esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Concentration**} & \multicolumn{3}{c}{**Exposure Time**} & **Median Lethal** \\ \cline{3-5}  & & **72 HAT** & **144 HAT** & **216 HAT** & **Time** \\ \hline  & 10\({}^{3}\) & 0.0 \(\pm\) 0.0 c & 10.0 \(\pm\) 5.8 c & 20.0 \(\pm\) 10.0 c & 205.0 \(\pm\) 0.6 a \\  & 10\({}^{4}\) & 0.0 \(\pm\) 0.0 c & 13.0 \(\pm\) 3.8 c & 23.0 \(\pm\) 5.8 c & 203.0 \(\pm\) 1.7 a \\ _F._ & 10\({}^{5}\) & 0.0 \(\pm\) 0.0 c & 13.0 \(\pm\) 12.3 b & 37.0 \(\pm\) 5.8 b & 195.0 \(\pm\) 2.1 b \\ _tonkinense_ & 10\({}^{6}\) & 3.4 \(\pm\) 5.8 b & 20.0 \(\pm\) 10.0 b & 50.0 \(\pm\) 10 a & 177.0 \(\pm\) 0.2 c \\  & 10\({}^{7}\) & 10.0 \(\pm\) 10.0 a & 23.0 \(\pm\) 11.5 a & 67.0 \(\pm\) 11.5 a & 162.0 \(\pm\) 2.8 d \\  & 10\({}^{8}\) & 13.0 \(\pm\) 15.3 a & 33.0 \(\pm\) 15.3 a & 71.0 \(\pm\) 5.8 a & 158.0 \(\pm\) 0.9 e \\ \hline Control & H\({}_{2}\)O + Tergitol Np\({}^{9}\) & 0.0 \(\pm\) 0.0 c & 0.0 \(\pm\) 0.0 d & 2.7 \(\pm\) 1.3 d & 215.0 \(\pm\) 0.2 f \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean mortality (\(\pm\)sd) and Median Lethal Time of _H. _armigen_ adults exposed to _F. _tonkinense_ for 216 h of two season experiments. Means of the same column followed by the same letter are not significantly different (Tukey test, a = 0.05), Median Lethal Time (hours \(\pm\) sd) was estimated by the Kaplan–Meier method (F = 3.893, df = 6, \(p<0.001\)), HAT: Hours After Treatment. F = 2.111, df = 6, 1002, \(p\) \(<\) 0.001; Exposure Time: F = 3.216, df = 2, 1002, \(p\) \(<\) 0.001; Concentration \(\times\) Exposure Time: F = 1.913, df = 15, 1002, \(p\) \(<\) 0.001). Finally, _Fusarium fujikuroi_ Nirenberg (Hypocreales: Nectriaceae) also appeared to induce varying degrees of larval mortality (Table 8). Larval mortality ranged on the 9 th day of the experiment between 17 to 60%. All applied doses differed significantly from the control, with the lower doses of 10\({}^{3}\), 10\({}^{4}\), 10\({}^{5}\) and 10\({}^{6}\) causing mortalities from 17 to 69% by the 9 th day. Concerning the higher doses of 10\({}^{7}\) (73%) and 10\({}^{8}\) (77%), they induced a mortality that proved to be significantly lethal. The main effects and interactions for all factors proved to be significant (Concentration: F = 1.811, df = 6, 1002, \(p\) \(<\) 0.001; Exposure Time: F = 2.647, df = 2, 1002, \(p\) \(<\) 0.001; Concentration \(\times\) Exposure Time: F = 2.913, df = 15, 1002, \(p\) \(<\) 0.001). _Fusarium algeries_ I. Laraba and O'Donnell (Hypocreales: Nectriaceae) also resulted in differing degrees of larval mortality (Table 9). Larval mortality ranged on the 9 th day of the experiment between 10 to 61%. All applied doses differed significantly from the control, and all doses of had produced mortality of 10 to 60% by the 9 th day. The main effects and interactions for all factors proved to be significant (Concentration: F = 3.411, df = 6, 1002, \(p\) \(<\) 0.001; Exposure Time: F = 3.992, df = 2, 1002, \(p\) \(<\) 0.001; Concentration \(\times\) Exposure Time: F = 2.943, df = 15, 1002, \(p\) \(<\) 0.001).
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 4 SDM Communication System Model
### Linear MMSE MIMO Receiver  
The most widely used linear MIMO receiver for SDM systems is based on the design of filter \(\underline{Q}(\omega)\) in Figure 1 to minimize the mean squared error (MSE), which is called a linear MMSE MIMO receiver [28; 37; 39]. Mathematically, the MSE for the linear MIMO receiver under the MMSE criterion is defined as:  
\[\sigma^{2}_{\text{MMSE-LE}}=\text{E}\Big{[}\varepsilon^{H}[n]\varepsilon[n] \Big{]} \tag{16}\]  
where  
\[\varepsilon[n]=\underline{\varsigma}[n]-\underline{\varsigma}[n] \tag{17}\]  
with \(\underline{\varsigma}[n]\) as the output vector of the linear MIMO receiver \(\underline{Q}(\omega)\).  
It is well known that the structure of a linear MMSE MIMO receiver can be divided into a matched filter \(\underline{Q}^{H}(\omega)\) operating in continuous time, a sampler operating at the symbol rate, and a discrete-time equalizer of response \(\underline{W}(\Omega)\), as presented in Figure 2.  
We denote as \(\underline{y}^{\prime}[n]\) the vector of samples after the match filter \(\underline{Q}^{H}(\omega)=\mathcal{F}\{\underline{q}^{H}(-t)\}\) in the receiver and a symbol rate sampling, being \(\underline{q}(t)=\mathcal{F}^{-1}\{\underline{\underline{Q}}(\omega)\}\) defined in (14). Now, we define the sampled impulse response at \(t=nT\) of the convolution of \(\underline{q}(t)\) and its matched filter \(\underline{q}^{H}(-t)\), which represents the equivalent discrete channel response as:  
\[\underline{\underline{g}}[n]=\left.\underline{q}(t)*\underline{q}^{H}(-t) \right|_{t=nT}, \tag{18}\]  
and its discrete Fourier transform pair as:  
\[\underline{\underline{G}}(\Omega)=\sum_{n=-\infty}^{\infty}\underline{g}[n] \cdot e^{-i\Omega n}. \tag{19}\]  
Hence, the optimal discrete-time MIMO equalizer \(\underline{W}_{opt}(\Omega)\) according to the MMSE criterion becomes:  
\[\underline{W}_{opt}(\Omega)=\left[\underline{\underline{G}}(\Omega)+ \underline{I}\cdot\left(\frac{N_{0}}{2}\right)\right]^{-1}, \tag{20}\]  
where we are considering a normalized transmission power equally distributed in each of the \(D\) modes.  
When \(\underline{\underline{G}}(\Omega)\) satisfies the Nyquist criterion for MIMO systems \(\underline{\underline{G}}(\Omega)=\underline{I}\) there is neither ISI nor cross-talk at the matched filter output, further equalization would not be needed, and the optimum linear receiver consists only in the matched filter. However, if such a criterion is not fulfilled, the equalizer \(\underline{W}(\Omega)\) is essential and some SNDR loss at the output will be unavoidable w.r.t. the ideal case.  
Figure 2: SDM communication system model with linear minimum mean square error (MMSE) MIMO receiver.
###### Abstract  
In this paper, we present the design of a compact and highly sensitive microwave sensor based on a metamaterial complementary split-ring resonator (CSRR), for liquid characterization at microwave frequencies. The design consists of a two-port microstrip-fed rectangular patch resonating structure printed on a 20 \(\times\) 28 mm\({}^{2}\) Roger RC3035 substrate with a thickness of 0.75 mm, a relative permittivity of 3.5, and a loss tangent of 0.0015. A CSRR is etched on the ground plane for the purpose of sensor miniaturization. The investigated liquid sample is put in a capillary glass tube lying parallel to the surface of the sensor. The parallel placement of the liquid test tube makes the design twice as efficient as a normal one in terms of sensitivity and Q factor. By bending the proposed structure, further enhancements of the sensor design can be obtained. These changes result in a shift in the resonant frequency and Q factor of the sensor. Hence, we could improve the sensitivity 10-fold compared to the flat structure. Subsequently, two configurations of sensors were designed and tested using CST simulation software, validated using HFSS simulation software, and compared to structures available in the literature, obtaining good agreement. A prototype of the flat configuration was fabricated and experimentally tested. Simulation results were found to be in good agreement with the experiments. The proposed devices exhibit the advantage of exploring multiple rapid and easy measurements using different test tubes, making the measurement faster, easier, and more cost-effective; therefore, the proposed high-sensitivity sensors are ideal candidates for various sensing applications.  
microwave sensors; complementary split-ring resonator; bended; highly sensitive; resonant frequency; Q factor +
Footnote †: journal:  
_Applied_  
_sciences_
## 4 Proposed DM EMI Estimation Method for Interleaved Units  
In this part, time-frequency analytical modeling methods are used for DM EMI noise prediction that is important in the DM EMI filter design in order to fulfill the standard  
Figure 5: Relationship between attenuation requirement and the switching frequency (up to four units interleaved) in (**a**) Band A. (**c**) Band-B. The relationship between a two-stage filter corner frequency and switching frequency (up to four units interleaved) in (**b**) Band A (**d**) Band B for CCM based on the attenuation requirement (1)–(2). The conventional phase shift is considered 360\({}^{\circ}\)/N.  
Figure 6: Input ripple current on the basis of the number of the interleaved converters with \(f_{sw}\) = 37.5 kHz, \(P_{0}\) = 1 kW, and \(L_{boost}\) =1 mH based on PLECS simulations.  
requirements. Additionally, the proposed method is on the basis of the closed-loop input impedance and the double Fourier analysis of the noise source spectrum. The suggested technique characterizes the production emissions of the power converter within the frequency range of 9-500 kHz considering the double Fourier analysis and closed-loop impedance. Extra details about the modeling of DM noise, closed-loop input impedance, and the frequency behavior have been reported in [10] for single-phase non-interleaved boost PFC. The DM noise spectrum of each switch is presented by (6), where it contains a DC offset value, baseband harmonics, carrier group harmonics, as well as sideband harmonics [19]:  
\[\begin{array}{l}u_{s}(t)=\frac{A_{00}}{2}+\sum\limits_{n=1}^{\infty}\left[A_ {0n}\cos(nw_{0}t)+B_{0n}\sin(nw_{0}t)\right]+\sum\limits_{m=1}^{\infty}\left[A_ {m0}\cos(mw_{c}t)+B_{m0}\sin(mw_{c}t)\right]\\ \quad\quad\quad\quad\quad\quad+\sum\limits_{m=1}^{\infty}\sum\limits_{n=-\infty }^{\infty}A_{mn}\cos([mw_{c}+nw_{c}]t)+B_{mn}\sin([mw_{c}+nw_{c}]t)\\ \quad\quad\quad\  
where \(h\) is harmonics order number. Thereby, the LISN input current without EMI filter is obtained by (12):  
\[i_{cnv}(s)=i_{d}(s)i_{L}(s) \tag{12}\]  
The relationship between the LISN input current and EMI receiver voltage should be added in the proposed analytical method to complete it. Hence, the relationship between the input current LISN and EMI receiver branch by considering the EMI filter is given as (13) [10]:  
\[i_{rcc}(s)=\frac{C}{D}i_{cnv}(s) \tag{13}\]  
\(C\) and \(D\) are defined in Equations (14) and (15), [10] as  
\[C=L_{1}L_{2}C_{1}C_{2}s^{4}+R_{2}C_{1}C_{2}(L_{2}+L_{1})s^{3}+C_{1}(L_{2}+L_{1 })s^{2} \tag{14}\]  
\[D=C_{1}C_{2}(R_{2}L_{1}+L_{2}L_{1})s^{4}+C_{1}C_{2}(L_{2}R_{1}+L_{2})s^{3}+(L_ {1}C_{1}+L_{2}C_{2}+L_{2}C_{1}+R_{1}R_{2}C_{2}C_{1})s^{2}+(R_{1}C_{1}+R_{2}C_{ 2})s+1 \tag{15}\]  
In addition, the EMI receiver voltage noise is  
\[U_{meas}(s)=R_{1}i_{rcc}(s) \tag{16}\]
## 1 Introduction
SPA is then applied to the corresponding factor graph to compute the marginals of the desired random variables. The exact implementation of SPA results in a significant computational complexity. To reduce this considerably, we propose the use of pre-defined look-up tables (LOTs) at the receiver. The dimension of LOTs depends on the constellation symbols' dimensionality and the discretization levels of unknown channel parameters. Average signal power at the receiver determines the noise variance. However, when we compensate the reflected components in the means of Gaussain observations, we should remove the power of those components from the noise variance. This can also be accomplished through the provided LOTs for the receiver. We found that the constellation size dictates the quantity of estimated patterns of reflected rays, with only one occurring at each time epoch, similar to additive noise. Therefore, the receiver relies on making hard decisions based on the provided posteriors by the decoder to select the most suitable one among them. Performance evaluations are conducted through bit error rate (BER) curves in both distributed and concentrated pilots scenarios. As none of the channel parameters are known to the receiver, previous works, such as [9], were unable to effectively track these parameters. This limitation highlights the need to modify existing algorithms. For this reason, we introduce a relative solution as the upper bound of the system performance. The relative solution can also serve as a comparison benchmark in this context. Our simulation results prove that by increasing receiver' iterations, the BER curves differ from the relative solution considerably. The paper is organized as follows: Section 2 introduces the discretization strategy for channel parameters, followed by the system model in Section 3. Section 4 discusses the corresponding factor graph and its SPA equations. Receiver implementations are detailed in Section 5, with performance analysis provided in Section 6. Finally, conclusions are drawn in Section 7.
# Active Learning Strategy for Surrogate-Based Quantile Estimation of Field Function
## 4 Applications
### Launch Vehicle Design Problem
#### 4.2.2 Test Case Settings  
The initial input \(\mathcal{U}_{M}\) and output datasets \(\mathcal{X}_{M}\) are defined by a Monte Carlo simulation with \(M=200\) (see Figure 13). For each realization of the input uncertain variables, a multidisciplinary analysis is carried out with optimal control problem solving to determine the output stochastic process realization \(\mathbf{X}(t,\mathbf{u}_{i}),\ i=1,\ldots,M\). The altitude as a function of time is considered as the quantity of interest resulting from the multidisciplinary analysis. In order to account for different trajectory durations depending on the input uncertain variables, the discretization is carried out with respect to a percent of flight from 0% to 100%. The objective of this test case is the determination of the 99% quantile on the altitude as a function of percent of flight. For that purpose, considering the computational cost of one multidisciplinary analysis, 10 new samples are added into the database with the active learning algorithm to improve the estimation of the quantile over the stochastic process.  
To evaluate the uncertainty on the 99% quantile estimation, \(N_{GP}=150\) GP trajectories are sampled and \(\beta=0.025\) is used to define the upper and lower bounds on the quantile estimates in the set \(\mathbb{Q}_{N_{GP}}\). Figure 14 presents the scaled KL models for the centered altitude.  
Figure 14: Scaled KL models for the centered altitude.  
Figure 13: Visualization of the launch vehicle ascent trajectories in 3D due to the uncertain input vector.  
The KL expansion validation is carried out using additional 800 validation samples. In that purpose, the exact multidisciplinary process is executed for the different input uncertain parameter values as defined in Table 2. The output samples \(\mathbf{X}_{\text{o}}^{*}(t,\mathbf{u})\) are used for the validation.  
Using the validation samples, it is possible to determine the residual samples as illustrated in red in Figure 15. The centered altitude samples are represented in blue. From Figure 15, it seems that with 2 modes in KL expansion, the obtained residual samples are non negligible meaning that the KL decomposition is not accurate enough. However, the increase of the number of modes leads to residual samples that becomes negligible validating the KL expansion accuracy.  
Table 3 gives the \(Q2\) factor depending on the number of KL modes in the expansion. As expected from the Figure 15, by considering only 2 KL modes, the decomposition does not provide accurate results. As the number of modes in KL expansion increases, the prediction accuracy converges to 1.0. The level of truncation is a compromise between the number of nodes and the KL expansion accuracy. For the altitude variable, a number of  
Figure 15: Karhunen–Loeve validation with respect to the number of modes: 2 (a), 5 (b), 10 (c), 20 (d) and 40 (e).  
20 modes is a good trade-off between the number of GPs involves in the overall surrogate model and the \(Q2\) factor.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
In Section 4, the compatibility between the designed signals and GNSS signals is assessed using the spectral separation coefficient (SSC) and the degradation of effective carrier-power-to-noise density ratio. Finally, the summary is made in Section 5.
## 2 Materials and Methods  
### High-Frequency Radar  
The array of HF radars has been applied in our study, with an operating frequency of 9.305-9.355 MHz. The number of transmitting antennas at each station is \(\geq\)3, and the number of receiving antennas is \(\geq\)8 to form an antenna array. This array is capable of detecting the flow field over a large area of the sea in real time. Moreover, it is feasible to build a synchronous network between the different radar stations. The two HF radars adopted in this study were deployed at Wanshan Island and Shangchuan Island (Figure 1), both of which were OSMAR-071G high-frequency surface wave radars operating at a nominal frequency of 9 MHz developed by Wuhan University. The software we used for radar data processing is the SeaMonitor for single-station radial flow. The data processing is divided into a pre-processing module, current extraction module, and radial flow merging module [25; 26]. The pre-processing module is mainly to perform channel data validity checks, interference suppression, and channel correction on the basic distance metadata read from the FT1 file, so as to ensure the validity of the processed data and the directional response of the antenna channel to maintain the ideal characteristics. The function of the current extraction module is range-by-distance metadata processing, through spectrum analysis, first-order spectrum separation, azimuth estimation based on the Music algorithm, and other steps, to realize the extraction of radial current velocity. The radial flow merging module aims to combine the results of short-time radial velocity, wind direction, and wave parameters of multiple fields with the median method according to the specified time interval.  
The radar coverage ranged from 112.40\({}^{\circ}\) to 114.70\({}^{\circ}\) E and from 20.20\({}^{\circ}\) to 22.00\({}^{\circ}\) N, covering an area of approximately 40,000 km\({}^{2}\). In the overlapping area observed by the two radars, the longitude and latitude spatial resolution was 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), and the time range was from 12:00 a.m. on 1 March 2020 to 11:50 p.m. on 31 March 2020, with a time resolution of 10 min. Due to the interference of environmental factors, there were missing values in the synthesized current fields. To ensure the reliability of the data, points with a data acquisition rate of more than 90% were selected (Figure 1).
## 2 Literature Review
### Models
Subsequently, the DT model decides its splits (i.e., nodes) upon the split with the lowest Gini value. The greatest advantage of the DT model is that it uses a \"white box\" model: this allows the user to fully interpret the decisions of the model. As a result, the DT model is considered to be very useful within a medical context [22]. However, a great disadvantage of DT is that it typically strongly overfits the training data, which makes the model less robust to unseen observations (i.e., the test data). The overview in Table 1 indicates that it is mainly the ensemble methods of ADA and GB that have not yet been assessed. Ensemble methods use multiple models (i.e., \"learners\"), typically decision trees. These models base their predictions on the majority predictions of each individual model within the ensemble, so-called \"voting\". For instance, RandF is an ensemble method that is constructed by a multitude of decision trees at the training time [23]. The predictive performance of the ensemble models is often better than what would be obtained from a single model alone. However, the interpretability of ensemble methods is often poor when compared with conventional methods, resulting in a \"black box\" model. Furthermore, ensemble models are often less prone to overfit towards the training data, as the multiple models within the ensemble (i.e., \"learners\") make them more robust towards unseen data. Although RandF, ADA, and GB all rely on the principle of multiple learners, there are some key differences. For instance, while RandF trains its learners on a randomly selected subset of the data (i.e., \"bagging\"), ADA includes all features in each decision tree. As a result, ADA is more affected by noise (e.g., unimportant features) [24]. Moreover, ADA includes the principle of \"boosting\". Boosting relies on the principle that during each learning iteration, the learners (i.e., decision trees) that perform bad incrementally receive less votes. This way, the learners that are performing well become more important with respect to the predictions, which typically results in a higher accuracy. Another important difference between the two models is that ADA only uses a depth of one (i.e., two leaves) in each of its decision trees, while in RandF, the optimal number of leaves is decided by the user (often using grid search cross validation). As a result, a disadvantage of RandF is that there is more hyperparameter tuning necessary. Lastly, GB [25] can be considered as a similar model to ADA; both these models rely strongly on the aforementioned boosting principle. However, while ADA only uses a depth of one within its trees (i.e., learners), GB uses a depth of more than one. Typically, the depth is set to a maximum of 8 to 32 leaves.