esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydraulic Characterization of Green Roof Substrates by Evaporation Experiments
## 2 Materials and Methods
### Determination of the Soil Hydraulic Functions  
On the assumption that the soil column is homogeneous, determination of the water retention characteristic involved the following iterative procedure:  
1. An initial guess fitting of the van Genuchten [18] water retention curve was achieved from the mean values of the pressure head at the three depths, \(\overline{h}_{i}=(h_{1,i}+h_{2,i}+h_{3,i})/3\) and the average sample water contents, \(\overline{\theta}_{i}\), measured at the same time;
2. Using the fitted water retention curve, water contents, \(\theta_{k,i}\), were estimated at depths and times at which the pressure heads were measured;
3. The estimated average water contents were compared with the measured water storages of the soil sample obtained from weighting and the differences equally redistributed among the three compartments;
4. From these \(\theta_{k,i}\) vs. \(h_{k,i}\) pairs, an updated water retention curve was obtained;
5. Steps 3 and 4 were repeated until the maximum absolute change in water storage values between two successive iterations was \(<\) 0.0001 m\({}^{3}\)m\({}^{-3}\). In general, three iterations were sufficient to reach convergence. Temporal changes in water content for each of the three compartments were used to compute hydraulic conductivity by means of a modified instantaneous profile method. During the time interval \(\Delta t=t_{i+1}-t_{i}\), the water flux, \(q_{k}\) (L T\({}^{-1}\)) from the compartment \(k\) upward in the compartment \(k\) + 1 is approximated by: \[q_{k}=-\frac{(\theta_{k,\,i+1}-\theta_{k,\,i})\Delta z}{\Delta t}+q_{k-1,i}\] (1) in which \(\Delta z\) (L) is the compartment thickness (in this case \(\Delta z\) = 4 cm). The hydraulic conductivity, \(K\) (L T\({}^{-1}\)), was consequently computed according to the [PERSON] equation as follows: \[K_{k}\Big{(}\overline{h}\Big{)}=-\frac{q_{k}}{(\Delta h/\Delta z)_{k,k+1}+1}\] (2) where \((\Delta h/\Delta z)_{k,k+1}\) is the average pressure head gradient between two consecutive compartments as measured from tensiometer readings at two consecutive times (\(t_{i}\) and \(t_{i+1}\)).  
Corresponding \(h\) (or \(\theta\)) values for the \(K\)(\(h\)) (or \(K\)(\(\theta\)) relationships were calculated from:  
\[\overline{h}=\frac{h_{k,i}+h_{k,i+1}+h_{k+1,i}+h_{k+1,i+1}}{4} \tag{3}\]  
\[\overline{\theta}=\frac{\theta_{k,i}+\theta_{k,i+1}+\theta_{k+1,i}+\theta_{k+1,i+1}}{4} \tag{4}\]  
In view of the high uncertainty at low gradients due to the limited sensitivity of tensiometers, all \(K\) values obtained from hydraulic gradients lower than \(-\)0.2 m m\({}^{-1}\) were excluded from the analysis.
## 5 Conclusions  
This paper proposes CNN models to predict WC and \(\rho_{dry}\) using surface images of soil and presents a digital image processing procedure for training data construction. The original image was segmented with various resolutions and used as training data for the CNN model. Proper image segmentation increased the number of training data and enabled prediction of WC and \(\rho_{dry}\) even with few original images. The results showed excellent classification and quantitative results in predicting WC. Classification accuracy in \(\rho_{dry}\) prediction was relatively low, but quantitative results showed that \(\rho_{dry}\) was predicted well. In the segmented image, the range of colors and voids that could significantly affect WC and \(\rho_{dry}\) increased compared to that in the original image, but the CNN algorithm learned features in the soil surface by itself and successfully predicted WC and \(\rho_{dry}\).  
The resolution of the original images used in this study was fixed at 0.018 mm/pixel. It is expected that the accuracy of the model would vary with the resolution of the original image. Increasing the distance per pixel may represent a larger area but may not clearly reveal detailed features of the soil surface. Future research should take this into account when determining the appropriate resolution for deep learning training data.  
Additionally, the most fundamental form of the CNN algorithm was applied in this study. The reason for this was to determine whether the most common form of the CNN algorithm could learn the features of soil images. The results showed that learning was successful. Therefore, it is predicted that the application of various CNN algorithms currently being developed will greatly increase the accuracy of the model. However, as mentioned above, the construction of a good soil surface image database must precede.  
Conceptualization, D.K. and Y.S.; methodology, D.K.; software, D.K.; validation, T.K. and J.J.; formal analysis, T.K.; investigation, T.K.; resources, J.J.; data curation, J.J.; writing--original draft preparation, D.K.; writing--review and editing, Y.S.; visualization, D.K.; supervision, Y.S.; project administration, Y.S.; funding acquisition, D.K. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2021R1A6A3A03045271), and the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Agricultural Energy Self-Sufficient Industrial Model Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (321007-02).  
Not applicable.  
The authors declare no conflict of interest.
## 7 Experimental Results
### Input Tasks Load  
Any real-time scheduling algorithm and the underlying embedded hardware depend on the number of tasks they process. It is referred to as Input load of the system. In this section, the input load is varied, and its effect is recorded. The rest of the parameters are kept constant to find the impact of the proposed work on the input load. The experiments are repeatedly performed for the various number of tasks starting from 2 to until 100 tasks. It can be seen from Table 4 that the rest of the parameters are kept constant to observe the effect of input tasks better.  
Figure 6: Flow of different processes in experimental setup.  
Figure 7 shows the effect of hyper-period, optimal threshold and overhead for a varying number of input tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Tasks** & **Period Range** & **Execution Time Range** & **Arrival Time Range** \\ \hline
2 & 1–10 & 1–5 & 1–5 \\
4 & 1–10 & 1–5 & 1–5 \\
6 & 1–10 & 1–5 & 1–5 \\
8 & 1–10 & 1–5 & 1–5 \\
10 & 1–10 & 1–5 & 1–5 \\
12 & 1–10 & 1–5 & 1–5 \\
14 & 1–10 & 1–5 & 1–5 \\
16 & 1–10 & 1–5 & 1–5 \\
18 & 1–10 & 1–5 & 1–5 \\
20 & 1–10 & 1–5 & 1–5 \\
25 & 1–10 & 1–5 & 1–5 \\
50 & 1–10 & 1–5 & 1–5 \\
75 & 1–10 & 1–5 & 1–5 \\
100 & 1–10 & 1–5 & 1–5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario: Varying number of input tasks.  
Figure 7: Scenario 1: The effect of varying Number of Input Tasks, given Period = 1–10, Execution Time Range = 1–5, Arrival Time Range = 1–5. (**a**) effect of hyper-period, optimal threshold and overhead on number of tasks; (**b**) effect of optimal threshold on the number of tasks.  
It can be observed from Figure 7a. Even though the number of tasks rises, the hyper-period always falls within the optimal threshold. The top line shows that the optimal threshold and the hyper-period have always fallen below the threshold. The optimal threshold is adjusted as the number of tasks increases and so too for the hyper-period. The overhead is very nominal for less number of tasks, but, when tasks grow, the overhead starts to get noticed. The pattern of hyper-period is randomly growing with an increasing number of tasks; however, it always falls within the optimal threshold. For instance, the hyper-period for 12 tasks is more than that of 14 tasks. Similarly, for 75 tasks, the hyper-period is lower than that for 50 tasks. In Figure 7b, the effect of the optimal threshold is shown. As the tasks increase, there is more probability that the hyper-period will get a higher value. Thus, it will increase the overhead more than the minimum threshold. The minimum threshold is initially set to 500 and increased after every 100 th failed iteration. The amount of increase is computed based on Algorithm 2. Once the threshold is adjusted to better adapt to the input load, the probability that the hyper-period will fall below the new optimal threshold increases.
## 4 Proposed Approach
### Pseudocode  
Algorithm 1 presents the pseudocode for the proposed Hybrid Genetic Algorithm.  
```
Input: Distance-Time Matrix \(M\), number of locations \(N\), algorithm parameters Output: Best route and performance metrics
1 Select \(N\) random locations from \(M\) Initialize population with \(P\) random routes for each generationdo for each routedo
2 Evaluate fitness using: \[\text{Fitness}(R)=\frac{1}{\text{Distance}(R)+\gamma\cdot\text{Time}_{LSTM}(R)}\]
3 Select parents using tournament selection Apply ordered crossover to generate offspring ifadaptive mutation enabledthen
4 Mutate offspring based on population diversity elseiffixed mutation enabledthen
5 Mutate offspring with fixed rate Select elite individuals and replace worst ifTabu Search enabledthen
6 Apply Tabu Search to top candidates to refine solutions Record metrics: best fitness, travel time, distance, diversity return best route and evolutionary history
```  
**Algorithm 1**Genetic Algorithm with Adaptive Mutation and Tabu Search (GAAM-TS)  
### Pseudocode  
Algorithm 2 presents the pseudocode for the proposed Hybrid Genetic Algorithm.  
LSTM Integration into the Optimization Process  
The Long Short-Term Memory (LSTM) model is embedded directly into the route evaluation phase of the hybrid genetic algorithm. Unlike classical heuristics that use static or precomputed values for travel time, our algorithm queries the LSTM model dynamically to obtain more realistic time estimations.  
During the fitness computation of each candidate route \(R=\{r_{1},r_{2},\ldots,r_{N}\}\), the LSTM model is invoked to estimate the travel time between every consecutive pair \((r_{i},r_{i+1})\), considering features such as the time of departure, the geospatial context, and historical traffic data. The total predicted travel time \(T(R)\) is then incorporated into the cost function as described in Equation (2), allowing the optimizer to evaluate both spatial and temporal efficiency simultaneously.  
This integration enables the algorithm to adapt to real-world traffic variability and temporal dynamics, prioritizing not only geographically short routes but also those that are faster and more efficient under expected traffic conditions. By combining the global search capacity of GAs with the predictive power of deep learning, the algorithm can better navigate the trade-off between distance and delivery time.
## 3 Effect of Current on Wave Height  
### Analytical Solution for Current-Affected Wave Height  
Studying how a coexisting current affects the wave characteristics (e.g., wave height and wavelength) is beneficial for gaining a deeper understanding of the mechanism of wave-current action on structures, and research into how the wave characteristics change under the action of a current has yielded several achievements [24; 25; 26]. Assuming uniform flow and based on linear wave theory and the conservation of wave action, [PERSON] and [PERSON] [24] provided a relatively accurate analytical solution for the current-affected wave height \(H\), i.e.,  
\[\frac{H}{H_{0}}=\left(1-\frac{U}{C}\right)^{0.5}\left(\frac{L_{0}}{L}\right)^ {0.5}\left(\frac{A_{0}}{A}\right)^{0.5}\left(1+\frac{U}{C}\frac{2-A}{A}\right) ^{0.5} \tag{1}\]  
\[\frac{L}{L_{0}}=\frac{c}{c_{0}}=\left(1-\frac{U}{c}\right)^{-2}\frac{\tanh kd }{\tanh k_{0}d} \tag{2}\]  
\[A_{0}=1+\frac{2k_{0}d}{\sinh 2k_{0}d} \tag{3}\]  
\[A=1+\frac{2 kd}{\sinh 2 kd} \tag{4}\]  
where \(L\), \(k\), and \(A\) are the wavelength, wave number, and wave energy transmissivity, respectively. The subscript 0 denotes parameters under wave action with no current, \(U\) is the current velocity, \(C\) is the wave celerity in the current, and \(d\) is the water depth. Equations (1)-(4) are all related to the wave number \(k\) under wave-current action, and the modified dispersion relation is as follows:  
\[\left(\frac{2\pi}{T}-kU\right)^{2}=gk\text{tanh }kd \tag{5}\]
## 5 Discussions of Wave Velocities, Dispersion and Attenuation Characteristics  
In the cases of infinite and zero permeability, the theoretical velocities in Equations (33) and (41) of the three body waves are all real numbers and independent of circular frequency. This indicates that the three waves have constant velocities and the propagation procedures are not attenuated and dispersed. For zero permeability, the density of the mixture medium is used to compute the velocity of the shear wave. Both of the solid and fluid phases undertake the shear force. For infinite permeability, only the density of the solid phase is used to compute the velocity of the shear wave. So, the saturated soil can be treated as a one-phase soil and the shear force is only undertaken by the solid skeleton.  
For the finite permeability, the relation of \(\rho_{m}=\frac{\rho_{f}}{n}-\frac{1}{\overline{k}}\frac{j}{\omega}\) is used in Equations (21) and (30). The theoretical velocities of three waves are complex numbers, which are functions of the permeability and the circular frequency. Therefore, the propagation properties of the three waves in saturated soils are analyzed from the three aspects of dispersion, velocity, and attenuation characteristics, respectively.
###### Abstract  
Airborne pollutant transport in an aircraft cabin is greatly affected by the created airflow. The seat layout can impact the flow and thus the pollutant transport. Most studies have adopted symmetric upright seats for simplicity. The influence of seat inclination and seat misalignment on airflow and pollutant transport is still unclear. This investigation adopted a validated computational fluid dynamics (CFD) method to study the airflow and airborne pollutant distribution in a single-aisle cabin with seven rows of seats. The pollutant was assumed to be released from a passenger seated in the middle of three adjacent seats. A total of five different seat layouts were considered, including all of the upright seats, the inclination of three adjacent seats, the inclination of all of the seats in half a cabin, the inclination of all of the seats in a whole cabin, and the misalignment seat rows across the aisle. The flows in both the cross and longitudinal sections were compared. The pollutant concentrations in the respiratory zone of the passengers in different seats were adopted to evaluate the cross-contamination. The results revealed that the symmetric seat layout aids to circumscribe the released pollutant in a small region and reduces the cross-contamination either by maintaining the upright seats or inclining all of the seats. Contrarily, any inclination of seats or a misalignment of seat rows should be avoided during the pandemic since an asymmetric seat layout would generate asymmetric flow and strengthen the spreading of pollutants.  
aircraft cabin; airborne pollutant; airflow; cross-contamination; CFD +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON] Impact of Seat Inclination and Misalignment on Airborne Pollutant Transport in a Single-Aisle Aircraft Cabin. _Appl. Sci._**2022**, 12, 4538. [[https://doi.org/10.3390/app12094538](https://doi.org/10.3390/app12094538)]([https://doi.org/10.3390/app12094538](https://doi.org/10.3390/app12094538))  
Academic Editor: [PERSON]
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 5 Game Theory Analysis of the CSMA/CA Problem When \(N_{p}=2\)
### Nash Equilibrium Concept  
The CSMA/CA game posed when \(N_{p}=2\) is a non-zero sum, two player game. A very popular equilibrium concept for these games is the Nash equilibrium (NE) concept: it defines a situation in which no player can obtain a better payoff by deviating unilaterally. Non-zero sum games might have more than one NE (Chapter 3, [24] and finding all of them might be hard (see [26; 27; 28]). However, it is well known that every non-zero sum game has, at least, one NE in mixed strategies (Theorem 3.2, [24]). In a mixed equilibrium, each player has access to a randomizing device which outputs which pure action the player should play, with a given probability. This probability is the mixed NE.  
If there are two players, each of them with two actions to choose, we can define the payoff matrices as:  
\[R_{1}=\left(\begin{array}{cc}r_{11}^{1}&r_{12}^{1}\\ r_{21}^{1}&r_{22}^{1}\end{array}\right)\quad R_{2}=\left(\begin{array}{cc}r_ {11}^{2}&r_{12}^{2}\\ r_{21}^{2}&r_{22}^{2}\end{array}\right) \tag{14}\]and we can obtain a mixed NE as it is shown in (Chapter 3, [24]). We define \(y\) as the probability that player 1 chooses her action 1, and \(1-y\) the probability that she chooses action 2 (for player 2, we define in an equivalent form \(z\) and \(1-z\)). The equilibrium conditions are the following:  
\[(y_{v}^{*})^{T}R_{1}z_{v}^{*}\geq y_{v}^{T}R_{1}z_{v}^{*}\qquad(y_{v}^{*})^{T}R _{2}z_{v}^{*}\geq(y_{v}^{*})^{T}R_{2}z_{v} \tag{15}\]  
where \(y_{v}=(y,1-y)\), \(z_{v}=(z,1-z)\) and \(y_{v}^{T}\) denotes the transposed of vector \(y_{v}\). In (15) we assume that both players are maximizers: otherwise, the inequality is reversed. One mixed NE for (15) can be obtained as (pp. 85-87, [24]):  
\[y^{*}=\frac{r_{22}^{2}-r_{21}^{2}}{r_{11}^{2}+r_{22}^{2}-r_{21}^{2}-r_{12}^{2} }\qquad z^{*}=\frac{r_{22}^{1}-r_{12}^{1}}{r_{11}^{1}+r_{22}^{1}-r_{21}^{1}-r_ {12}^{1}} \tag{16}\]  
where \(y^{*}\in[0,1]\) and \(z^{*}\in[0,1]\) is the mixed NE.
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 2 Materials and Methods
### Methodology
#### 2.3.3 Precipitation Intensity Classes  
In addition to assessing overall performance, an evaluation has been carried out specifically for different precipitation intensities. To categorize precipitation intensity, we utilized five classes based on the World Meteorological Organization guidelines [62]. These classes were further modified and tailored to suit the specific climate conditions of our study area (Table 5).
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 2 Materials and Methods
### Cross-Comparison Analysis between PS and SBAS Methods  
The subsidence velocity maps obtained by PS and SBAS were classified with the same grade, so that they can be compared and analyzed on the same scale. According to the different subsidence velocities, the study area was divided into different levels of subsidence. Then, a statistical analysis of the area, maximum subsidence velocity and average subsidence velocity in different subsidence zones was performed. Besides, corresponding points were selected to further explore the cross-comparison between PS and SBAS based on regression analysis and time series analysis, respectively.  
In the regression analysis, the subsidence velocity difference of the corresponding points was compared using the correlation coefficient (R). The calculation formula of R is as follows [37]:  
\[\text{R}=\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})\cdot(y_{i}-\overline{y})}}{ \sqrt{\sum_{i=1}^{n}{(x_{i}-\overline{x})^{2}\cdot\sum_{i=1}^{n}{(y_{i}- \overline{y})^{2}}}}} \tag{1}\]  
where \(n\) is the total number of corresponding points, and \(x\) and \(y\) represent the values of the points taken from the corresponding points. When the value of \(R\) is 0, it means there is no linear relationship between the subsidence velocities of the corresponding points. On the contrary, when \(R\) is 1, it indicates a perfect linear relationship.  
The standard deviation (SD) value was used to evaluate the precision of mean subsidence rate derived from the MT-InSAR technique [12].  
\[SD=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(x_{i}-\mu)^{2}}}} \tag{2}\]  
where \(n\) is the total number of pixels involved in the MT-InSAR method, \(x_{i}\) stands for each temporal subsidence rate of the pixel, and \(\mu\) represents the mean value of all the temporal subsidence rate. A small standard deviation indicates that the dispersion is low, and the corresponding accuracy is higher.
## 4 Jasmonates: Biosynthesis, Signaling, and Functions  
Jasmonic acid (JA) and its related derivatives--collectively known as jasmonates (including MeJA, JA-amino acid conjugates such as JA-isoleucine, and various oxylipin intermediates)--play fundamental roles in regulating plant growth, defense, and stress responses [31]. Their biosynthesis largely follows the octadecanoid pathway, unfolding in the chloroplasts and peroxisomes through a series of well-orchestrated steps. Initially, wounding, insect feeding, or other stress triggers the release of \(\alpha\)-linolenic acid (18:3) from membrane lipids via phospholipases, such as phospholipase A (PLA). The liberated \(\alpha\)-linolenic acid is then transformed into 13(S)-hydroperoxyl linolenic acid (13-HPO) by lipoxygenase (LOX), a process whose specificity can vary according to different LOX isoforms and their tissue-specific expression. Subsequent reactions in the chloroplast involve allene oxide synthase (AOS) converting 13-HPO to an unstable epoxide (13-HPO), and allene oxide cyclase (AOC) catalyzing intramolecular cyclization to produce 12-oxo-phytodienoic acid (OPDA). OPDA is transported to the peroxisome, where it is reduced by OPDA reductase and subjected to three rounds of \(\beta\)-oxidation, ultimately yielding (+)-7-iso-JA. From there, JA can be conjugated to various amino acids, such as isoleucine (JA-Ile), by the JAR1 enzyme, forming bioactive molecules that are critical for receptor binding [32]. Another important derivative is MeJA, produced when JA is methylated by JA carboxyl methyltransferase (JMT). As MeJA is volatile, it can diffuse or be transported systemically, extending its signaling influence across tissues or even neighboring plants [31].  
The enzymes involved in these steps, including LOX, AOS, AOC, and OPDA reductase, are tightly regulated at both the transcriptional and post-translational levels in response to environmental inputs, such as pathogen attack, mechanical injury, UV irradiation, or temperature stress [33]. Specialized transporters also facilitate the flux of OPDA and other oxylipins between subcellular compartments, coordinating the production of jasmonates. The reversible conversion of JA to MeJA enhances the complexity of this hormonal network by allowing for the rapid transmission of signals that trigger defense in distant tissues or adjacent plants [34]. Beyond their contributions to plant immunity, jasmonates oversee a wide range of developmental processes, including anther dehiscence, pollen maturation, tuber formation, and leaf senescence [35]. They also interact with additional phytohormones, such as salicylic acid, gibberellins, and ethylene, to navigate the crucial balance between plant growth and defense.
## 3 Modeling and Error Analysis of the Navigation Process in RINS
### Simulation of Measurement Error Influence
From the simulation results in Figure 5, we could see that a 0.02\({}^{\circ}\) initial azimuth estimation error would result in a position error of several kilometers in the navigation algorithm. Installation misalignment induced navigation error: In the navigation algorithm, installation misalignment is another error source. Due to the installation error, the gyro could not measure the precise angular information in IMU body coordinate. This measurement inaccuracy would also introduce a navigation error. In our simulation, we assume that there is an installation error along the azimuth direction in our IMU. As shown in Figure 6, we could see that a 0.2\({}^{\circ}\) initial azimuth installation error would result in position errors less than 1 km.