced by the corals in situ, perhaps other environmental properties, including many not measured herein (e.g., food supply [51] or nutrient levels), could be modulated in an effort to foster the resilience of farmed or out-planted corals.
## 3 Application to Gimpo Airport
### Three Types of Topology  
#### 3.2.1 Linear Topology  
In the linear topology, several TLOF pads and gates are arranged in a line. In the absence of a gate, the TLOF pad takes over the role of a gate, so the aircraft turnaround  
Figure 16: Two topologies for the Gimpo Airport parking lot: (**a**) current parking space; (**b**) expanded parking space.  
Figure 15: Gimpo Airport vertihub design of the Korea Airport Corporation [21].  
is performed on the TLOF pad. The linear topology is arranged to support simultaneous operation so that the aircraft operating interval is short, which is useful when it is installed in a narrow and long space [22]. If the space width is large enough, the TLOF pad and gate are installed in a line, which enables simple and fast operation. For the linear topology of the Gimpo Airport parking lot, two layouts were considered, as shown in Figures 17 and 18, that respectively comply with the FAA's and EASA's new regulations, and the components of the TLOF pad, gate, and taxiway including the passenger waiting area were included.
## 3 Description of the State of Knowledge
### Mechanisms of Antifungal Activity of Silver Nanoparticles  
The mechanism of action of silver nanoparticles on cells has not yet been thoroughly investigated, but several most likely mechanisms have been identified (Figure 5). These include the ability to attach AgNPs to the cell wall and the ability to penetrate the cell interior, induction of oxidative stress, and interference with signal transduction and protein synthesis [97]. Researchers suggest that the ability of AgNPs to connect with the cell wall occurs as a result of the electrostatic interaction between positively charged silver ions and the negatively charged surface of the cell membrane and to then penetrate it, thus causing structural changes in the cell membrane, which in turn increases its permeability. Other researchers suggest that silver nanoparticles lead to the inactivation of enzymes and proteins associated with the membrane or change the composition of the lipid bilayer, which translates into a change in its integrity. Another example is the interaction of silver ions with the disulfide bonds of enzymes involved in cellular metabolism, causing their inactivation. It was also found that silver ions bind to the 30S ribosomal subunit, deactivate the ribosome complex, and stop protein synthesis [98]. Generally, the main mechanism of activity of AgNPs is the ability to produce reactive oxygen species that cause oxidative stress in the cell. Free radicals can cause, among other things, lipid peroxidation, damage to the integrity of cell membranes, and DNA damage, which leads to cell apoptosis. The mechanisms mentioned here were confirmed in the studies of [PERSON] et al. on the action of AgNPs on _Trichosporon asahiii_. Analysis under an electron microscope showed that silver nanoparticles caused damage to the cell wall and membrane and then penetrated the cells and damaged mitochondria and ribosomes. They also caused condensation and marginalization of chromatin [39]. In the study by [PERSON] et al. using the plant pathogen _Ustilaginidae virens_, it was also confirmed that AgNPs disrupt the integrity of the cell wall and cell membrane and also affect the transcription process [99]. An increase in the permeability of the cell membrane was also identified in the case of studies on the influence of AgNPs on the cells of four pathogens causing rot of kiwi fruit: _Alternaria alternata_, _Pestalotiopsis microsppora_, _Diaporthe actinidae_, and _Botryosphareia dothidea_[100].
## 5 Analysis
### Security
#### 5.1.4 Avoiding Location Tracking  
Due to the access control to the trajectory data, we can prevent an outside attacker as well as any unauthorized entity from learning vehicle's driving trajectory stored on the cloud. On the other hand, to prevent an outside attacker from tracking driving path of a vehicle by eavesdropping on the vehicle-to-RSU communications, it must be difficult for an outside attacker to guess that the observed vehicle at different RSU's coverage is the same vehicle while a vehicle connects to contact point RSUs for receiving a message over VANET on its driving. In our proposed system, a vehicle \(V_{d}\) has a set of pseudonyms \(\{pid_{di},..,pid_{dw}\}\), which can be independently generated random values, and it is recommended to use a different pseudonym for identification and authentication whenever \(V_{d}\) connects to a different contact point \(RSU_{i}\) deployed at the location of \(loc_{i}\) along \(V_{d}\)'s driving path. Therefore, we can make it hard for an outside attacker to track moving locations of a vehicle if any two pseudonyms \(pid_{di}\
eq\mathit{pid}_{dj}\), respectively, observed by the attacker at \(RSU_{i}\) and \(RSU_{j}\) are unlinkable to the same vehicle from attacker's viewpoint.  
Moreover, \(\langlepid_{di},loc_{i}\rangle\) pairs are only known to the sRSUs authorized by \(V_{d}\) by means of a re-encryption technique. Another possible attack for an outside attacker is to compromise an sRSU to extract \(V_{d}\)'s pseudonyms and driving trajectory data \(\langlepid_{di},loc_{i}\rangle\) kept by the sRSU on the VANET. As a countermeasure to this threat, in this paper, we just assume that all RSUs are inspected by the TA and compromise of an RSU can be preventable and detectable by means of a security module such as tamper proof device. RSUs would not disclose any information without the authorization of the TA. Nevertheless, if an sRSU is detected as abused, TA can take an action to recover the sRSU and the vehicle can generate new re-encryption keys for sRSUs to protect the changed trajectory data after that.
## 4 Simulations and Results  
In order to illustrate the developed optimization framework for identifying parameters of the simplified SFR model and designing UFLS schemes, simulations were carried out on the Da Dan power grid. As shown in Figure 9, the Da Dan power grid is a typical small receiving-end power system. The goal is to maximize active power import into the Da Dan power system from the main power network, because the small receiving-end power system has limited local generation but heavy load. Some large disturbances, for instance, a double circuit fault outage on the tile-line between the receiving-end system and the main network, would island the small receiving-end system from the main power system. Robust UFLS schemes must be deployed to safeguard the small receiving-end power system against such large impacts.  
The Da Dan power grid consists of five power plants adding up to 3030 MVA. The imported active power is 800 MW and 1000 MW under valley demand and peak scenarios, respectively, as shown in the following.  
The developed technology could be used for practical problems including the identification of parameters for the simplified SFR and the optimal design of UFLS schemes.  
The whole optimization procedure was implemented in Matlab 7.1 (The MathWorks, Inc., Natick, MA, USA) and has been executed on an Intel (R) Core (TM) i3 3.2-GHz computer. During the optimization process using PSO, each swarm generation contained 60 position particles. The coefficients in Equation (8), \(\omega\), \(c_{1}\), and \(c_{2}\) are set to 0.8, 2, and 2, respectively.
## 4 Simulation Results
### Output C/N0 Performance When Interferences with the Same Polarization Mode  
In Section 4.2, a lot of space has been allocated to polarization discrimination. To emphasize the performance of the proposed algorithm, all the 12 satellite signals and four wideband interferences are assumed to have the same polarization mode, which is RHCP. Analogously, the output C/N\({}_{0}\) performance of the proposed STPAP algorithm based on the MVDR-CMS criterion is compared with those of the STPAP algorithms based on the criterions in [29; 30; 31] when there are multiple GNSS satellites in the FOV. As depicted in Figure 11, the results are identical to those in Section 4.2.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 5 Discussions
### Artifact Suppression
By filling the frequency gaps via the Super-SVA-based method at each antenna position, SAR imaging based on BPA and RMA can be modified correspondingly and the artifacts can be effectively suppressed. For example, the SAR image of the metallic plate obtained by RMA after frequency gap filling is shown in Figure 20. It can be learned that, after using the Super-SVA-based method, the high-level artifacts can be suppressed, giving a better imaging quality, which is more clearly shown in the right subfigure of Figure 20.  
Since each TV channel is filled separately at each antenna position, the relationship of different TV channels at different antenna positions is not exploited by the Super-SVA-based method. If all the frequency gaps for all the antenna positions can be processed at the same time, the gapped signal estimation accuracy can be increased [37]. Besides, when the SNR is low, the performance of the Super-SVA-based method with only the flat spectrum will be degraded. Reference signal reconstruction can be conducted to provide a template for inverse filtering to solve this problem. Therefore, as a key technique for the performance improvement of PB-GB-SAR, more advanced frequency gap filling methods should be applied. Moreover, apart from the artifacts in the range direction, caused by the limited synthetic aperture length and frequency bandwidth, sidelobes are generated in the range and azimuth directions by the conventional matched filtering based imaging algorithms (BPA and RMA). To further improve the imaging quality with reduced sidelobe level, other advanced methods can be used, such as the sparse representation or compressive sensing-based [52; 53] imaging method or the coherence factor or phase coherence factor-based [54; 55] filtering method.