esa-sceva/satcom-chunk-collection huggingface dataset:
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Study Areas and Drought Events
#### 2.1.3 Pampa: The 2012 Drought  
The Brazilian Pampa is predominantly a natural grassland region located in the extreme south of the country, within the South Temperate Zone, and has both subtropical and temperate climates with four well-characterized seasons [59]. The region has regular \(P\) during the whole year, even though it is often subject to prolonged and severe droughs, which can greatly affect the growth and harvest of crops [60]. The biome faced significant drought impacts between 2009 and 2015, with municipalities declaring an emergency and constantly reporting economic losses in the agricultural sector [11]. In this context, most of Brazilian southern region presented drought conditions through an extensive area in 2011-2012, which affected water supplies in rural properties, agriculture, and livestock production [43]. The studied area was mostly located in the Pampa biome (southern portion), while the northern portion had a mixture of agricultural lands and remaining vegetation from the Atlantic Forest biome. This area is predominantly covered by farming (45%), grassland (26%), and forest (20%).
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Comparison of the Results Obtained by MLP, Two Other ML Models, and the Weather Service
#### 4.6.2 Minimum Temperature Prediction  
Table A18 in Appendix E contains examples of the minimum temperature observed values, the values predicted by the MLP, and those predicted by the weather service. Table 10 includes the quantitative values of the effectiveness measures of the MLP, two other predictive models, and the weather service in forecasting the minimum temperatures. Figure 4 displays a comparison of the effectiveness of the MLP and weather service in forecasting the minimum temperatures.  
Here, the effectiveness of the MLP was higher than that of the weather service. The MAE was 1.0496 lower for the MLP, and the MSE had a value of 9.3824 less for the MLP than for the weather service. The determination factor R\({}^{2}\) score used to interpret the model effectiveness was 0.1133 higher for the MLP compared with for the weather service. These results show the much higher effectiveness of the MLP model over the weather service in forecasting the minimum temperature.  
Figure 3: Comparison between the observed values and those predicted by the MLP and weather service maximum temperature.  
For the compared models, the prediction accuracy of the daily minimum temperature was slightly higher for the MLP, while the LSTM model performed better than the SVR. The higher effectiveness in forecasting the minimum temperature for the MLP model compared with for the weather service is evidenced because the chart of values predicted by the MLP is closer to the chart of observed values, especially for the time range from April to October. In the spring and summer months, the weather service forecasts show greater divergence from the observed values than they do in the autumn and winter months, i.e., from January to March and from November to December. The MLP model showed high accuracy throughout the year.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 3 Results
### Instantaneous Power Calculation by Method 1  
In this section, the instantaneous power calculation by method 1 is presented, relying on Equation (8) to evaluate the power consumed by the resistance of the RC load at each time lapse.  
The derivative of \(P_{C}(t)\) for a given time step k can be approximated as follows:  
\[\frac{dP_{C}(k)}{dt}\approx\frac{P_{C,fit}(k)-P_{C,fit}(k-1)}{T_{S}}, \tag{21}\]  
where \(T_{S}\) is the sampling period and \(P_{C,fit}(k)\) is the filtered value of the pressure at the compliance at time step k.  
Therefore, the power delivered to the load at a time step k can be expressed as  
\[\dot{E}_{load}(k)=\frac{V}{\gamma P_{m}}\cdot\left(P_{E,fit}(k)-P_{C,fit}(k) \right)\cdot\frac{P_{C,fit}(k)-P_{C,fit}(k-1)}{T_{S}}, \tag{22}\]  
where \(P_{E,fit}(k)\) is the filtered value of the pressure in the resonator at time step k.  
To determine the average value of the power delivered to the load, the following expression can be used:  
\[\overline{\dot{E}_{load,method1}}=\frac{\sum_{j=1}^{N_{S}}P_{ac}(j)}{N_{S}}, \tag{23}\]  
where \(N_{S}\) is the number of samples considered for averaging.  
According to Equation (17), the average value of \(P_{ac}(t)\) can alternatively also be calculated as  
\[\overline{\dot{E}_{load,method1}}=\frac{\dot{E}_{load,max}}{2}, \tag{24}\]  
where \(\dot{E}_{load,max}\) is the maximum value of \(\dot{E}_{load}(k)\).
## 4 UAV-TPUA: UAV Trajectory Progress Updating Algorithm
### Updating Trajectory Progress
\begin{split}&\dot{C}^{j+1}_{EA}=\left(\dot{x}^{j+1}_{EA},\dot{y}^{j+1 }_{EA}\right)=\left(\sum\limits_{i=1}^{M}\frac{\hat{x}_{u_{ij+1}}}{M},\sum \limits_{i=1}^{M}\frac{\hat{y}_{u_{ij+1}}}{M}\right)\\ &\dot{H}^{j+1}_{EA}=\max_{i=1}^{M}\{\hat{z}_{u_{ij+1}}+H_{0}\} \\ &\dot{\rho}^{j+1}_{EA}=\max_{i=1}^{M}\biggl{\{}\left\|P_{u_{ij+1}} -\dot{C}^{j+1}_{EA}\right\|\biggr{\}}=\max_{i=1}^{M}\biggl{\{}\sqrt{\left(\hat {x}_{u_{ij+1}}-\dot{x}^{j+1}_{EA}\right)^{2}+\left(\hat{y}_{u_{ij+1}}-\dot{y} ^{j+1}_{EA}\right)^{2}}\biggr{\}}\end{split}  \tag{10}\]  
After estimating \(\dot{C}^{j+1}_{EA}\), \(\dot{H}^{j+1}_{EA}\) and \(\dot{\rho}^{j+1}_{EA}\), the next coordinate of the UAV can be predicted by the following formula:  
\[ \begin{split}&\vec{p}^{next}_{uav}=\left(x^{(j+1)}_{uav},y^{(j+1 )}_{uav},z^{(j+1)}_{uav}\right)=f(x^{(j)}_{uav},y^{(j)}_{uav},z^{(j)}_{uav}, \hat{x}_{EA},\hat{y}_{EA},\Delta s)\\ &=\left(\begin{array}{c}\frac{\Delta s\left(\hat{x}_{EA}-x^{(j )}_{uav}\right)}{\sqrt{\left(\hat{x}_{EA}-x^{(j)}_{uav}\right)^{2}+\left(\hat {y}_{EA}-y^{(j)}_{uav}\right)^{2}+\left(\hat{R}^{(j)}_{EA}-z^{(j)}_{uav}\right) ^{2}}+x^{(j)}_{uav}},\\ \frac{\Delta s\left(\hat{y}_{EA}-y^{(j)}_{uav}\right)}{\sqrt{\left(\hat{x}_{ EA}-z^{(j)}_{uav}\right)^{2}+\left(\hat{R}^{(j)}_{EA}-z^{(j)}_{uav}\right)^{2}+\left( \hat{R}^{(j)}_{EA}-z^{(j)}_{uav}\right)^{2}}+y^{(j)}_{uav}},\\ \frac{\Delta s\left(\hat{R}^{(j)}_{EA}-z^{(j)}_{uav}\right)}{\sqrt{\left(\hat {x}_{EA}-z^{(j)}_{uav}\right)^{2}+\left(\hat{y}_{EA}-y^{(j)}_{uav}\right)^{2}+ \left(\hat{R}^{(j)}_{EA}-z^{(j)}_{uav}\right)^{2}}+z^{(j)}_{uav}}\end{array} \right)\\ \end{split}  \tag{11}\]  
where \(v\) and \(\Delta T\) represents the flight speed and decision time of the UAV. **Stage 3: DL-SSB+PRS Orbit+Convergence. ** In the final phase of our approach, we aim to maximize the observation of the victim's environment and minimize the positioning inaccuracies that arise due to the UAV's current location and the presence of UEs in NLOS conditions. To achieve this, we alter the UAV's flight strategy to involve hovering and descending towards the target destination, thereby enhancing the precision of the localization process. Furthermore, the targeted angle measurement and positioning methodology remains consistent with the previous stage, leveraging the DL-SSB+PRS framework. To mitigate the adverse effects of NLOS conditions on the base station's location determination, the UAV and UEs maintain a pitch angle of approximately 45 degrees during the descent trajectory of the UAV's hovering phase, as it converges towards the destination. This strategic positioning aids in reducing interference and ensuring accurate location updates. The specific steps involved in the location updating process are outlined in detail as shown in Algorithm 1.
## 3 Mathematical Equations of the Conceptual Model  
### Governing Equations of Coal Deformation  
Considering thermal expansion/contraction and matrix swelling/shrinkage are isotropic, the constitutive relations for a non-isothermal dual-porosity coal seam becomes (negative in compression) [33; 34]:  
\[\varepsilon_{ij}=\frac{1}{2G}\sigma_{ij}-(\frac{1}{6G}-\frac{1}{9k})\sigma_{kk }\delta_{ij}+\frac{\alpha}{3K}p_{m}\delta_{ij}+\frac{\beta}{3K}p_{f}\delta_{ij} +\frac{\alpha_{T}}{3}T\delta_{ij}+\frac{\varepsilon_{s}}{3}\delta_{ij} \tag{1}\]  
where \(\sigma_{ij}\) is the component of total stress tenor and \(\varepsilon_{ij}\) is the component of total stress tenor. \(G=D/2(1+v)\), \(D=\left[1/E+1/aK_{n}\right]^{-1}\), \(K=D/3(1-2v)\), \(\alpha\)= \(1-K/K_{s}\), \(\beta\)= \(1-K/a\cdot K_{nt}\), \(K\) is the bulk modulus of coal, \(K_{s}\) is the bulk modulus of coal grains and \(K_{n}\) is the normal stiffness of individual fractures. \(E\) is the [PERSON]'s modulus of coal, \(G\) is the shear modulus of coal, \(v\) denotes the [PERSON]'s ratio of coal, \(\alpha\) and \(\beta\) are Biot coefficients, \(\delta_{ij}\) is the Kronecker delta, \(p\) is gas pressure, subscript \(f\) represents the cleat system, \(m\) represents the matrix system, \(T\) is temperature, and \(\alpha_{T}\) is thermal expansion coefficient. From Equation (1), we obtain  
\[\varepsilon_{v}=-\frac{1}{K}(\overline{\sigma}-\alpha p_{m}-\beta p_{f})+ \alpha_{T}T+\varepsilon_{s} \tag{2}\]  
where \(\varepsilon_{v}=\varepsilon_{11}+\varepsilon_{22}+\varepsilon_{33}\) is the volume strain of coal and \(\overline{\sigma}=-\sigma_{kk}/3\) is the mean stress. The sorption-induced strain \(\varepsilon_{s}\) is generally represented by a Langmuir-type equation, which is defined as:  
\[\varepsilon_{s}=\varepsilon_{L}\frac{p_{m}}{p_{m}+p_{L}} \tag{3}\]  
Figure 3: The geometry of the model used for numerical simulation (CT) graph adapted from [11]).  
where \(\varepsilon_{L}\) is the Langmuir constant representing the maximum volume strain and \(p_{L}\) is the Langmuir pressure constant.  
The equations of stress equilibrium and the strain-displacement relationships can be expressed as:  
\[\sigma_{ij,j}+f_{i}=0 \tag{4}\]  
\[\varepsilon_{ij}=\frac{1}{2}\Big{(}u_{i,j}+u_{j,i}\Big{)} \tag{5}\]  
where \(f_{i}\) is the body force component and \(u_{i}\) is the displacement component. The combinations of Equations (1)-(5) yields the Navier-type equations:  
\[Gu_{i,jj}+\frac{G}{1-2v}u_{j,ii}-\beta_{m}p_{m,i}-\beta_{f}p_{f,i}-K\alpha_{T} T_{,i}-K\varepsilon_{L}\frac{P_{L}}{p_{m}+P_{L}}p_{m,i}+f_{i}=0 \tag{6}\]
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 3. Results
### Biological Analysis
#### 3.3.2 Hemolysis Activity  
Blood compatibility is a gold standard for all new materials developed specifically for biomedical applications. Hemolytic activity is a requirement to be tested for any medical device that encounters blood and is calculated as the percentage of red blood cell lysis produced as a result of the interaction with the biomaterial.  
The hemolysis effects were between 0.2 and 0.4%/cm\({}^{2}\). Also, no significant differences were recorded between 1 h and 2 h. It was noted that with hemolysis, the amount of Ht (%) increased when the CS concentration was smaller (0.5%) in the mixture's biofilm (Figure 5) as a result of the weakening of the bonds between chitosan and the plant extract compounds. The biofilm formed by the CS-Ly mixture allowed the amine groups to bind to the polyphenols in the extract [38]. Chitosan interacts not only with red blood cells, but also with platelets through amine groups that are attracted to the negative charge of blood elements [32]. In the case of the analyzed membranes, the hemolysis effect was low and perhaps determined by the groups left free after the interaction with the extract.  
The percentage values of the hemolysis from the tested samples were graphically interpreted according to Figure 5.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Samples** & **Biological Effect** & \multicolumn{2}{c}{**Induction Factor (IF) ***} \\ \hline  & & without microsomal activation & microsomal activation \\  & & activation & (+S9) \\  & & (-S9) & (+S9) \\ Negative control & Non effect & - & - \\ C+ (4-NQO) & High genotoxic & \textgreater{}2 & \textgreater{}2 \\ C+ (2-AA) & Moderate genotoxic & 1.5–2 & 1.5–2 \\ CS 1 & Non-genotoxic & \textless{}0.01 & \textless{}0.1 \\ CS 0.5 & Non-genotoxic & \textless{}0.01 & \textless{}0.1 \\ Ly 1 & Non-genotoxic & 0.78 \(\pm\) 0.0005 & 0.42 \(\pm\) 0.001 \\ Ly 2 & Non-genotoxic & 0.26 \(\pm\) 0.0001 & 0.21 \(\pm\) 0.001 \\ CS–Ly 2 & Non-genotoxic & 0.46 \(\pm\) 0.0030 & 0.04 \(\pm\) 0.001 \\ CS–Ly 1 & Non-genotoxic & 0.36 \(\pm\) 0.0047 & 0.04 \(\pm\) 0.01 \\ \hline \hline \end{tabular}  
* Evaluating criteria: IF \(<\) 1.5: non-genotoxic; 1.5 \(<\) IF \(>\) 2: moderate genotoxic; IF \(>\) 2: high genotoxic; the mean \(\pm\) SE.  
\end{table}
Table 1: Results of SOS-Chromo assay samples (mean \(\pm\) SE, n = 3). 4-NQO (4-hydroxyquinoline 1-oxide) and 2-AA (2-aminoanthracene) were used at positive control (C+).  
Figure 5: Hemolytic activity, Ht (%/cm\({}^{2}\)), induced by the tested membranes (mean \(\pm\) SE, n = 3) compared with MS (medical steel) and negative control (physiological saline solution).
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 7 Conclusions  
In this paper, we develop an estimation-cancellation approach for GNSS spoofing detection and navigation solution recovery based on the MLE-based positioning technique. The proposed method distinguishes the spoofing signals from the authentic ones based on the fact that the spoofing and authentic signals form peaks at different positions of the MLE cost function. After canceling the composite signal constructed with the ML estimates of the PVT parameters, another peak can still be detected in the MLE cost function of the residual signal if evil signals such as spoofing signals are present. In order to analyze the theoretical performance and set the threshold appropriately, a test statistic is derived based on the GLRT. Furthermore, once evil signals are detected, spoofing validation can be performed by decomposing the cost function and inspecting the consistency of the signals. We also present theoretical and simulation results to investigate the anti-spoofing performance. Finally, we evaluate the proposed countermeasure with the TEXBAT GPS spoofing datasets. Results show that the method can detect and recover navigation solution even when the code phase differences between authentic and spoofing signal are much less than one code chip, which greatly improves the availability of GNSS service under spoofing attacks.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61571255).  
[PERSON] proposed the anti-spoofing method and wrote the paper. [PERSON] supervised and supported the study and revised the paper. [PERSON], [PERSON] and [PERSON] discussed the study and read the paper.  
The authors declare no conflict of interest.  
Figure 14: (a) \(r_{2}\) in Scenario 6; results larger than four indicate that consistent signals are detected in the residual signals and the spoofing attack is validated; (b) Biases of user position in the z-coordinate from the position in Epoch 0: \(\delta z_{u}=z_{u}(t)-z_{u}(0)\). The green trace shows the receiver’s unspoofed response; the blue trace shows the receiver’s spoofed response; and the red dots show the recovered results.
## 6 Summary and Conclusions  
Based on the viscoelastic theory of asphalt, the finite element model in software ABAQUS was established according to the pavement structure and the material properties of the HMAC, the stress field and the temperature field was analyzed; then, the creep behavior and permanent deformation of the HMAC pavement was achieved. The following conclusions were obtained:  
1. Compared to conventional asphalt pavement, vertical stress and shear stress generated in HMAC pavement increased, while the vertical strain, shear strain, and vertical displacement decreased. The vertical strain and the shear strain decreased by 24.5% and 32.8% respectively. The vertical displacement that was generated in the HMAC road surface also reduced from 0.16 mm to 0.13 mm; this is an 18.8% reduction, which improved the evenness of the road surface.  
2. The pattern variations of the temperature field and the thermal stress were consistent. When the surface temperature reached the maximum of 59 \({}^{\circ}\)C, the maximum thermal stress of \(-\)0.7 MPa was found. As the depth increased, the influence of outside air temperature on the pavement structural layers gradually decreased.  
3. In both the HMAC pavement and conventional asphalt pavement, the creep deformation only occurred in the asphalt surface layers. At the road surface, the maximum relative deformation of conventional asphalt pavement reached 10.84 mm, which was 7.13 mm higher compared to that of HMAC pavement. Therefore, the HMAC layer could effectively reduce the relative deformation of pavement.  
4. With the increase of tire contact pressure from 0.7 MPa to 1.5 MPa, the maximum relative deformation increased by 2.27 mm and 6.54 mm in HMAC pavement and conventional asphalt pavement, respectively. This suggested that HMAC pavement had better resistance to road deformation and rutting.  
Conceptualization, C.S.; methodology, E.C.; software and formal analysis, H.C.; validation, R.T.; writing--original draft preparation, H.C.; writing--review and editing, R.Z., Z.Y.; visualization, J.G.  
This work was supported by the National Natural Science Foundation of China (No. 11202142, 11302138, and 11872253), Princal Project of Hebei Province Department of Transportation (No. Y-2010021, No 2013-2-5), Youth Talent Support Program of Hebei Province and Distinguished Young Scientists Foundation of Hebei Provincial Education Department (YQ2014038), Hebei science and technology project(18242219G).  
The authors declare no conflict of interest.
# Quantitative Characterization of Complex Systems--An Information Theoretic Approach
## 5 Case Study--Entropy Metric Formulation and Analysis of Simulated Complex Networks
### Transfer Entropy to Understand the Transfer of Information in Network over Time  
The use of entropy in the field of networks is a well-studied area. Entropy has been identified as a possible metric for playing a crucial role in analyzing the complexity of a network [29]. To further extend entropy towards measuring uncertainty between two random variables \(X\) and \(Y\), a measure of mutual information is used. Mutual information is based only on the present state/symbol of each variable; considering all such present states, mutual information, \(I\) (\(X\), \(Y\)), is defined by:  
\[I(X,Y)\ =\sum_{x\in X}\sum_{y\in Y}P(x,y)\log\frac{P(x,y)}{P(x)\ \times\ P(y)} \tag{6}\]  
However, the fact that the measure of mutual information is symmetrical, i.e., \(I\)(\(X\), \(Y\)) = \(I\)(\(Y\), \(X\)), implies that the future state of a random variable has a casual effect on the past state. In the case of complex networks, where a network consisting of several nodes evolves over a period of time, the structure of a network at a time \(t\) depends upon the evolution of the network until time \(t\)\(-\)1, not the other way around. Thus, in order to address the symmetrical limitation of the mutual information measure, the concept of transfer entropy was proposed [30].  
Transfer entropy (TE) was coined [31] to address the time symmetric limitation of a mutual information measure. Considering two sample spaces of information represented in time by \(X\) = {x\({}_{1}\), \(x\), \(x\), \(x\), \(\ldots\), \(x\)} and \(Y\) = {y\({}_{1}\), \(y\), \(y\), \(\ldots\), \(y\)}, transfer entropy is defined as the additional amount of information gained for the next observation of one of the two processes being considered, given the past observation of the other process.  
_TE_, the transfer of information between two random variables \(X\) and \(Y\), is given by [30; 31]:  
\[TE\;(X,Y)\;=\sum_{t=1}p(y_{t+1},y_{t},x_{t})\log\frac{p(y_{t+1},y_{t},x_{t})\; \times\;p(y_{t})}{p(y_{t},x_{t})\;\times\;p(y_{t+1},y_{t})} \tag{9}\]  
Moreover, the transfer entropy from Y to X can be inferred similarly based on the above as:  
\[TE\;(Y,X)\;=\sum_{t=1}p(x_{t+1},x_{t},y_{t})\log\frac{p(x_{t+1},x_{t},y_{t})\; \times\;p(x_{t})}{p(x_{t},y_{t})\;\times\;p(x_{t+1},x_{t})} \tag{10}\]  
To analyze the flow of information generated from one node to the other of a considered network based on the observed individual node degree evolution over several time-steps of network formation, the concept of transfer entropy is used. When applied to a network evolution scenario, TE helps in understanding how much of the information generated at one node is responsible for the information obtained by the other node.
## 4 On-line Algorithm
### Strategy for Dealing with Flying Range Limitations  
Based on [4], we consider a dynamic buffer that ensures that the UAV has enough flying capacity to finish the exploration route at the DMC at any time. The dynamic buffer represents the distance that the UAV has to be able to fly to explore the next selected unexplored road edge _edgeTolnsert_ and and fly to the closest refueling station. For example, Figure 7 exemplifies how the buffer is calculated, assuming that the DMC is the only reachable refueling station and that the _edgeTolnsert_ is (4,2), so the aerial edge (2,0) refers to the road edge connecting (4,2) with the reachable refueling station and the next selected unexplored road edge. In Figure 7a, the current position of the UAV is a disruption location, so the buffer is calculated as the sum of the distances of the aerial edge (disruption location, 4), road edge (4,2), and aerial edge (2,0). Similarly, in Figure 7b, the current location of the UAV is a road crossing, whereby the buffer is calculated as the sum of the distance of the road edge (4,2) and aerial edge (2,0). The buffer depends on the current location (_position_) of the UAV and the location of the closest refueling station. The buffer (\(buffer\)), the flying range (\(flyingRange\)), and the flying capacity (\(flyingCapacity\)) of the UAV are expressed in units of distance. When the UAV is fully fueled, it is assumed that the flying capacity of UAV is the flying range limitation. After, the first exploration, the flying capacity is defined as the difference between the flying range and the travel distance of the UAV since its last refueling operation. We propose three refueling strategies to deal with the flying range limitation:  
* _Extremely conservative refueling strategy_ (_EC Conservative_): refuel each time when at the refueling station or refuel when the flying capacity is lower than the buffer needed at that time. * _Moderately conservative refueling strategy_ (_MC Conservative_): refuel when the flying capacity is lower than 50% of the flying range. * _Conservative refueling strategy (Conservative)_: refuel when the flying capacity is lower than the buffer needed at that time. The sort insertion procedure applies an additional function to control the flying capacity of the UAV, which refueling stations are available at any given time \(t\) during the exploration, in turn, to control the buffer depending on the current location of the UAV and available refueling stations. Algorithm 1 presents the extension of the on-line algorithm presented in [4].