esa-sceva/satcom-chunk-collection huggingface dataset:
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 4 Proposed Methodology
### Architectural Overview
#### 4.1.2 Feasible and Non-Dominated Path Computation (Algorithm 1)  
This algorithm computes the set of high-quality paths from the BR to all other nodes in the network, where each path must (i) satisfy multiple Quality of Service (QoS) constraints and (ii) be non-dominated concerning the considered metrics (e.g., delay, energy, and reliability). The output of this process is a collection of Pareto-optimal routing options that serve as candidates for DODAG construction.  
The algorithm operates iteratively in the following manner:  
1. [label=0., ref=0]
2. **Initialization:** Each node is assigned an initially empty set of paths. The algorithm starts by injecting a fictive zero-weight path containing only the BR.
3. **Path Expansion:** Using a breadth-first search style expansion, every path is iteratively extended by one-hop neighbors. For each new candidate path: * The path's cumulative QoS metrics are computed. * If the path exceeds any QoS threshold, it is discarded.
4. **Dominance Checking:** Feasible paths are compared to existing paths at the destination node: * If a new path dominates an existing one, the latter is discarded. * If it is dominated by another, it is not stored.
5. **Path Limiting:** At most, \(k\) non-dominated paths are stored per node to limit complexity.
6. **Termination:** The expansion continues until no new feasible non-dominated paths are found.  
The final result is a structure that captures up to \(k\) high-quality paths per node, all originating from the BR, and forms the basis for conflict-free DODAG construction.  
```
1:Network graph \(G=(V,E)\)
2:QoS weights per edge
3:Maximum number of paths per node \(k\)
4:\(Paths[n]\): feasible, non-dominated paths for each node \(n\)each node \(n\in V\)
5:Initialize:
6:for all nodes \(n\in V\)do
7:\(Paths[n]\leftarrow\emptyset\)
8:endfor
9:Insert initial trivial path at BR: \(Paths[BR]\leftarrow\{[BR]\}\)
10:Mark all paths as unprocessed
11:repeat
12:\(new\_paths\_found\leftarrow\)false
13:for all nodes \(current\_node\in V\)do
14:for all unprocessed paths \(path\in Paths[current\_node]\)do
15:for all edges \(link\) from \(current\_node\)do
16:\(next\_node\leftarrow\) target of \(link\)
17:\(extended\_path\leftarrow\) append \(link\) to \(path\)
18:if\(extended\_path\) satisfies QoS constraints then
19:\(dominated\leftarrow\)false
20:for all\(existing\in Paths[next\_node]\)do
21:if\(extended\_path\) dominates \(existing\)then
22: Remove \(existing\) from \(Paths[next\_node]\)
23:elseif\(existing\) dominates \(extended\_path\)then
24:\(dominated\leftarrow\)true
25:endif
26:endfor
27:if not \(dominated\) and \(|Paths[next\_node]|<k\)then
28: Add \(extended\_path\) to \(Paths[next\_node]\)
29:\(new\_paths\_found\leftarrow\)true
30:endif
31:endif
32:endfor
33: Mark \(path\) as processed
34:endfor
35:endfor
36:until\(new\_paths\_found=\)false returnPaths
```  
**Algorithm 1** Feasible and Non-Dominated Path Computation
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### System Model
#### 3.1.3 Attack Detection  
A preferred parent node is selected from the potential parents list and is checked for the attacker node using the attack detection procedure. For Blackhole attack detection, the node is checked for the success threshold and the trust index. Whereas for the Rank attack detection, the node is checked for the rank and DIO_seq as compared to the neighboring nodes. If the selected preferred parent is detected as an attacker node, it is added to the suspicious list and a new parent is required to be selected from the list of potential parents list using the parent selection procedure.
## 2 Data and Methods
### Inversion and Modeling Methods  
#### 2.3.1 TSF Inversion Method  
The regional stress tensor damping inversion method is adopted, and the damping coefficient is introduced based on the linear stress inversion method to control the mismatch between the theoretical and observed values and the relative weight of the model complexity [40]. First, the optimal damping coefficient in the inversion process is determined by an automatic program. Second, the FMSs are divided into many grids (each grid adopts at least one FMS), and stress inversion is carried out to obtain the optimal stress tensor for each grid point. Finally, the bootstrap method is used to resample the data of each grid to obtain the confidence interval of the stress tensor axis [41].  
In this paper, the tectonic stress field near the epicenter is calculated by using the regional stress tensor damping inversion method and MSATSI program [42]. The number of earthquakes in every grid is set to 1, the number of automatic resamplings is 1000, and the confidence level is 95%.
## 3 Mutual Coupling Suppression using Frequency-Selective Surface  
This section describes the mutual coupling reduction technique proposed in this work. It is explained using several design case studies. The major components of the presented approach are isolation improvements using a frequency-selective surface (FSS), a FSS with a metallic strip, and the ground slot.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)
### Moon-Based Constellation Simulation
#### 5.2.3 Effect Observed on Small Satellites Due to SRP (Moon's Side)  
The Solar Radiation Pressure is most influential on the satellites propagating the Moon's orbital environment. Severe issues were noticed several times in recent past missions [39; 40]. Hence, it is important to analyze the intensity in terms of SRP force and torque on each satellite in a constellation. Figure 13 gives a thorough insight on its variations and determines the normal and worst possible scenarios.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Probability of Collision (Pc) & 0.032097 \\ Probability of Collision (Pc)—with Monte Carlo & 0.028883 \\ The difference in TCAs & 59 ms \\ Difference in Pc & +6.3491\% \\ No. of potential hits & 94 \\ Closest Avg. Range & 0.047099 Km \\ Closest Avg. Range— & 1.209042 Km \\ with Monte Carlo & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Observations from Case—2 (Moon).  
Figure 13: Solar Radiation Pressure variations in different situations in operations [16]. **(a)** Variation of SRP force-Normal conditions (Newtons); **(b)** Variation of SRP torque—Normal conditions (N\(\cdot\)m); **(c)** Variation of SRP force-Critical conditions (Newtons); **(d)** Variation of SRP torque-Critical conditions (N\(\cdot\)m).
## 3 Results and Discussion
### PST Removal from G. catenatum Cultures  
Along the experiment, that lasted for 48h, the toxins GTX2&3 and STX were not detected, while the toxins dcGTX2&3, C3&4, GTX1&4, NEO, and dcNEO were detected at levels below the corresponding LOQ (for all replicates). On the other hand, the toxins C1&2, dcSTX, GTX5 and GTX6 were quantified. The results of the PST removal experiments over 48 h with the H-form resin are shown in Table 1. For toxins C1&2 and GTX6, no changes in concentration were observed throughout the experiment. However, after 48 h of contact with the H-form resin we obtained an average removal of 55% and 100% of the concentration of GTX5 and dcSTX, respectively. The removal percentage was determined following Equation (1), where \(C_{i}\) is the concentration for each toxin. These are very promising results, especially due to the complete removal of dcSTX, which has a high toxic potential. Considering the toxic potential of each toxin (proposed by EFSA) and calculating the total toxicity equivalent to saxitoxin (Equation (2)), a decrease in the total toxicity close to 80% is obtained after 48 h of contact with the resin (relative to the same period without resin--48 h control). No differences were observed between the zero-time experiment (before addition of resin) and the 48-h control (with toxins but without resin), this meaning that there was no degradation of the toxins along the experiment. As such, we can safely conclude that the observed decrease in the concentrations of GTX5 and dcSTX along the 48 h was due to chemical adsorption to the H-form resin. \[\%\ Removal_{48h}=(1-(C_{i,\ 48\ h}/C_{i,\ 0\ h}))\times 100 \tag{1}\]  
\[C_{total}(\mu M)=\sum TE_{i}\times C_{i} \tag{2}\]  
To better understand the selectivity/specificity of a given type of reaction over another, here expressed by the differences in affinity to the resin evidenced by the various quantified toxins, Figure 7 shows a representation of their molecular structures. Unlike the C1&2, GTX5 and GTX6 toxins, the decarbamoyl toxin dcSTX bears a hydroxyl group (-OH) at R4, which renders it less sterically hindered than the other toxins (all of which contain an \(N\)-sulfocarbamoylI group, -OCONHSO\({}_{3}\)\({}^{-}\), at R4). This steric effect, combined with the overall positive charge, will probably facilitate the interaction of the toxin with the resin structure, promoting an easier adsorption through exchange of H\({}^{+}\). In addition to the \(N\)-sulfocarbamoylI group on R4, toxins C1&2 and GTX6 differ from dcSTX and GTX5 in other functional groups: C1&2 bear an \(O\)-sulphate group on R2/R3, while GTX6 bears an -OH group in R1. The presence of the \(O\)-sulphate group contributes to an even greater steric impediment and affects the charge balance (neutralizes), disfavouring the exchange of cations with the resin. In turn, the -OH group in GTX6 introduces the additional effect of electronegativity, disfavouring interactions with the resin through electronic effects. In
## 4 Results
### Spray Droplet Diameter Distribution  
Figure 8 presents the effects of injection temperature and pressure on the variation of the Sauter droplet diameter (D32) in the spray axial direction (Z). As can be seen in the figure, the diameter of the droplets decreases violently in a rather short distance (about 10 mm) outside of the nozzle exit and then changes little in the far field. The difference in inject pressures and inject temperatures also indicates little influence on the sharp decrease process, but they have a significant effect on the final stable droplet diameter. A higher injection temperature and pressure all contribute to a smaller droplet diameter at the spray downstream region. The reason for the rapid decrease is mainly due to the sudden release of superheated iso-pentane liquid, which shutters liquid bulk into smaller droplets and causes intense evaporation. After that, the droplets established a balance between drag force and surface tension force in their surface and achieved a relatively uniform distribution in the far area of injection. In addition, the figure shows that the injection temperature contributes little to the droplet penetration distance. However, a lower injection pressure can actually lead to further droplet penetration before full evaporation. This observation is consistent with the result shown in Figure 6. As discussed before, a high pressure and inject temperature are beneficial to the spray breakup process, creating smaller droplets and finer atomization. This will shorten the penetration distance of the liquid droplet due to the more intense evaporation.  
Figure 8: Effects of injection temperature and pressure on the variation of droplet D32 diameter along spray central axis. (**a**) Effect of \(T_{inj}\) on D32 variation under \(P_{inj}\) = 1.5 MPa; (**b**) Effect of \(P_{inj}\) on D32 variation under \(T_{inj}\) = 60 \({}^{\circ}\)C.  
Figure 7: Temperature of gas phase and vapor concentration distributions of gas phase under the condition of initial injection temperature of 60 \({}^{\circ}\)C and pressure of 1.5 MPa. (**a**) Gas temperature distribution; (**b**) Vapor concentration distribution.  
Figure 9 exhibits the effects of the injection temperature and pressure on the D32 variation along the spray's radial direction (R). The droplet diameter decreases smoothly in the radial plane of iso-pentane flashing spray, exhibiting a Gaussian distribution. A higher injection temperature leads to smaller droplets in the radial direction, while the injection pressure affects the radial diameter distribution much less. The possible explanation is that increasing the injection superheat degree leads to an increase in the spray angle and expansion width, causing a more intense evaporation rate in the radial panel.
## 4 Case Study--Safety Assessment of the Rockfill Specim in Triaxial Test
### Numerical Modeling
#### 4.4.1 FEM Model Setup  
The cylindrical triaxial reference specimen is idealized with an axisymmetric two-dimensional FEM model using Roccience software RS2 [36], exploiting the specimen's rotational symmetry and the symmetry of the loading conditions. Only one half of the sample is discretized: a square domain of the unit height and unit radius is meshed with one eight-noded quadratic element [37]. The symmetry axis is defined along the left-hand vertical boundary, where radial displacements are constrained, while the base is fully fixed in the vertical direction to mimic the actual test conditions. Figure 10 illustrates the model geometry and restraints.  
The selected constitutive model is linear elastic: a perfectly plastic Mohr-Coulomb model, due to its simplicity, intuitive interpretation and wide practical use, especially in stability problems. The limitations of the Mohr-Coulomb model with respect to the actual material behavior are as follows: constant stiffness for different confining stress levels, constant dilation angle at different stress levels as well as a linear failure envelope.  
The triaxial test loading procedure is reproduced in two sequential phases (Figure 11). During the isotropic compression phase, the model is subjected to equal radial and axial stresses, i.e., \(\sigma^{\prime}{}_{1}=\sigma^{\prime}{}_{2}=\sigma^{\prime}{}_{3}=2000\) kPa, applied as uniform pressure boundary conditions on the lateral and upper free surfaces. In the subsequent shearing phase of the test, confining (radial) stress \(\sigma^{\prime}{}_{2}=\sigma^{\prime}{}_{3}=2000\) kPa is held constant, whereas the axial stress is increased to the value \(\sigma^{\prime}{}_{1}=5860\) kPa.  
Figure 11: FEM model setup. (**a**) phase one (isotropic loading); (**b**) phase two (shearing).  
Figure 10: FEM model setup: (**a**) real specimen representation; (**b**) FEM model geometry.  
Once the triaxial loading phase concludes, the FEM model retains the specimen's current stress-strain state and exports it into the shear strength reduction (SSR) procedure. The SSR method then evaluates the factor of safety by gradually lowering the shear strength parameters by a trial factor \(F\) while keeping the external loads and boundary conditions fixed. The solver attempts to reach equilibrium with such reduced parameters. If the convergence is reached, the specimen is still stable at that strength level; if convergence fails or the incremental displacements increase excessively, it is considered that the failure has occurred at that reduction factor \(F\). By successively increasing \(F\), the analysis arrives at a critical value \(F_{crit}\) at which stability is lost. This value is reported as the factor of safety (FS), and it represents the margin by which the material's shear strength could be uniformly scaled down before the reference specimen, in its current stress state, would collapse. Because the loading history is fixed and only strength is modified, the SSR procedure provides a consistent safety metric for every Monte Carlo simulation.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 3 Hydrological Hazards
### Flooding
#### 3.1.1 Example Case Study: Wildman Coastal Floodplains (Northern Territory)
(**A**) Red-green-blue (RGB) map with red assigned to the Coefficient of Variation (COV), green assigned to the minimum (Min), and blue assigned to the temporal gradient (Grad), to show seasonally flooded areas, vegetated, and rapidly flooded areas, respectively. (**B**) Thresholding of the images (at \(-15\) dB) over three dates during the early wet season shows the progression of flooded extent. (**C**) Thresholding of the images (at \(-15\) dB) over three dates at the end of the wet season shows the recession of flooded areas. (**D**) The time-series of precipitation (blue) and SAR-derived percentage of flooded areas (orange circles, where the orange line is a moving average of five images). The images used in B and C are labelled with color-coded letters.
## 2 MOFs for Fuel-Cell Applications
### MOFs as Electrode Application
#### 2.1.1 H\({}_{2}\)/O\({}_{2}\) Fuel Cells  
The past few years have witnessed H\({}_{2}\)/O\({}_{2}\) fuel cells gaining increasing popularity as a result of their high efficiency and environmental friendliness. However, a potential problem is associated with the slow kinetics of the ORR [73; 74] that can be a hindrance for practical applications. In order to commercialize ORR technologies in the future, it is vital to develop efficient catalysts. A range of studies reported over the past few years have focused on SACs to address durability without impeding the atomic utilization of the catalyst. MOF-derived nanomaterials have also been deliberated for their potential role as functionally viable substitutes for platinum, which is a highly expensive metal in consideration of its excellent catalytic activity [49]. It has been demonstrated by a theoretical study that M-N\({}_{x}\) is the most catalytically active species in the ORR [75; 76]. The subject of Fe-doped ZIF-8 to pyrolysis as a straightforward methodology for synthesizing Fe-N-C electrocatalysts for membrane fuel cells based on the principle of proton exchange (PEMFCs) has been suggested, which takes advantage of less expensive approaches without diminishing the superior catalytic properties of Fe. As a result of its atomic dispersion, Fe-N\({}_{4}\) functions as the active molecule for a highly efficient and stable acidic PEMFC. There is widespread use of MOFs for controlling the morphology and atom distribution of the desired products in MNC catalysts by using them as sacrificing templates. They have been effectively used to fabricate individual or unaccompanied Fe atomic sites on an N, P, and S co-doped carbon polyhedron with empty cavities (denoted as Fe-SAs/NPS-HC) using the ZIF-8/Fe@PZS method (PZS = poly(cyclo-triphospazene-co-4,4\({}^{\prime}\)-sulfonyldiphenol)) in a pyrolysis experiment at 900 \({}^{\circ}\)C in Ar [56]. In order to successfully synthesize highly ordered FeN\({}_{4}\)-doped struc
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 2 Materials and Methods
### Behavior Based Algorithm  
To reduce GPS error in virtual fencing systems, we proposed a method which uses an animal's real-time behavior and speed.  
First, the method calculates animal position in two dimensions with signals received from the smart collar worn around the animal's neck (as shown in Figure 2). Simultaneously, the method retains position calculation time and helps to calculate the animal's travel speed between two consistently received GPS position data (points). Secondly, it checks animal's behavior for two received GPS position time periods. If the animal's behavior was \"running\" or \"walking,\" the method calculates maximum probable travel distance for the time period. The device then compares the distances  
Figure 1: Virtual Fence Smart Collar: (**a**) illustration of the main parts of the smart collar, which are an Arduino Nano, GPS module, XBee, shocker controller, SD card reader, accelerometer and gyroscope sensor, and mp3 player; (**b**) completely constructed smart collar on the goat’s neck.  
between two GPS receiver points and maximum probable traveled distance. If the distance between the two GPS points is more or less than the calculated maximum probable traveled distance, the method creates a new geolocation which lies somewhere on an imaginary line between the two points received from the GPS receiver. For the other conditions, the method does the same as well. For example, if the animal's condition is \"standing\" or \"laying,\" the method reports the last position as current.  
The main components that build up the complete system are the algorithm for measuring the distance between two points on a spherical surface, the classification of behavior using machine learning algorithms and calculated destination points using a given distance and bearing from the starting point. All algorithms used are discussed in the following sections.
# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Traditional Features and Their Advantages and Disadvantages for Common Weed Detection
#### 3.1.4 Color Features  
The accuracy of color-based detection highly depends on the plant being studied and its color differences. Color is insensitive to the adjustment of scale, size, and position. In addition, it can provide information about unusable objects. It is a common method used to segment plants from the background by using the difference in color features. [PERSON] et al. [85] summarized the advantages and disadvantages of plant segmentation for color index-based methods. [PERSON] et al. [86] proposed to modify the color component (\(2G-R-B\)) and use the excessive green component \(ExG=2G-R-B\) of the RGB color space to segment. [PERSON] et al. [87] and [PERSON] et al. [88] used the color moments of the RGB color space (including mean, standard deviation, and skewness) to represent the color features of plant leaves. [PERSON] et al. [89] used the color difference between green weeds and senescent cereals to propose a simple, semi-automatic, and robust procedure for weed detection in pre-harvest cereals, which has strong practical significance.  
In addition, R, G, and B components have a high degree of correlation, which is suitable for color display but not for segmentation and analysis [90]. Therefore, many methods transform images from the RGB color space to other color spaces, such as HIS, HSV, Lab, and YCrCb. [PERSON] et al. [44] used the YCrCb color space \(Cg\) (\(Cg=G-y\)) to describe the green features of green crops under different illumination conditions. [PERSON] et al. [91] believe that the HSV color space is more in line with human color perception than other color spaces and has strong robustness to illumination changes. The HSV color space was used to distinguish weeds, soil, and other residues in cauliflower fields under actual field conditions. [PERSON] et al. [92] utilized 18 color features (r, g, b; Y, Cb, Cr; H, S, L; H, S, V; L*, a*, b*; L*, u*, v*), which were defined in 6 color spaces (RGB, YCbCr, HSL, HSV, CIEL*a*b*, and CIEL*u*v*). [PERSON] et al. [93] and [PERSON] [94] also utilized different color spaces.  
Color is the most unstable feature used for plant identification. When the color difference is unobvious, color-based methods may not be able to distinguish weeds from crops accurately. These methods can be affected by leaf disease, plant seasonal changes in color, or different lighting conditions. Table 4 compares the advantages and disadvantages of four common image features for weed detection.
## 3 Active Disturbance Rejection Control Design
### Observer Design
#### 3.1.1 Extended Sliding Mode Observer Design
 \tag{22}\]  
where \(X_{1}=B_{1}^{T}P_{1}^{T}P_{1}B_{1}\), \(Y_{1}=C_{1}^{T}P_{1}^{T}P_{1}C_{1}\); \(\lambda_{\max}(X_{1})\), and \(\lambda_{\max}(Y_{1})\) are the maximum eigenvalues of the matrices \(X_{1}\) and \(Y_{1}\), respectively. \[ \begin{split} a_{1}&=\frac{1}{\lambda_{\max}(P1)} (\omega_{1}-\frac{1}{2}\lambda_{\max}(X_{1})k_{f_{2}}^{2}-1)\\ b_{1}&=\frac{1}{2\omega_{1}^{2}}\lambda_{\max}(Y_ {1})\delta_{1}^{2}\end{split}  \tag{23}\]  
where \(\lambda_{\max}(P_{1})\) is the maximal eigenvalue of the matrix \(P_{1}\). Theorem 1 is completely proven. To eliminate the chattering in the estimated values of the mismatched disturbance and angular velocity, the \"sign\" function in (12) is replaced by the hyperbolic tangent function, i.e., \"\(\tanh\)\" function.