esa-sceva/satcom-chunk-collection huggingface dataset:
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 3 **Scanners**
However, this paper proposes a new adaptive multimodal image fusion strategy based on the combination of variational mode decomposition (VMD) and local energy maxima (LEM) to address the challenges mentioned above. The highlights of the proposed method are as follows:  
1. VMD is an adaptive decomposition scheme that decomposes the images as band-limited sub-bands called intrinsic mode functions (IMFs) without introducing boundary distortions and mode-mixing problems. Indeed, the band-limited sub-bands characterize the edge and line features of source images. This decomposition technique can effectively extract the image features from the other transform methods such as wavelet transform \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Fusion Types** & **Fusion Methods** & **Advantages** & **Drawbacks** \\ \hline \multirow{4}{*}{Spatial domain} & Average, minimum, maximum, morphological operators [11], Principal Component Analysis (PCA) & Easy to implement. Computationally efficient & Reduces the contrast, produces brightness or color distortions. May give desirable results for a few fusion datasets. \\  & & Contrast Pyramid [30], Ratio of the low-pass pyramid [31], Laplacian [19] & Provides spectral information & May produce artifacts around edges. Suffer from blocking artifacts \\ \cline{2-4}  & & Discrete wavelet transform (DWT) [15], Shift invariant discrete wavelet transform (SIDWT) [32], Dual-tree complex wavelet transform (DcxDWT) [20] & Provides directional information & May produce artifacts around edges because of shift variant nature. Computationally expensive and demands large memory. \\ \hline \multirow{4}{*}{VMD} & Curvelet [24], Contourlet [33], Shearlet [34], Nonsubsampled Shearlet transform (NSST) [28] & Provides the edges and texture region & Loss in texture parts, high memory requirement, demands high run time. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief summary of the image fusion methods. (WT), bi-dimensional empirical mode decomposition (BEMD), and empirical wavelet transform (EWT);  
2. The LEM fusion rule extracts the local information from decomposed modes corresponding to two source images pixel by pixel using a windowing operation (3 \(\times\) 3) and then measures the maximum information value. Hence, using the LEM fusion rule, we can preserve the required complementary visual, edge, and texture information in the IMFs;  
3. The proposed approach aims to preserve the information and details of both MRI and CT images into the fused image using VMD and LEM. From visual perception and objective assessment of the fusion results, it is evident that our new image fusion method accomplishes good performance over other existing fusion methods. The remainder of the paper is arranged as follows: The proposed framework and its mathematical representation are presented in Section 2. The detailed analysis of the simulation results and necessary discussion is presented in Section 3. A final note on the proposed method and future directions is given in Section 4.
# Estimate of Passive Time Reversal Communication Performance in Shallow Water
###### Abstract  
Time reversal processes have been used to improve communication performance in the severe underwater communication environment characterized by significant multipath channels by reducing inter-symbol interference and increasing signal-to-noise ratio. In general, the performance of the time reversal is strongly related to the behavior of the \(q\)-function, which is estimated by a sum of the autocorrelation of the channel impulse response for each channel in the receiver array. The \(q\)-function depends on the complexity of the communication channel, the number of channel elements and their spacing. A \(q\)-function with a high side-lobe level and a main-lobe width wider than the symbol duration creates a residual ISI (inter-symbol interference), which makes communication difficult even after time reversal is applied. In this paper, we propose a new parameter, \(E_{q}\), to describe the performance of time reversal communication. \(E_{q}\) is an estimate of how much of the \(q\)-function lies within one symbol duration. The values of \(E_{q}\) were estimated using communication data acquired at two different sites: one in which the sound speed ratio of sediment to water was less than unity and one where the ratio was higher than unity. Finally, the parameter \(E_{q}\) was compared to the bit error rate and the output signal-to-noise ratio obtained after the time reversal operation. The results show that these parameters are strongly correlated to the parameter \(E_{q}\).  
underwater acoustic communication; passive time reversal; \(q\)-function; \(E_{q}\)
## 4 Experiment
### Performance Metrics  
The purpose of this article is to detect various attacks and anomalies based on industrial control data. To measure the performance of the architecture, we will focus on the metrics of accuracy, recall, precision, F1-score, FNR and FPR.  
The accuracy represents the proportion of the number of samples correctly predicted by the classifier out of all samples:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{12}\]  
Figure 4: The heatmap of the correlation coefficients of input variables.  
The recall rate indicates the proportion of the number of samples that the classifier correctly predicts as positive examples to the number of samples of all true examples:  
\[Recall=\frac{TP}{TP+FN} \tag{13}\]  
The precision represents the ratio of the number of samples that the classifier correctly predicts as positive to the number of all samples that are predicted to be positive:  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
The F1 score represents the harmonic mean of precision and recall:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{15}\]  
The FNR represents the ratio of the number of positive samples that the classifier incorrectly predicts as negative samples to the sum of the number of positive samples that are correctly predicted as positive samples and the number of positive samples that incorrectly predicted as negative samples:  
\[FNR=\frac{FN}{FN+TP} \tag{16}\]  
The FPR represents the proportion of the number of negative samples that the classifier incorrectly predicts as positive samples to the sum of the number of negative samples that are incorrectly predicted as positive samples and the number of negative samples that correctly predict negative samples:  
\[FPR=\frac{FP}{FP+TN} \tag{17}\]  
where \(TP\) is the number of positive samples correctly predicted as positive samples, \(FP\) is the number of negative samples incorrectly predicted as positive samples, \(TN\) is the number of negative samples correctly predicted as negative samples and \(FN\) is the number of positive samples incorrectly predicted as negative samples.
# Practical Performance Analyses of 5G Sharing Voice Solution
## 5 Performance Analysis  
### Testing Environment  
The data were processed from field measurements with a large sample size (i.e., 50 VoLTE/EPS-fallback/VoNR calls for each scenario). The performance testing was conducted in the same-area LTE/NR commercial networks. The test area has residential buildings, industrial parks, small commercial streets, and schools, which is a typical general urban test scenario. The network architecture is shown in Figure 1. Network uplink and downlink configuration: 30% loading.  
Figure 9: Handover process from VoNR to VoLTE.  
In the NSA sharing VoLTE feature test, two carriers were configured at operator A as anchor point carriers for operator A and operator B, respectively, broadcasting their respective PLMNs, which were used independently by operator A and operator B. The shared area was configured by the contractor, and the shared NR base station was closed.  
We next verified whether users of different operators could fall back to their respective 4G networks when EPS fallback was adopted as a handover/redirection strategy. In this case, the 5G cells were configured as sharing carriers, and the 4G cells of operators A and B were set up with each other in a neighboring relationship. The 4G cell was configured to cover the same area as the 5G SA cell.  
In the VoNR test, the NR cell supported the VoNR feature, and the LTE cell supported the VoLTE feature. NR cells and LTE cells were working properly and configured with neighbors.  
The carrier configuration is shown in Table 2.  
During the test, for the SIM cards of both sides, data services were contracted by the default bearer QCI9 and kept the priority of SIM cards in each test area consistent. QCI1 was used by both the contractor and the sharer of the voice-dedicated load.  
The results were derived from the UE side through post-processing scripts of the collected logs.
## 3 Data Source and Methods
### Methods
#### 3.2.4 Calibration Information of Spectral Data  
The conversion from raw data (such as Digital Numbers, DNs) to methane concentration in [ppm] mainly involves four steps. First is the radiometric calibration: converting DN values to radiance. Next is the atmospheric correction: removing atmospheric effects from the remote sensing data to obtain the surface reflectance. The third step is spectral analysis: analyzing the changes in reflectance in specific bands to invert the methane column concentration. Finally, there is the unit conversion: converting the inverted methane column concentration to the volume mixing ratio (ppm).  
The Conversion from DN Values to Radiance  
DN values are the raw digital signals recorded by the sensor, usually being dimensionless integers. Radiance is the radiant power per unit area per unit solid angle, typically measured in units of \(W/m^{2}/sr\). The conversion formula is as shown in Equation (7):  
\[L=G\times DN+B \tag{7}\]  
Figure 3: The relationship between the 10 m wind speed and the effective wind speed in the study area. (**a**) Changzhi in Shanxi, China; (**b**) the Permian Basin in the United States.  
In the formula, \(L\) represents the radiance, \(G\) represents the gain, and \(B\) represents the bias. These parameters are typically obtained through laboratory calibration and in-orbit calibration.  
Atmospheric correction is the process of removing the atmospheric effects from remote sensing data to obtain the surface reflectance or atmospheric constituent concentrations. This step is usually performed using an atmospheric radiative transfer model, such as MODTRAN. The formula for calculating reflectance is as shown in Equation (8):  
\[\rho=\frac{L-L_{path}}{E_{sun}\times cos(\theta)} \tag{8}\]  
In the equation, \(\rho\) represents the surface reflectance, \(L_{path}\) represents the atmospheric path radiance, \(E_{sun}\) represents the solar irradiance, and \(\theta\) represents the solar zenith angle.  
Spectral Analysis  
Methane has strong absorption features in certain specific spectral bands (1600 nm and 2300 nm). By analyzing the changes in reflectance in these bands, the methane concentration can be inverted. The inversion formula is as follows:  
\[\Delta X_{CH4}=\frac{S^{\mathrm{T}}C}{S^{\mathrm{T}}S} \tag{9}\]  
In the formula, \(S\) represents the preset methane spectral signature, and \(C\) represents the observed spectrum.  
Methane concentration is typically expressed as a volume mixing ratio (ppm), which represents the number of methane molecules per million air molecules. The unit conversion is as follows:  
\[CH4\ concentration\ (ppm)=\frac{\Delta X_{CH4}}{air\ column\ density} \tag{10}\]  
Here, \(\Delta X_{CH4}\) is the inverted methane column concentration, and air column density refers to the density of the air column. From this, a methane plume map in units of ppm can be obtained.
## 2 Materials and Methods
### Determination of Amino Acid Content  
In our study, we modified the method outlined in the Shimadzu technical note titled \"Auto-precolumn Derivatization for Amino Acid UHPLC Analysis by Using SIL-30 AC\". Amino acid (AA) content in food samples was analyzed by ultrafast liquid chromatography (UFLC) with automated o-phthalaldehyde (OPA)/9-fluorenylmethyl chloroformate (FMOC). Standard solutions of the amino acids alanine (ALA), aspartic acid (ASP), arginine (ARG), cystine (CYS), glycine (GLY), valine (VAL), leucine (LEU), isoleucine (ILE), threonine (THR), serine (SER), proline (PRO), methionine (MET), glutamic acid (GLU), phenylalanine (PHE), lysine (LYS), histidine (HIS), tyrosine (TYR), and tryptophan (TRP) were analyzed (A9781 Sigma-Aldrich, Taufikirchen, Germany). For the analysis, the sample (0, 1 g) was hydrolyzed with 25 mL of 6 M HCl for 24 h at 37 \({}^{\circ}\)C. Prior to injection, all samples were filtered through 0.45 \(\upmu\)m filters. The AAs were separated with UHPLC column YMC-Triart C18 (1.9 pm, YMC Co., Ltd.) using a UFLC instrument Shimadzu Prominence LC20 AD (Shimadzu, Japan) equipped with a fluorescence detector RF-20 Axs and pretreatment function equipped with automatic injector SIL-30 AC (Shimadzu, Japan). Analytical conditions were as follows: mobile phase--solvent A (20 mmol/L potassium phosphate buffer (pH 6.5), solvent B (45/40/15 acetonitrile/methanol/water); flow rate--0.8 mL/min, column temperature 35 \({}^{\circ}\)C; detection--RF-20 Axs Ex. at 350 nm, Em. at 450 nm to Ex. at 266 nm, Em. at 305 nm (9.0 min) [23]. A five-level calibration set was used, covering a concentration range of 9.375-150.00 \(\upmu\)mol/L.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 5 Conclusions and Future Work  
In this paper, a new adaptive data dissemination protocol named TrAD is proposed for both highway and urban scenarios, taking into account both road traffic and network traffic status. The realistic simulation results show that TrAD outperforms three well-known reference data dissemination protocols in both highway and urban scenarios. On the one hand, TrAD is flexible enough to adapt to irregular road topology and considers both road traffic and network traffic status for the transmission delay-contention scheme and sorting technique. On the other hand, TrAD uses  
Figure 16: The data dissemination speed of TrAD, AMD and UV-CAST with 50 m GPS drift.  
Figure 17: The data dissemination speed of TrAD, AMD and UV-CAST with 100 m GPS drift.  
Figure 15: The data dissemination speed of TrAD, AMD and UV-CAST with perfect GPS.  
double broadcast suppression techniques to limit data message transmissions. This technique not only performs the broadcast suppression technique in a well-connected network, but also limits the rebroadcast of SCF-agents in disconnected networks. Consequently, TrAD only needs a moderate amount of data message transmissions to achieve high data coverage with low delay. Furthermore, TrAD can alleviate well the impact of GPS drift up to 75 m in a complex traffic environment, e.g., the Clermont-Ferrand scenario. In conclusion, TrAD provides good realistic simulation results in terms of PDR, data message transmission and delay, but our next challenge is to implement TrAD in vehicles and to evaluate its field test performance. Moreover, on the one hand, we will investigate the security and privacy issues to improve the TrAD protocol and we know that this will be a big challenge to overcome [37; 38]. On the other hand, how to implement a standard large scale field test of IVC protocol is also still an open research issue.  
This work has been sponsored by the French government research program \"Investissements d'avenir\" through the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01), by the European Union through the program Regional competitiveness and employment 2007-2013 (ERDF-Auvergne region), and by the Auvergne region.  
We would like to thank [PERSON], [PERSON] and [PERSON] for revising the manuscript of this paper, and [PERSON] and [PERSON] for creating the realistic scenarios.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
###### Abstract  
Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.  
dynamic properties; objective functions; evolutionary algorithm; regularization; noisy conditions +
Footnote †: journal: Applied Sciences
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 2 Structure and Production of Pectins  
### Structure  
Pectin compositions and structures are strongly dependent on the pectin source, developmental stages of plants, and extraction conditions. Pectin is composed of D-galacturonic acids (Gal\(p\)A) \(\alpha\)-(1,4) linked to form a backbone interrupted by (1,2)-linked \(\beta\)-L-rhannose (Rha\(p\)) [13]. Indeed, they encompass a very complex group of polysaccharides covalently linked to each other and the most abundant classes are homogalacturonan (HG) and rhamn I (RG-I). Minor components are substituted galacturonans which include rhamn II (RG-II), xylogalacturonan (XGA), and apiogalacturonan (AGA). The latter has been reported only in aquatic plants (Figure 1). In dicots, fenites are ester linked to arabinose and galactose residues in pectin. On the backbone, a proportion of the carboxyl groups can be methyl esterified, while a certain number of short chains composed of galactose (Gal\(p\)), arabinose (Ara), xylose, and glucuronic acid (Glc\(p\)A) might be present as side chains (hairy regions). Acetyl groups can esterify Gal\(p\)A at C2 and/or C3 positions, giving a degree of acetylation (DAC) (especially in sunflower or beet pectins) (Figure 2). Mono- or divalent ions can neutralize carboxylic groups of pectins. The pectic chains, in a solid state or solution, have a helical conformation [6,14].  
Figure 1: Pectin structure [15,16].
# An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
## 2 Review of Corresponding Algorithms
### Review of the SM Principle and Corresponding Algorithm  
The model space in which includes input-output vector pairs is defined as \(\Theta\). A upper bound of estimated error is \(\gamma\). The criterion of set-membership is to seek the optimization subject to  
\[|e(n)|^{2}\leq\gamma^{2}. \tag{8}\]  
When \(\hat{\mathbf{w}}(n)\) does not belong to \(\Theta\), the problem of solving optimization of the SM-PNLMS can be described as  
\[\min\|\hat{\mathbf{w}}(n)-\hat{\mathbf{w}}(n-1)\|_{2}^{2}\ \ \text{s.t.}\ \ d(n)- \mathbf{x}^{T}(n)\hat{\mathbf{w}}(n)=\gamma. \tag{9}\]  
The updating equation of the SM-PNLMS is  
\[\hat{\mathbf{w}}(n)=\hat{\mathbf{w}}(n-1)+\frac{\mu_{\text{SM}}\mathbf{x}(n) \mathbf{Q}(n-1)e(n)}{\mathbf{x}^{T}(n)\mathbf{Q}(n-1)\mathbf{x}(n)+\varepsilon _{\text{SM}}}, \tag{10}\]  
where  
\[\mu_{\text{SM}}=\left\{\begin{array}{ll}1-\frac{\gamma}{|e(n)|},\text{if}\,| e(n)|>\gamma\\ 0,\ \ \text{otherwise}\end{array}\right.. \tag{11}\]  
Herein, the matrix \(\mathbf{Q}(n-1)\) is the same as Equation (4). The role of \(\varepsilon_{\text{SM}}\) in Equation (10) is the same as that of \(\varepsilon\) in Equation (3).
###### Abstract  
The Solar System Boundary Exploration (SSBE) mission is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as a super long space exploratory distance, a super long flight time in orbit, and a significant communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. Traditional navigation schemes have been unable to provide high-accuracy autonomous intelligent navigation for the probe independent of the ground. Therefore, high-accuracy intelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE probe. The probe of the SSBE is disturbed by multiple sources of solar light pressure and a complex, unknown environment during its long cruise operation while in orbit. In order to ensure the high-accuracy position state and velocity state error estimation for the probe in the cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar/solar and target planetary Doppler velocity measurements is proposed. The reinforcement \(Q\)-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of state and observation noise error covariance parameters. The federated extended Kalman filter (FEKF) based on the \(Q\)-learning (QLFEKF) navigation algorithm is proposed to achieve high-accuracy state estimations of the autonomous intelligence navigation system for the SSBE probe cruise phase. The main advantage of the QLFEKF is that \(Q\)-learning combined with the conventional federated filtering method could optimize the state parameters in real-time and obtain high position and velocity state estimation (PVSE) accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSE navigation accuracy of the federated filter integrated based the Q-learning navigation algorithm is improved by 55.84% and 37.04%, respectively, demonstrating the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher navigation accuracy and is able to satisfy the demands of autonomous high accuracy for the SSBE cruise phase.  
solar system boundary exploration; X-ray pulsar; solar/target planetary Doppler; Q-learning FEKF; intelligent integrated navigation; cruise phase +
Footnote †: journal: Remote Sensing  
1202416465[[https://doi.org/10.3390/rs16234465](https://doi.org/10.3390/rs16234465)]([https://doi.org/10.3390/rs16234465](https://doi.org/10.3390/rs16234465))