esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Quantification with the gPC  
In this section, we focus on the UQ methodology for chaotic systems described by Equation (1) for the QoI (2). Uncertainty is introduced through \(\mathbf{s}\) which is modeled by a vector of \(m\) random independent variables \(\boldsymbol{\xi}=(\xi_{1},\dots,\xi_{m})\), where each \(\xi_{i}\) follows a given probability density function (PDF) \(w_{i}(\xi_{i})\), defined in the domain \(\mathcal{E}_{i}\). Under these conditions, the QoI is also uncertain, becomes a function of \(\boldsymbol{\xi}\), i.e., \(G_{\infty}=G_{\infty}(\boldsymbol{\xi})\), and we are interested in evaluating the statistical moments of \(G_{\infty}\). The vector \(\boldsymbol{\xi}\) follows a PDF given by the product \(W=\prod_{i=1}^{m}w_{i}(\xi_{i})\) defined in the domain \(\mathcal{E}=\prod_{i=1}^{m}\mathcal{E}_{i}\). The set of stochastic components \(\xi_{i}\) defines a set of stochastic orthonormal bases \(\psi^{(i)}=\{\psi_{0}^{(i)},\psi_{1}^{(i)},\dots\}\) with a tensor product in the form \(\Psi:=\otimes_{i=1}^{m}\Psi^{(i)}=\{\Psi_{0},\Psi_{1},\dots\}\) that is orthogonal to the PDF \(W\), i.e.,  
\[\left\langle\Psi_{j},\Psi_{k}\right\rangle=\int_{\mathcal{E}}\Psi_{j}\Psi_{k} Wd\xi=\delta_{jk}\left\langle\Psi_{j},\Psi_{j}\right\rangle, \tag{16}\]where \(\delta_{jk}\) denotes the [PERSON]'s symbol, and repeated superscripts do not imply [PERSON] summation. Using this approach, an uncertain quantity \(F=F(\xi)\) can be represented in spectral form as  
\[F(\xi)=\sum_{i=0}^{\infty}F^{i}\Psi_{i}(\xi), \tag{17}\]  
which is commonly referred to as the PCE. This expansion is usually truncated after a finite number of terms, given by  
\[q+1=\frac{(m+c)!}{m!c!}, \tag{18}\]  
where \(c\) is a user defined quantity, known as chaos order. The computation of the statistics of \(F\) requires computing the spectral coefficients \(F^{i}\). These coefficients can be computed through Galerkin projection, as in  
\[F^{i}=\int_{\mathcal{E}}F\Psi_{i}d\xi. \tag{19}\]  
In practice, the Galerkin integral is approximated by a numerical quadrature technique,  
\[\int_{\mathcal{E}}F\Psi_{i}d\xi\approx\sum_{j=1}^{\alpha}\omega_{j}(\xi_{j}) \Psi_{i}(\xi_{j}), \tag{20}\]  
that requires the evaluation of the QoI at points \(\xi_{j}\). The first 4 moments of \(F\) are computed through the following expressions:  
\[ \begin{array}{ll}\text{Mean value}&E[F]=F^{0}\qquad\text{Standard deviation} \quad\sigma^{2}[F]=E[F^{2}]-E^{2}[F]\\ \text{Skewness}&S[F]=\frac{E[F^{3}]-3E[F]\sigma^{2}[F]-E^{3}[F]}{\sigma^{3}[ F]}\\ \text{Kurtosis}&K[F]=\frac{E[F^{4}]-4E[F]E[F^{3}]+6E^{2}[F]\sigma^{2}[F]+3E^{4}[ F]}{\sigma^{4}[F]}\end{array}  \tag{21}\]  
This framework can be used for conducting UQ either intrusively or non-intrusively. In this work, all Galerkin integrals are computed using Gauss Quadrature due to its numerical accuracy, although it is also common to use sparse grid approaches. For unsteady systems, that may or may not display chaotic behavior, the intrusive approach is not guaranteed to converge to correct statistics [24]. We illustrate this below with the aid of an example.
## 3 Results
### Effect of Endophytic Colonization of Plants on Aplid Fecundity  
The likelihood ratio tests to evaluate the effect of the experiment for both fungus sprayed leaves and unsprayed leaves were not significant (\(\chi^{2}\)1; \(p>0.05\)) and therefore the two experiments were combined. The analysis of the pooled fecundity data indicated that there was a significant effect of time on the number of nymphs deposited by aphids feeding both on fungus-sprayed leaves and unsprayed but endophytically-colonized leaves (F\({}_{2,69,8}\) = 52.01; \(p<0.001\) and F\({}_{2,69,8}\) = 44.25; \(p<0.001\) respectively). However, there was no significant effect of treatment (F\({}_{3,55,07}\) = 0.29; \(p=0.84\)) or a treatment*day interaction for the fungus-sprayed leaf (F\({}_{6,84,2}\) = 0.82; \(p=0.56\)) (Figure 2). Conversely, for the unsprayed but endophytically-colonized leaf, the effect of treatment and the treatment*day interaction were significant, at \(\alpha\) = 0.1 (treatment: F\({}_{3,56,6}\) = 2.35; \(p=0.08\); treatment*day interaction: F\({}_{6,81,7}\) = 2.03; \(p=0.07\)) (Figure 3). The maximum reproductive activity of aphids was attained earlier in aphids feeding on endophytically-colonized leaves than in the controls (Figure 3). In fact, significant differences were detected by day 3 of the assay, if the number of nymphs laid by aphids feeding on leaves endophytically-colonized by isolate EABb 01/33-Su is compared with the number laid by aphids feeding on control leaves (t\({}_{103}\) = 2.89; \(p=0.01\)).  
Figure 2: _Per capita_ aphid fecundity over time (5 days) after aphid exposure to a sprayed leaf (with both surface conidia and endophytic colonization by one of three fungal isolates or the control). Evaluation began (day 1 on graph) 72 hours after leaves had been sprayed. Data represents the average of two trials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Mortality (\%)**} & \multicolumn{2}{c}{**Kaplan-Meier Survival Analysis**} \\ \cline{2-5}  & **Mean (\(\pm\) SE) 1 ** & **AST 2 ** & **Confidence Interval (95\%)** \\ \cline{2-5}  & & & **Lower Limit** & **Upper Limit** \\ \hline Control & 13.74 \(\pm\) 0.08 & 6.62 \(\pm\) 0.21 a & 6.22 & 7.03 \\ EABb 01/33-Su & 49.99 \(\pm\) 14.09 1 ** & 5.90 \(\pm\) 0.32 b & 5.28 & 6.52 \\ EABb 04/01-Tip & 37.71 \(\pm\) 13.61 & 6.41 \(\pm\) 0.28 a & 5.85 & 6.96 \\ EAMa 01/58-Su & 40.08 \(\pm\) 13.56 & 6.11 \(\pm\) 0.25 b & 5.62 & 6.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Susceptibility of _A. gossypii_ adults to entomopathogenic fungal isolates after exposure to leaves that were endophytically-colonized by those fungi.  
Nonetheless, although fecundity was accelerated, this did not result in an overall increase in the aphid population compared with the control (Figure 4). At the end of the assay (day 5), aphids feeding on endophystically-colonized leaves laid, in general, fewer nymphs than ones feeding on control plants. This was particularly apparent in aphids feeding on leaves endophystically-colonized by isolate EAMa 01/58-Su (t \({}_{103}=-2.49\); \(p=0.04\)).
# On the Design and Implementation of a Mixed-Platform Collaborative Virtual RAS Planner
## 3 Materials and Methods
### System Description  
All of the components of the proposed virtual RAS planner focus on being able to perform the surgical planning process, which is recreated by means of a virtual operating room. The virtual operating room includes an operating table and a 3D model representing the patient. Our work is focused on RAS-type surgeries as it includes tools capable of simulating the placement of a surgical robot on a virtual patient represented by a segmented 3D model. To create a versatile system that can adjust to each clinical case, segmented 3D models generated from medical images can be imported. For this demonstration, a medical CT image study of the abdominal area from _The Cancer Archive Imaging_ (TCIA) open repository was used [21]. The 3D model of this case was segmented using the open software _Slicer_[22] and the automatic segmentation tool _Total Segmentator_[23]. This tool can segment more than 100 different tissues in the human body from CT images. However, the ViPRAS system can also accept models from other software as long as the 3D model meshes are in STL or OBJ format, both widely known 3D file formats. Figure 1 shows a snapshot of the virtual RAS planner. It is important to highlight that our system is able to show a generic scenario that can be used as a teaching resource but can also represent a real clinical case of a particular person who is about to be operated on. This way, the surgeons have customised 3D models of the patient and are able to study their case and plan the surgical intervention accordingly. This of the utmost importance for the success of the surgical process.
## 2 Technologies for Indoor Localization
As reported in the literature [2; 5], the accuracy of Wi-Fi-based localization systems ranges between 1 m and 5 m, while in the case of Bluetooth, accuracies range between 2 m and 5 m. Similar values (from 3 to 5 m) were reported for ZigBee-based systems, while RFID technology provides an accuracy ranging between 1 m and 2 m. Examples of wireless localization systems are the hybrid fingerprint location technology based on Received Signal Strength (RSS) and Channel State Information (CSI) proposed in [52] and the Wi-Fi system exploiting a Round Trip Time (RTT) positioning method based on Line of Sight (LoS) identification and range calibration presented in [53], which achieves a mean localization error of 0.862 m and a root-mean-square error of 0.989 m.  
Machine learning techniques and deep learning algorithms have been also investigated. As an example, ref. [54] presents a localization model employing a convolutional neural network (CNN) and the Gaussian Process Regression (GPR) based on Wi-Fi RSS indication fingerprinting data. code.
## 4 Results and Discussion
### Edge Device Computing  
The PQUM-DNS, with its reduced and compressed model size, is well suited for lightweight edge-computing devices. Therefore, it is applied to replace traditional IPCs with embedded systems, such as a Raspberry Pi. This substitution reduces the size and weight, conserves resources, lowers costs, and supports large-scale deployments.
# Biomass Dynamics in a Fragment of Brazilian Tropical Forest (Caatinga) over Consecutive Dry Years
## 3 Results and Discussion
### Biomass
From 2008 to 2011, the total rainfall depth was 31% higher than the climate normal: 47%, 6% and 66% respectively, with a negative mean water balance of 558.4 mm yr\({}^{-1}\). Between 2012 and 2018, the total rainfall was below the climate normal by 7%, 4%, 4%, 21%, 7%, 24% and 2% respectively, with a negative mean water balance of 1237.0 mm yr\({}^{-1}\), more than double the previous period. Thus, the biomass seen at the end of 2015 was influenced by the sequence of four dry years (2012 to 2015), and was worse during the final year (2015), which registered a water deficit of 1422.61 mm. The same was seen for 2016 and 2018. In semi-arid regions, water is the main limiting factor to increases in biomass [12], since under conditions of consecutive periods of drought there is an increase in tree and shrub mortality due to the continuous scarcity of water [16]. Furthermore, the capacity of dry forests to produce biomass year after year is highly variable, precisely because they are influenced by consecutive years of drought, which can reduce this capacity [35], demonstrating the impact of climate conditions on biomass production in the DTF [16]. Therefore, limitations on water availability are not only the result of the total annual amount of rainfall, but also of the spatial and temporal distribution of the available water, in addition to solar energy being available for use in the process of evapotranspiration [33]. In addition to the existing limitations, recent simulation models suggest that semi-arid regions will suffer the greatest impact from climate change [3]. The mortality which took place demonstrates the reduction in total and potential biomass produced by the DTF/CPD over consecutive dry years, and which may worsen in the face of climate change. Climate change is expected to affect rainfall patterns and increase temperatures [36], culminating in droughts that, though previously tolerable, in the future may reach intolerable levels for plants, thereby reducing forest formations [10; 36].
## 7 Experimental Verification and Analysis
### Inter-Satellite Link Accessibility (from Beidou-3 Constellation to Lunarprobe-1 to CEI) Analysis  
Based on the above inter-satellite link visibility (from Beidou-3 constellation to lunarprobe-1) analysis, in this subsection, the inter-satellite link accessibility (from Beidou-3 constellation to lunarprobe-1 to CEI) analysis will be given to further demonstrate the feasibility of the proposed method.  
Figure 17 presents the accessibility of the link from the Beidou-3 constellation to lunarprobe-1 to CEI, while Figures 18 and 19 illustrates the chain access AER of the link (CEI stations located at Beijing and Kashi, respectively).  
It can be seen in Figure 17 that, with the collaboration of ISL of the Beidou-3 constellation, the link can be almost connected all the time, from 24 March 2023 04:00:00.000 to 24 April 2023 04:00:00.000. Meanwhile, in Figures 18 and 19, the GEO-1 satellite in the BeiDou-3 constellation was selected as the radiation source for transmitting the ISL, which illustrates that the distance between GEO-1 and lunarprobe-1 ranged from \(0.5\times 10^{5}\) km to \(4.7\times 10^{5}\) km, while that between lunarprobe-1 and CEI stations ranged from \(0.1\times 10^{5}\) km to \(4.5\times 10^{5}\) km, with azimuth and elevation fluctuating periodically, with only short jumps, and each duration remaining at almost 18 h.  
The Complete Chain Access of BD3-lunarprobe-CEI (from 24 March 2023 04:00:00.000 to 31 March 2023 10:00:00.000) is shown in Table 7, which shows that the Min Duration is 18,282.217 s/day, while the Max Duration is 50,998.804 s/day, and the Mean Duration is 44,801.100 s/day, with a Total Duration of 358,408.797 s in 7 days.  
Figure 21: Carrier-to-noise ratio at the CEI receiver in the case of CEI Receiving Antenna Aperture variations (BD-3 in GEO/IGSO).  
Figure 22: Carrier-to-noise ratio at the CEI receiver in the case of CEI Receiving Antenna Efficiency variations (BD-3 in GEO/IGSO).  
Figure 20: Carrier-to-noise ratio at the CEI receiver in the case of spacecraft RCS variations (BD-3 in GEO/IGSO).  
It can be seen that when the distance of cislunar space targets to Earth range from \(8\times 10^{4}\) km (which has not reached Earth-Moon transfer orbit) to \(45\times 10^{4}\) km (which is beyond the farthest Earth-Moon Lagrangian point), the \(C/N_{0}\) of ISL at the CEI receiver will reach from 93.2 dBHZ to 56.1 dBHZ, and the level will be slightly higher (approximately 0.95 dB higher) from the ISL radiation source on GEO and IGSO compared to that on MEO. The above analysis demonstrates that the carrier-to-noise ratio of this method meets the reception and observation requirements in the practical application of lunar exploration scenarios, which can achieve the effective tracking and measurement of cislunar spacecraft.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## Proposed eEFA Approach
### Filtering Unicast Frames in DANH Rings  
As specified in the standard HSR protocol, each DANH periodically multicasts a supervision frame called HSR_Supervision with an interval called LifeCheckInterval [5]. Upon receiving an HSR_Supervision frame, each access QuadBox learns the MAC addresses of DANHs in its DANH ring and stores these in a MAC table called NodesTable. Supervision frames allow the NodesTable of access QuadBoxes to keep track of the presence of DANH nodes in their DANH rings. When a DANH does not send an HSR_Supervision frame within a given timeout called NodeForgetTime, its address is removed from NodesTable.  
Instead of using PL for filtering traffic for nondestination DANH rings, eEFA looks-up NodesTable to make forwarding decisions. When an access QuadBox receives an HSR unicast frame, it looks up its NodesTable and checks whether there is an entry matching the destination MAC address of the frame. If so, it means that the DANH ring of the QuadBox contains the destination DANH; the QuadBox then forwards the frame to its DANH rings. If not, the access QuadBox sends the frame over the other port connecting to its QuadBox ring and does not send the frame to its DANH ring. Figure 5 shows the process of forwarding a unicast frame at access QuadBoxes in eEFA.  
By looking up the NodesTable, eEFA avoids forwarding HSR frames, including the first sent frame, to nondestination DANH rings, as shown in Figure 6.  
Figure 5: Process of forwarding a unicast frame in access QuadBoxes in eEFA. eEFA: Enhanced effectively filtering approach.  
Figure 6: Forwarding the first sent frame in eEFA.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 3 Results and Discussion
### Permittivity of Leaves  
The complex-valued permittivity of eight different types leaves were extracted from measurements taken from three various locations with different percentage of WC in them. Furthermore, on every location, measurements were recorded using four different orientations of the leaves to observe any anisotropic behaviour. Figure 2(a) shows that for a coffea arabica leaf, neither the location, nor the leaf orientation had any effect on the real part of permittivity on day 1. However, the effect of location was notable on the day 4 as shown in Figure 2(b). We believe that the drastic decrease in the leaf thickness is responsible for this behaviour.  
Similar patterns were observed for other types of leaves as well. Figure 4 shows the real part of the permittivity for all the leaves measured on four consecutive days. It is significant to observe that all the leaves revealed the highest permittivity on day 1 when the WC in fresh leaves was the highest, and as the days progressed, permittivity showed a decrement when leaves became water stressed. Hence, dielectric parameter measurements differed significantly on days 1 and 4 for fresh, and water-stressed leaves. From these observations, it also showed a clear correlation between the permittivity and WC of leaves, i.e., fresh leaves with a higher amount of WC would have a high permittivity and vice versa. From Figure 5, it is evident that the real part of permittivity shows a strong decaying correlation with WC. As observed in Figure 4, various leaves also showed distinct decreasing responses from each other, attributed to different lead composition and structure. In another study, we have shown that the real part of permittivity can be used to classify leaves, that are described here with an accuracy of 98.2%.  
Figure 4: _Cont._  
Figure 4: Real part of permittivity of all eight leaves measured on four consecutive days. Leaves become transparent to electromagnetic waves with the passage of days as seen by the decrease in the permittivity.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 3 Our Method
### Improved Feature Extraction Algorithm
#### 3.1.2 Construct a Gaussian Pyramid  
The previous approaches frequently employed the image pyramid with a fixed number of levels of 8 to offer scale invariance for features, such as ORB-SLAM3 [12; 22]. The high layer of the image, which is scaled down to create the image pyramid, contains less image information when the image is small. In this case, the high-level image has little effect on scale invariance. The amount of calculation may increase when the pyramid's layers are added. For the purpose of reducing the quantity of calculations and better tracking, this paper designs an adaptive image pyramid layer decision method, facing the input pictures with different resolutions. The number of layers is set as follows:  
\[\textit{Level}=\text{Round}((width+height)/200), \tag{3}\]  
We divided the sum of the picture's width and height by 200 and rounded up to obtain the number of levels of the pyramid's construction plan because the picture's proportions could not be taken into consideration when utilizing the picture's area. After calculating the necessary number of pyramid layers, the picture is down-sampled based on the scale factor to generate an image of all pyramid layers and set the number of feature points to be acquired for each layer.
# Analysis of Injected Electron Beam Propagation in a Planar Crossed-Field Gap
###### Abstract  
This paper examines basic crossed-field device physics in a planar configuration, specifically electron beam perturbation and instability as a function of variation in magnetic field, and angle between magnetic and electric field. We perform a three-dimensional (3-D) simulation of electron perturbation in a planar crossed-field system using the full 3-D particle trajectory solver in CST Particle Studio (CST-PS). The structure has a length, height, width and anode-sole gap of 15 cm, 2 cm, 10 cm, and 2 cm, respectively. The anode to sole voltage is fixed at 3 kV, and the magnetic field and injected current varied from 0.01 T to 0.05 T and 1.5 mA to 1 A, respectively. The simulations show that applying a magnetic field of 0.05 T makes the beam stable for a critical current density of 94 mA/cm\({}^{2}\) for an anode-sole gap of 20 mm. Above this current density, the beam was unstable, as predicted. Introducing a 1\({}^{\circ}\) tilt in the magnetic field destabilizes the beam at a current density of 23 mA/cm\({}^{2}\), which is lower than the critical current density for no tilt, as predicted by our theory. The simulation results also agree well with prior one-dimensional (1-D) theory and simulations that predict stable bands of current density for a 5\({}^{\circ}\) tilt where the beam is stable at low current density (<13.3 mA/cm\({}^{2}\)), unstable above this threshold, and then stable again at higher current density, (>33 mA/cm\({}^{2}\)).  
planar crossed-field device; beam perturbation simulation; instability +
Footnote †: journal: Physics Letters B
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 4 Experimental Results and Discussion
### Results of GCI Classification
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No** & **Class** & **Pr (\%)** & **Re (\%)** & **F1\_score (\%)** & **Acc (\%)** \\ \hline
1 & Ac & 92.38 & 97.00 & 94.63 & \\
2 & Cb & 91.92 & 91.00 & 91.46 & \\
3 & Ci & 94.04 & 89.33 & 91.62 & \\
4 & Cl & 98.36 & 100 & 99.17 & 94.24 \\
5 & Cu & 99.66 & 97.00 & 98.31 & \\
6 & Mi & 97.30 & 96.00 & 96.65 & \\
7 & Sc & 86.45 & 89.33 & 87.87 & \\ \hline \end{tabular}
\end{table} 
Table 5: The classification results for the ASGC dataset.
## 3 Common Latitude Power Series Expansions with Geodetic Latitude as Variable
### Power Series Expansion of Authalic Latitude with Geodetic Latitude as Variable  
According to map projection theory, given the constant  
\[A=\frac{1}{2(1-e^{2})}+\frac{1}{4e}\ln\frac{1+e}{1-e} \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Expand to \(e^{10}\) Based on the First** & **Expand to \(n^{5}\) Based on the Third** \\
**Eccentricity \(e\)** & **Flattening \(n\)** \\ \hline \(\left\{\begin{array}{l}\alpha_{2}=-\frac{3}{8}e^{2}-\frac{3}{8}e^{2}-\frac{ 111}{1024}e^{4}-\frac{111}{205}e^{6}-\frac{141}{205}e^{8}-\frac{1533}{32768}e^{ 10}\\ \alpha_{4}=\frac{156}{256}e^{4}+\frac{156}{256}e^{6}+\frac{156}{8192}e^{8}- \frac{165}{4005}e^{10}\\ \alpha_{6}=-\frac{356}{357}e^{6}+\frac{35}{2192}e^{8}-\frac{4935}{262144}e^{10} \\ \alpha_{8}=\frac{31 the authalic latitude formula \(\vartheta(B)\) with geodetic latitude as the variable is  
\[\vartheta(B)=\arcsin\biggl{[}\frac{1}{A}\biggl{(}\frac{\sin B}{2(1-e^{2}\sin^{2}B )}+\frac{1}{4e}\ln\frac{1+e\sin B}{1-e\sin B}\biggr{)}\biggr{]} \tag{8}\]  
The third flattening \(n\) and the first eccentricity \(e\) of the ellipsoid are related by  
\[n=\frac{a-b}{a+b}=\frac{1-\sqrt{1-e^{2}}}{1+\sqrt{1-e^{2}}} \tag{9}\]  
We expand this expression to \(e^{10}\) and \(n^{5}\), so the authalic latitude \(\vartheta(B)\) is  
\[\vartheta(B)=B+\gamma_{2}\sin 2B+\gamma_{4}\sin 4B+\gamma_{6}\sin 6B+\gamma_{8} \sin 8B+\gamma_{10}\sin 10B. \tag{10}\]  
The coefficients in Equation (10) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 2.