esa-sceva/satcom-chunk-collection huggingface dataset:
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
###### Abstract  
Ground-based GNSS-R (global navigation satellite system reflectometry) can provide the absolute vertical distance from a GNSS antenna to the reflective surface of the ocean in a common height reference frame, given that vertical crustal motion at a GNSS station can be determined using direct GNSS signals. This technique offers the advantage of enabling ground-based sea level measurements to be more accurately determined compared with traditional tide gauges. Sea level changes can be retrieved from multipath effects on GNSS, which is caused by interference of the GNSS L-band microwave signals (directly from satellites) with reflections from the environment that occur before reaching the antenna. Most of the GNSS observation types, such as pseudo-range, carrier-phase and signal-to-noise ratio (SNR), suffer from this multipath effect. In this paper, sea level altimetry determinations are presented for the first time based on geometry-free linear combinations of the carrier phase at low elevation angles from a fixed global positioning system (GPS) station. The precision of the altimetry solutions are similar to those derived from GNSS SNR data. There are different types of observation and reflector height retrieval methods used in the data processing, and to analyze the performance of the different methods, five sea level determination strategies are adopted. The solutions from the five strategies are compared with tide gauge measurements near the GPS station, and the results show that sea level changes determined from GPS SNR and carrier phase combinations for the five strategies show good agreement (correlation coefficient of 0.97-0.98 and root-mean-square error values of <0.2 m).  
GNSS reflectometry; tide gauge; sea level; SNR; carrier phase geometry-free linear combinations 1
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Implementation of PV Method  
To the best of our knowledge, the PV method is the only method that detects the saccade timing from the EOG signal only and observes the EFRP. We implemented the PV method that was proposed in [3]. All parameters in this section have the same values as in the previous study. First, ICA was applied to all measured data, and after visually extracting the IC data corresponding to eye movements, the following equation was applied:  
\[EOG=\sqrt{IC_{EOG_{v}}^{2}+IC_{EOG_{h}}^{2}}, \tag{5}\]  
where \(IC_{EOG_{v}}\) indicates the IC data corresponding to the vertical eye movement and \(IC_{EOG_{h}}\) indicates the IC data corresponding to the horizontal eye movement. Subsequently, the square of the EOG velocity was calculated by taking the derivative, as follows:  
\[dEOG=diff(EOG)^{2}, \tag{6}\]  
where \(diff\) is the derivative. A median filter with a width of 80 ms was applied to the \(dEOG\), and the peak point was defined as the saccade time. Note that the PV method detects the saccade time, which is the timing of the peak velocity, rather than the SO timing.  
If the saccade was detected within \(\pm\)120 ms relative to the blink timing, the detected saccade point was excluded, as in [3]. The median filter with a width of 80 ms was applied to the \(IC_{EOG_{v}}\) data, following which, the peak point was defined as the blink time, to detect the blink.  
As we only focused on left-to-right eye movements, we used only the \(IC_{EOG_{h}}\) data for the saccade time detection. The _findpeaks_() function (see [[https://www.mathworks.com/help/signal/ref/findpeaks.html](https://www.mathworks.com/help/signal/ref/findpeaks.html)]([https://www.mathworks.com/help/signal/ref/findpeaks.html](https://www.mathworks.com/help/signal/ref/findpeaks.html)) (accessed on 1 April 2023) for details), which is a built-in function in Matlab version R2018a that is designed to determine local maxima, was used for the peak detection. Table 1 presents the parameters of the _findpeaks_() function for the saccade and blink time detection in the PV method.  
Figure 1: Description of the proposed model. (**a**) Structure of GRUs corresponding to Equations (1)–(4). The corresponding parts of Equations (1)–(4) are highlighted in blue. (**b**) Network structure of the proposed model.  
To compare the performance of the proposed model directly with that of the VD and PV methods, their performances were evaluated using the same trials. Thus, among the saccades that were detected by the PV method, the saccades within \(\pm\)100 ms relative to the SO timing determined by the VD method were considered to correspond to the same saccade across the two methods, and we used trials including the saccades for the following analysis. The time interval was determined considering the time lag between the saccade time detected by the PV method and the SO timing determined by the VD method, as the PV method detects the time point when the velocity of the saccade is maximal, rather than the SO timing.
## 2 Methodology
### Seasonal Autoregressive Integrated Moving Average (SARIMA)
Identification: This stage focuses on selecting the appropriate level of differencing to transform the time series into a stationary form. It also involves determining the desired order of the model and analyzing the autocorrelation function (ACF) and the partial autocorrelation function (PACF). These functions help to uncover the temporal correlation structure of the transformed data. Specifically: * The ACF is used to assess whether past values have a significant association with the current values. * The PACF quantifies the correlation between the variable and its time-lagged values, while controlling for intermediate lags. 2. Model Selection: The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) (also referred to as Schwarz's BIC) are commonly employed to identify the optimal model. These criteria are defined mathematically in Equations (6) and (7), respectively, and provide a trade-off between model complexity and goodness-of-fit. 3. Parameter Estimation and Diagnostic Testing: Once the model structure is identified, parameters are estimated, and diagnostic testing is performed to ensure the model adequately fits the data. The \(AIC\) and \(BIC\) serve as key tools in balancing model parsimony with accuracy, enabling the selection of an effective ARIMA model [75]. \[AIC=-2\text{log}(L)+2k=-2\text{log}(L)+2(p+q+P+Q) \tag{10}\]  
\[BIC=-2\text{log}(L)+k\,*\,In(n)=-2\text{log}(L)+(p+q+P+Q) \tag{11}\]  
In this context, \(n\) represents the number of observations in the time series, while \(k\) denotes the set of ARIMA parameters. Through empirical analysis, it was observed that the model's efficiency improved as the \(AIC\) value decreased. The forecasting model with the lowest \(AIC\) score was identified as the best-fitting model, indicating its superior balance between complexity and goodness-of-fit [75; 76]. In order to determine the most suitable model, many models are defined and among these, residuals are examined. Following the selection of the stochastic time series model, the stationarity and reversibility of the model parameters are examined. The most popular method among those suggested in the literature is to look at the residual terms' correlogram (ACF) after testing them as independent series in order to choose the best model for explaining the process. The residual terms' correlogram should be independent, random, and below a selected confidence level, which is often 95%. The model's residual terms must be regularly distributed and independent of time. The Ljung-Box test [11], which is provided as follows, is used to verify it. \[Q=n(n+2){\sum}_{k=1}^{m}(n-k)^{-1}{r_{k(\epsilon)}}^{2} \tag{12}\]  
where \(m\) is the maximum lag, \(r_{k(\epsilon)}\) is the residual correlogram, and \(n\) is the number of observations. It is compared with the \(\chi^{2}\) (chi square) statistic, which is the degree of freedom (\(m\)-\(p\)-\(q\)) at a chosen significance level with the calculated \(Q\) statistic.
## 3 Proposed Mechanism
### Multi-channel Communications System  
The basic idea of LPMC is that all nodes in a single path use the same channel. Nodes do not need to switch channels in order to receive and forward packets. Therefore, in a tree-structure, the channel used by a neighbor of the sink (one-hop away node) is to be used by all of the nodes that forward their data through this node. We refer to this node as the _channel deciding node_ (CDN). Figure 1 shows a typical WSN environment, in which a set of nodes sends data to the sink using a tree. There are four CDN nodesin the figure (\(c_{1}-c_{4}\)). The nodes that forward their data through a CDN create a _tree-branch_ (TB), and all nodes within a TB use the same channel. When a channel assignment takes place, all of the nodes in a TB change channels. However, multiple TBs can use one channel. Figure 1 shows four TBs (\(tb_{1}-tb_{4}\)) rooted at four CDNs.  
When the network starts, all of the nodes use a predefined channel. We refer to this channel as the _primary channel_. If the primary channel is overloaded, the most heavily loaded TB of the network is assigned a new channel. This continues as long as a channel is available and one of the allocated channels is overloaded. If there is no available channel, we assume that a rate control mechanism will restrict the data rates of the sources in order to avoid the packet losses. The rate control mechanism is beyond the scope of this paper. However, interested readers can refer to [16] and [18], where two well described rate control mechanisms for WSNs can be found. In contrast, when the network load decreases, the added channels are removed. If a single channel can handle the loads of two or more channels, the lowest channel ID is assigned to all of the nodes using these channels.
## 2 Results
### Functional Classification and Enrichment Analysis of DEGs  
To analyze the functions of differentially expressed genes and predict the functional classification of non-differentially expressed genes, we conducted GO enrichment analysis. We categorized gene sequences into three groups: biological process, cellular component, and molecular function. The husk and leaf callus libraries exhibited significant differences in \"carbohydrate metabolic process\", \"heme binding\", \"hydrolase activity, acting on glycosyl bonds\", \"iron ion binding\", \"oxidoreductase activity, acting on paired donors with incorporation or reduction of molecular oxygen\", and \"tetrapyrole binding\". Most of these pathways primarily consist of down-regulated DEGs. The significant differences between the stem and root libraries are primarily concentrated in \"lipid biosynthetic process\", \"ATPase activity\", \"enzyme inhibitor activity\", \"enzyme regulator activity\", \"molecular function regulator\", \"transferase activity, transferring acyl groups\". Most of these pathways consist mainly of up-regulated DEGs (Figures 7 and S7).  
Figure 6: Analysis of differentially expressed genes. (**A**) PCA analysis of transcriptome data in different tissue parts of _Aquilaria sinensis_. (**B**) Heatmap based on hierarchical clustering analysis of differentially expressed genes (DEGs) in various tissue sites.  
To conduct an analysis of the gene functional pathways associated with differentially expressed genes across various tissue sites, we performed a KEGG enrichment analysis. We selected the top 20 most significant pathways for visualization using scatter plots (Figure S8). The results of the KEGG enrichment analysis revealed a significant enrichment of differentially expressed genes in the metabolic pathways \"ABC transporters\", \"Starch and sucrose metabolism\", \"Plant hormone signal transduction\", \"Biosynthesis of various plant secondary metabolites\", \"Flavonoid biosynthesis\", and \"MAPK signaling pathway--plant\". The differentially expressed genes in the comparison group between the callus and the rest of the tissues were mainly concentrated in pathways involved in \"ABC transporters\", \"Biosynthesis of various plant secondary metabolites\", \"Plant-pathogen interaction\" and \"Starch and sucrose metabolism\" as well as other metabolic pathways. Notably, the \"Diterpenoid biosynthesis\" pathway was enriched exclusively in the control group of husk and leaf callus, while the \"Pantotheate and CoA biosynthesis\" pathway showed significant enrichment solely in the control group of root and leaf callus. Additionally, \"Carbon metabolism\" exhibited significant enrichment only in the control group of leaf and root.  
Figure 7: Analysis of differentially expressed genes. (**A**) Enriched GO terms of DEGs between husk and leaf callus. (**B**) Enriched GO terms of DEGs between stem and root.
## 4 Design Combination of SMC and NESO
### NESO Design
This bound will ensure that the NESO errors of the estimation meet these conditions:  
\[|e_{1}|\leq\tanh^{-1}\Big{(}\frac{\rho}{h^{2}}\Big{)},\qquad|e_{2}|\leq h \tanh^{-1}\Big{(}\frac{\rho}{h^{2}}\Big{)} \tag{29}\]  
By selecting an appropriate parameter \(h>0\), the estimation errors \(e_{1}\) and \(e_{2}\) will eventually approach the vicinity of zero. According to (23), the NESO based on the HTF to estimate the mismatched disturbance for each state of the FPPMSM model is given as follows:  
* Defining \(x\), \(u\), \(z_{1}\), and \(z_{2}\) as \(i_{dp}\), \(v_{dp}\), \(\hat{i}_{dp}\), and \(\hat{d}_{1}(t)\), respectively, \(d_{1}(t)\) can be estimated by \[ \begin{cases}\dot{i}_{dp}=\hat{d}_{1}(t)-h_{1}(\hat{i}_{dp}-i_{dp})+f_{dp}+b_ {1}v_{dp}\\ \dot{d}_{1}(t)=-h_{1}^{2}\tanh(\hat{i}_{dp}-i_{dp})\end{cases} \] (30)
* Defining \(x\), \(u\), \(z_{1}\), and \(z_{2}\) as \(i_{dp}\), \(v_{dp}\), \(\hat{i}_{dp}\), and \(\hat{d}_{2}(t)\), respectively, \(d_{2}(t)\) can be estimated by \[ \begin{cases}\dot{i}_{qp}=\hat{d}_{2}(t)-h_{2}(\hat{i}_{qp}-i_{qp})+f_{qp}+b_ {2}v_{qp}\\ \dot{d}_{2}(t)=-h_{2}^{2}\tanh(\hat{i}_{qp}-i_{qp})\end{cases} \] (31)
* Defining \(x\), \(u\), \(z_{1}\), and \(z_{2}\) as \(i_{qs}\), \(v_{qs}\), \(\hat{i}_{qs}\), and \(\hat{d}_{4}(t)\), respectively, \(d_{4}(t)\) can be estimated by \[ \begin{cases}\dot{i}_{qs}=\hat{d}_{4}(t)-h_{4}(\hat{i}_{qs}-i_{qs})+f_{qs}+b_ {4}v_{4}\\ \dot{d}_{4}(t)=-h_{4}^{2}\tanh(\hat{i}_{qs}-i_{qs})\end{cases} \] (33)* Defining \(x\), \(u\), \(z_{1}\), and \(z_{2}\) as \(\Omega\), \(T_{e}\), \(\hat{\Omega}\), and \(\hat{d}_{5}(t)\), respectively, \(d_{5}(t)\) can be estimated by \[ \begin{cases}\hat{\Omega}=\hat{d}_{5}(t)-h_{5}(\hat{\Omega}-\Omega)+a_{1}T_{e} \\ \hat{d}_{5}(t)=-h_{5}^{2}\tanh(\hat{\Omega}-\Omega)\end{cases} \] (34)
* Defining \(x\), \(u\), \(z_{1}\), and \(z_{2}\) as \(i_{\beta s}\), \(v_{\beta s}\), \(\hat{i}_{\beta s}\), and \(\hat{d}_{6}(t)\), respectively, \(d_{6}(t)\) can be estimated by \[ \begin{cases}\hat{i}_{\beta s}=\hat{d}_{6}(t)-h_{6}(\hat{i}_{\beta s}-i_{\beta s })+f_{\beta s}+b_{\beta s}v_{\beta s}\\ \hat{d}_{6}(t)=-h_{6}^{2}\tanh(\hat{i}_{\beta s}-i_{\beta s})\end{cases} \] (35) where \(h_{i}>0\) (\(i=1,\ldots,6\)) represent the constant gains of the observer.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 1 Introduction
### Agent-Based Modelling and Community-Based Adaptation to Climate Change in the Literature
Some discuss the potential for combining ABM with broader socio-environmental models to address multi-scale and multi-stakeholder dynamics [57]. Such approaches, while promising, require significant resources for model calibration and validation, as well as a greater emphasis on aligning outputs with stakeholder needs. Collectively, these studies illustrate the versatility of ABM in addressing the multifaceted challenges of CBA. However, they also reveal important gaps. Many models depend on localised data and assumptions, limiting their transferability across diverse socio-economic and environmental contexts. The integration of socio-cultural dimensions, particularly across multiple scales, remains underexplored in most studies. Additionally, while ABM offer significant potential for informing policy, few studies demonstrate robust validation mechanisms or clear pathways for translating findings into actionable governance strategies.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 4 Proposed EEG VL-OCC System Modelling  
This section explains the system modelling of the proposed VL-OCC for EEG. Figure 3 shows the proposed system modelling in healthcare for EEG. The data bits transmitted are presented by \(b_{n}\).  
The OLED screen working as a transmitter is a matrix of LEDs arranged in the formation of rows and columns. The total number of LEDs is \(N=R_{1}\times C_{1}\). Thereafter the signal extracted from the microprocessor and fed to the OLED screen after serial to parallel conversion (S/P) is given by \(s^{(t)}=[r_{1}\), \(c_{1}]\). The spatial discrete coordinates were presented in the OLED screen as rows and columns given by \(r_{1}\) and \(c_{1}\), respectively. The number of bits that were transmitted per frame of the OLED screen was dependent upon the size of pixels; D given by  
\[(S_{z_{rows}}\ /D)\times(S_{z_{column}}\ /D)=R_{1}\times C_{1} \tag{1}\]  
where \(S_{z_{rows}}\) and \(S_{z_{column}}\) are rows and columns number, respectively. The optical signal transmitted from the OLED screen can be presented by  
\[x^{(t)}[r_{1},c_{1}]=(s*h)^{(t)}[r_{1},c_{1}\ ] \tag{2}\]  
In Equation (2), h is the impulse response and Equation (3) presents the received signal given by  
\[y^{(t)}[r_{1},c_{1}]=(x)^{(t)}[r_{1},c_{1}]+(y)^{(t)}[r_{1},c_{1}] \tag{3}\]  
Figure 3: Proposed Visible Light Optical Camera Communication (VL-OCC) system model.  
where \((v)^{(t)}[r_{1},c_{1}]\) is the dominant white Gaussian noise (WGN) and was independent of the pixels. Thereafter, the camera was used to capture a video, which was followed by processing of images and threshold detection in MATLAB, to decode the images and bits, to get the original EEG signal and to calculate BER. The nomenclature is listed in Table 1.  
The bits transmitted through the OLED screen were received by the camera at different distances to calculate the BER and to depict the successful transmission of the EEG signal. The video captured by camera was downloaded to MATLAB using a USB cable or memory card; thereafter offline processing was done to determine the BER at different distances. In the experiment, the ambient light was kept very low to avoid interference to the receiver.
## 4 Analysis of Numerical Results
### Influence of Tunnel Excavation Surface Spacing
#### 4.1.3 Plastic Area Analysis  
From the contour distribution of the plastic zone at the top of Figure 9, it can be seen that the difference in the distance between the tunnel excavation faces affects the distribution of the plastic areas in the strata above the tunnel. The plastic zone of the right line tunnel is larger than that of the left line, which shows that the first excavated tunnel caused more plastic deformation than the later excavated tunnel. The largest plastic area appears at the location of the sidewalls on both sides of the cavern, and the plastic area in the left line is affected by the excavation in the right line increases with the decrease in the distance of the tunnel excavation face. The plastic area of the overlying rock forms a complete funnel over the smaller distance between the two tunnel excavation faces, and the central location directly above the two tunnels will not generate large shear stress due to the overall subsidence. The plastic area becomes asymmetric as the distance between the tunnel excavation faces increases. After a small funnel-shaped plastic area is generated in the first tunnel, the plastic strain generated in the second tunnel expands in the area close to the side of the second tunnel and eventually becomes an asymmetric funnel shape.  
Figure 8: Variation curve of displacement of monitoring point 1 with tunnel excavation surface spacing.  
Figure 9: _Cont._Figure 9: Contour map of plastic strain area (tunnel excavation surface spacing 0 m/25 m/50 m/100 m). (**a**) A distance of 0 m. (**b**) A distance of 25 m. (**c**) A distance of 50 m. (**d**) A distance of 100 m.  
After the excavation of the right line tunnel, a settlement area and plastic areas are formed above the tunnel as shown in Figure 10a. Shear areas occur between the settlement area and the surrounding unsettled area due to the incompatibility of displacements, and the plastic strain in the overlying soil layer occurs mainly in this area. When the left line passes through the tunnel, the part of the settlement area formed by the left line and the settlement zone formed by the right line overlap because the settlement has already occurred, so the shear area formed after the excavation of the left line mainly occurs on the left side of the left settlement ellipse, which is further expanded on top of the plastic strain area formed by the right line as seen from the plastic strain contour map. Moreover, the plastic area generated by the surrounding soil of the first tunnel is larger than that of the later tunnel because the stresses in the surrounding rock are released after the excavation of the first tunnel.
# A Contribution of Shortest Paths Algorithms to the NetworkX Python Library
## 3 Foundations
### Dynamic A-Star (D*) and D* Lite  
The D* or \"Dynamic A-Star\" algorithm presented by [PERSON]. (1994) [33] is an informed incremental search algorithm. D* maintains a list of nodes used to propagate information about changes of the cost function during the search of the path. In contrast to canonical A*, which follows the path from start to finish, D* begins by searching backwards from the goal node. It works by iteratively selecting a node from a called OPEN list, evaluating it, and then propagates the node's changes to all the neighbouring nodes and places them on the OPEN list. The algorithm distinguishes between current cost (important at the time of collection) and minimum costs (important for the sorts of the OPEN list). Another particularity is that the function which returns the minimum cost is always the lowest cost to the current point since it is the first entry of the OPEN list.  
[PERSON] and [PERSON] (2002) [34] presented D* Lite, reformulating the strategy between the goal and the current position of an object, changing the search direction compared to A* and D* by fixing the target coordinate and by only considering a changing starting point through time. With this strategy, D* Lite increases the search efficiency, attaining a fast-replanning task.  
[PERSON] et al. (2014) [73] presented an experimental comparison of A* and D* Lite in the context of path planning for differential drive automated guided vehicles. The simulation and experimental results showed that, in general, D* Lite can plan the shorter path with faster computation time compared to A*. However, there are some cases when D* Lite is less effective than A* and so depends on the requirements of the system or context environment and variables.  
[PERSON] et al. (2017) [74] presented an improvement to D* Lite, in particular for environments where the effect of changes is costly. They proposed the setting of a threshold on the ratio of the traversed path length to the total path length. [PERSON] and [PERSON] (2018) [75] also explored D* Lite as a search-based planning method in robotics and autonomous systems.  
More recently, [PERSON] et al. (2023) [76] adapted D* Lite to improve guidance, navigation and control of a tail-actuated underwater vehicle in unknown environments. They presented promising results in terms of effectiveness by adding a waypoint tracking controller to correct the vehicle's swimming performance inside three scenarios when the planner does not consider broader free spaces for replanning and in two scenarios where the vehicle passes too close to obstacles inside hallways.  
Algorithm 3 presents the pseudo code of D* Lite.  
[PERSON] and [PERSON] (2017) [46] proposed an enhancement of SMA*, providing a new open list, a simplified implementation, and a culling heuristic function that improves search performance through a priori knowledge of the search space.