esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Asynchronous Duty-Cycled MAC Protocols
#### 5.1.2 Wise-MAC  
In order to reduce the length of the wakeup preamble, [PERSON] et al. proposed the Wise-MAC [29] protocol in 2004. This protocol is also specifically designed for asynchronous duty-cycled sensor nodes. Wise-MAC is based on the preamble sampling technique (listening to the channel for carrier sensing). Even though it is made for asynchronous duty-cycled sensors (no synchronization scheme for the sleep-awake schedule), the sleep interval of all the nodes in the network is the same. It does not use RTS/CTS but uses ACK, which also carries information about the next wakeup time of the receiver. Only neighbors within a one-hop distance can know the wakeup schedule of the receiver node. Using the next wakeup time of the nearest receiver node, the sender node can send data at an exact time, with a short preamble added before the data.  
In Figure 6, node A has a packet to send, so it wakes up and waits until the receiver node wakes up. Node A starts sensing the carrier just before the receiver wakes up, and if the medium is idle, it then sends a small-length preamble. After obtaining a short preamble from A, node B will respond by an ACK that also contains the next wakeup time of node B. This information will be saved by the neighbor nodes that are in a one-hop distance from the B node, and can later be used if they want to send data to this specific receiver node B.  
However, it does not provide a mechanism by which nodes can adapt to changing traffic patterns. Moreover, the authors did not address the hidden node problem because the RTS/CTS is absent. On the other hand, the first communication between two nodes will always be performed using a long wakeup preamble. Hence, the per-packet overhead decreases with increasing traffic, whereas in low traffic conditions, the per-packet overhead is high, which is again energy-consuming because most of the major applications of WSNs are related to environment monitoring, wherein nodes are idle most of the time and traffic is relatively low.
## 3 Results
### Deformation Law Analysis of the Deep Exaxation Channel Slope Based on TerraSAR-X Data
On the other hand, point P3 is located in zone B, which is characterized by an overall weak settlement deformation. It experienced continuous uplifting deformation from 10 June 2020, reaching its maximum value on 29 August 2020, before transitioning into subsidence deformation. The subsidence process halted on 27 December 2020, and then, uplifting deformation occurred again, but the uplifting deformation value did not exceed the previous peak value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Date** & **Deformation Characteristic** & **Deformation Value (mm)** \\ \hline \multirow{4}{*}{P1} & 10 June 2020–29 August 2020 & Uplift & 0\(\rightarrow\)12 \\  & 29 August 2020–28 October 2020 & Settlement & 12\(\rightarrow\)4.3 \\  & 28 October 2020–5 February 2021 & Uplift & 4.3\(\rightarrow\)10 \\  & 10 June 2020–29 August 2020 & Uplift & 0\(\rightarrow\)9.8 \\ \multirow{4}{*}{P2} & 29 August 2020–28 October 2020 & Settlement & 9.8\(\rightarrow\)3 \\  & 28 October 2020–5 February 2021 & Uplift & 3\(\rightarrow\)10 \\ \multirow{4}{*}{P3} & 10 June 2020–29 August 2020 & Uplift & 0\(\rightarrow\)3 \\  & 29 August 2020–27 December 2020 & Settlement & 3\(\rightarrow\)\(-\)1.7 \\ \cline{1-1}  & 27 December 2020–5 February 2021 & Uplift & \(-\)1.7\(\rightarrow\)1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deformation process characteristics of the three monitoring sites. Figure 8: Severe uplift deformation zone (C zone) and fracture.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 3 Single Detection
This is the steering vector angular threshold \(\xi_{\text{Th}}\):  
\[\mathbf{C}=\mathbf{B}^{H}\times\mathbf{B}\,, \tag{14}\]0  
This symbolic angle provides an intuitive understanding of how the detector functions, as opposed to merely setting an arbitrary threshold value. The threshold is used to detect each value in the matrix \(\mathbf{C}_{\text{norm}}\). If the value is lower than the threshold then the null-hypothesis \(H_{0}\) of no spoofing is accepted. Otherwise, the alternative hypothesis \(H_{1}\), where the signals have similar origins and are considered spoofed, is accepted:  
\[\mathbf{C}=\mathbf{B}^{H}\times\mathbf{B}\,, \tag{14}\]1  
A detection matrix \(\mathbf{D}\) can be generated accordingly:  
\[D(n,m)=\left\{ \begin{array}{ll}1,&\text{if }C_{\text{norm}}(n,m)\leq \lambda_{\text{Th}}\\ 0,&\text{if }C_{\text{norm}}(n,m)>\lambda_{\text{Th}}\end{array} \right.\,. \tag{22}\]  
Both normalized the correlation matrix \(\mathbf{C}_{\text{norm}}\) and the detection matrix \(\mathbf{D}\) are used in the subsequent sections to develop system-level detectors. As such, obtaining these matrices are the first step for the subsequent blind detection methods.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 3 The Proposed System Methodology
### Feature Extraction  
To track the number of heads in depth frames, local and global feature extraction plays a vital role. Therefore, Fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are passed to an Apriori Association algorithm to remove the unnecessary redundant features (See Algorithm 1).  
```
Input: Y: Detected head region; Output: Extracted Features; /* Fused joint HOG-LBP*/ /* Energy based point clouds*/ /* Fused intra-inter trajectories*/ Fused joint HOG-LBP /* For a sample \(p\) HOG feature can be calculated as; \(\text{HOG}_{d_{i},d_{j}}(p)\); /* The Horizontal and vertical gradient */ \(h_{gx}(x,y)=l(x+1,y)-l(x-1,y)\); \(h_{gy}(x,y)=l(x,y+1)-l(x,y-1)\); /* The magnitude and direction of the pixel gradient */ \(h_{gy}(x,y)=\sqrt{h_{gx}(x,y)^{2}+h_{gy}(x,y)^{2}}\); \(\theta(x,y)=tan^{-1}\frac{h_{gy}(x,y)}{h_{gy}(x,y)}\); /* HOG features description of heads and shoulders */ \(H_{d_{i},d_{j}}=\left\{H_{d_{i}},H_{d_{j}}\right\}=v_{1},v_{2},\ldots,v_{L+1}\); Energy based point clouds /* Energy based point clouds w...r.t central point of head and other 5 fiducial points marked */ \(D=\left\{\begin{subarray}{c}A=(A-D_{x}+D_{y}+\sqrt{\Delta}\text{ }where\text{ }\Delta\geq 0\\ B=\text{min}(D_{x},D_{y})+a\\ \Delta=2a^{2}-\left(D_{x}-D_{y}\right)^{2}\end{subarray}\right.\); Fused intra-inter point trajectories /* Five fiducial points are marked on head, neck, two shoulders and chest. Then, find the change in displacement of the trajectories */ \(\Delta p_{t}=(x_{t+1}-x_{t},y_{t+1}-y_{t})\); \(d_{x,y}=\frac{(\Delta p_{t},\Delta p_{x},\Delta p_{T-1})}{\sum_{i=1}^{L-1}|| \Delta p_{i}||}\); Augment all features extracted; \(A=\left|H_{d_{i},d_{j}}\right|||D||\Delta p_{t}|d_{x,y}|\); Pass to Apriori-Association end;
```  
**Algorithm 1**Feature Extraction.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 4 Study on Synchronization and Desynchronization Time
### Results Analysis  
This subsection compiles and analyzes the results of the analog-discrete and discrete-analog synchronization and desynchronization time. The results are grouped by chaos oscillators and the state variables in Figures 22 and 23. The benefit of using \(\theta\) (time scaled by the time constant of the oscillator) is that the results for two chaos oscillators become comparable despite the different frequencies of oscillations.  
Analyzing the results of the two figures, the synchronization and desynchronization times change very little over at the high SNR levels. Decreasing the SNR demonstrates two types of behavior. The first behavior is the decrease of the synchronization and desynchronization time (Figures 22 and 23c,d), which is attributed to the lower level of synchronization, i.e., the lower the synchronization level, the less time is required to reach 80% of the synchronization level threshold. The cases demonstrating the increase in synchronization and desynchronization times (Figure 23a,b) are when the synchronization level is so low (less than 0.2) that it barely stands out from the noise present when the oscillators are not synchronized.  
The results also demonstrate that chaotic synchronization takes more time than desynchronization. The Vilnius chaos oscillator is the fastest in terms of analog-discrete synchronization, while the RC chaos oscillator is faster in terms of analog-discrete desynchronization. The RC chaos oscillator is also faster in terms of discrete-analog synchronization and desynchronization, making this oscillator the fastest overall in synchronization and desynchronization speed.  
Like in the first study, the use of LTspice to model the behavior of the hardware circuit produces similar results in most cases, but there are exceptions like the Figure 23a,b, where the desynchronization time shows a completely different behavior in LTspice than the hardware circuit.  
Figure 21: Time diagram of window correlation of \(z\) state variables in the case of RC chaos oscillator discrete model synchronization with the hardware prototype.  
Figure 23: _Cont._  
Figure 22: RC chaos oscillator analog-discrete synchronization and desynchronization times of the \(x\) (**a**) and \(z\) (**b**) state variables and discrete-analog synchronization and desynchronization times of the \(x\) (**c**) and \(z\) (**d**).
## 2 Fundamentals
### Sand Cat Swarm Optimization (SCSO)  
Sand Cat Swarm Optimization (SCSO) is a metaheuristic algorithm inspired by the unique survival and hunting behaviors of sand cats, a species adapted to harsh desert environments [14]. Unlike domestic cats, sand cats can detect low-frequency sounds below 2 kHz, which plays a crucial role in locating prey in challenging conditions. SCSO simulates this remarkable sensory and hunting capability to navigate the search space and locate near-optimal solutions. Similar to other swarm-based optimization techniques, the algorithm begins with the random initialization of a population of search agents within defined lower and upper bounds. Each agent represents a potential solution in a matrix where rows correspond to individual agents and columns to the problem's dimensions. A fitness function is employed to evaluate how well each solution satisfies the optimization objective--whether minimization or maximization--and guides the search process across iterations. The algorithm iteratively updates the agents' positions based on fitness values until convergence, with the final solution representing the best outcome found. While the principal goal across metaheuristics is similar, the strategies employed, such as the foraging-inspired mechanism in SCSO, distinguish each algorithm in achieving optimal or near-optimal results.  
Each search agent begins with a sensitivity range starting at 2 kHz, controlled by the \(\overset{\rightarrow}{R}_{G}\) parameter, which linearly decreases over iterations to balance exploration and exploitation. Position updates are based on both the best candidate and the current position, modulated by the sensitivity range \(\overset{\rightarrow}{r}\), ensuring adaptive movement across the search space. A circular movement strategy is employed using a random angle \(\theta\), enabling agents to effectively avoid local optima and converge toward the best solution:  
\[\overset{\rightarrow}{r}_{G}=s_{M}-\left(\frac{S_{M}\times iter_{c}}{iter_{ Max}}\right) \tag{9}\]  
\[\overset{\rightarrow}{R}=2\times\overset{\rightarrow}{r}_{G}\times rand(0,1 )-\overset{\rightarrow}{r}_{G} \tag{10}\]  
\[\overset{\rightarrow}{r}=\overset{\rightarrow}{r}_{G}\times rand(0,\ 1) \tag{11}\]  
\[\overset{\rightarrow}{Pos}(t+1)=\overset{\rightarrow}{r}.\left(\overset{ \rightarrow}{Pos_{bc}}(t)-rand(0,1)\cdot\overset{\rightarrow}{Pos_{c}}(t)\right) \tag{12}\]  
\[\overset{\rightarrow}{Pos_{rnd}}=\left|rand(0,1)\cdot\overset{\rightarrow}{Pos _{b}}(t)-\overset{\rightarrow}{Pos_{c}}(t)\right| \tag{13}\]  
\[\overset{\rightarrow}{S}(t+1)=\left\{\begin{array}{cc}\overset{\rightarrow }{Pos_{b}}(t)-\overset{\rightarrow}{Pos_{rnd}}\cdot\cos(\theta).\overset{ \rightarrow}{r}&|R|\leq 1; exploitation\\ \overset{\rightarrow}{r}.\left(\overset{\rightarrow}{Pos_{bc}}(t)-rand(0,1) \cdot\overset{\rightarrow}{Pos_{c}}(t)\right)&|R|>1;exploration\end{array}\right. \tag{14}\]
## 4 Conclusions  
Correlations available in the open literature to predict nanofluid thermal conductivity were reviewed. Using experimental data for the H\({}_{2}\)O-LiBr solution with Al\({}_{2}\)O\({}_{3}\) and CNT nanoparticles, the most appropriate correlation was selected. Values of the nanofluid properties were calculated using the concentration of Al\({}_{2}\)O\({}_{3}\), CuO, and CNT nanoparticles up to 5% in volume.  
The performance of a membrane microchannel desorber was studied using a previous validated model and the three types of nanoparticles. The largest increase in the desorption rate (7.9%), with respect to using the pure H\({}_{2}\)O-LiBr solution, was obtained using CNT nanoparticles and the maximum volume fraction simulated.  
Regarding the combination of the absorption chiller simulated with one solar thermal collector, the best performance for the cases simulated is obtained using the minimum mass flow rate through the channels (maximum number of desorber modules) and the H\({}_{2}\)O-LiBr-CNT nanofluid. In these conditions, the maximum cooling power is 645 W, obtained at 13:00 local time, the daily SCOP reaches 14%, and the total cooling effect is 2.3 kWh.  
However, the relative increase in the cooling effect owing to the use of nanoparticles reduces as the number of desorber modules grows. In addition to this, a higher number of modules increases the total cost of the absorption chiller. For this reason, a thermo-economic analysis was performed to identify the optimum number of hot water channels in the desorber.  
When the price of the first module increases, the number of modules to be competitively used reduces. For the absorption chiller to be economically competitive with respect to the mechanical compression system, a maximum value of 35 euros for the first module is obtained. The optimum number of desorber modules varies with the price of the first module. When this price raises, a decrease in the benefit and in the optimum number of modules is obtained. The optimum number of modules is always lower than 50.  
Conceptualization, M.V. and M.d.V.; investigation, M.V., M.d.V., N.G.-H., and A.Z.; resources, M.V., M.d.V., and N.G.-H.; writing--original draft preparation, M.V.; writing--review and editing, M.V., M.d.V., N.G.-H., and A.Z.; funding acquisition, M.V., M.d.V., N.G.-H., and A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FEDER/Ministerio de Ciencia, Innovacion y Universidades--Agencia Estatal de Investigacion/_Proyecto (DPI2017-83123-R), and science research grant SIP20190076, by the National Polytechnic Institute of Mexico (IPN). A.Z. thanks CONACYT for the 740638 scholarship granted.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Getting Young People to Farm: How Effective Is Thailand's Young Smart Farmer Programme?
## 2 The Young Smart Farmer Programme Background and Evaluation Framework  
### The Young Smart Farmer Programme  
According to the Farmer Development Division [27], the YSF programme has been designed to develop the farming business capabilities of young farmers. The development approach of the programme is based on the principle that farmers are the centre of the development (bottom-up approach) and relies on the process of knowledge sharing and network building among farmers as a development means. The programme has been implemented annually in every province of the country by the Department of Agricultural Extension (DOAE) since 2014.  
There are three long-term aims of the programme:  
1. to increase the number of young farmers by motivating young people to continue, return to, or enter farming to replace older farmers;
2. to help young farmers to become agricultural leaders in their communities; and
3. to create collaborative networks among relevant stakeholders for the development of the agricultural sector of the country.  
It is expected that these aims will be achieved through the completion of two short- or medium-term outcomes of the programme:  
1. to make young farmers become financially independent with their own farming businesses and
2. to enhance the adoption of innovative farming methods by young farmers.  
The three primary activities of the programme are, first, to provide training, workshops, seminars, and field trips to participants, as per their needs; second, to create 77 provincial, nine regional, and one national young smart farmer networks and channels for sharing knowledge among participants; and third, to establish and support the services of 27 young farmer development learning centres.  
Young farmers who can participate in the programme must have the following qualifications. First, they must be between 17 and 45 years old and have just started their own farming. Second, they must be determined to improve their farming capability and quality of life. Third, they must volunteer to participate in the programme and join all activities throughout the programme period. Fourth, they must be registered as farmers with the DOAE. Each year, 25 to 30 young farmers are recruited to participate in the programme in each province. Between 2014 and 2018, 12,569 participants nationwide were recruited [28].
## 3 Methods  
### BIOME-BGC Description  
BIOME-BGC is a mechanistic ecosystem model that simulates biogeochemical and hydrologic processes of terrestrial ecosystems based on the assumption that differences in the process rates are a function of climate and general life-form characteristics. BIOME-BGC simulates carbon, nitrogen and water cycles within a forest ecosystem across several compartments (including leaf, root, stem, soil and atmosphere) [5]: the main processes described by the model are evapotranspiration (Penman-Monteith equation), photosynthesis (Farquhar model, [38]) and respiration, modeled as the sum of autotrophic respiration [39] and heterotrophic respiration.  
NEE (kgC m\({}^{-2}\) day\({}^{-1}\)) was modeled as the net accumulation or loss of carbon by the entire soil-stand system and was determined by the differences between GPP (kgC m\({}^{-2}\) day\({}^{-1}\)), resulting from the processes of photosynthesis, and R\({}_{\rm{eco}}\) (kgC m\({}^{-2}\) day\({}^{-1}\)), resulting from the respiration processes. The LAI (m\({}^{2}\) m\({}^{-2}\)) is a key variable of BIOME-BGC controlling canopy radiation absorption, water interception, photosynthesis and litter input to detrital pools [40].  
The model requires three types of information: ecophysiological parameters, site parameters and meteorological data. The main ecophysiological properties include the carbon to nitrogen ratios of the different plant pools (e.g. carbon to nitrogen leaf ratio, C\(\cdot\)N\({}_{\rm{leaf}}\)), carbon allocation parameter (e.g. new fine root to new leaf carbon, FR:LC), maximum stomatal conductance (g\({}_{\rm{c,MAX}}\)), canopy water interception, light extinction coefficients and SLA.  
The main site parameters include soil depth, soil texture, initial standing biomass (e.g. maximum leaf carbon during the simulated year, LC\({}_{\rm{MAX}}\) and maximum stem carbon) and initial soil carbon in the different soil pools. The model also requires the bud-burst date (ONDAY), in which the growing season starts, and the day of the end of growing season (OFFDAY). These phenological parameters strongly influence the seasonal pattern and magnitude of simulated carbon fluxes [25].  
With regards to meteorological data, the model requires the maximum, minimum and mean daily air temperature, mean daylight VPD, daily precipitation, mean daily incoming shortwave radiation and day-length.Model outputs include GPP, NEE, evapotranspiration and LAI simulated at a daily time-step. We used version 4.1.1 of the BIOME-BGC code available on the web site of the Numerical Terradynamics Simulation Group (NTSG) of the University of Montana. More information on the design of the model and its functioning can be found in [6; 25; 40].
# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 2 Materials and Methods  
Figure 1 outlines the workflow for the proposed study, starting with dataset preparation, where water quality data undergoes preprocessing to address missing values, remove duplicate records, and ensure data consistency. In the Correlation Analysis phase, the relationships between the water quality variables and the target variables (Chl-a and TSS) are examined using Pearson's correlation coefficient. Additionally, the relative importance (RI) index is employed to determine the influence of each predictor variable, while bivariate scatter plots visualize the linear relationships. This analysis identifies the key water quality parameters that exhibit statistically significant correlations with the target variables. The third stage involves Multivariate Regression Analysis, where two separate models are developed: Model 1, which focuses on predicting Chl-a concentrations, and Model 2, which targets the prediction of TSS levels. These models aim to uncover the functional relationships between the predictors and the target variables, considering the complexity and interdependence of the environmental factors. All statistical analyses and visualizations were performed using Python (v3.11.7) with the statsmodels, scikit-learn, and matplotlib packages.
## 2 Materials and Methods  
### Computational Model  
The NREL's S833 airfoil has been extensively used to design wind turbine blades [26]. However, with the rapid development of wind farms, noise emission has become a significant issue. The ultimate goal of this study is to explore a novel airfoil design to reduce the noise generation associated with the shedding vortices from wind turbine blades. Thus, the S833 airfoil is selected to investigate the aerodynamic interactions between the vortex shedding in the wake and the flapping fringe installed at the trailing edge. Two-dimensional CFD simulations are performed for the airfoil model. The dimensions and boundary conditions applied in simulations are shown schematically in Figure 1.  
The rectangular area of the outside domain is 20C by 10C (\(C\): the chord length of the airfoil), with 5C from the inlet to the leading-edge of the airfoil. The flow boundary conditions applied in the simulation are adopted from the parameters in our previous experimental study [24]. The upstream velocity is set at 6 m/s, and the density and viscosity of the air are assumed as 1.23 kg/m\({}^{3}\) and 1.84 \(\times\) 10\({}^{-5}\) Pa s, respectively, to match the air property at room temperature. This boundary condition corresponds to a Reynolds number (Re) of 40,000, which is the same as the Re number evaluated in the experiment. This Re number falls within the range of the Re number of owls' flight. The outlet boundary condition is defined as zero static pressure, and surfaces on the airfoil, as well as the top and bottom walls, are defined as non-slip wall conditions. The airfoil's angle of attack is set at 9 degrees, which is also consistent with the experimental study [24]. The fringe length (\(L_{e}\)) ranges from 8% to 12% of the chord, with a step increment of 2%. The flapping frequency (\(f_{e}\)) ranges from 80 Hz to 170 Hz, with a step increment of 30 Hz. The initial \(f_{e}\) is determined as the shedding vortex frequency (\(f\)) of the baseline bare airfoil model,which is 140 Hz, obtained via the power spectral density analysis of the velocity in the downstream wake [25]. The dynamic motion of the trailing edge fringe can be expressed as \(\theta=A\sin(2\pi\theta)\), where \(A\) is the amplitude flapping angle (\(A=5^{\circ}\)). The connection point of the flapping motion is fixed at the tip of the trailing edge.