esa-sceva/satcom-chunk-collection huggingface dataset:
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 2 All-Digital True Random Number Generators  
### Information Content and Entropy  
This section aims to introduce some concepts about the theory of information to define a metric to compare different TRNGs. Let \(X\) be a random variable with probability mass function \(p_{X}\), the self-information or information content of an event \(x\) is defined as:  
\[I_{X}(x)=-logp_{X}(x)=log\frac{1}{p_{X}(x)} \tag{1}\]  
This quantity is a measure of how much an event is likely to be the outcome of a random experiment on variable \(X\). From this equation a duality arises between the information content of an event and its probability: the more an event is rare, the more information it carries with it. Usually, the logarithmic base 2 is used and the unit measure is a bit. The most probable outcome carries less information since it is the most likely result of the random experiment.  
Given a stochastic information source (the random experiment), the entropy is a measure of the average number of bits of information contained in the raw data. Shannon entropy measure, \(H_{S}\), of a random experiment is defined as the expectation of the self-information:  
\[H_{S}=E[I_{X}(x)]=-\sum_{i}p_{X}(i)log_{2}p_{X}(i) \tag{2}\]  
Another measure of the entropy is Min-Entropy. The name contains the prefix Min because it is the strictest definition of entropy available. Min-Entropy of a random experiment \(X\) is defined as the minimum value of the self information:  
\[\begin{split} H_{min}&=min[I(x)]=min_{i}[-log_{2} (p_{i})]\\ &=-log_{2}[max_{i}(p_{i})]\end{split} \tag{3}\]  
Min-Entropy is expressed in bits and poses an upper-bound estimation on the ability of the attacker to predict the result. A property of Min-Entropy is that it is never larger than the Shannon Entropy and it is the same if \(X\) has a uniform probability mass function.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 3 Hardware Verification Setups
### Outdoor Measurement Setup  
To perform the outdoor measurements presented in this paper, nodes were placed at three locations in the city of Ghent, Belgium. The exact locations of these sites are marked on Figure 6, which also shows the (sub)urban propagation environment in between the sites. In each of the outdoor experiments, small packets of data were sent from the transmitter location in the center of the city of Ghent to be received by two receivers located near the south of the city.  
Figure 5: Compact PCB implementation of the wireless sensor node. (One square equals one cm\({}^{2}\)).
## 8 Conclusions and Future Work  
In this paper, we have presented an offline model-assisted online optimization method to automatically tune the gain-scheduled PID gains for an exhaust gas recirculation (EGR) valve with large nonlinear friction. The optimized gain-scheduled PID controller can adaptively compensate for the increased friction force in low-velocity regions. In the optimization problem, two objective functions are proposed to consider commonly used performance indices such as the settling time, overshoot, and steady-state error. The NSGA-II optimization results can provide decision-makers with a Pareto front that clearly shows the trade-offs. The optimal initial gain-scheduled functions are obtained using the multi-objective NSGA-II algorithm, eliminating the process of manually tuning control gains. Next, the proposed online iterative NSGA-II optimization approach is used to further optimize the closed-loop system performance, significantly reducing the need for bench tests. Our experimental results show that the proposed automatically tuned optimal gain-scheduled PID controller has better overall performance than a fixed-gain controller.  
In future work, the proposed algorithm based on offline and online co-optimization can be extended to optimize advanced controllers, such as model predictive control and learning-based control. Although this paper provides a meaningful attempt to combine prior model knowledge with black-box online learning, it is interesting and worthwhile to develop analytical tools to study the resulting system's robustness and optimality. Earlier work on model-based control can provide useful insights in this direction [37]. Of particular interest, constraints on states and control inputs as well as learning-based control can be further included in the proposed algorithm to enable computationally efficient tuning of online-learning control. In addition, comparison studies with other data-driven PID automatic tuning algorithms, such as the Monte Carlo method, are meaningful and will be part of our future work.  
Conceptualization, S.Q. and G.Z.; methodology, S.Q. and G.Z.; software, S.Q.; validation, S.Q. and G.Z.; formal analysis, S.Q., T.H. and G.Z.; investigation, S.Q.; resources, S.Q.; data curation, S.Q.; writing--original draft preparation, S.Q.; writing--review and editing, T.H. and G.Z.; visualization, S.Q.; supervision, G.Z. and T.H.; project administration, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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.  
Figure 17: Step responses of offline–online co–optimized controller.
## 2 Method
### Definition of the Modelled Geophysical Gravity Signals-South America  
The South American (SA) continent hosts several different climatic conditions due to its peculiar orography and wide latitudinal extension. The northern part of the continent is characterized by tropical climate, and hosts one of the largest hydrologic basins worldwide (the Amazon basin), which is recharged by the large precipitations occurring at the orographic barrier of the Andes. The precipitations feed the sub-surface aquifer system of the Amazon plain and a network of rivers and lakes, among these is the Amazon River, which then drains the waters to the Atlantic Ocean.  
The southern sector of the SA continent is more arid and is characterized by a large glaciered area, presently subjected to systematic melting episodes associated with climate change [19]. The waters drain to a nearby drainage system constituted by lakes and rivers, which then contribute to the Atlantic Ocean and in particular to the sea water level variations.  
These processes are associated with large amounts of water involving seasonal variations, inter-annual transients, and long-period trends. Tracking and understanding these water mass transports is fundamental for the scientific community, especially for constraining models for evaluating climate change impacts. Gravity missions such as GRACE have already proved to be fundamental to such scopes [19]; the modeling of the sensitivity of MOCAST+ allows for the quantitative assessment of the impact of the mission in observing these mass changes.
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 3 Performance Analysis  
We initially analyze the performance of passive RIS and active RIS systems before investigating the performance of the hybrid active-passive RIS system, and compare the three. In this subsection, we derive the cumulative distribution functions (CDFs) for the end-to-end (e2e) SNR of the three RIS systems initially, and then derive the closed-approximation expression of outage probabilities by CDFs.
## 3 Example of FireBIRD and Himawari-8 Fire Scene Observation
Figure 6 also illustrates that smaller or weaker fire clusters derived from the BIROS data are omitted in the AHI active fire list. This is due to the 2.1 km \(\times\) 2.5 km ground pixel size of the AHI-band 7, which allows detecting only flaming fires with an effective fire area greater than 500 m\({}^{2}\), whereas the BIROS pixel size of 0.36 km \(\times\) 0.36 km permits the detection of flaming fires with a minimum effective fire area of 15 m\({}^{2}\). This is due to the fact that the area proportion of a flaming fire in an MIR pixel must be \(>\)1.0 \(\times\) 10\({}^{-4}\) to be reliably detected, which is explained in detail, for instance, in [3]. Figure 5: FRP diurnal cycle of agricultural residue burning in eastern China on 11 June 2015 derived from Himawari-8 and MODIS data [13].
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 5 Examples
### Peristatic Solutions
Cantilever beam \[w(x)=-\frac{2}{cL}\sum_{n=1}^{\infty}\frac{M\frac{(2n-1)\pi}{2L}(-1)^{n+1}}{ \left[\int\limits_{-\delta}^{\delta}\frac{1}{\xi^{2}}\Big{(}1-\cos\frac{(2n-1 )\pi\xi}{2L}\Big{)}d\xi\right]^{2}}\bigg{[}1-\cos\frac{(2n-1)\pi\xi}{2L}\bigg{]}\] based on PD and \[w(x)=-\frac{M}{2 EI}x^{2}\] based on CBT 5.2. Peridynamic Solutions Consider the simply supported beam subjected to a harmonic load, as shown in Figure 14.  
Figure 14: Simply supported beam under harmonic load. Let us set \(L=2\) m, \(A=0.01\)\(\times\)\(0.01\) m \({}^{2}\), \(E=200\times 10^{9}\) Pa, \(\rho=7850\) kg/m\({}^{3}\), \(\delta=0.01\) m. The initial conditions are assumed as follows: \(u(x,0)=0.01x(x-L)\) and \(\dot{u}(x,0)=\sin\frac{\pi x}{L}\). The harmonic concentrated load is  
\[p(x,t)=50\sin\left(\frac{\omega_{1}}{3}t\right)\delta\left(x-\frac{L}{2} \right),\]  
where \(\delta\) is the Dirac delta function. Figure 15 illustrates the transverse displacement vs. time plots for two different points along the beam axis. We observe that for the considered value of the PD horizon, the results according to the PD beam theory and the classical beam theory almost coincide. As the next example, consider a mass drop on the midpoint of a simply supported beam; see Figure 16.  
As in the previous example, we set \(L=2\) m, \(A=0.01\)\(\times\)\(0.01\) m\({}^{2}\), \(E=200\times 10^{9}\) Pa, \(\rho=7850\) kg/m\({}^{3}\), and \(\delta=0.01\) m. The balance of momentum for the mass yields  
\[-mv(t_{1^{+}})-mv(t_{1^{-}})=-m[v(t_{1^{+}})+v(t_{1^{-}})]=-m\overline{v}, \quad\overline{v}=-[v(t_{1^{+}})+v(t_{1^{-}})].\]  
Specifying the concentrated force acting on the beam by \(F\) and considering the interaction over \(\Delta t\), we obtain  
\[m\overline{v}\{H(t-t_{1})-H[t-(t_{1}+\Delta t)]\}=F\Delta t,\]  
where \(H\) is the Heaviside function. Consequently, the force is obtained as follows  
\[w(x)=\frac{F}{48 EI}\Big{[}x^{2}(9L-8x)-(L-2x)^{3}H(2x-L)\Big{]}\]0  
\[w(x)=\frac{F}{48 EI}\Big{[}x^{2}(9L-8x)-(L-2x)^{3}H(2x-L)\Big{]}\]1 Simply supported beam under impact load. \[w(x)=\frac{F}{48 EI}\Big{[}x^{2}(9L-8x)-(L-2x)^{3}H(2x-L)\Big{]}\]2 Displacement vs. time for a simply supported beam under harmonic load. (**a**) Orbit of \(x=L/2\), (**b**) orbit of \(x=L/4\). or  
\[w(x)=\frac{F}{48 EI}\Big{[}x^{2}(9L-8x)-(L-2x)^{3}H(2x-L)\Big{]}\]3  
as \(\Delta t\to 0\). The initial conditions are \(u(x,0)=0.01x(x-L)\) and \(\dot{u}(x,0)=\sin\overline{\frac{mx}{L}}\) The load is assumed as follows:  
\[w(x)=\frac{F}{48 EI}\Big{[}x^{2}(9L-8x)-(L-2x)^{3}H(2x-L)\Big{]}\]4  
Plots of the transverse displacement vs. time for two different points along the beam axis are shown in \[w(x)=\frac{F}{48 EI}\Big{[}x^{2}(9L-8x)-(L-2x)^{3}H(2x-L)\Big{]}\]5  
Again, the results according to the PD beam theory and the classical beam theory agree if the horizon size is considered small.
## 3 Results  
### Rheological Model of KL, PAM, and XC Drilling Fluids  
Based on previous studies [16], 1 L H\({}_{2}\)O + 15% NaCl + 0.5% NaOH was the basic fluid selected for this experiment. The rheological properties of the three kinds of SFPDF, KL, PAM and XC, were studied. First, shear stress was tested at different shear rates and with different concentrations of these three kinds of fluid (0.2% \(w/v\), 0.4% \(w/v\), 0.6% \(w/v\) and 0.8% \(w/v\)); Figure 1 shows the curves of shear stress for each shear rate.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Concentration**} & \multicolumn{4}{c}{**KL (\%)**} & \multicolumn{4}{c}{**PAM (\%)**} & \multicolumn{4}{c}{**XC (\%)**} \\ \cline{3-14}  & & **0.2** & **0.4** & **0.6** & **0.8** & **0.2** & **0.4** & **0.6** & **0.8** & **0.2** & **0.4** & **0.6** & **0.8** & **0.2** & **0.4** & **0.6** & **0.8** \\ \hline \multirow{2}{*}{Bingham plastic model} & \(R^{2.1}\) & 0.9931 & 0.9925 & 0.9961 & 0.9263 & 0.9843 & 0.9881 & 0.9889 & 0.9986 & 0.9981 & 0.9821 & 0.9763 & 0.9227 \\  & SSE\({}^{2.}\) & 0.042 & 0.055 & 0.396 & 66.22 & 0.051 & 0.029 & 0.011 & 0.008 & 0.011 & 1.063 & 4.155 & 27.789 \\ \hline \multirow{2}{*}{Power law model} & \(R^{2}\) & 0.9934 & 0.9870 & 0.9986 & 0.9986 & 0.9785 & 0.9772 & 0.9793 & 0.9812 & 0.9796 & 0.9805 & 0.9885 & 0.9902 \\  & SSE & 0.045 & 0.041 & 0.143 & 3.667 & 0.05 & 0.075 & 0.134 & 0.128 & 0.133 & 1.3805 & 1.998 & 3.258 \\ \hline \multirow{2}{*}{Herschel–Bulkley model} & \(R^{2}\) & 0.9958 & 0.9995 & 0.9989 & 0.9993 & 0.9958 & 0.9985 & 0.9975 & 0.9983 & 0.9979 & 0.9978 & 0.9926 & 0.9972 \\  & SSE & 0.039 & 0.013 & 0.127 & 0.257 & 0.038 & 0.026 & 0.013 & 0.007 & 0.01 & 1.03 & 1.015 & 0.657 \\ \hline \multirow{2}{*}{Carson model} & \(R^{2}\) & 0.9933 & 0.9791 & 0.9983 & 0.9692 & 0.9615 & 0.9682 & 0.9901 & 0.9818 & 0.9703 & 0.9813 & 0.9843 & 0.9846 \\  & SSE & 0.045 & 0.012 & 0.174 & 27.084 & 0.053 & 0.056 & 0.063 & 0.057 & 0.063 & 1.194 & 0.947 & 5.48 \\ \hline \hline \end{tabular} \({}^{1}\) Goodness-of-fit, \({}^{2}\) Sum of square errors.  
\end{table}
Table 2: Regression analysis of the degree of fitting \(R^{2}\) and sum of square errors (SSE) for different concentrations of the three kinds of polymer drilling fluid.  
The Herschel-Bulkley model is the ideal rheological model that can be used to characterize the rheological properties of solid-free polymer drilling fluid. The linear correlation coefficients (\(R^{2}\)) found with all fluids tested here were greater than 0.99 and the sum of square errors (SSE) remained lower than others model for different kinds of drilling fluids and concentrate.
## 3 Two Methods for BDS-3 Orbit Determination  
Concerning observations two factors must be considered in BDS-3 orbit determination: (1) Due to the restriction of the number and distribution of BDS-3 tracking stations, it is insufficient for BDS-3 observations required in BDS-3 orbit determination. As visible in Figure 7, the number of BDS-3 tracking stations increased from 9 to 17 at the end of the experiment period. According to the station distribution theory [33], this number of stations was far from meeting the precise orbit determination requirements; (2) The observation types from different tracking networks were different from each other, especially for BeiDou. Based on the analysis of the observations from iGMAS and MGEX, BDS-2 (mainly B1 and B2) could be received by the MGEX and iGMAS, while BDS-3 was only acquired by iGMAS with B1 and B3. Therefore, the orbit determination suffered from the inconsistency between two tracking networks. However, in the study of multi-GNSS orbit determination [19], the full use of various observations improved the strength of the parameter estimation and indirectly enhanced the accuracy of the orbit parameters. Therefore, two methods, namely, NES and step-by-step (SS), for improving the accuracy of BDS-3 orbit estimations using the combination of different networks, were proposed.
## 2 System Model
### Nonlinear PA Model
The distortion of the NPAs on the \(l\)th satellite is given by \(\mathbf{d}_{l}=\left[d_{l,1},d_{l,2},\cdots,d_{l,N}\right]^{T}\in\mathbb{C}^{N \times 1}\), which is assumed to be independent of the signal vector \(\mathbf{u}_{l}\). According to Equation (9), we can obtain that the \(n\)th element of the emitted signal \(\mathbf{x}_{l}\) on the \(l\)th satellite can be written as \(\mathbf{x}_{l,n}=\left[\mathbf{\bar{G}}_{l}\right]_{n,n}u_{l,n}+d_{l,n}\), where  
\[\left[\mathbf{\bar{G}}_{l}\right]_{n,n} =\frac{\mathbb{E}\Big{\{}x_{l,n}u_{l,n}^{*}\Big{\}}}{\mathbb{E} \Big{\{}\big{|}u_{l,n}\big{|}^{2}\Big{\}}}=\frac{1}{\mathbb{E}\Big{\{}\big{|} u_{l,n}\big{|}^{2}\Big{\}}}\left(\beta_{1}\mathbb{E}\Big{\{}\big{|}u_{l,n} \big{|}^{2}\Big{\}}+\beta_{3}\mathbb{E}\Big{\{}\big{|}u_{l,n}\big{|}^{4}\Big{\}} \right) \tag{10}\] \[=\beta_{1}+2\beta_{3}\mathbb{E}\Big{\{}\big{|}u_{l,n}\big{|}^{2} \Big{\}}.\]  
and where \(\mathbb{E}\Big{\{}\big{|}u_{l,n}\big{|}^{2}\Big{\}}=\left[\mathbf{U}_{l}\right] _{n,n}\) denotes the average input power of the \(n\)th PA on the \(l\)th satellite. Thus, the average linear amplification gain matrix of the PAs on the \(l\)th satellite can be obtained as \(\mathbf{\bar{G}}_{l}=\beta_{1}\mathbf{I}_{N}+2\beta_{3}\text{diag}\{\mathbf{U }_{l}\}\). From the above derivation, it follows that \(\mathbf{d}_{l}=\left(\mathbf{\bar{G}}_{l}-\mathbf{G}_{l}\right)\mathbf{u}_{l}\). Hence, we can obtain [21]  
\[\mathbf{D}_{l}=\mathbb{E}\Big{\{}\mathbf{d}_{l}\mathbf{d}_{l}^{H}\Big{\}}=2| \beta_{3}|^{2}\mathbf{U}_{l}\odot\mathbf{U}_{l}\odot\mathbf{U}_{l}^{T}=2| \beta_{3}|^{2}\mathbf{F}_{l}\mathbf{F}_{l}^{H}\odot\Big{|}\mathbf{F}_{l} \mathbf{F}_{l}^{H}\Big{|}^{2}. \tag{11}\]  
Introducing the above derivation into a system with \(L\) satellites, we can obtain that \(\mathbf{G}=\text{blkdiag}[\mathbf{G}_{1},\mathbf{G}_{2},\ldots,\mathbf{G}_{L }]\in\mathbb{C}^{LN\times LN}\) and \(\mathbf{\bar{G}}=\text{blkdiag}\big{[}\mathbf{\bar{G}}_{1},\mathbf{\bar{G}}_{ 2},\ldots,\mathbf{\bar{G}}_{L}\big{]}\in\mathbb{C}^{LN\times LN}\). Similarly, \(\mathbf{D}=\text{blkdiag}[\mathbf{D}_{1},\mathbf{D}_{2},\cdots,\mathbf{D}_{L }]\in\mathbb{C}^{LN\times LN}\).
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Network Depth Influence  
To further evaluate the performance of ELP-Net, we investigated the impact of two critical parameters: the number of layers in the GNN module and the number of layers in the transformer module. Specifically, Figure 10 presents the test accuracy results with varying numbers of GNN and transformer layers.  
For the GNN module, the number of transformer layers was fixed at 1, and the number of GNN layers was varied from 1 to 5. The results (Figure 10a) show that using a single GNN layer achieves the best performance across most top-x levels. However, for broader prediction ranges like top 15 and top 50, a depth of two layers yields slightly better accuracy. This suggests that, for more localized predictions, a single layer is sufficient to capture the spatial relationships effectively, while additional layers provide a benefit when broader contextual information is required. For the transformer module, we fixed the number of GNN layers at 1 and varied the number of transformer layers from 1 to 5. The general trend, as presented in Figure 10b, showed a decrease in accuracy as the number of layers increased. A single transformer layer achieved optimal performance for most top-x levels, while for top 3, top 15, and top 50, a depth of four layers delivered comparable results. This could be attributed to the model ability to capture longer temporal dependencies at these specific prediction levels when additional layers are used. However, beyond this, the increased complexity likely leads to overfitting and diminished performance.  
These findings suggest that both the transformer and GNN modules require careful tuning of their respective layer depths to avoid overfitting and to maximize performance. A smaller number of layers often provides a more optimal trade off between model complexity and generalization.  
Figure 9: Heatmap of the predicted enemy locations and true locations of the tank piece over different time periods.
## 4 Optimization
### Optimization Methodology
Regression analysis is an analysis method that measures the fitness of how well the response surface function represents the actual value, and it is determined by the R2 adj (correction coefficient of determination) of the response surface function. That is, if R2 adj > 0.9, the response surface function is very accurate, and if 0.9 > R2 adj > 0.7, the response surface function is relatively accurate [15; 16]. An optimal point of the response surface provides a new design geometry. The performance of the optimal design geometry is then evaluated. This optimization process repeats until the objective function reaches the target value. Figure 8: Flowchart of response surface meta-model-based optimization methodology.
## 3 Methodology  
Figure 1 shows the proposed framework based on the MAH-based outlier-removal method. Our methodology was divided into three distinct interlinked modules. Initially, data preparation was performed, and the raw data were meticulously organized and structured. Data preprocessing involves cleaning and feature selection for optimal dataset refinement. Finally, predictive analysis applies advanced statistical models to make informed predictions. Each module is crucial for the integrity and accuracy of a diabetes prediction model. A detailed description of Figure 1 is provided in the subsequent subsections, where we break down the components, methodologies, and insights depicted in the figure for a thorough understanding.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Authors** & **Years** & **Algorithms** & **Comments** \\ \hline
[25] & 2024 & SECNN & The SECNN model has an accuracy of 94.12\% in NHANES dataset and an accuracy of 89.47\% on the PIMA Indian dataset. \\ \hline
[26] & 2023 & CBC & CatBoost classifier best performance, with an accuracy of 85\% and an AUC of 93\%. \\ \hline
[27] & 2023 & DDLNN & The model is best, with an accuracy of 84.42\%. \\ \hline
[31] & 2023 & PCA + ELM & PCA has the best performance with an accuracy of 90.57\%. \\ \hline
[29] & 2020 & AB + XB & The best model reaches an AUC of 95\%. \\ \hline
[30] & 2023 & RFWBP & The best performance, with an accuracy of 95.83\%. \\ \hline \hline \end{tabular}  
* MAH: Mahalanobis distance. ML: machine learning. SECNN: Attention-Oriented Convolutional Neural Network. PIMA Indian dataset: Public Investment Management Assessment of India. NHANES: National Health and Nutrition Examination Survey. CBC: CatBoost Classifier. DDLNN: Deep Dense Layer Neural Network. PCA: Principal Component Analysis. ELM: Extreme Learning Machine. RFWBP: Random forest algorithm with the best parameters. AB + XB: The ensembling of two boosting (adaptive (AB) and gradient (XB))-type classifiers.  
\end{table}
Table 1: Summary of existing studies integrating the MAH and ML methods for predicting diabetes risk 1.