esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 3 A Three Step MUSIC-Taylor-Based Localization Approach  
This section presents the proposed localization algorithm, which combines the MUSIC and Taylor-series techniques. The algorithm consists of three key steps: (1) Coarse localization using the MUSIC-based JAEDE algorithm; (2) Enhancement of the first step results using a Taylor-series-based approach, which helps mitigate the searching grid constraints introduced by the previous step; (3) Achieving precise localization with a single BS based on the estimated angles and delays obtained from the JAEDE process.
# Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool
## 2 ANN-Based System Identification of an Irrigation Main Canal Pool
Therefore, the synaptic weights and biases of the NARX-ANNs were adjusted by the algorithm [76]:  
\[x_{k+1}=x_{k}-\left[J^{T}(x_{k})J(x_{k})+\lambda_{k}I\right]^{-1}J^{T}(x_{k}) e(x_{k}) \tag{9}\]  
where, \(J\) is the Jacobian matrix, \(J^{T}J\) is the Hessian matrix, \(e\) is the error vector of ANN between the real output and the estimated ANN output, \(I\) represents the identity matrix of appropriate size, \(k\) is the number of iterations, and \(\lambda_{k}\) is the learning coefficient that is automatically updated based on the error at each iteration to secure the convergence. The FIT crossover performance index was used to quantify the degree of precision of the obtained models. It is defined by the expression [69]:  
\[FIT=\left\{1-\frac{\|y_{i}-\hat{y}_{i}\|^{2}}{\|y_{i}-mean\ (y_{i})\|}\right\} \times 100\% \tag{10}\]  
where \(y_{i}\) are the observed (measured) data of the downstream end water level, \(\hat{y}_{i}\) are the estimates of the downstream end water level developed by the model, and \(mean(y_{i})\) is the means of the downstream end water level. The simplest procedure to evaluate the quality of the derived ANN model is to train it with the training dataset and validate its performance with different test datasets, e.g., with the validation and testing data sets [77]. The validation data set is used to improve the model performance by fine-tuning the model, while the testing data set is used to provide an unbiased assessment of the final model fit [41]. Validation results of these models with the validation and training data sets are given in Figure 6, which show that the two models adequately describe the dynamic behavior of the water distribution in the canal pool under study with FIT performance indices of 91.0% for the NARX-ANN- based model and 94.0% for the TD-NARX-ANN-based model. The third model is linear. It was obtained by applying the step response method [69] and is represented by the following transfer function with time delay:  
\[G_{p}(s)=\frac{K_{p}}{\tau_{p}s+1}e^{-Ls}=\frac{0.835}{250s+1}e^{-300s} \tag{11}\]  
where \(K_{p}\) is the static gain, \(\tau_{p}\) is the time constant, and \(L\) is the time delay. For linear system identification the Matlab system identification toolbox was used. Figure 7 illustrates the validation results of the linear model (11) with the estimated nominal parameters with a FIT performance index of 87.0%, which indicates that (11) is a suitable model since models with FIT greater than 80% are considered adequate for process control [69]. The FIT indexes obtained with the three models demonstrate the better performance of NARX-ANN-based models due to their ability to model the non-linear dynamic behaviors. Figure 6: Validation results of the NARX-ANN and TD-NARX-ANN-based models with the validation and testing data sets. Figure 7: Validation results of the obtained linear mathematical model.
## 2 Materials and Methods
### Data Collection and Model Calibration
#### 2.3.1 Solid-Liquid Partition Experiment  
Hydrocarbon adsorption to soil particles was assessed in batch assays, using serum bottles of 70 mL total volume. The assays were performed in quadruplicate. Hexadecane (C16:0) (ACROS ORGANICS, 99%) was chosen as a model compound due to its relative abundance in crude oil (up to 50% of the hydrocarbon fraction) [17; 36] and its intermediate molecular weight (226.44 g/mol) [17]. Soil was collected from an agricultural site in Barcelos, Portugal, at approximately 1.5 m depth using hand augers. Soil samples were kept at 4 \({}^{\circ}\)C, thoroughly homogenized through a 2 mm sieve and characterized. The soil presented a moisture of 2.5 \(\pm\) 0.05% (\(w/w\)), an organic matter content of 0.28 \(\pm\) 0.004%, pH of 5.2 \(\pm\) 0.2 (in water) and a silt loam texture from 25% clay, 65% silt and 8.5% sand.  
In each bottle, a 1:1 soil-water mixture was prepared [37] with 20 g of dry soil and 20 g of distilled water. Additionally, 100 \(\upmu\)L of sodium azide (0.2%, \(w/v\)) was used as a microbial activity inhibitor [38]. Different amounts of hexadecane (2.5, 5, 10, 25 and 50 mg) were added with a glass syringe. These corresponded to the following concentrations, expressed relatively to the amount of soil added: 0.125 g/kg, 0.25 g/kg, 0.5 g/kg, 1.25 g/kg and 2.5 g/kg. Blank assays without hexadecane were also prepared. All bottles were closed with Viton rubber stoppers and were kept at 20 \({}^{\circ}\)C, 200 rpm, for 7 days. This time period was defined based on preliminary tests carried out to guarantee that adsorption equilibrium was reached. Afterward, the liquid and solid phases were separated by decantation. The liquid phase was acidified at pH 2.0 with HCl and preserved at 4 \({}^{\circ}\)C until further hydrocarbon analysis. The hexadecane present in the solid phase was immediately extracted and quantified.
###### Abstract  
Ultrasound images are corrupted with multiplicative noise known as speckle, which reduces the effectiveness of image processing and hampers interpretation. This paper proposes a multiplicative speckle suppression technique for ultrasound liver images, based on a new signal reconstruction model known as sparse representation (SR) over dictionary learning. In the proposed technique, the non-uniform multiplicative signal is first converted into additive noise using an enhanced homomorphic filter. This is followed by pixel-based total variation (TV) regularization and patch-based SR over a dictionary trained using K-singular value decomposition (KSVD). Finally, the split Bregman algorithm is used to solve the optimization problem and estimate the de-speckled image. The simulations performed on both synthetic and clinical ultrasound images for speckle reduction, the proposed technique achieved peak signal-to-noise ratios of 35.537 dB for the dictionary trained on noisy image patches and 35.033 dB for the dictionary trained using a set of reference ultrasound image patches. Further, the evaluation results show that the proposed method performs better than other state-of-the-art denoising algorithms in terms of both peak signal-to-noise ratio and subjective visual quality assessment.  
Keywords: ultrasound; speckle reduction; medical image processing; sparse representation; K-singular value decomposition; dictionary learning; B-mode imaging +
Footnote †: journal: Medical Image Processing
# On Efficiency of Two-Degree-of-Freedom Galloping Energy Harvesters with Two Transducers
## 1 Introduction
Research [23] demonstrates that a GEH composed of two oscillators coupled by a nonlinear magnetic interaction can exhibit a lower critical speed. Furthermore, an appropriate selection of the distance between the magnetic segments can contribute to an increase in the system's efficiency. A similar coupling, but in reference to a single-degree-of-freedom system, was discussed in [24]. This approach resulted in a tristable system, which exhibited an efficiency over 35% greater than that of a standard GEH. According to the literature, enhancing the energy extraction efficiency of a GEH can be also achieved by extending its mechanical structure with an additional degree of freedom. In [25], two variants of GEHs with two degrees of freedom (GEH2 Ds), differing in the location of the applied aerodynamic force, were compared to a single-degree-of-freedom device. Experimental comparisons of the electric voltage generated by the variants were presented in [26] and extended to numerical studies of systems with up to three additional masses in [27]. Comprehensive analytical studies of the voltage generated by a GEH2D, considering the possibility of two different vibration modes, were presented in [28]. A two-mass system, whose segments are magnetically coupled, was discussed in [29]. It was demonstrated that such a system can exhibit both a reduced critical speed and enhanced efficiency. The literature review clearly indicates that extending the mechanical structure to include an additional degree of freedom is seen as a potential way to increase the efficiency of a GEH. However, the literature does not discuss the fact that within the family of two-mass systems, three device variants can be distinguished, differing in the placement of the electromechanical transducer: it can be located between the stationary base and the first mass, between the masses, or in both of these places. The aim of this work is therefore to analytically examine the efficiency of all three GEH2D variants, compare them, and assess whether any of them can exhibit greater efficiency than the device of basic design.
# Spatiotemporal Mechanical Effects of Framework-Slope Systems Under Frost Heave Conditions
## 3 Results and Discussion
### Effect of Frost Heave on Shear Strength of Soils  
Based on a triaxial shear testing system, strength parameter tests were conducted on natural and frozen specimens. An axial strain-controlled loading procedure was implemented to obtain peak deviator stress datasets under consolidated undrained conditions for four standard confining pressures. Following the Mohr-Coulomb strength criterion, Mohr's circles representing four standard stress states were constructed in a two-dimensional stress space. The specimens exhibited strain-softening behavior in their stress-strain curves. By measuring failure stresses at confining pressures of 100 kPa, 200 kPa, 300 kPa, and 400 kPa, the shear strength envelope was plotted using MATLAB R2022b. Figure 4 illustrates the Mohr's circles and strength envelope for specimens with a moisture content of 12.6% under varying confining pressures.  
Table 1 summarizes the strength parameters under both conditions. In the natural state, the soil retained its primary structure, with particle interaction governed mainly by mineral surface friction, yielding a cohesion of 41.2 kPa and an internal friction angle of 30.9\({}^{\circ}\). In the frozen state, the formation of ice crystals enhanced interparticle bonding and densified particle packing. Additionally, ice infill in the pores formed a composite ice-soil cementation network [25]. As a result, the cohesion increased significantly to 72.5 kPa and the peak friction angle rose to 42.7\({}^{\circ}\). These findings confirm that the shear strength of the soil during the frozen phase significantly exceeds that in the natural state. The quantified strength parameters obtained from the triaxial tests serve as key inputs for the multi-field numerical modeling of slope behavior under coupled thermal and hydraulic conditions.  
Figure 4: _Cont._  
Figure 3: Stress-strain diagram of the specimen under different circumferential pressures. Specim shear strength envelope: (**a**) natural state; (**b**) frozen state.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Evaluation
#### 6.1.2 Average End to End Delay  
Average end to end delay of the comparing protocol is calculated using Equation (13) and is plotted in Figure 6. ESDR performs better than its counterparts in terms of average end to end delay. Packet segregation causes significant decrease in delay. ESDR put comparatively lesser forwarding overhead on nodes and hence these nodes cause less retransmission and delay. It is also important to note that delay in DBR is lesser than EEDBR as the later selects forwarders on the basis of energy. Hence a node with lesser depth but more energy can become forwarder involving more number of nodes in forwarding process, which eventually accumulates into larger transmission delays. Delay  
Figure 5: Network lifetime.  
comparison between different types of nodes in ESDR is given in Figure 7. Delay of normal packets is calculated as the sum of delay from a normal node to nearest courier node and the delay that would have been suffered by an acoustic signal from that courier node to surface sink. Average end to end delay suffered by normal packets is much more than the delay sensitive packets. Very critical packets are forwarded using higher priority values in forwarding functions so they suffer minimum average delay. Critical packet delay remains in between very critical and normal packets. Moreover as the delay in each type increases as the nodes start die out and large distance communication becomes more prominent.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Data Center Network Requirements and Challenges
#### 3.2.2 Energy Consumption  
The high economic impact of DCNs regarding energy efficiency has been attracting considerable attention. The required energy for an effective operation usually results in higher operational expenditure (OPEX) costs. For instance, it has been observed that DCNs consume about 10-20% of the entire information technology power of the sites [72,74]. Consequently, substantially energy-efficient interconnections are required for sustainable higher network traffic with relatively less energy consumption [74].
## 3 Results  
### Group Analysis--Number of Functional Links and Their Pre- and Post-Session Location  
Figure 3 displays the number of FC links for \(G(\rho,r)\) according to the studied frequency bands and thresholds. Overall, regardless of the thresholds' choice, the same tendency can be observed for the group's adjacency matrix, by means of the number of functional links shown in Figure 4, which tended to increase (decrease) for the higher (lower) frequency ranges. Indeed, results in Figure 3 indicate that FC patterns are more consistent across subjects for the higher frequency bands, with no common functional links being found in the \(\alpha\) range. Furthermore, results in Figure 3 also indicate that, with exception of \(\beta_{1}\) for some \((\rho,r)\) combinations, FC patterns were more consistent across subjects prior to the intervention.  
Figure 3: Number of functional links in the group’s adjacency matrix, \(G(\rho,r)\). Threshold values indicate the persistence of functional links among the recordings’ time (\(r\)) and their strength (\(\rho\)), and they were set at \(\rho=0.30,0.40,0.50\) and \(r=0.55,0.65,0.75\). Pre- and post-intervention data are displayed as blue and red bars, respectively.  
Figure 4: Functional connections in the group’s adjacency matrix for all threshold values under study. Blue, red and green edges indicate functional connections that were present only during the first RS-recording, the second RS-recording, and both, respectively. Plots are shown for the \(\beta_{1}\) (**A**), \(\beta_{2}\) (**B**) and \(\gamma\) (**C**) frequency bands.  
Figure 4 displays the FC links for \(G(\rho,r)\), for the three frequency bands that presented a non-empty group adjacency matrix, for both the pre- and post-session conditions. Following the color scheme in Figure 3, functional connections present only during the pre-intervention recording are shown in blue, whereas the ones found only at the post-session recording are displayed in red. Connections that were present at both instances are shown in green. These results allow complementing the findings in Figure 3 by providing a notion that the functional reorganization following the AO+MI-VR practice not only generally results in a lesser number of common links, but it also shifts their locations to more posterior nodes.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 2 Overview of Existing KF RAIM  
Both fault detection test statistics and PLs of KF RAIM presented in [16] are briefly discussed in this section for ease of understanding of the subsequent developments.
## 3 Sensitivity Optimization  
### Finite Element Modelling  
In this section, the optimization of the magnetic eddy current sensor was carried out by simulations on Comsol Multiphysics. The problem of imposing an alternating current field on a DC bias magnetic field is governed by two different sets of equations. A two-step approach of the magnetic transient study was adopted to optimize the sensitivity of the sensor while achieving a smaller probe size.
## 3 Materials and Methods
### Analytical Performance  
The optimized LLE procedure was validated in terms of precision, linearity and sensitivity based on the peak areas of heptanal, octanal, nonanal, decanal, hexanoic acid, heptanoic acid and octanoic acid used as external standards. The standard solutions were extracted using the optimized method. Precision was calculated and expressed as the RSD of six replicates of an aqueous stock standard solution with a concentration of 0.01 \(\mu\)L/mL (interday precision), followed by repeating the interday precision the next day (intraday precision). The linearity was determined by evaluation of the regression curves of the standard peak areas versus the concentration and expressed as the squared determination coefficient R\({}^{2}\). The linear ranges were obtained by creating the calibration curves using six sequential dilutions of the working standard solutions. Sensitivity expressed as LOD and LOQ was calculated based on the signal-to-noise ratio (S/N). LOD was defined as the lowest concentration with a S/N ratio of 3, whereas the LOQ used a S/N ratio of 10.
## 2 Theory and Analysis
### The Classification and Identification Method  
Based on the discussion presented in Section 2.1, three types of radiation emission components can represent the composition of the internal sources of the equipment. Thus, the classification and identification of the equipment spectrum can be transformed into the classification and identification of these three types of radiation emission components.  
Figure 4 shows the specific steps involved in classification and identification through the algorithm based on radiation emission components described in this paper. In the characteristic process, the dissimilarities between all the data of the spectrum can be verified based on the radiation emission components corresponding to the equipment. Then, the classification of the different equipment based on the spectra can be realized by clustering analysis based on radiation emission components. Finally, according to the classification results and clustering features, the identification thresholds of each class of dissimilarities can be extracted. In the identification process, the radiation emission components extracted in the characteristic process are used to characterize a new spectrum and then verify the  
Figure 3: The electromagnetic test spectrum diagram of a typical piece of equipment and the extraction of emission components.  
dissimilarity with the standard equipment spectrum found in the characteristic process. Finally, by using the identification threshold, the spectrum of a specific piece of equipment can be easily identified.  
No matter in classification or identification, the most important thing is to establish the similarity measurement of radiation emission components.
# Design of the Input and Output Filter for a Matrix Converter Using Evolutionary Techniques
## 4 Output Filter
#### Output Filter Optimization  
In this subsection, the problem of the output filter is solved using DE, optimizing the performance by means of the elements that make it up. The output filter shown in Figure 5 contains the elements \(C_{o}\) and \(L_{o}\). Considering the damping \(\zeta_{o}\) and the resonant frequency of the filter \(f_{out0}\), therefore the design variables are as in Expression (32):  
\[\mathbf{x}=\{C_{o},\ L_{o},\ \zeta_{o},\ f_{out0}\}^{T} \tag{32}\]  
Expression (33) is obtained by representing the design vector in terms of the optimization problem,  
\[\mathbf{x}=\{x_{1},\ x_{2},\ x_{3},\ x_{4}\}^{T} \tag{33}\]  
The objective function to minimize corresponds to the amplitude response of the output filter in the frequency behavior, declared in Equation (34);  
minimize:  
\[\begin{split} f(x_{1},x_{2},x_{3},x_{4})&=-20 log |H_{1}(j\omega_{1})|_{\omega_{1}=x_{4}}\\ &\quad-20 log|H_{2}(j\omega_{2})|_{\omega_{2}=x_{4}+500}\\ &\quad-20 log|H_{3}(j\omega_{3})|_{\omega_{3}=x_{4}-500}\end{split} \tag{34}\]  
subject to Expressions (35) to (41):  
\[h_{1}(x_{1},x_{2},x_{3},x_{4})=x_{1}-\frac{1}{(2\pi x_{4})^{2}x_{2}}=0 \tag{35}\]  
\[h_{2}(x_{1},x_{2},x_{3},x_{4})=x_{3}-\sqrt{\frac{r_{i}^{2}x_{1}}{4x_{2}}}=0 \tag{36}\]  
\[h_{3}(x_{1},x_{2},x_{3},x_{4})=x_{2}-\frac{1}{2\pi\sqrt{x_{2}x_{1}}}=0 \tag{37}\]  
\[1\times 10^{-6}\leq x_{1}\leq 6\times 10^{-6} \tag{38}\]  
\[1\times 10^{-6}\leq x_{2}\leq 1\times 10^{-2} \tag{39}\]  
\[0.01\leq x_{3}\leq 0.9 \tag{40}\]  
\[1000\leq x_{4}\leq 4000 \tag{41}\]The expression \(H_{i}(j\omega_{i})\) in Equation (34) is defined by Equations (43) to (44):  
\[H_{i}(j\omega_{i})=\sqrt{\left[G_{1i}\right]^{2}+\left[G_{2i}\right] ^{2}}\hskip 14.226378 pti=1,2,3 \tag{42}\] \[G_{1i}=\frac{\frac{1}{x_{2}^{2}x_{1}^{2}}-\frac{(\omega_{i})^{2} }{x_{2}x_{1}}}{\left(\frac{1}{x_{1}x_{2}}-(\omega_{i})^{2}\right)^{2}+\left( \frac{r_{a}\omega_{i}}{x_{2}}\right)^{2}}\hskip 14.226378 pti=1,2,3\] (43) \[G_{2i}=\frac{\frac{-r_{a}\omega_{i}}{x_{2}^{2}x_{1}}}{\left( \frac{1}{x_{1}x_{2}}-(\omega_{i})^{2}\right)^{2}+\left(\frac{r_{a}\omega_{i}} {x_{2}}\right)^{2}}\hskip 14.226378 pti=1,2,3 \tag{44}\]  
In order to test the proposed methodology, it was applied to the design of a converter with a nominal power of \(P_{n}=746\) W, with a maximum variable output frequency of \(f_{out0}=480\) Hz, switching frequency of \(f_{s}=1200\) Hz, and a variable voltage up to \(117\) V.