esa-sceva/satcom-chunk-collection huggingface dataset:
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### Local Dependence in IRT Models  
Assume that there is local dependence in an IRT model, and the items are arranged in testlets that reflect the dependency structure of items in groups of items [19]. In other words, there exist testlets \(t\) such that items \(X_{it}\) are locally independent between testlets but dependent within testlets. Define \(\mathbf{X}_{t}=(X_{1t},\ldots,X_{l_{t}t})\), where \(l_{t}\) denotes the number of items in testlet \(t\).  
The joint distribution of all item responses \(\mathbf{X}=(\mathbf{X}_{1},\ldots,\mathbf{X}_{T})\) fulfills  
\[\mathsf{P}(\mathbf{X}|\theta)=\mathsf{P}(\mathbf{X}_{1},\ldots,\mathbf{X}_{T}|\theta)= \prod_{t=1}^{T}\mathsf{P}(\mathbf{X}_{t}|\theta). \tag{7}\]  
However, local dependence means that the items within a testlet are not conditionally independent (i.e., local dependence exists):  
\[\mathsf{P}(\mathbf{X}_{t}|\theta)=\mathsf{P}(X_{1t},\ldots,X_{l_{t}t}|\theta)\
eq \prod_{i=1}^{l_{t}}\mathsf{P}(X_{it}|\theta). \tag{8}\]  
Consequently, we obtain, for an item pair \((X_{it},X_{it})\) of items within the same testlet \(t\),  
\[\mathsf{P}(X_{it},X_{ji}|\theta)\
eq\mathsf{P}(X_{it}|\theta)\mathsf{P}(X_{ji} |\theta). \tag{9}\]  
This property reflects the residual dependence of the items within a testlet and potentially biases the estimation of IRT models that incorrectly attribute the residual dependence to the ability variable \(\theta\). In general, item discriminations are distorted and positively biased in many instances [20]. The neglected local dependence entails inflated reliability estimates [21; 22].  
However, it should be noted that there is local independence among items \(X_{it}\) and \(X_{ju}\) within different testlets \(t\) and \(u\)  
\[\mathsf{P}(X_{it},X_{ju}|\theta)=\mathsf{P}(X_{it}|\theta)\mathsf{P}(X_{ju}| \theta), \tag{10}\]  
Local dependence can be detected at the level of item pairs by means of standardized correlations of item residuals. This \(Q_{3}\) statistic [9] can be interpreted as a measure of the deviation from local independence, and positive residual correlations (e.g., larger than 0.1 or 0.3) can be considered non-negligible deviations from local independence [23].
## 4 Results  
### Simulations  
To validate theoretical advantages of the proposed fuzzy logic controller, the following MATLAB/SIMULINK simulations present the performance of the conventional P and O algorithm with a fixed perturbation intensity 0.1 V/trial and proposed FLC with adaptive perturbation intensities. The configuration of the simulation is shown in Table 2.  
To test the performance of the improved MPPT algorithm using FLC, several irradiation changes are added into the simulation so that the fast and smooth power transitions (caused by the changes of P-V characteristics of the simulated PV cells) conducted by the proposed FLC can be observed. The MATLAB/Simulink block fulfilling the functionalities of the FLC is shown in Figure 10.  
The membership functions of the FLC can be tuned for achieving better performances in terms of initial tracking time and transition responses, according to practical characteristics of solar panels. Based on the parameters given by Table 2, the output surface of the FLC is decided as is shown in Figure 11. Irradiation variations are represented by the changes in PV short-circuit current shown in Figure 12. To track the initial MPP, the proposed algorithm uses 0.2 s, while the conventional P and O algorithm uses 2.2 s in Figure 13. The initial tracking time is shown in Figures 13 and 14. The first and second transition periods of the two simulated algorithms are demonstrated by Figures 15 and 16. As seen in these two figures, the theoretical advantages of the proposed algorithm using FLC can be confirmed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters of the Simulated Solar Panel @ STC** \\ \hline Short circuit current, \(I_{sc}\) & 4.75 A \\ Open circuit voltage, \(V_{oc}\) & 27.03 V \\ Maximum power, \(P_{mpp}\) & 98.23 W \\ \hline
**Configuration Settings** \\ \hline Initial operation point (\(V\),\(P\)) & 0 V, 0 W \\ Perturbation offset of P and O & 0.1 V \\ Perturbation interval & 100 ms \\ Temperature condition, \(T\) & 25 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of the simulation.  
Figure 10: The MATLAB/Simulink diagrams of the FLC.  
Figure 11: The output surfaces of the FLC.  
Figure 12: I\({}_{\rm sc}\) vs. time.  
Figure 13: The proposed algorithm (FLC) vs. the conventional P and O algorithm.
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
###### Abstract  
This paper proposes CONCERTS: Coverage competency-based target search, a failure-resilient path-planning algorithm for heterogeneous robot teams performing target searches for static targets in indoor and outdoor environments. This work aims to improve search completion time for realistic scenarios such as search and rescue or surveillance, while maintaining the computational speed required to perform online re-planning in scenarios when teammates fail. To provide high-quality candidate paths to an information-theoretic utility function, we split the sample generation process into two steps, namely Heterogeneous Clustering (H-Clustering) and multiple Traveling Salesman Problems (TSP). The H-Clustering step generates plans that maximize the coverage potential of each team member, while the TSP step creates optimal sample paths. In situations without prior target information, we compare our method against a state-of-the-art algorithm for multi-robot Coverage Path Planning and show a 9% advantage in total mission time. Additionally, we perform experiments to demonstrate that our algorithm can take advantage of prior target information when it is available. The proposed method provides resilience in the event of single or multiple teammate failure by recomputing global team plans online. Finally, we present simulations and deploy real hardware for search to show that the generated plans are sufficient for executing realistic missions.  
multi-robot systems; target search; multi-robot search; coverage path planning; exploration +
Footnote †: journal: Applied
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
## 4 Results and Discussion
### Q-criterion Contours at z/D = 5 and y/D = 0  
In the Q-criterion, Q is defined as the difference between the magnitude of the rotation rate and the strain rate, and it represents the dominance of the rotation rate compared to the strain rate [28; 29]. Figure 9a,b shows the instantaneous Q-criterion contours with iso-surfaces of levels 1000 and 500, colored with vorticity \(\omega_{\rm z}\) for the 32 Hz pulsation, and obtained with LES and URANS, respectively. The Q-criterion contours are colored with y-vorticity in each phase. Compared to the Q-criterion contours predicted using URANS, those predicted by LES indicate that LES can predict more complex vortex structures, including CRVP, hairpin, jet shear layer, and horseshoe vortices. URANS predicted only simple vortex structures, such as the horseshoe vortex and the hairpin vortex, as shown in Figure 9b. In the Q contour obtained using LES, complex vortical structures continuously exist under the injectant, but in the URANS result, they appear partially under the hairpin vortices.  
Figure 8: Phase-averaged dimensionless temperature contours at \(y/D=0\) (upper) and \(z/D=0\) (lower) for 32 Hz: (**a**) LES (Smagorinsky–Lilly) and (**b**) unsteady RANS (URANS) (realizable k-\(\epsilon\)).
## 4 Results and Discussion
### First Part: Factorial Design Multivariate Statistical Analysis
#### 4.1.1 Pareto Chart--Student's \(t\)-Test Analysis  
The Pareto chart shown in Figure 4 illustrates the relative importance of the main factors and their interaction effects with the output response. The Student's \(t\)-test was used to determine whether the measured effects were statistically different from zero. The values for each factor effect are represented by horizontal columns in the Pareto chart. The \(t\) value for a 95 percent confidence level is 2.037. The red vertical line on the Pareto chart represents the minimum statistically significant effect magnitude at a 95% confidence level. From the standardized effect Pareto chart, it can be seen that all the main factors, such as, the melt temperature, screw speed, and back pressure and their interaction, cross the red dotted lines. From this, it is clear that all the main factors and their interactions were significant at the 95% confidential interval (\(\alpha\) = 0.05). Therefore, we cannot exclude any of these factors, and we need to consider all the main factors and their interaction to evaluate the effect on air bubble generation.  
Figure 3: Experiment run number versus counted air bubble number.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 3 Results
### Desorption  
The elution solvent must be capable of disrupting all retentive interactions between the analyte and sorbent but not too strong as to remove tightly bound contaminants. One milliliter of pure methanol was sufficient to obtain a complete desorption for all the analyzed bisphenols, which were adsorbed on the microgel. However, when 100% organic solvent is injected into liquid chromatography, the peaks for analytes become broad and asymmetric; this is why the different compositions of methanol and water were also studied for bisphenols removal. In order to obtain a 5-fold preconcentration of 1 mL of the sample, 200 \(\upmu\)L of each methanol/water mixture was tested for the elution of all bisphenols. As presented in Figure 3, 80% of MeOH (80/20 methanol and deionized water) obtained the best desorption efficiency for all four bisphenols. This is why it was selected as the optimum elution solvent for further extraction studies.  
Figure 3: The recovery obtained for a 5-time preconcentration of 50 \(\upmu\)g\(\cdot\)L\({}^{-1}\) mixture of four bisphenols with each of the elution mixture (different ratio of methanol and water). Data are means with RSD (\(n=3\)).  
Figure 2: The effect of the initial concentration of BPA (0.025–1 mg\(\cdot\)L\({}^{-1}\)) and the amount of microgel on the sorption. Data are means with RSD (\(n=3\)).
## 2 Model and Methodology
### Numerical Simulation Method and Verification  
In this study, liquid oxygen is considered incompressible, with a density of 1142 kg/m\({}^{3}\) and a viscosity of 0.00019582 kg/(m\(\cdot\)s), at 90 k and 0.8 MPa approximately [16]. This incompressibility assumption is supported by the following three considerations: First, the density variation in liquid oxygen at 90 K between 0.6 MPa and 1.0 MPa is less than 0.1%. Second, the applied oscillation frequencies fall within the low-frequency regime, where liquid compressibility effects become negligible, as demonstrated in orifice flow impedance analyses. Third, the impedance of gas-filled accumulators is mainly derived from the usage gas compliance rather than contributions from the liquid phase or structure.  
The continuity equation and momentum equation are established for the communication port model. Since the fluid is incompressible and heat transfer has a minimal influence on this problem, the energy equation is omitted. Therefore, this study is conducted by solving the Navier-Stokes equations without the energy equation.  
In the 1980s, [PERSON] studied flow through the orifice using the \(k\)-\(\epsilon\) turbulence model and algebraic stress turbulence model, and the simulation results had less than a 2% difference from empirical results [17]. Relevant studies [12; 13; 18; 19] have also demonstrated that it is reasonable and reliable to conduct numerical simulations of orifice flow using the \(k\)-\(\epsilon\) turbulence model and standard wall functions, which is the approach adopted in this study.  
To verify the reliability of this research method, the experimental results and 2D/3D simulation results of an orifice flow example were compared. In reference [15], an experimental study was conducted on a cylindrical orifice connected to a large chamber downstream. The inlet pipe diameter was 10 mm, the orifice diameter was 2 mm, and the orifice length was 10 mm. We applied the same conditions and reached the same conclusion that three cycles of the input sinusoid were sufficient for conditions to stabilize. The relationship between impendence amplitude \(M_{\mathrm{I}}\) and oscillation frequency \(f\) are plotted in Figure 4, along with reference data for comparison.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology
### Optimization Process  
The last step in the implemented SBO methodology is to solve the optimization problem, which is a multi-objective one, as we want to maximize aerodynamic performance in terms of the lift-to-drag ratio (\(C_{L}/C_{D}\)) and minimize structural mass while ensuring that the flutter speed (\(V_{\text{flutter}}\)) is far from the cruise speed (\(V_{\text{cruis}}\)). Mathematically, the optimization problem can be stated as follows:  
\[\begin{array}{ll}\text{minimize}&f(-C_{L}/C_{D},\text{ mass})\\ \text{with respect to}&x=(\text{AR},\ \Lambda,\ GJ,\ \alpha)\\ \text{subject to}&c=V_{\text{cruisie}}-(V_{\text{flutter}}/1.5)\leq 0\end{array}, \tag{1}\]  
where \(f\), \(x\), and \(c\) denote the classical notation for objective functions, the design variable set, and constraints, respectively. The factor 1.5 provides a safety factor against flutter, a standard in aerospace engineering [35]. Since this is a multi-objective problem and the run time of the surrogate model is inexpensive, a gradient-free optimizer was chosen to better explore the design space, namely, the implementation of Non-dominated Sorting Genetic Algorithm II (NSGA-II) [36] in pygmo [37]. However, as NSGA-II is an unconstrained optimization algorithm, a penalty was added to the objective function when the flutter constraint is not respected.  
In this work, the population size, mutation rate, and crossover rate were adjusted to improve the resulting Pareto front while ensuring a good balance between the computational cost, hypervolume, and ranking. It is worth mentioning that hypervolume measures the space occupied by solutions in the target space, while ranking helps select solutions based on levels of non-dominance [36].  
After obtaining the Pareto fronts with the initial design variable boundaries (Table 1), a significant discrepancy was noted in some results. A concentration of solutions was found near the lower limit for the sweep angle and torsional stiffness, as can be observed in Figure 4, suggesting that extending these limits might produce better solutions. In addition, surrogate models showed high prediction errors for high aspect ratios.  
Figure 4: Distribution of optimization solutions along design variable boundaries for both objectives: \(C_{L}/C_{D}\) maximization (**above**, in green) and mass minimization (**below**, in red). Units of mass are given in kg.  
To solve this problem, the boundaries of the design variables were extended and the dataset was augmented, focusing particularly on the critical aspect ratio interval. An iterative analysis and modification of the dataset show a systematic approach to addressing optimization challenges, emphasizing the importance of identifying weaknesses in the model and making necessary changes.
## 3 System Configuration
### Mid-Infrared Tunable Lasers
#### 3.1.1 Quantum Cascade Lasers  
QCL, first demonstrated by [PERSON] et al. in 1994 [46], emits by intersubband transitions between energy levels inside superlattice quantum wells rather than the material bandgap energies in conventional lasers. The most attractive distributed feedback (DFB) QCLs were commercialized in 2004. Now, many commercial providers offer cw- and RT-operated QCLs in different configurations ranging from Fabry-Perot devices, to DFB resonators, to external cavities-based (EC-) QCLs, as well as high-power devices (with watts) in the infrared to terahertz spectral region. QCLs are attractive for infrared countermeasure, metrology, high-resolution spectroscopy, and chemical sensing applications [7; 47; 48; 49; 50; 51; 52; 53].  
QCLs enjoy a broad gain profile of hundreds of wavenumbers and a narrow linewidth about 0.5 MHz by providing monochromatic feedback, i.e., DFB and EC [54]. The natural linewidth can be as low as a few hundred hertz. The emission wavelength of DFB-QCLs can be tuned only a couple of wavenumbers, whereas EC-QCLs can provide hundreds of wavenumbers of coverage with drawbacks of slow mechanical speed, instability, alignment of multiple optical components, and highprice. Broadband QCLs have been also built by an array of DFB QCLs with closely spaced emission wavelengths and fabricated monolithically with wavelength coverage of several micrometers [55]. QCL arrays enjoy broad tuning and high spectral resolution, which opens up a wide range of new possibilities for fast, compact, and mechanically robust solutions with high customizability [56].  
Overall, QCLs have been greatly improved, but many challenges remain, such as intervalley scattering, heat removal from the core region, and interface scattering, which limit the performance of QCLs, especially at short wavelengths [57]. Although InP-based QCLs emitting at wavelengths of 3-4 \(\upmu\)m have been demonstrated recently [58; 59], they are still not commercially available.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 4 Simulation Results
### Mi  
Figure 6 compares the numerical results of (16) and (33). The average mutual information represents the maximum data rate, which could be achieved using infinitely long ergodic ideal code (capacity achieving code). Channel code has an inherent diversity capability with a diversity order that is equal to the minimum code distance and thus, with the ideal channel code, the array gain dominates the performance. This can be observed in Figure 6. Due to the loss in array gain, our proposed scheme had poor MI performance compared to the OFDM system and the corresponding SNR loss ranged from 2\(\sim\)5 dB. This loss was mainly on account of the power penalty of the ZF precoder and hence, it could be remedied by using alternative precoders, such as DPC, BD-SLNR or MMSE-SLNR [37; 38].  
It is worth noting that \(K\) users had \(NK\) samples altogether at the output of the ADC units in the conventional OFDM system, while this number was \(N\) for the proposed system. In other words, the samples in the proposed system were much more informative. This can be observed in Figure 7.  
Figure 6: The comparison of average mutual information. (**a**) \(M=2\), \(N=64\); (**b**) \(M=4\), \(N=64\); (**c**) \(M=2\), \(N=256\); (**d**) \(M=4\), \(N=256\).
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 2 Elastoplastic Representation of Mechanical Properties
### Elastic Parameters  
As shown in Figure 1a, the conventional triaxial compression stress-strain curve of a rock can be divided into four stages, defined by three characteristic stresses. Initially, during the elastic stage before the dilatational stress \(\sigma_{d}\), corresponding to the crack's initial compaction, linear elastic deformation, and stable propagation stages, the overall deformation can be considered as linear elastic. Next, in the hardening stage after \(\sigma_{d}\), corresponding to the accelerated crack propagation stage, the overall deformation exhibits plastic yielding. Following this, in the softening stage after the peak stress \(\sigma_{f}\), corresponding to the post-peak crack damage stage, the overall strength rapidly declines. Finally, in the residual stage after the residual stress \(\sigma_{r}\), the rock is fully damaged, and the overall deformation can be considered ideally plastic.  
Therefore, assuming that the elastoplastic constitutive model of rock exhibits linear elastic deformation before \(\sigma_{d}\), it can be expressed as follows:  
\[\begin{cases}\epsilon_{1}^{e}=\left[\sigma_{1}-\mu(\sigma_{2}+\sigma_{3}) \right]/E\\ \epsilon_{2}^{e}=\left[\sigma_{2}-\mu(\sigma_{1}+\sigma_{3})\right]/E\\ \epsilon_{3}^{e}=\left[\sigma_{3}-\mu(\sigma_{1}+\sigma_{2})\right]/E\end{cases} \tag{1}\]  
where \(\epsilon_{1}^{e}\), \(\epsilon_{2}^{e}\), and \(\epsilon_{3}^{e}\) are the first, second, and third principal elastic strains, respectively, and \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are the first, second, and third principal stresses, respectively. Under conventional triaxial compression, \(\epsilon_{2}^{e}=\epsilon_{3}^{e}\) and \(\sigma_{2}=\sigma_{3}\) are the constant confining pressures. The elastic parameters \((E,\mu)\) are also constants. By differentiating Equation (1), we obtain the following:  
\[\begin{cases}\mu=-\Delta\epsilon_{2}^{e}/\Delta\epsilon_{1}^{e}=-\Delta \epsilon_{3}^{e}/\Delta\epsilon_{1}^{e}\end{cases} \tag{2}\]where \(\Delta\) denotes the increment. Due to the assumption of linear elastic deformation before \(\sigma_{d}\), \((E,\mu)\) are taken as the secant modulus and corresponding Poisson's ratio at \(\sigma_{d}\) on the stress-strain curve, respectively.
###### Abstract  
Intense solar radio bursts (SRBs) can increase the energy noise and positioning error of the bandwidth of global navigation satellite system (GNSS). The study of the interference from intense L-band SRBs is of great importance to the steady operation of GNSS receivers. Based on the fact that intense L-band SRBs lead to a decrease in the carrier-to-noise ratio (\(C/N_{0}\)) of multiple GNSS satellites over a large area of the sunlit hemisphere, an intense L-band SRB detection method without the aid of a radio telescope is proposed. Firstly, the valley period of a single satellite at a single monitoring station is detected. Then, the detection of SRBs is achieved by calculating the intersection of multiple satellites and multiple stations. The experimental results indicate that the detection rates of GPS L2 and GLONASS G2 are better than those of GPS L1 L5, GLONASS G1, and Galileo E1 E5. The detection rate of SRBs can reach 80% with a flux density above 800 solar flux unit (SFU) at the L2 frequency of GPS. Overall, the detection rate is not affected by the satellite distribution relative to the Sun. The proposed detection method is low-cost and has a high detection rate and low false alarm rate. This method is a noteworthy reference for coping with interference in GNSS from intense L-band SRBs.  
global navigation satellite system; solar radio bursts; carrier-to-noise ratio; detection rate +
Footnote †: journal: Sensors