esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 3 The Improved Spherical Simplex-Radial Cubature Rule
### Spherical Simplex-Radial Cubature Rule  
By substituting Equation (14) into Equation (20), we obtain the following improved spherical simplex-radial cubature rule which is composed of \(N_{p}=N_{r}N_{s}=4(n+1)\) points.  
\[\begin{array}{ll}I_{N}&=\int_{\mathbb{R}^{n}}g(x)N(x;\overline{x},\mathbf{P}_{x})dx\\ &=\frac{n}{4(n+1)\left(n+2+\sqrt{2n+4}\right)}\sum\limits_{i=1}^{n+1}\left[g \left(\overline{x}+\sqrt{\left(n+2+\sqrt{2n+4}\right)\mathbf{P}_{x}}\mathbf{a}_{i} \right)+g\left(\overline{x}-\sqrt{\left(n+2+\sqrt{2n+4}\right)\mathbf{P}_{x}}\mathbf{a }_{i}\right)\right]+\\ &\frac{n}{4(n+1)\left(n+2-\sqrt{2n+4}\right)}\sum\limits_{i=1}^{n+1}\left[g \left(\overline{x}+\sqrt{\left(n+2-\sqrt{2n+4}\right)\mathbf{P}_{x}}\mathbf{a}_{i} \right)+g\left(\overline{x}-\sqrt{\left(n+2-\sqrt{2n+4}\right)\mathbf{P}_{x}}\mathbf{a }_{i}\right)\right]\end{array} \tag{21}\]  
If we define matrix \(\mathbf{a}=\left[\mathbf{a}_{1}\quad\mathbf{a}_{2}\quad\cdots\quad\mathbf{a}_{n+1}\right]\), and the calculation methods of the cubature points and weights are extracted from cubature rule (21), we obtain the following.  
\[\begin{array}{ll}\overline{\mathbf{x}}^{(i)}=\left\{\begin{array}{ll}\overline {x}+\sqrt{\left(n+2+\sqrt{2n+4}\right)\mathbf{P}_{x}}\left[\begin{array}{cc}\bm {a}&-\mathbf{a}\end{array}\right]_{i^{\prime}}i=1,2,\cdots,2n+2\\ \overline{x}+\sqrt{\left(n+2-\sqrt{2n+4}\right)\mathbf{P}_{x}}\left[\begin{array}[] {cc}\mathbf{a}&-\mathbf{a}\end{array}\right]_{i^{\prime}}i=2n+3,\cdots,4n+4\end{array}\right. \\ \\ \omega_{i}=\left\{\begin{array}{ll}\frac{n}{4(n+1)\left(n+2+\sqrt{2n+4} \right)},i=1,2,\cdots,2n+2\\ \frac{n}{4(n+1)\left(n+2-\sqrt{2n+4}\right)},i=2n+3,\cdots,4n+4\end{array}\right. \end{array} \tag{22}\]  
The order of the quadrature rule determines the accuracy of the cubature rule. The relationship of the number of quadrature points and the accuracy is proportional [8; 32]. The radial rule proposed in Section 3.2 uses two points to achieve fifth-degree accuracy. In contrast to the third degree adopted by the CKF and SSRCKF, when only one point is used, fifth-degree is more accurate. And the spherical simplex rule performs better than the spherical rule; therefore, the improved spherical simplex-radial cubature rule, which is shown in Equation (21), can achieve higher accuracy than the conventional CKF and SSRCKF.
## 3 Results
### Integration Strategies for Nanosensors in Bridge Maintenance
#### 3.6.2 Connection and Communication Infrastructure  
To collect and analyze data from the nanosensors, an efficient connection and communication infrastructure is essential [79]. The sensor data need to be transmitted from the sensors to a central monitoring system for analysis and interpretation.  
Various wired or wireless communication technologies can be utilized for data transmission. Wired connections, such as Ethernet or fiber optic cables, can provide reliable and high-bandwidth communication [80]. Wireless communication technologies, such as Wi-Fi, Bluetooth, or cellular networks, offer flexibility and mobility but may have limitations in terms of bandwidth and range [81].  
The choice of communication infrastructure depends on factors such as the sensor density, data volume, distance, and power consumption considerations [82]. For large-scale bridge monitoring, a combination of wired and wireless communication systems may be employed to ensure reliable and efficient data transmission.
# Simulation of Gas Fracturing in Reservoirs Based on a Coupled Thermo-Hydro-Mechanical-Damage Model
## 4 Analysis of Numerical Simulation Results
### Variation of Rock Mass Physical Parameters under Pneumatic Fracturing
These findings suggest that gas fracturing is an effective method for enhancing rock permeability, increasing gas extraction efficiency within the rock. Moreover, higher temperatures can contribute to improved gas fracturing efficiency and subsequently increase rock permeability. The observation results in Figure 7 indicate that as the degree of rock damage increases, the pressure applied to the borehole gradually increases. In addition, the temperature field has a significant impact, leading to a wider range of pressure increase around the borehole. This is because as the surrounding rock of the borehole is destroyed, the fractured rock mass loses its integrity, resulting in a sharp increase in pressure applied to the surrounding area of the borehole, and the range expands inward. Under the influence of temperature field, the increase in gas molecule activity leads to higher pressure and a larger range of rock damage. Figure 5: Graph of the variation of the third principal stress with time during the damage process. Figure 6: The graph depicting the change in permeability over time during the damage process. Figure 7: The graph depicting the change in pressure exerted on the rock specimen over time, without considering the temperature field. The stress around the borehole decreased significantly after damage evolution compared to before, with a greater reduction range under the influence of the temperature field. This suggests that gas fracturing can cause a significant decline in the mechanical properties of rock and generate more cracks, while higher temperatures can increase the efficiency of gas fracturing and produce better results. Additionally, the figures show that the rock is subjected to greater pressure under the influence of the temperature field than when the temperature field is not considered. This indicates that the temperature field can increase the gas pressure during the fracturing process, thereby achieving better results. In summary, gas fracturing technology can cause damage to the surrounding rock of the borehole, reduce its compressive strength, and increase its permeability. Furthermore, under the influence of the temperature field, gas fracturing technology can achieve better results.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 5 Results
Figure 8: The time-averaged scores, \(\overline{s}\), are plotted (**a**) for the raw data with the standard deviation given by the stationary limit and (**b**) for the same dataset as in (**a**) except that the uncertainty is multiplied by the appropriate Dilution-Of-Precision (DOP) variable. (**c**) \(\overline{s}\) from the unscented Kalman filter with OU noise model and (**d**) the unscented Kalman filter with iid Gaussian noise model. The colours are consistent with Figure 4. The scales of the ordinates are different in each figure; most importantly, in (**d**), it is \(3\times 10^{3}\) bigger than in the other figures. Figure 6: (**a–c**) Log scores, \(S\), for latitude (**a**) longitude (**b**) and altitude (**c**) inferences from the Port Douglas data using the KF with the OU noise model. (**d–f**) The same as above, but for the KF with iid Gaussian noise model. Figure 7: Performance of the predictions of the Unscented Kalman Filter with (**a**) the OU noise model and (**b**) iid Gaussian noise. Note that the \(\overline{s}\) scale in (**b**) is \(3\times 10^{3}\) bigger than in (**a**).
# Design of External Rotor Ferrite-Assisted Synchronous
## 3 Design of Fe-PMaSynRM for High Power Density
### Optimal Design Variables  
The converged values of the design variables are shown in Table 2. The design variables include the slot/pole combination, split ratio, and several other dimensional parameters. The optimal value of the split ratio was found to be 0.82 for the proposed model. The selection of optimal slots/pole combination is another critical step toward designing a high power density motor. The slot/pole combination is related to the MMF harmonics components generated by the windings and defines whether the winding is of distributed or concentrated type. The differential evolution algorithm generates different random designs with different slot/pole combination. The LPM tool calculates the motor performance by  
Figure 8: Workflow diagram to obtain the optimal Fe-PMaSynRM model.  
finding saliency ratio, torque, and losses while the obtained optimal winding configuration is a distributed winding with coil span 2 which has 25 slots and 12 poles, as given in Table 2. Although, for in-wheel applications, the concentrated windings are more suitable owing to the very low space required for their end winding, they offer lower saliency ratio and higher copper loss. Hence, the distributed winding has been found as an optimal one due to their higher salient torque, lower torque ripple, and lower overall loss compared to the concentrated winding configurations. In fact, the coil span is comparatively lower than the conventional distributed winding machines which ensures less space taken by the end winding and less coil overlapping. Considering the MMF harmonics for different slot/pole configuration, the differential evolution algorithm-based design optimization saves the best design and discards the poor ones.
## 3 Results
### Polyamine Levels in Peripheral Blood Cells and Plasma for Patients and the Control Group
In patients with AMI, in terms of cardiac damage markers, troponin levels were negatively correlated with the plasma spermine-to-spermidine ratio (\(r_{s}=-0.538\), \(p=0.008\), \(n=24\)), as well as with plasma isoamylamine levels (\(r_{s}=-0.459\), \(p=0.024\), \(n=24\)). Lactate dehydrogenase (LDH) levels were positively correlated with N-acetyl putrescine levels in mononuclear cells (\(r_{s}=0.733\), \(p=0.016\), \(n=10\)) and negatively correlated with plasma isoamylamine levels (\(r_{s}=-0.709\), \(p=0.022\), \(n=10\)). The other markers of cardiac damage were correlated among them. Alanine aminotransferase positively correlated to N-acetyl spermidine in erythrocytes (\(r_{s}=0.475\), \(p=0.04\), \(n=19\)) and to spermine in plasma erythrocytes (\(r_{s}=-0.665\), \(p<0.001\), \(n=22\)); the aspartate aminotransferase-to-alanine aminotransferase ratio positively correlated to N-acetyl putrescine in mononuclear cells  
Figure 4: Box-and-whisker plots showing the levels of putrescine (**), spermidine (**b**), spermine (**c**), N-acetyl putrescine (**d**), N-acetyl spermidine (**e**) (pmod/mg of protein), and isoamylamine (**f**) (mmol/mg of protein) in peripheral blood plasma from the control group and from patients with acute myocardial infarction (AMI) or chronic heart failure (CHF). Control–CHF: Control subgroup matched for age with CHF patients. ***\(p\leq 0.01\) and ***\(p\leq 0.001\) compared to the control group; \(\big{\updownarrow}p\leq 0.01\) and \(\big{\updownarrow}p\leq 0.001\) compared to patients with AMI vs. CHF using the Kruskal–Wallis test. (\(r_{s}=0.646\), \(p=0.009\), \(n=15\)), but no significant correlation was observed for aspartate aminotransferase. The days of stay in the Coronary Care Unit among patients with AMI were negatively correlated with the levels of putrescine (\(r_{s}=-0.474\), \(p=0.022\), \(n=23\)) and spermine (\(r_{s}=-0.485\), \(p=0.019\), \(n=23\)) in mononuclear cells. The total days of hospitalisation correlated negatively with spermine levels (\(r_{s}=-0.599\), \(p=0.002\), \(n=24\)) in mononuclear cells. In patients with CHF, LVEF exhibited a positive correlation with plasma spermidine levels (\(r_{s}=0.503\), \(p=0.033\), \(n=18\)). 7. Lineal Regression Analysis of Clinical Parameters of Patients with AMI and CHF as Dependent Variables and Polyamines as Predictor Variables  
Multiple regression analysis was undertaken to predict clinically relevant variables in patients with AMI and CHF, with cardiac damage or structural or functional changes included as dependent variables and polyamines and isoamylamine as independent variables or predictors. The results show that determining polyamine and isoamylamine in peripheral blood cells with different models could predict LVEF in 66.6% of patients with AMI and 86% of patients with CHF. In patients with CHF, the models could predict 51.2% of the heart rate and 55% of the septum size (Table 3). Other dependent variables analysed were not predicted.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Bistatic Deployment  
Bistatic deployment defines a JSC system in which the transmitter and receiver are located at separate sites. Unlike monostatic configurations, bistatic setups are better suited for communication systems, eliminating the need for full-duplex architectures and SI cancellation techniques [23]. These configurations present a compelling opportunity to extend sensing areas while utilizing receivers that can be both simple and cost-effective. Consequently, bistatic JSC systems are particularly suited for vehicular scenarios. In vehicle-to-vehicle (V2V) contexts, JSC facilitates information exchange among vehicles while simultaneously sensing nearby objects and vehicles. In V2X scenarios, sidelink sensing enables vehicles to detect and share information about their surroundings, including nearby vehicles [6; 7], VRUs such as pedestrians, and other objects, as illustrated in Figure 1. These sensing applications are essential for collision avoidance, cooperative driving, and enhanced road safety.
## 4 Numerical Assessment of the Strategy Using 3D and 2D Models
### Material and Geometry  
The numerical simulation test is performed to verify the efficiency of the proposed VBSHM method. A cantilever steel beam was taken into consideration for the numerical test and beam properties are given below in Table 1.  
The updating procedure is done for 2D and 3D healthy FE models with respect to an experimental beam presented in Section 5. [PERSON]'s modulus is updated from an initial value of 210 GPa according to Section 3.2 by obtaining the maximum value of the function expressed in Equation (7). Seven bending frequencies are used in the procedure, which are indicated in Table 2 for the healthy experimental beam. In the 3D model, a procedure is first applied to filter the results and consider only the frequencies corresponding to bending modes. The updated results of [PERSON]'s modulus are found to be 189.26 GPa with 0.8635 maximized value for the 2D beam and 188.90 GPa with 0.8466 maximized value for the 3D beam. The frequencies of the updated 2D and 3D FE models of the beam structure are reported in Table 2. Note that the mean error values are 0.569% for 3D model and 0.584% for 2D model, respectively.  
Note that, just as in the initial updating procedure, the seven lowest frequencies are assumed to be sensitive enough to find damage parameters with the help of FSC. Remember that Ref. [21] suggests that the modes can be selected in a smarter way, which is not done here for the sake of simplicity. Finally, in this study, the bending frequencies with two digits after the decimal are considered, which proves necessary to correctly correlate the FE models.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 5 Security-Analysis Tools
### Recent Security-Tools Support  
Table 3 shows how each tool covers the most ubiquitous vulnerabilities. Many tools have their own set of vulnerabilities and methods to find them (see Table 4). There are overlapping vulnerabilities that many tools try to detect. We consider reported accuracy above 90% as **high**, below 50% as **low**, and the rest as **medium**. The accuracy works in the realm of each tool's defined vulnerabilities, so this comparison will give readers an impression of how each tool works for their own set of vulnerabilities. **smartCheck. ** This tool is a static analysis tool that uses ANTLR [92] (a parser generator) to translate Solidity source code into an XML pare tree [93] (an XML-based intermediate representation), and checks it against XPath [94] patterns [38]. **DefectChecker. ** The defectChecker tool takes bytecodes as input, disassembles them into opcodes, splits the opcodes into several basic blocks and symbolically executes instructions in each block [43]. Then it generates the control flow graph (CFG) and records all stack events. Using CFG and stack events information, it detects three pre-defined features: money call, loop block and payable function. After feature detection, it applies rules to detect eight vulnerabilities: _transaction state dependency_, _DoS under external influence_, _strict balance equality_, _re-entrancy_, _nested call_, _greedy contract_, _unchecked external calls_, and _block info dependency_. **contractWard. ** The contractWard applies supervised learning to find vulnerabilities [44]. It extracts 1619 dimensional bigram features from opcodes using an n-gram algorithm [95] and forms a feature space. Then it labels contracts in training set with six types of vulnerabilities using Oyente [13]. The label is stored in a six-dimension vector (e.g., [1 0 1 0 1 1]) where each bit stands for an existing vulnerability. Based on the feature space and labels of the training set, contractWard uses five classification algorithms to detect vulnerabilities. **NPChecker. ** This tool analyzes the _non-determinism_ in the smart-contract execution context and then performs systematic modelling to expose various non-deterministic factors in the contract execution context [46]. Non-deterministic factors are factors that could impact final results to the end-user and make them unforeseeable. Possible factors discussed in NPChecker are _block and transaction state_, _transaction execution scheduling_, and _external callee_. The NPChecker disassembles the EVM bytecode and translates them into LLVM intermediate representation (IR) code [96], recovers the control flow structures and enhances the LLVM IR with function information, identifies state and global variables, and performs information-flow tracking to analyze their influences on the fund's transfer.
## 3 Results and Discussion
### Vis-NIR Spectra  
Vis/NIR absorption spectra for all three bell pepper cultivars are presented in Figure 1A-C. To mitigate noise present at the spectral data's beginning and end, each variety was examined within a distinct spectral range. The cultivars exhibit varying qualitative properties and absorption levels within their respective noise-free spectral regions. In all three varieties, absorption increased or decreased in all samples with a similar trend, which may be affected by the color of the samples [53]. According to Figure 1, there is a clear peak for all spectra around the wavelengths of 920 to 940 nm, which may be caused by the protons absorbed by OH or NH [54].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Variety** &
\begin{tabular}{c} **Firmness** \\ **(N)** \\ \end{tabular} & **pH** & **SSC (Brix)** & **TA (\%)** & **Acorbic Acid (mg in 100 g)** & **TP (mg/g)** & **Anthocyanin (mg/g)** \\ \hline \multirow{3}{*}{Mean} & Red & 14.051 & 4.5263 & 7.963 & 7.627 & 200.49 & 202.27 & 1.6294 \\  & Yellow & 12.947 & 4.5437 & 9.060 & 8.459 & 208.36 & 200.15 & 1.7149 \\  & Orange & 12.229 & 4.41 & 10.542 & 10.811 & 244.98 & 262.03 & 1.165 \\ \hline \multirow{3}{*}{Minimum} & Red & 9.213 & 4.33 & 6.2 & 6.4 & 169.08 & 165 & 0.8013 \\  & Yellow & 8.212 & 4.39 & 7 & 6.72 & 151.65 & 128.64 & 0.8013 \\  & Orange & 6.9 & 4.3 & 8.9 & 6.57 & 190.48 & 146.82 & 0.481 \\ \hline \multirow{3}{*}{Maximum} & Red & 19.250 & 4.77 & 9.1 & 9.6 & 275.88 & 301.36 & 3.0451 \\  & Yellow & 19.363 & 4.78 & 10.40 & 11.52 & 299.08 & 415 & 2.8849 \\  & Orange & 18.625 & 4.52 & 12.30 & 13.44 & 305.94 & 333.18 & 4.007 \\ \hline \multirow{3}{*}{StDev} & Red & 2.249 & 0.0961 & 0.759 & 0.733 & 21.89 & 31.27 & 0.4612 \\  & Yellow & 2.666 & 0.0845 & 0.828 & 1.062 & 35.02 & 52.44 & 0.5360 \\ \cline{1-1}  & Orange & 2.929 & 0.0534 & 0.898 & 3.145 & 30.42 & 40.84 & 0.650 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quality parameters measured in three bell pepper varieties using standard methods.
## 3 Results and Discussion
### TDS-1 Data Analysis at Regional Scale
#### 3.2.3 Yanco/NSW/Australia  
The New South Wales region in Australia, from \(-\)39.45\({}^{\circ}\) to \(-\)28.39\({}^{\circ}\) South, and from 155\({}^{\circ}\) to 140\({}^{\circ}\) West, has also been studied. As in the previous cases, it has been analyzed the SMOS L3/L4 SM sensitivity with respect to the TDS-1 derived GNSS-R reflectivity. Although for this area the VOD values are lower than in the Iberian Peninsula, the scattering is still significant. In general, the higher slope values are obtained for the incidence angles between 0\({}^{\circ}\) and 30\({}^{\circ}\) when the vegetation correction is applied.  
SMOS L3 SM Sensitivity  
Figure 15a shows the main results, when different types of corrections are applied, and for different ranges of incidence angles for SMOS L3 SM (25 km gridded resolution).  
SMOS L4 SM Sensitivity  
Similarly, Figure 15b shows the main results, when all corrections are applied, and for different ranges of incidence angles for SMOS L4 SM (1 km resolution). See figure caption for details. Results will be discussed at the end.  
The graphical results obtained in the previous Sections are now summarized and discussed. Table 3 shows the sensitivities of the TDS-1 GNSS-R reflectivity to the descending-pass SMOS Soil Moisture both for low (25 km) and high resolution (1 km), and for different levels of corrections. Table 4 shows the corresponding number of points used in the regressions.  
Figure 15: Scatter plot between the (**a**) Low Resolution (25 km) SMOS L3 SM and (**b**) High Resolution (1 km) SMOS L4 SM and the TDS-1 reflectivity value over Yanco/NSW/Australia from June 2016 to June 2017, with vegetation and roughness corrections applied. Each column represents the values for a specific range of incidence angles: (**left**) 0\({}^{\circ}\)–30\({}^{\circ}\); (**center**) 30\({}^{\circ}\)–60\({}^{\circ}\); and (**right**) 60\({}^{\circ}\)–90\({}^{\circ}\).  
This suggests that sensitivity is higher over large, relatively flat areas, with low vegetation (e.g., Australia), but as the topography effects and/or vegetation effects increase, this sensitivity decreases, although pixels affected by topography are flagged and discarded, and vegetation effects have been --in principle--compensated for. In any case, all these sensitivities are much smaller than the ones obtained in [1,2], \(\sim 38\) dB/100%, but it has to be noted that these sensitivities where derived using the SNR as variable, and not the calibrated reflection coefficient, and also, the standard deviation when using the SNR, was almost twice larger than when using the reflections coefficient.