esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cumulant-Based Method for Acquiring GNSS Signals
## 3 Cumulant-Based Acquisition Method  
### Definition of Cumulants  
In this section, we will provide a brief overview of the definition of cumulants. Considering a one-dimensional, zero-mean random process \(x(n)\) with finite length, the first-order cumulant of this process is simply the mean:  
\[C_{1x}:=E\{x(n)\} \tag{5}\]  
For a zero-mean stationary process, the second-, third-, and fourth-order cumulants are given by [22]:  
\[C_{2x}(\tau_{1}):=E\{x^{*}(n)x(n+\tau_{1})\} \tag{6}\]  
\[C_{3x}(\tau_{1},\tau_{2}):=E\{x^{*}(n)x(n+\tau_{1})x(n+\tau_{2})\} \tag{7}\]  
\[C_{4x}(\tau_{1},\tau_{2},\tau_{3}):=E\{x^{*}(n)x(n+\tau_{1})x(n+\tau_{2})x^{*}( n+\tau_{3})\} \tag{8}\]  
\[-C_{2x}(\tau_{1})C_{2x}(\tau_{2}-\tau_{3})-C_{2x}(\tau_{2})C_{2x}(\tau_{1}- \tau_{3})-M_{2x}^{*}(\tau_{3})M_{2x}(\tau_{1}-\tau_{2})\]  
where \(E\{\bullet\}\) denotes expectation, and \(M_{2x}(\tau)=E\{x(n)x(n+\tau)\}=C_{2x}(\tau)\) for a real-valued process. The higher-order cumulants are invariant to a shift of mean. Therefore, one should replace \(x(n)\) with \(x(n)-E\{x(n)\}\) in the case of a nonzero mean process. As previously mentioned, first- and second-order cumulants correspond to the mean and variance, respectively, and are not applicable for time-delay estimation. For Gaussian noise, the fourth-order cumulants are approximately zero, making them unsuitable for GNSS signals. In this paper, we focus on using third-order cumulants for signal acquisition.  
Here are some important properties of cumulants that are relevant to our study:  
* If \(x(n)\) is symmetrically distributed, then \(C_{3x}(\tau_{1},\tau_{2})=0\). If \(x(n)\) is Gaussian distributed, then \(C_{4x}(\tau_{1},\tau_{2},\tau_{3})=0\).
* If \(\lambda\) is a constant, then \(C_{kx}\{x(n)+\lambda\}=C_{kx}\{x(n)\}\) for \(k>2\), where \(C_{kx}\{\bullet\}\) denotes \(k\)th-order cumulant.
* If \(y(n)=x(n)+w(n)\), where \(x(n)\) and \(w(n)\) are statistically independent random process, then \(C_{ky}\{y(n)\}=C_{kx}\{x(n)\}+C_{kw}\{w(n)\}\), with similar relationships holding for cumulants of all orders.
* Assuming \(w(n)\) is Gaussian (colored or white) and independent of \(x(n)\), then for \(k>2\), \(C_{ky}\{y(n)\}=C_{kx}\{x(n)\}\). Hence, cumulants can recover the non-Gaussian signals even in the presence of colored Gaussian noise.  
Figure 2: Comparison of autocorrelation between GPS C/A code and Galileo E1-C.
## 2 Previous Works  
In this section, we outline some prior efforts aimed at comparing different grid-based numerical methods or drawing connections between them.  
Some authors rigorously showed the equivalence of the finite volume method to either mixed finite element [10] or Petrov Galerkin finite element methods [11; 12]. With regards to the finite difference and finite element method, [PERSON] showed early on that the FEM can be understood as a somewhat equivalent, yet generalized, variant of taking finite differences on arbitrary grids [13]. Some general differences between these schemes were outlined by [PERSON] and [PERSON] [14]. In the work of [PERSON], some analogies were brought up between finite volume and finite difference schemes in WENO formulation [15]. A theoretical and numerical comparison between higher-order finite volume and discontinuous Galerkin methods was conducted by [PERSON] et al. [9]. Additionally, [PERSON] et al. constructed a unifying framework to accommodate high-order finite volume and discontinuous Galerkin schemes [16]. In the context of elliptic PDEs, [PERSON] et al. presented a theoretical and empirical comparison between the comparably new weak Galerkin, discontinuous Galerkin, and mixed finite element schemes [17]. A comparative study between discontinuous Galerkin and the streamline upwind Petrov Galerkin method for flow problems can be found in [18]. These works in summary draw point-wise comparisons between some grid-based approximation schemes. Despite being quite useful for disseminating individual advantages and disadvantages for a given application, one may still lack an understanding of the general properties. Furthermore, [PERSON] presented an encompassing analysis and application of the hybridizable discontinuous Galerkin method (HDG) to solve a wide variety of PDE-governed problems. It was, therefore, shown that this numerical scheme is general and powerful enough to form a unified baseline [19]. In addition, due to the generality of this method, there have been works that attempt to benchmark DG methods to more conventional and widely adopted continuous Galerkin methods (CG) [20; 21; 22]. Some authors proposed combinations of numerical schemes that operate optimally to solve hyperbolic [23] or parabolic [24] systems of PDEs.  
In summary, we draw the following conclusion from this brief review of the relevant literature. To this date, there only exist a few comparisons between grid-based approximation schemes that outline common properties in a mostly ad hoc or point-wise manner. We have, however, outlined in the previous section that it would be beneficial from an application-oriented perspective to have an encompassing taxonomy for these numerical schemes. Furthermore, many different variants of numerical schemes have been proposed to tackle a wide variety of PDE problems. What appears to be missing, though, is a general guideline on how to choose between these vast alternatives to obtain a method, or possibly a combination of different methods, to solve a system in a stable (above all) and reasonably efficient way.
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 2 Types of 2D Nanofluids
### Titanium Carbide MXene
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Authors/Year** & **Concentration** & **Base Fluid** & **Main Findings** & **Remarks** \\ \hline \multirow{4}{*}{[105]} & & & & Maximum of 63.4\% & \\  & & & & in the & \\  & & MXene at 5, 10, 20, & 40, and 60 p.p.m. & Water & photothermal & Near-zero spectral \\  & & 40, and 60 p.p.m. & & conversion & & transmittance at \\  & & & & efficiency at & 60 p.p.m. \\  & & & & 20 p.p.m. & \\ \hline \multirow{4}{*}{[106]} & & & Mxene at 0.1\%, & Diethylene Glycol & Increase of 78.5\% & Increase of 6\% in \\  & & 0.2\%, 0.3\%, and & and Ionic Liquid & in the PV/T & the PV/T heat \\  & & 0.4\% wt. & Mixtures & thermal efficiency. & transfer coefficient. \\ \hline \multirow{4}{*}{[107]} & & & MXene-Al\({}_{2}\)O\({}_{3}\) at & & & Approximate \\  & & 0.05\%, 0.1\%, and & Therminol 55 & 61.8\% increase in the TC. & 17.1\% increase in the viscosity. \\  & & & 0.2\% wt. & & & the viscosity. \\ \hline \multirow{4}{*}{[108]} & & & & Approximate 70\% & & Approximate \\  & & MXene at 0.05\%, & & increase in the TC & 0.50\% increase in \\  & & 0.08\%, and & Silicone Oil & at 25 \({}^{\circ}\)C and near & & the viscosity at \\  & & 0.1\% wt. & & 89\% at 150 \({}^{\circ}\)C and & 25 \({}^{\circ}\)C and near \\  & & & & at 0.1\% wt. & 2.4\% at 150 \({}^{\circ}\)C and \\  & & & & & at 0.1\% wt. \\ \hline \multirow{4}{*}{[109]} & & & & Thermal stability & \\  & & & & up to near 393 \({}^{\circ}\)C. & \\ \cline{1-1}  & & MXene- & & The viscosity & \\ \cline{1-1}  & & functionalized Gr & & & decrease by \\ \cline{1-1}  & & at 0.02\% wt. & & increase in the TC. & approximately 31 \\ \cline{1-1} \cline{2-4} \cline{6  
MXene formulations, rather than Ti3C2 Tx, should be considered in future studies on MXene nanofluids. More practical measures are required to understand and improve the oxidation resistance of the MXene family, which is a relevant issue when studying the long-term stability of nanofluids containing MXene. Apart from solar thermal energy conversion and storage, new applications of MXene nanofluids should be explored. Moreover, there is a lack of studies regarding economic analyses, operating uncertainties, and the impacts on the environment and human health through a life cycle assessment. Finally, more bio-friendly solutions should be explored, such as dispersions of MXene nanostructures in palm oil.
## 3 Experimental Setup and Methods  
### Experimental Setup  
The experimental setup is shown in Figure 2. The experiments were carried out in a glass parallelepiped vessel, 60 mm in height, 30 mm in length, and 25 mm in width. PES ultrafiltration membrane (Model MSC80010, Shanghai Mosu Tech. Co. Ltd., Shanghai, China) with a MWCO of 30 kDa was tested. The ultrafiltration membrane wetted for 30 min at room temperature by permeating deionized (DI) water was fixed in the vessel by flange and rubber ring, enabling the same initial tension to be applied and thus obtaining reproducible results. The space under the membrane was used as a part of the membrane gas chamber. Three different volumes of the membrane gas chamber were realized by the connection of three ultrafiltration amicon cells (300 mL, Model MSC 300, Shanghai Mosu Tech. Co. Ltd., Shanghai, China) with the space under the membrane in the glass parallelepiped vessel. The water depth above the membrane was adjusted. Nitrogen gas was used as the pressurized gas. The pressure values in the membrane gas chamber and gas tank were converted into digital code by two pressure transducers (Range 1: 0-100 kPa; Range 2: 0-200 kPa, Model MIKP 300, Hangzhou Meacon Tech. Co. Ltd., Hangzhou, China), and the outputs of these two pressure transducers was checked against each other. The signal was received by the data acquisition card (Model USB 1210, Beijing SMACQ Tech. Co. Ltd., Beijing, China). The discrete signal frequency was adjusted to 10 Sa/s. For each run, the pressure value of the gas chamber was collected. Every experiment was repeated on three pieces of membranes with the same specification, and the average value was taken as the experimental results. During all runs, the indoor temperature was kept at 20 \(\pm\) 1 \({}^{\circ}\)C.
# Moment-Based Parameter Estimation for the \(\Gamma\)-Parameterized TWDP Model
## 1 Introduction
This hinders accurate observation of the impact of the ratio between \(V_{1}\) and \(V_{2}\) on a system's performance metrics and causes anomalies within the expressions obtained by integration or derivation with respect to parameter \(\Delta\)[14]. Considering that previously mentioned, \(\Gamma\)-based parameterization was recently proposed in [14], by introducing parameters \(K\) and \(\Gamma\):  
\[K=\frac{V_{1}^{2}+V_{2}^{2}}{2\sigma^{2}} \tag{3}\]  
\[\Gamma=\frac{V_{2}}{V_{1}} \tag{4}\]  
where \(\Gamma\) (\(0\leq\Gamma\leq 1\), for \(0\leq V_{2}\leq V_{1}\)) obviously ensures linear dependence between \(V_{1}\) and \(V_{2}\). On the other side, the definition of parameter \(K\) remains unchanged, but the definition expression of normalized \(K/K_{Ricc}\) with respect to the ratio between \(V_{1}\) and \(V_{2}\), e.g., \(K/K_{Ricc}=1+(V_{2}/V_{1})^{2}\) (where \(K_{Ricc}\) is the Rician parameter), is affected by the choice of second parameter (\(\Delta\) vs. \(\Gamma\)). Obviously, parameter \(\Delta\) completely changes its character since \(K/K_{Ricc}=2(1-1\sqrt{1-\Delta^{2}})/\Delta^{2}\), while \(\Gamma\) does not, since \(K/K_{Ricc}=1+\Gamma^{2}\)[14]. Despite that aforementioned, \(\Gamma\)-based parameterization has up to now been considered only in [14], by elaborating its benefits on ASEP observation accuracy. However, no other benefits of \(\Gamma\)-based parameterization or the anomalies caused by the nonphysical definition of a \(\Delta\) parameter within the expressions which involve integration or derivation with respect to \(\Delta\) have been presented yet. Accordingly, in this paper, one such anomaly is identified within \(\Delta\)-parameterized TWDP moment-based estimators and overcome by introducing the appropriate estimators for \(\Gamma\)-parameterized TWDP fading model. In that sense, this paper focuses on performance analysis of moment-based parameter estimators, for both \(\Delta\)- and \(\Gamma\)-based TWDP parameterizations. Considering that aforementioned, in Section 2, an overview of the results related to estimation of TWDP parameters is foremost presented, elaborating in detail anomalies caused by conventional \(\Delta\)-based parameterization and indicating the absence of those related to a \(\Gamma\)-parameterized TWDP model. In Section 3, a closed-form moment-basedestimator is derived for \(\Gamma\)-based parameterization, and their behavior is examined for various values of proposed parameters. In Section 4, performance analysis of proposed estimators is performed. First, corresponding asymptotic variances are derived and graphically presented and then the limits of estimation problem are explored and determined in terms of Cramer-Rao bound. Finally, \(\Delta\)- and \(\Gamma\)-based estimators are compared in Section 5, in order to gain insight into the differences in their behavior and their estimation accuracy. Conclusions are outlined in Section 6.
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 5 Model Predictive Control Strategies for the EWMS
### Short Term
#### 5.1.1 Climate Controller  
With the predictive climate models presented in Section 4, it was possible to design a model predictive control strategy for greenhouse climate conditions. This strategy included a predictive controller with a 48 h horizon to determine the openness of windows in the future. This design had the advantages of being low-cost and able to appropriately regulate greenhouse climate conditions.  
For the successful regulation of greenhouse conditions, future decisions regarding the openness of windows (\(W_{s}(k)\ldots W_{s}(k+N_{u}-1)\)) were obtained by minimizing the following fuzzy objective function:  
\[\begin{array}{ll}J&=\sum\limits_{j=1}^{2H}\bar{\mu}_{T}^{k+j}\Big{(}T_{gr}(k +j)-T_{ref}(k+j)\Big{)}^{2}+\bar{\mu}_{HR}\Big{(}HR_{gr}(k+j)-HR_{ref}(k+j) \Big{)}^{2}\\ &+\lambda_{W_{s}}\bar{\mu}_{W_{s}}\left(\sum\limits_{j=1}^{N_{u}}\Delta W_{s}( k+j-1)^{2}\right)^{2},\end{array} \tag{22}\]  
which was defined based on several fuzzy membership functions \(\bar{\mu}\) and the concept of fuzzy optimization [18; 25]. In Equation (22), the objective function includes three main fuzzy membership functions \(\bar{\mu}\), which are used to evaluate each of the control objectives defined. In particular, \(\bar{\mu}_{T}\) is the optimal range for the greenhouse temperature \(T_{gr}\) around a given reference value \(T_{ref}\). The function \(\bar{\mu}_{HR}\) represents the acceptable range for the differences between the greenhouse humidity \(HR_{gr}\) and the reference value \(HR_{ref}\). The third membership function \(\bar{\mu}_{W_{s}}\) represents the penalization term for the number of changes in the window state. These three membership functions are used to simultaneously maintain temperature and humidity at desired values while opening or closing as few windows as possible.  
The trapezoidal shapes of the membership functions for temperature and humidity can be observed in Figure 3; they were based on the definitions of temperature and humidity limits for tomatoes presented in [22; 26]. These limits were defined for a security zone and a normal operation zone, and their values are reported in Table 1. To determine which membership function to use for temperature, the controller considers any instance with \(Ir\geq 100\) Wm\({}^{-2}\) each day at an hourly scale, and \(Ir<100\) Wm\({}^{-2}\) is used as the reference condition at night. Moreover, the membership function for window openness is plotted as a simple sloped line, because linear penalization is sufficient in this case.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Min. Security** & **Min. Normal** & **Max. Normal** & **Max. Security** \\ \hline \(T_{gr}\) at day (\({}^{\circ}\)C) & 10 & 22 & 26 & 30 \\ \(T_{gr}\) at night (\({}^{\circ}\)C) & 10 & 13 & 16 & 30 \\ \(HR_{gr}\) (\%) & 30 & 55 & 75 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Greenhouse climate limits.  
Figure 2: Short-term management optimization flow chart, where PSO is used for both optimizations.
## 5 MATLAB Simulation Results
### Analysis  
We analyze the computational load of our control law. To compute the pursuer's heading vector \(\mathbf{u}_{k}\) at each time index \(k\), it takes \(0.03\) s. This is negligible compared to the sampling time interval \(T=2\) s. Hence, we can argue that latency due to the computational load is negligible.  
Next, we run simulations while varying the initial heading angle of the pursuer. We set the evader's speed as \(v_{k}^{p}=100\) in m/s. In addition, the evader does not maneuver, i.e., \(u_{k}^{e}=0\).  
We introduce two metrics, \(T_{f}\) and \(E_{f}\). \(T_{f}=k_{f}T\) is the spent time(seconds) to capture the evader. Here, \(k_{f}\) is the time index when the pursuer captures the evader. Here, \(E_{f}=\sum_{k=1}^{k_{f}}\|v_{k}^{p}\angle(\mathbf{u}_{k},\mathbf{u}_{k-1})/T\|\). Here, \(\angle(\mathbf{u}_{k},\mathbf{u}_{k-1})/T\) is the pursuer's angular acceleration at time index \(k\). \(E_{f}\) can be regarded as the energy (acceleration sum in m/s\({}^{2}\)) required to capture the evader.  
The pursuer's initial location is the origin. We calculate both \(T_{f}\) and \(E_{f}\) while changing the initial heading angle, say \(H=atan2([0,1]\times\mathbf{u}_{0},[1,0]\times\mathbf{u}_{0})\), of the pursuer from 0 to 180 degrees. Here, the initial heading angle is measured counter-clockwise from the x-axis. Table 1 shows \(T_{f}\) and \(E_{f}\) while changing \(H\). See that the evader is captured regardless of the initial heading angle of the pursuer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**H (degrees)** & \(T_{f}\) **(s)** & \(E_{f}\) **(m/s\({}^{2}\))** \\ \hline
0 & 47 & 238 \\
30 & 49 & 321 \\
60 & 49 & 242 \\
90 & 49 & 164 \\
120 & 49 & 242 \\
150 & 49 & 321 \\
180 & 51 & 405 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results.  
Figure 9: The **(top)** figure depicts the pursuer’s speed with respect to time. The **(bottom)** figure depicts the distance between the evader and the pursuer with respect to time (maneuvering evader).
## 3 Results
### Trophic Factor Release in Individual Extracts  
The gene expression levels of three trophic factors were analyzed in the human neural stem cell cultures exposed to oxidative stress: NRF2, NGF and VEGF (Figure 5).  
Regarding NRF2, this trophic factor was significantly increased by approximately 17.8% when the NSCs were submitted to oxidative stress. Among the botanical extracts, only age significantly increased the expression of NRF2 by approximately 1.5-fold, at 0.001% and 0.01%.  
Figure 4: Caspase-9 gene expression analysis in neural stem cells exposed to H\({}_{2}\)O\({}_{2}\) and in the presence of the botanical extracts. Dashed line indicates control with H\({}_{2}\)O\({}_{2}\) levels. * \(p\) < 0.05, **_p_ < 0.01, ***_p_ < 0.005, ***_p_ < 0.001. Ratio corresponds to the gene relative expression ratio.  
As for NGF, H\({}_{2}\)O\({}_{2}\) exposure caused an average 70.1% increase in this growth factor in NSCs. With the exception of arctic root, all botanical extracts further increased their expression. Bilberry (at 0.0001%), blueberry (at 0.001%), age (at 0.0001%) and saffron (at 0.00005%) increased NGF by around 3-fold. Bacopa (at 0.0001%) and gotu kola (at 0.0001%) seemed to be the extracts with the highest increase in NGF, around 4-fold. On the other hand, ginkgo (at 0.0001%) and ginseng (at 0.01%) increased only around 2-fold.  
Finally, VEGF expression was significantly decreased in NSCs submitted to oxidative stress by an average of 40.3%. As in the case of NRF2, only wage revealed a significant increase, at both 0.001% and 0.01% concentrations, with an over 10-fold increase in expression.  
A summary of the results observed, with the exception of cell death markers, among the botanical extracts is given in Table 2.  
Figure 5: _Cont._  
Figure 5: Expression levels of trophic factors NRF2, NGF and VEGF in neural stem cells exposed to H\({}_{2}\)O\({}_{2}\) and in the presence of the botanical extracts. Dashed line indicates control with H\({}_{2}\)O\({}_{2}\) levels. *\(p<0.05\), **\(p<0.01\), ***\(p<0.005\), ***\(p<0.001\). Ratio corresponds to the gene relative expression ratio. NRF2: nuclear factor erythroid 2-related factor 2, NGF: nerve growth factor, VEGF: vascular endothelial growth factor.  
Based on the results obtained with the various individual extracts, sage was revealed to possess the most significant effects overall, with positive results observed in both ROS and RNS, reduction in the pro-apoptotic marker caspase-9, and was the only extract to significantly increase the expression of all three trophic factors analyzed: NRF2, NGF and VEGF.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Topology Management
#### 5.2.2 Mobility Management  
Sensor nodes in an SDN-based WSN are susceptible to movement and this can cause variation in packet transmission and execution of tasks making it necessary to monitor and manage the movement of nodes in the network. The challenges to consider when integrating a mobility management scheme includes handling the effect of nodes entering the network and the nodes leaving the network on QoS, execution of functions and other network attributes. A few solutions and processing steps that the SDN controller can provide to prevent problems associated with nodes entering and leaving the WSN have been outlined by [PERSON] et al. [87]. Considering IP support for nodes in SDN-based WSNs [79; 82] research has been conducted on mobility management in IP based  
Figure 3: Topology based on virtual overlay [87] (Republished with permission of IEEE, from SDN-Based Application Framework for Wireless Sensor and Actor Networks, [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON], 4, 1583–1594, 2017 Copyright).  
WSNs based on TinyOS implemented and tested on a 6 LoWPAN [101]. A mechanism based on IEEE 802.15.4 [102] standard has been investigated and implemented to manage mobility in a distributed network architecture with promising implementation capacity in a real system [103]. There has been several contributions on mobility management in Wireless Cellular Networks (WCNs) based on SDN [104; 105; 106; 107], whether or not these management mechanisms can be applied and integrated in SDN-based WSNs is a worthwhile research area.