esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The scope of this study was to explore whether particles of the nanomaterials silver (AgNPs), titanium nitride (TiN), and laponite can be mechanically-released from the food contact surface of polymer-nanocomposites under material stress conditions caused by severe interactions with foods or food simulants including thermal effects as well as solvent based and mechanical stress.  
It was demonstrated that in case of abrasion each nanomaterial can be separated from a dry food simulant matrix and redispersed in a suitable solution for subsequent analysis by AF4/MALLS, AF4/MALLS/ICP-MS (laponite), or ICP-MS (AgNPs and TiN) techniques at detection limits of 16.0 ng/dm\({}^{2}\) for AgNPs, 39.3 ng/dm\({}^{2}\) for TiN, and 12.0 ug/dm\({}^{2}\) for laponite. Assuming a surface to volume ratio of 6 dm\({}^{2}\)/kg of packed food this corresponds to detection limits of approx. 0.1 ug/kg for AgNPs, 0.3 ug/kg for TiN and 71.8 ug/kg for laponite (ug nanomaterial per kg food (ppb)).  
In previous studies it was found and concluded that the nanomaterials investigated in this study cannot migrate based on Fickian diffusion in the polymer. The results of this study confirm that AgNPs and laponite particles are also not released from the nanocomposites' surface under or after severe materials stress conditions. In case of TiN-nanocomposites a release of TiN was detectable at very low levels which were most likely due to abrasion of polymer flakes with incorporated TiN particles and not due to pure TiN particles. In any case, it is most likely that the surface of the composite was severely damaged. However, the measured release of TiN in this case was very low and below 1 ug/kg. Furthermore, the used nanocomposite TiN in LDPE at 1000 mg/kg loading level has the character of a model test sample which will not find application as such in practice.  
In conclusion, based on our previous studies and the results of this study, migration, or release of nanoparticles from polymer nanocomposites is very unlikely under usual and even exaggerated conditions of use. Severe and polymer matrix damaging conditions may lead to physical disintegration of the composite which will not necessarily lead to release of the pure nanomaterial but may cause detachment of host matrix particles with embedded nanomaterial as small composite pieces. The crucial prerequisite for non-migration of a nanomaterial from a polymer composite is that it is fully embedded in and covered by the host polymer or matrix.  
Conceptualization, J.B. and R.F.; Methodology, J.B. and R.F.; Validation, R.F., Investigation, J.B.; Writing--Original Draft Preparation, J.B.; Visualization, J.B.; Supervision, R.F.; and Project Administration, R.F.  
We gratefully acknowledge the financial cosupport of PlasticsEurope and the Cefic FCA (Food Contact Additives) sector group for this study.  
The authors declare no conflicts of interest.
## 4 Experiments
### Evaluation Metrics  
The peak signal-to-noise ratio (PSNR) is a widely recognized metric for evaluating image quality, especially effective in comparing the discrepancies between fully sampled reference images and their processed or reconstructed versions. Within the realm of MRI, PSNR serves as a valuable tool for assessing quality of a reconstructed image, and thus assessing the performance of reconstruction methods. Higher PSNR values indicate a higher signal-to-noise ratio in the reconstructed image, corresponding to a reconstruction method with better suppression of impurities and noise. The PSNR is defined as:  
\[\text{MSE}=\frac{1}{\text{N}}\|x-x\|_{2}^{2} \tag{13}\]  
\[\text{PSNR}\big{(}x^{\prime},x\big{)}=10\text{log}_{10}\,\frac{\max(x^{\prime} )^{2}}{\text{MSE}(x,x^{\prime})} \tag{14}\]  
where N is the number of pixels, \(x\) is the fully-sampled MR images of the auxiliary modality or the target modality, and \(x\prime\) is the reconstruction image and PSNR in decibels (dB). The relative \(l_{2}\) norm error (RLNE) is a standard metric employed in MRI for assessing the accuracy of reconstructed images against the fully sampled images. The RLNE quantifies the relative error between the reconstructed and fully sampled images, providing a measure of how closely the reconstruction process adheres to the original data. The lower the RLNE, the smaller the difference between the reconstructed image and the fully sampled image. The RLNE is defined as:  
\[\mathrm{RLNE}\big{(}x^{\prime},x\big{)}=\frac{\left\|x-x^{\prime}\right\|_{2}}{ \left\|x\right\|_{2}} \tag{15}\]  
The structural similarity index (SSIM) is a metric designed to assess image quality by focusing on the quantification of structural information within images. In the context of MRI, SSIM plays a crucial role in evaluating the performance of image reconstruction techniques, particularly with respect to the preservation of detail. Higher SSIM indicates greater structural similarity between two images, which usually means better image quality. The SSIM is defined as:  
\[\mathrm{SSIM}\big{(}x^{\prime},x\big{)}=\frac{(2\mu_{x}\mu_{x^{\prime}}+C_{1})( 2\sigma_{xx^{\prime}}+C_{2})}{\big{(}\mu_{x}^{2}+\mu_{x^{\prime}}^{2}+C_{1} \big{)}\big{(}\sigma_{x}^{2}+\sigma_{x^{\prime}}^{2}+C_{2}\big{)}} \tag{16}\]  
where \(\mu_{x}\) and \(\mu_{x^{\prime}}\) denote the mean values of \(x\) and \(x\prime\), \(\sigma_{x}\) and \(\sigma_{x^{\prime}}\) denote the local variances, and \(\sigma_{xx^{\prime}}\) denote the local covariance between these two MR images. \(C_{1}=0.03\) and \(C_{2}=0.07\) are the constant relaxation terms.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GNSS & Global Navigation Satellite Systems \\ LOFAR & Low Frequency Array (www.astron.nl/telescopes/lofar) \\ POLFAR & Polish LOFAR Consortium \\ LOFARSW & LOFAR for Space Weather \\ EU & European Union \\ HBA & High Band Antennas \\ LBA & Low Band Antennas \\ ILT & International LOFAR Telescope \\ VLBI & Very Large Baseline Interferometry \\ TEC & Total Electron Content \\ EISCAT & European Incoherent Scatter Scientific Association \\ RO & Radio Occultation \\ TID & Travelling Ionosphere Disturbances \\ ISM & Interstellar Medium \\ RM & Rotation Measure \\ DM & Dispersion Measure \\ SNR & Supernova Remnant \\ S/N & Signal to Noise ratio \\ DSPSR & The Digital Signal Processing for Pulsars package ([[http://dspsr.sourceforge.net/](http://dspsr.sourceforge.net/)]([http://dspsr.sourceforge.net/](http://dspsr.sourceforge.net/))) \\ AGW & Atmospheric Gravity Waves \\ STEC & Slant Total Electron Content \\ IGS & International GNSS Service \\ ROT & Rate of TEC \\ ROTI & Rate of TEC Index \\ IPP & Ionospheric Pierce Point \\ GPS & Global Positioning System \\ GLONASS & GLObal NAvigation Satellite System \\ FORMSAT & Formosa Satellite Mission \\ COSMIC & Constellation Observing System for Meteorology, Ionosphere, and Climate \\ \end{tabular}
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 2 Mvido: A Monocular Vision-Based System for Docking a Hovering Auv
### Resilience to Occlusions and Outliers Rejection  
Just by itself, the color-based detector algorithm proved to be insufficient for a robust later pose estimation because of the false detections, the outliers and the noise in the color detections. On the other hand, partial occlusions of the target implies only a partial detection which would not allow a pose estimation. To overcome these limitations, we decided to develop a filter algorithm in a color-based context. The implemented algorithm allows an adaptive color detection, in the sense that each marker model is updated over time in order to make it more robust against changes in water turbidity conditions and underwater illumination changes. The main goal is to track the state of each of the three markers in the captured images. In our color tracker, we used a probabilistic Bayesian approach and in particular a sequential Monte Carlo technique and we considered two types of constraints related to the target in order to improve the performance of the filter: the geometrical constraints of the target and the constraints of the markers in the color space. The developed solution allowed to improve the performance of the relative pose estimator by rejecting outliers that occurs during the detection process and made it possible to obtain pose estimates in situations of temporary occlusion of the markers.  
For the proposed tracking system, we developed the filters based on a particle filter framework. For the formal model of the particle filter we adopt the one presented on [24] and we choose the resampling method presented in [29]. The Figure 7 presents the scheme we propose for the proposed tracking system. As shown in the Figure 7, the filter block appears between the markers detector block and the pose estimator block. The filter block consists of three particle filters, one filter per each marker. Each particle filter receives from the detector block the position \(u,v\) and the radius \(R\) of the respective marker in the image.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 2 Preliminaries
### Iterative Algorithm  
The M-equation (3) is a non-linear function in general, and there is no efficient way to find the root of the equation. This point will be more problematic when estimating multiple parameters, since one has to solve coupled multivariate equations. A simple iterative method is usually used in robust statistics to find an approximated solution [8]. Let us rewrite Equation (3) as:  
\[0 =\sum_{i=1}^{n}\psi(x_{i}-\mu)\] \[=\sum_{i=1}^{n}W(x_{i}-\mu)\,\left(x_{i}-\mu\right),\]  
where \(W(x)=\frac{\psi(x)}{x}\). This can be put into the form:  
\[\mu=\frac{\sum_{i=1}^{n}W\left(x_{i}-\mu\right)x_{i}}{\sum_{i=1}^{n}W\left(x_{ i}-\mu\right)}. \tag{7}\]  
We start with an initial choice for \(\mu^{(0)}\) and then iterate it according to:  
\[\mu^{(a+1)}=\frac{\sum_{i=1}^{n}W\left(x_{i}-\mu^{(a)}\right)x_{i}}{\sum_{i=1} ^{n}W\left(x_{i}-\mu^{(a)}\right)}. \tag{8}\]  
After several iteration steps (\(a=1,2,\dots,a_{\text{fin}}\)), we stop the algorithm. Alternatively, any stopping rule can be adopted. A common choice for the initial value is the median when estimating the expectation value. As explicitly demonstrated in Section 5.3, this iteration algorithm is efficient.  
We will now summarize the method of M-estimators in practice. First, we choose an appropriate \(\psi\) function to apply. Second, we set the M-equation up by plugging an observed datum. Third, we solve this M-equation for the parameters of interest. One method is to use the above simple iterative algorithm, but other methods can be applied as well. The obtained value is the estimate based on this M-estimator. As a last remark, it is better to apply several M-estimators and compare them. Based on the comparison, we adjust the tuning parameters of the chosen \(\psi\) function. Repeating this procedure, we can obtain a more reliable estimate.
## 2 Methods
### Bidirectional Gated Recirculation Units (BiGRUs)  
BiGRU is an improved model based on GRU, which consists of a pair of forward-backward GRUs, where both GRUs simultaneously learn feature mapping from the forward and reverse directions [38] and extract the sequence features, and then, the two output vectors from the forward and backward GRUs are concatenated to yield the final result. Since the model is able to capture the information from forward and backward directions, it is able to better understand the features and structures in the sequence and thus has higher model efficiency, and the related principle is shown in Figure 4.  
When dealing with classification problems, BiGRU is usually used as a feature extractor to convert sequence data into fixed-length feature vectors, which are then fed into a classifier for classification.  
1. Calculate the hidden state sequence of BiGRU. The hidden state sequences are computed separately for forward GRU and reverse GRU, and then, they are connected to form the hidden state sequences of BiGRU, the mathematical expression of which is shown below: \[\begin{array}{l}z_{t}=\sigma(w_{z}*[h_{t-1},x_{t}]+b_{z})\\ r_{t}=\sigma(w_{r}*[h_{t-1},x_{t}]+b_{r})\\ \widetilde{h}_{t}=\tanh(w_{h}*[r_{t}*h_{t-1},x_{t}]+b)\\ h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\widetilde{h}_{t}\end{array}\] (16) where \(z_{t}\) is an update gate, \(r_{t}\) is a hidden gate, \(x_{t}\) denotes an input at moment \(t\), \(\widetilde{h}_{t}\) represents a candidate state at moment \(t\), \(h_{t}\) stands for a hidden state value at moment \(t\), \(w_{z}\) represents the weight matrices of the corresponding update and \(w_{r}\) represents reset gates, respectively, while \(b_{z}\) and \(b_{r}\) are their biases, respectively.
2. Feature Extraction Process. Pooling operations, such as average pooling or maximum pooling, are performed on the hidden layer, and the computational formulas are given in (13) and (14), to convert the variable-length hidden state sequence into a fixed-length feature vector.
3. Classification of feature vectors. Its mathematical expression is as follows: \[\text{Output}=\text{softmax}(W_{\text{fc}}\cdot\text{ Feature}+b_{\text{fc}})\] (17)  
In the context of neural networks, the symbol \(W_{\text{fc}}\) represents the weight matrix of the fully connected layer, while \(b_{\text{fc}}\) denotes the bias vector, and sofmax is the activation function of the classifier, which is used to compute the probability distribution for each category.  
Figure 4: BiGRU Schematic.
## 2 Materials and Methods
### Frequency and Mode Selection  
Wave propagation in isotropic plates is characterized by an infinite set of Lamb modes, with their dispersion curves shown in Figure 2a. It is widely acknowledged that Mindlin plate theory is applicable when the wavelength of the mode is larger than the plate thickness, or when \(kh<1\), where \(k=f/c_{ph}\) (\(k\)--wavenumber, \(f\)--frequency, \(c_{ph}\)--phase velocity). For accurate thickness mapping, it is crucial to select a mode that is sensitive to thickness variations. The zero-order antisymmetric mode \(A_{0}\) is particularly suited for this purpose, as it is highly dispersive in the low-frequency regime.  
To avoid complex multi-mode propagation, we focus on lower frequencies, where \(kh<0.6\). At the low frequencies used in this work (\(kh\)\(\sim\)0.27), the Mindlin model ([PERSON], 1965) shows excellent agreement with the exact Rayleigh-Lamb theory ([PERSON], 1965), with the phase velocity of the \(A_{0}\) mode differing by only 0.9% at 50 kHz, as shown in Figure 2b. Although the velocity curves of different theories align well up to \(kh\)\(\sim\)1, which is critical for plate thickness reconstruction, it is important to note that these curves provide only velocity information. They do not enable the prediction of scattering effects in plates with varying thickness due to defects, which are crucial for the performance of GWT--the focus of this study.
## 2 Methods
### Signal Detection Method  
The calculation of DPA is based on the 2 \(\times\) 2 contingency table shown in Table 1. If a specific DEC in the database is significantly higher than the background frequency in the entire database and reaches a certain threshold, it is considered a positive signal.  
A represents the number of reports caused by the target drug and the target ADR, B represents the number of reports of the other ADRs caused by the target drug, C represents the number of reports of the target ADR caused by the other drugs, and D Represents the number of reports of the other ADRs caused by the other drugs.  
The PRR method is adopted for ADR signal detection. Based on Table 1, the calculation formula is as follows:  
\[\text{PRR}=\frac{A/(A+B)}{C/(C+D)} \tag{1}\]  
A positive signal is an output if \(\text{PRR}\geq 2\).
###### Abstract  
In this work, a prior-training-free and low-complexity modulation format identification (MFI) scheme, based on amplitude histogram distributions, was proposed and demonstrated, both numerically and experimentally, for autonomous digital coherent receivers. In the proposed scheme, after having performed power normalization, incoming polarization division multiplexed (PDM) signals were classified into QPSK, 8 QAM, 16 QAM, 32 QAM and 64 QAM signals, according to their ratios. Ratios were defined according to specific features of their amplitude histograms. The proposed MFI scheme used only amplitude information. As such, it was insensitive to carrier phase noise. Furthermore, the proposed scheme did not require any prior information, such as optical signal-to-noise ratio (OSNR). The performance of the proposed MFI scheme was numerically verified using 28 GBaud PDM-QPSK/-8 QAM/-16 QAM/-32 QAM/-64 QAM signals. The numerical simulation results showed that the proposed scheme was able achieve a 100% correct identification rate for all five modulation formats when their OSNR values were higher than the thresholds corresponding to the 20% FEC correcting bit error rate (BER) of 2.4 \(\times\) 10\({}^{-2}\). To further explore the effectiveness of the proposed MFI scheme, proof-of-concept experiments in 28 GBaud PDM-QPSK/-8 QAM/-16 QAM, and 21.5 GBaud PDM-32 QAM transmission systems were also undertaken, which showed that the proposed scheme as robust against fiber nonlinearities. To explore the scheme's feasibility for use in practical transmission systems, the computational complexity analysis of the proposed scheme was conducted. It showed that, compared with relevant MFI schemes, the proposed MFI scheme was able to significantly reduce the computational complexity.  
m _ethematics_**2023**, _10_, 472. [[https://doi.org/10.3390/photonics1004072](https://doi.org/10.3390/photonics1004072)]([https://doi.org/10.3390/photonics1004072](https://doi.org/10.3390/photonics1004072))
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 3 State of Health Estimation Model
### Feature Extraction and Data Processing  
Generally, the steps of building a SVM model contains feature extraction, data processing, optimal SVM parameters searching, data training, data prediction, etc.  
Feature variables selection is the key step for training the SVM model. The selected features should reflect the change of voltage curve for different cycle numbers, as discussed in Section 2. In [31], the lithium-ion battery life model is proposed which can be formulated as,  
\[Q_{loss}=B\cdot\exp\left(\frac{-E_{a}}{RT}\right)(A_{h})^{2}, \tag{4}\]  
where \(B\) denotes the pre-exponential factor, \(R\) is the gas constant, \(z\) is the power law factor and \(E_{a}\) is the activation energy in J mol\(-\)i and \(T\) is the absolute temperature. \(A_{h}\) is the \(A_{h}\)-throughput and can present different C-rates when charging the battery. In order to quantify the capacity fading behavior with same C-rate, a variable, i.e., time t during the cycle is introduced. Hence, we can see the loss capacity \(Q_{loss}\) is related with \(A_{h}\), and \(t\). In addition, energy signal \(E_{s}\) can effectively reflect the difference of voltage curve for different SOH [24]. To sum up, we consider three feature values, i.e., \(E_{s}\), \(A_{h}\), and \(t\), that need to be extracted from the CC charging data to track the SOH variation, which can be yielded as,  
\[\left\{\begin{array}{l}E_{s}=\int_{1_{1}}^{t_{2}}V^{2}dt\\ A_{h}=\int_{t_{1}}^{t_{2}}Idt\\ t=t_{2}-t_{1}\end{array}\right., \tag{5}\]  
where \(V\) is the battery voltage, \(I\) is the battery current, and \(t_{1}\) and \(t_{2}\) are time nodes when battery voltage reaches \(V_{l}\) and \(V_{h}\), respectively. Figure 4 describes the variation of these three features across cycles for Cell 2 under different voltage regions. We can observe that, with increase of cycle numbers, the variation trend of these three features is similar. Aiming to easily train the SVM model, the normalization process is imposed in this paper, as shown in Table 2.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 7 Concluding Remarks  
This research addresses the design and numerical testing of an attitude control architecture tailored to precise satellite reorientation and tracking, in two operational scenarios: (a) pointing toward a ground station, for downlink data routing, and (b) pointing toward a companion satellite, for intersatellite data sharing. In each scenario, two distinct phases are identified, i.e., (i) attitude tracking and (ii) reorientation. The related commanded attitude and angular rate are obtained in closed form through geometric analysis. An inertia-free nonlinear attitude control algorithm leads to obtaining a feedback law for the commanded torque components. Actuation is demanded to an array of four single-gimbal control momentum gyroscopes. They are driven through the use of an effective steering law that incorporates singular direction avoidance. Accurate modeling of the actuation devices allows finding the actual torque transferred to the spacecraft, as well as the net motor torque to apply to each device. Both mission scenarios include slewing and tracking phases. Nonlinear attitude control, in conjunction with actuation, leads to obtaining modest values for the misalignment angle and the angular velocity error. The single-gimbal control momentum gyroscopes are effectively steered while remaining within their (safe) operational range, without any saturation, and modest motor torques are needed. Lastly, the numerical simulations, assuming either nominal or nonnominal conditions, prove that the control architecture at hand is rather effective for precise attitude maneuvering, with the final aim of establishing downlink and intersatellite connections for data sharing.  
Figure 31: Average magnitude of the angular velocity error \(\bar{\omega}_{c}[^{\circ}/\mathrm{s}]\) for all simulations of the Monte Carlo campaign.  
Figure 30: Average pointing error \(\bar{\Phi}_{c}[^{\circ}]\) for all simulations of the Monte Carlo campaign.  
**Author Contributions:** Conceptualization, M.P. ([PERSON]), M.P. ([PERSON]) and P.G.; methodology, M.P. ([PERSON]) and P.G.; software, M.P. ([PERSON]); validation, M.P. ([PERSON]) and P.G.; formal analysis, M.P. ([PERSON]); investigation, M.P. ([PERSON]); resources, M.P. ([PERSON]), M.P. ([PERSON]) and P.G.; data curation, M.P. ([PERSON]); writing--original draft preparation, M.P. ([PERSON]); writing--review and editing, M.P. ([PERSON]) and P.G.; visualization, M.P. ([PERSON]); supervision, M.P. ([PERSON]) and P.G.; project administration, M.P. ([PERSON]), M.P. ([PERSON]) and P.G.; funding acquisition, not applicable. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The paper contains all the data needed for reproducing the numerical results.  
**Conflicts of Interest:** The authors declare no conflict of interest.