esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] [14] designed separate and compact sliding mode controllers for straight-line path following and roll reduction in random waves. [PERSON] et al. [15] studied proportion-derivative (PD) controllers based on the self-tuning neural network algorithm and the results indicate that the stabilizer fin control for roll reduction and the rudder control for track keeping in the seaway would be beneficial. A similar PD autopilot with RRR optimized by GA is presented in [16]. A model predictive control controller was proposed for the integrated path following and roll motion control problem by [PERSON] et al. [2]. A two-time scale decomposition method was used to analyze and design the rudder roll stabilization by [PERSON] et al. [17]. It is worth noting that, in the aforementioned study, the path following mainly focuses on the decrease of the tracking error, while, in the navigation practice of merchant ships, the track-belt is usually regarded as the reference standard for economic and safe manipulations. The parameter scheme of multi-objective control design is usually not optimal, and multi-objective optimization (MO) methods have been applied to solve many engineering problems [18]. One of the most prominent Pareto evolutionary algorithms is a fast and elitist multi-objective genetic algorithm (NSGA-II) [19; 20; 21; 22; 23]. Inspired by previous research, a multi-purpose autopilot with path following, course and anti-rolling control is discussed in this paper. The main contributions of this work can be summarized as following:  
* An improved line of sight (LOS) principle with course-keeping in track belt (LOS-CT) is firstly proposed to guide the ship in field of marine practice, which can reduce the rudder movements greatly by keeping the ship in track-belt rather than following the exact route. * The uniform asymptotic stability proofs of the developed concise robust control design for the course and roll are given. The control parameters have obvious physical significance and can be determined easily. * NSGA-II is recommended to solve the restrictions caused by the model perturbation, external disturbance and the performance trade-off for optimal control solution. The rest of the article is organized as follows: Section 2 presents the analysis and transformation of the nonlinear model of a container ship. Section 3 is devoted to systematic procedures for LOS-CT and concise robust controller design, together with stability proofs. Section 4 provides multi-objective optimization and Pareto-optimal solutions of the control scheme. In Section 5, simulation results and discussion are presented. Section 6 draws the conclusion.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 1 Introduction
### Motivation and Contribution  
In order to address these problems, this paper proposes two DL-based methods mixed with different resampling methods for better tackling the issue of an imbalanced dataset. The existing DL-based methods, especially CNN architectures, have been employed in a wide variety of challenges, and they have proven to be extremely powerful in terms of learning balanced datasets. Their efficacy has not been satisfactorily investigated when tackling imbalanced datasets [30].  
CNNs are types of architectures that contain convolutional blocks and can provide an end-to-end classification algorithm. These blocks are a stack of different layers, namely convolutional layers, pooling layers, and activation functions. The most significant attributes of such models are their learning capacity with fewer parameters and translational invariance concerning the input data. In CNNs, the input data are fed to multiple convolutional blocks, which are named mainly backbone as a whole, and then followed by a sequence of fully connected layers to be classified.  
The training procedure is performed using Focal Loss (FL), which optimizes the abstraction learned by the models to handle complex samples better.  
In particular, the main contributions of this paper are threefold. First, 24 popular imbalanced datasets from KEEL Dataset Repository, breast cancer dataset from KDD Cup, and Z-Alizadeh Sani are chosen. The proposed pipeline is trained and validated 100 times to achieve more reliable results. Second, this paper is the first research work that extensively investigates the most efficient mix of Deep Neural Networks (DNN) with the famous resampling method, SMOTE, for imbalanced data. Lastly, the mixed SMOTE-Normalization-CNN methodology has proved to produce superior results in comparison with related research works in terms of accuracy, precision, recall, Geometric mean (G-Mean), specificity, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Kappa.  
The rest of the paper is outlined as follows: Section 2 provides a brief review of the existing works on imbalanced datasets; Section 3 presents the details of our proposed methodology; Section 4 includes the implementation setup and the evaluation process and then focuses on the results obtained by the proposed methods; Section 5 presents a discussion on comparing our method to the others, and the last section draws a reasoned conclusion and future work.
## 4 Results and Discussion
### Measurements and APoffRCI  
The next stage in the development of the concept for the angular positioning method of a circular cutting insert (APoffRCI) torus cutter was the measurements.  
In order to be able to carry out an angle measurement, i.e., measure the angle of the blade in the base plane under given machining conditions, a reference line must first be marked. This line is applied to a round cutting insert. It passes through the center point of the RCI. In this way, when assembling the tool set (body with inserts), it is possible to position the insert for the first time in such a way that the reference line is parallel from the tool's axis of rotation, as shown in Figure 13. As can be observed in Figure 13b, the marked trace of the cross-sectional effect of the cut layer on the cutting blade is completely within the limits of the cutting blade working angle.  
Subsequently, each successive angular positioning of the RCI was realized according to the model dependencies once the limit value \(VB_{Blim}\) was reached. Machining tests, together with angular positioning, were repeated two more times, obtaining three active segments worn on the cutting edge, as illustrated in Figure 14.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **\(F\)-Value** & \(p\)**-Value** & \(R\) \\ \hline Regression & 1.3271 & 2.0 & 0.6635 & 1867.4383 & 0.0000 \\ \hline Residual & 0.0305 & 86 & 0.0003 & & \\ \hline Total & 1.3577 & 88 & & & 0.90 \\ \hline Adjusted grand total & 0.1675 & 87 & & & \\ \hline Regression of the adjusted total & 1.3271 & 2.0 & 0.6635 & 344.5687 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimation statistics and results of ANOVA analysis of variance.  
Analyzing the results obtained from the digital microscope measurement, it was found that all worn segments were within the cutting blade working angle (Figure 14a). This demonstrates the correctness of both the models developed and the procedure together with the measurement techniques used. These techniques made it possible to measure the maximum defect, that is, the abrasion of the blade material, as shown in Figure 14b. It was found that there were differences in the value of the maximum abrasion between the segments, which may be due to the radial runout of the cutting blade. In this case, further research and clarification of the proposed solution should be carried out. In addition, it was also possible to measure the distribution of clash variation relative to the nominal RCI outline, which, in turn, is shown in Figure 14c. Maximum abrasion values were obtained in the zone of maximum thickness of the cut layer acting on the cutting blade. And, in this case, further research is needed. In conclusion, it can be said that the proposed solution works well not only for angular positioning of the RCI but also for carrying out analysis on wear mechanisms.
## 2 Materials and Methods
### Preparation of the Samples  
There are two methods for adding the modifiers in the asphalt mix: the direct method and the indirect method. For the direct method, the modifier is added directly in the mixing process of the asphalt mix, while for the indirect method, the modifier is first blended with asphalt binder by using a high-speed shear mixer, and then the modified asphalt mixes are prepared with modified asphalt binder. Previous studies showed that the two methods have the same mix effect [40; 41; 42]. Consequently, the direct method was applied to the current research, and the preparation processes of DLFAM are detailed as follows:  
* Aggregates and asphalt were heated to temperatures of 170 \({}^{\circ}\)C and 160 \({}^{\circ}\)C, respectively, till the material temperature was stable.
* The preheated aggregates were mixed at a temperature of 175 \({}^{\circ}\)C in the mixing pot for 90 s.
* Lignin fiber was added to aggregates, and then they were mixed together about 90 s in order to improve the dispersion of fibers in the asphalt mix.
* Asphalt binder (Bitumen) was poured into the mixer and mixed for 90 s.
* Finally, diatomic and mineral filler were added and mixed 90 s to form DLFAM.  
Excluding the modified materials addition, the preparation processes of CAM, LFAMAM, and DMAM were the same as DLFAM. Time and temperature of mixing at each step were similar to those of DLFAM.
# Vulnerability and Seismic Exposure of Residential Building Stock in the Historic Center of Alcamo
## 6 Comparison of Comparant- and Building-Scale Surveys  
Table 5 shows, for the entire historic center, the effect of the behavior modifiers on the evaluation of the vulnerability indexes for the different types estimated with the building-scale analysis (for non-plastered buildings).  
The table shows the statistical parameters (mean, standard deviation, coefficient of variation, lower and upper extremes and width of the variation range, percent change in the mean value, and percent of the width of the variation range with respect to the mean value) of the vulnerability index for each of the CARTIS types. The percentage values of the variation range, ranging from 35.8% for MUR2/3 SCA up to 60.2% for MUR1, show that the influence of modifiers at the building scale is very significant on the individual building. However, it produces a modest average value of \(V_{I}\) for the typological class. Excluding the effect of the regional modifier, the greatest variation in the average value was found for the MUR2/3_CONF typology and was 5.1%, while it was only 0.4% for the MUR1 typology. A comparison with the influence of modifiers estimated with the compartment-scale survey, highlighted by the results shown in Table 2, shows that, in this case, the influence on the \(V_{I}\) value is significant, being equal to 22% for MUR2/3_CONF (excluding the rate due to the regional modifier), standing at values close to 10% for most masonry types and with smaller variations for reinforced concrete types. These circumstances had relevant repercussions on the expected mean damage (vulnerability curve) and damage distribution (fragility curve) estimations as the intensity of the seismic event changes for each building typology; however, the influence was greatly reduced when an overall assessment of the expected damage was performed.  
Figure 6: Distributions of (**a**) behavior modifiers; (**b**) vulnerability indices.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **MUR1** & **MUR2** & **MUR3** & **MUR23\_SCA** & **MUR23\_CONF** & **RC1** & **RC2** \\ \hline
**Most prob. Vuln. Index VI** & 0.873 & 0.740 & 0.740 & 0.616 & 0.511 & 0.644 & 0.484 \\ \hline
**Average prob. Index VI** & 0.870 & 0.753 & 0.770 & 0.643 & 0.537 & 0.653 & 0.494 \\ \hline
**Standard Deviation** & 0.081 & 0.077 & 0.090 & 0.073 & 0.078 & 0.071 & 0.065 \\ \hline
**Coeff. of Variation** & 0.093 & 0.103 & 0.117 & 0.113 & 0.145 & 0.109 & 0.131 \\ \hline
**Min** & 0.653 & 0.530 & 0.560 & 0.456 & 0.331 & 0.504 & 0.344 \\ \hline
**Max** & 1.000 & 1.000 & 1.000 & 0.846 & 0.659 & 0.904 & 0.644 \\ \hline
**Variation Range** & 0.347 & 0.470 & 0.440 & 0.390 & 0.328 & 0.400 & 0.300 \\ \hline
**Variation of av. Value \%** & 0.389 & 1.698 & 4.101 & 4.421 & 5.077 & 1.323 & 2.105 \\ \hline
**Variation range \%** & 60.252 & 36.486 & 40.541 & 36.688 & 35.812 & 37.888 & 38.017 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vulnerability index values estimated by the building-scale survey.
# Influence of Drift on Robot Repeatability and Its Compensation
## 4 Results and Discussion
The heat accumulated in the gearboxes is dissipated in the surrounding aluminium frame, but when the manipulator is not in motion, the frame is not air-cooled, resulting in a local temperature rise on the frame. For this reason, we did not perform heating and cooling evaluation for individual joints and all the joints were measured simultaneously instead. Taken together, the results indicate that the presented compensation system can significantly reduce the effect of the drift. Unlike previous graphs, which presented results for a single TCP-axis, Figure 12 presents the drift difference between non-compensated and compensated robot motion for all TCP axes. The significant increase of repeatability is especially noticeable for the TCP Z-axis (the red line of the graph), however, X and Y-axis are also suitable to compensate, as their differences in repeatability vary considerably. Figure 10: Target 5, working speed TCP Z Drift measurement and prediction. Data recorded during the initial measurement: purple—joint 1 temperature, light blue—joint 2 temperature, yellow—joint 3 temperature, black—joint 4 temperature, orange—joint 5 temperature, dark blue—joint 6 temperature, red dots express the uncompensated TCP Z-axis drift, green dots—TCP Z drift prediction based on MatLab regression learner. Figure 11: Target 5, working speed, compensated TCP Z Drift, measurement with interrupts. Data recorded during the initial measurement: purple—joint 1 temperature, light blue—joint 2 temperature, yellow—joint 3 temperature, black—joint 4 temperature, orange—joint 5 temperature, dark blue—joint 6 temperature, grey trend represents the TCP Z drift with drift compensation applied on measurement that was systematically disrupted. Figure 12: Target 5, working speed, 3 DOF repeatability comparison. Blue dots—TCP X not compensated, blue line—TCP X compensated, green dots—TCP Y not compensated, green line—TCP Y compensated, red dots—TCP Z not compensated, red line—TCP Z compensated. After evaluating all measurements and considering the presented results, the authors consider the proposed methodology to be fully functional. Application of this methodology allows compensating for the effect of temperature on TCP repeatability.
## 3 Results and Discussion
### RSSI Localization  
The data collected from the channel modeling and RSSI localization tests are presented in this section.
###### Abstract  
\(M^{2+}\)N\({}^{4+}\)Nb\({}_{2}\)O\({}_{8}\)-type ceramics (where \(M\) = Mg, Ca, Mn, Co, Ni, Zn and \(N\) = Ti, Zr) are essential for satellite communication and mobile base stations due to their medium relative permittivity (\(\epsilon_{r}\)) and high quality factor (\(Q\times f\)). Although ZnTi\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) ceramic exhibits impressive microwave dielectric properties, including an \(\epsilon_{r}\) of 29.75, a \(Q\times f\) of 107,303 GHz, and a \(\tau_{f}\) of \(-\)24.41 ppm/\({}^{\circ}\)C, its sintering temperature of 1150 \({}^{\circ}\)C remains a significant barrier for integration into low-temperature co-fired ceramic (LTCC) technologies. To overcome this limitation, a strategy involving the partial substitution of Zn\({}^{2+}\) with Cu\({}^{2+}\) and the addition of LiF as a sintering aid was devised for ZnTi\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\). The dual impact of Cu\({}^{2+}\) partial substitution and LiF as a sintering enhancer facilitated the successful sintering of Cu\({}_{0.3}\)Zn\({}_{0.7}\)Ti\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) ceramics at a reduced temperature of 950 \({}^{\circ}\)C using the conventional solid-state reaction method. These ceramics exhibited excellent microwave dielectric properties. Notably, Cu\({}_{0.3}\)Zn\({}_{0.7}\)Ti\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) ceramic with 40 mol% LiF addition demonstrated optimal microwave dielectric properties without any reaction with a silver electrode at a sintering temperature of 950 \({}^{\circ}\)C, yielding \(\epsilon_{r}\) = 32, \(Q\times f\) = 45,543 GHz, and \(\tau_{f}\) = \(-\)43.5 ppm/\({}^{\circ}\)C.  
Keywords:Cu\({}_{x}\)Zn\({}_{1-x}\)Ti\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) ceramics; LiF; LTCC; microwave dielectric properties +
Footnote †: journal: materials
## Material and Methods
### VitB9 Content Measurement  
For vitB9 content measurement, each sample was mixed with the extract buffer, boiled for 10 min, and cooled on ice for 10 min. Next, 20 \(\upmu\)L \(\alpha\)-amylase (40 mg/mL) was added to the mixture, which was incubated at 37 \({}^{\circ}\)C for 30 min. Subsequent parts of the procedure until supernatant collection were previously described [18,19]. The resulting supernatant was subjected to 3 kDa ultra-filtration and then used for high-performance liquid chromatography-mass spectrometry (HPLC-MS). Chromatographic analysis were performed using a 1260 HPLC system (Agilent) with an Akzo Nobel analytical column (Kromasil 100-5 C18, 50 \(\times\) 2.1 mm) at a flow rate of 0.30 mL/min. The mobile phases were 0.1% formic acid in water (phase A) and 0.1% formic acid in acetonitrile (phase B). The mobile phase volumes changed in the following time intervals: 0-2 min (95% phase A, 5% phase B; flow rate: 0.3 mL/min); 2-7.9 min (91% phase A, 9% phase B; flow rate: 0.3 mL/min); 7-9.8-2 min (90.5% phase A, 9.5% phase B; flow rate: 0.3 mL/min); 8.2-11.2 and 11.2-11.4 min (80% phase A, 20% phase B; flow rate: 0.6 mL/min); 11.4-14.4 min (95% phase A, 5% phase B; flow rate: 0.6 mL/min); and 14.4-14.5 min (95% phase A, 5% phase B; flow rate: 0.3 mL/min). An Agilent 6420 triple-quadruple tandem MS system coupled to an electron spray ionization interface was used for mass analyses and quantification of target analytes. The mass spectrometer was operated in positive ion mode. The parameters were optimized for target analyses at a gas temperature of 350 \({}^{\circ}\)C, drying gas flow of 11 L/min, nebulizer pressure of 35 psi, and capillary voltage of 3500 V (+). System operation, data acquisition, and data analyses were performed using the Agilent MassHunter soft ware (Beijing, China, www.agilent.com/zh-cn/promotions/masshunter-mass-spec). We purchased H\({}_{2}\)folate (\(m/z\): 444.2-178, 20 eV), 5,10-CH = THF (\(m/z\): 456-412, 30 eV), 5-CH\({}_{3}\)-H\({}_{4}\)folate (\(m/z\): 460-313, 20 eV), 5-CHO-H\({}_{4}\)folate (\(m/z\): 474-327, 20 eV), and H\({}_{4}\)folate (\(m/z\): 446-299, 20 eV) from Schircks Laboratories (Jona, Switzerland, [[http://www.schircks.ch/](http://www.schircks.ch/)]([http://www.schircks.ch/](http://www.schircks.ch/))).
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 1 Introduction
Considering the limited transmission power of the positioning signal and the complex electromagnetic environment of the metro, the interference signal from the environment poses a threat to the CPIS, which affects the safety of train operation based on the communication-positioning integrated system. Therefore, suppression of interfering signals is very important to the train control system. Interference mitigation techniques are mainly classified into the following categories, including spatial processing techniques, such as array signal processing, adaptive notch filtering (ANF), time-frequency analysis, and subspace decomposition. Spatial processing-based methods use beamforming to direct the main beam to the desired signal, improving the signal-to-noise ratio (SNR) of the received signal [15; 16; 17]. Beams with high directivity depend on the complexity of the equipment and are not suitable for metro train positioning scenarios [18]. ANF is widely used due to its simplicity of implementation. For single-tone interference scenarios, ANF performs well [19; 20; 21]. In the case of chirp signal interference or narrow-band signal interference, the filter cannot accurately separate the interference and real signals, and the filtering performance is degraded. Among the time-frequency analysis methods, wavelet packet coefficient transform (WPTC) [22; 23] and short-time Fourier transform (STFT) [24; 25] show good performance in the case of chirp signals. STFT has lower computational complexity, but the windowing process of STFT results in an inherent trade-off between time and frequency resolution. WPTC overcomes the shortcomings of STFT, but the parameter configuration of wavelet transform needs to be adjusted for interference scenarios. In the subspace projection and decomposition method, the Karhunen-Loeve transform (KLT) is used to orthogonally decompose a high amount of observational data to extract the eigenvalues of specific types of signals [26; 27]. This paper proposes an interference mitigation algorithm for metro train positioning based on Karhunen-Loeve decomposition. The signal is orthogonally expanded to obtain the eigenvalues of the interference signal, and the interference signal is reconstructed, and the reconstructed interference signal is subtracted from the received signal to achieve interference suppression. The remainder of this paper is organized as follows. Section 2 presents the signal model and interference pattern. Section 3 presents the proposed KL expansion-based interference mitigation algorithm. In Section 4, experiments are conducted to compare different methods and to verify the effectiveness of the proposed algorithm. The conclusions and future work are presented in Section 5.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Performance on Individual Distortion Types
The distortion types include additive Gaussian white noise (WN), JPEG compression (JPEG), JPEG2000 compression (JP2K), Gaussian blur (GB), fast fading (FF), pink Gaussian noise (PGN), and global contrast decrements (GCD). The best two algorithms are highlighted in boldface. Figure 9: Median SROCC results of 1000 train-test trials on TID2013 database [39] for DIIVINE [12], BRISQUE [15], GMLOG [19], BIQA-SC-I, BIQA-SC-II and BIQA-SC. There are 24 distortion types in TID2013 [39] database, including additive Gaussian noise (AGN), additive noise in color components is more intensive than additive noise in the luminance component (NC), spatially correlated noise (SN), masked noise (MN), high frequency noise (HFN), impulse noise (IN), quantization noise (QN), Gaussian blur (GB), image denoising (IDN), JPEG compression (JPEG), JPEG2000 compression (JP2K), JPEG transmission errors (JPTE), JPEG2000 transmission errors (J2 TE), non eccentricity pattern noise (NEPN), local block-wise distortions of different intensity (LBD), mean shift (MS), contrast change (CC), change of color saturation (CCS), multiplicative Gaussian noise (MGN), comfort noise (CN), lossy compression of noisy images (LCN), image color quantization with dither (ICQD), chromatic aberrations (CA), and sparse sampling and reconstruction (SSR).