esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a novel method based VMD and GBDT for ANC. The proposed method decomposes different types of noise data by VMD and obtains the approximate entropy of each IMF. The clustering analysis is carried out on the output of VMD, based on the PCA dimension reduction method and k-means++ method. Then, different GBDT regression learners are built for different noise types and their structural parameters are optimized to achieve high-performance active noise control for different noise types. As shown by the simulation analysis, the result shows the VMD method could extract the inherent characteristics of different noises and greatly improve the clustering effect of different noise types. Meanwhile, the PCA is also used to extract the features of each IMF of noise, thereby further enhancing the clustering effect of k-means++. In addition, since the GBDT regression learner is built for every IMF of noise separately, high-performance ANC for different types of noise is acheived. Compared with ANC methods using a LMS algorithm and RLS algorithm, the proposed method has a better noise reduction effect. Four simulation schemes were conducted to verify the effectiveness of the proposed method for ANC. The simulation results revealed that the algorithm proposed in this paper can meet the actual needs of practical engineering applications on the whole. Therefore, the proposed method provides sound technical support for ANC of multiple noise sources.  
[PERSON], [PERSON] and [PERSON] contributed to conceiving the research idea and developed the algorithm and procedure. [PERSON] and [PERSON], established the model, performed the calculations and wrote the manuscript. [PERSON] helped with data analysis, code debugging and experimental data processing. [PERSON] helped in editing the manuscript and in the modification the English grammar of the manuscript. [PERSON] and [PERSON] contributed some useful suggestions and were involved in the preparation of the manuscript. [PERSON] and [PERSON] reviewed the manuscript and supervised the whole project. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Technical Foundation Program grant number JSZL2019601C006. Project is supported by the Technical Foundation Program of the Ministry of Industry and Information Technology of China (Grant No. 327).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 15: The ANC simulation results of proposed methods and several other methods.
## 1 Introduction  
Measurement and detection of the magnetic field is of great importance in many branches of science. Areas such as biomagnetism require an ultra-sensitive magnetometer where the magnetic field to be measured is significantly lower than the geomagnetic field. Detailed discussion and application of this magnetometer can be found in previous work [1; 2; 3] and the references therein.  
Until recently, a superconducting quantum interference detector (SQUID) was considered to be the most sensitive magnetometer for many applications, where the sensitivity was reported to be in the femttesla (fT) region [4]. However, the need for cryogenic cooling and calibration as well as its indirect measurement of the magnetic field makes SQUID less attractive for many applications. As a result of these stringent working conditions and running costs, much work have been carried out to develop and improve alternative magnetometers, such as optical and atomic magnetometers, where the sensitivity was reported to match [5; 6; 7] and in some cases surpass that of SQUID [8; 9].  
One of the techniques that has been utilized for developing and creating a sensitive magnetometer is the creation of a coherent dark state in a multi-level quantum system and measurement of its response due to the presence of an external magnetic field [10; 11; 12; 13; 14]. The sensitivity of such a magnetometer is approaching that of SQUID, and therefore it has many potential applications, especially in the field of magnetocardiography (MCG), where the magnetic field to be detected is a few picoteslas (pT) [15].  
There are different techniques for creating a coherent dark state. One such method is the use of a single diode laser and an acousto-optics modulator (AOM). A sensitive magnetometer based on this method offers many advantages over other types of modulation; these include the creation of bichromatic coherent beams outside the laser source where the beams are equally affected by environmental perturbation to the laser source while the laser linewidth is kept at a minimum.  
Despite these advantages, the use of AOM as a method for modulation presents some technical challenges, and it introduces some undesired artifacts to the observed signal. Thus, one must overcome and eliminate these disincentives when a sensitive magnetometer based on this method is to be created. Consequently, in the following, we address technical challenges and artifacts using this method and report on the realization of a magnetometer based on a dark state using a single laser and an enhanced optical-AOM setup.
## 3 System Description  
The correct performance of the whole system relies on the operation of the modules attached to each solar cell in order to collect the relevant information and send it to the _Monitoring Center_. These modules are supplied from the PV panel itself and, therefore, must guarantee low consumption levels. A detailed description of the operation of these modules and an explanation of the communication protocol follow.
# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 3 The Proposed Methodology of ECFAR for Burst Detection and Pipeline Fault Diagnosis
### Leakage Detection for Pipeline with One-Versus-All Multiclass Support Vector Machine  
On the other hand, AE signals supply intrinsic information about mechanical characteristics of the object. To increase the reliability of the pipeline feature extraction method, traditional features were considered together with typical statistical features. For the AE burst features, the peak amplitude, duration, hit counts, and Measured Area under the Rectified Signal Envelope (MARSE) energy of the burst segment signal were calculated. In addition, statistical features such as root mean square, entropy, kurtosis, and skewness of the signal were also extracted from the time-domain. The extracted features were stored in n-dimensional matrix data structures. Afterward, to classify the features into respective classes, a one-versus-all multi-class support vector machine (OVA-MCSVMs) was utilized [28,29]. This strategy constructs \(m\)-SVMs as a sequence of binary classifiers, where the \(n^{th}\)-SVM classifier, \((n=1,2,\ldots,m)\), is trained against all the other \(m-1\) classes. In the testing stage of this approach, a classifier is considered to be a winner if it gives the decision output with maximum value. The obtained decision boundaries for an example of four classes are illustrated in Figure 3. For each classifier, the SVM finds the largest margin-width hyperplane which separates the two classes in the n-dimensional feature space. For instance, consider a given training set of sample-  
Here, \(l_{i}\) and \(l_{j}\) are two Lagrangian multipliers, \(C\) is a penalty coefficient, and \(\phi{(f_{i})}^{T}\phi{(f_{j})}=K(f_{i},f_{j})\) is the kernel function.  
The SVM was trained on a number of samples representing the normal state and leaks of various sizes in the pipeline monitoring system. This classifier machine could not only enhance the capability of distinguishing between classes, but could also accurately recognize the current state of a pipeline. According to specific engineering problems, the Gaussian radial basis kernel (RBF) was selected as the kernel function, and the hyperparameter of the SVMs could be obtained by the grid search algorithm corresponding to the maximum classification accuracy.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CH\({}_{4}\) Combustion
#### 3.2.2 Effect of Excess Air Coefficient \(\alpha\) on NO Production  
This section focuses on the effect of NH\({}_{3}\)/CH\({}_{4}\) combustion on NO emissions under different excess air coefficients. As shown in Figure 4, the emission of NO from combustion changed little when the excess air coefficient was greater than 1, but decreased significantly when the excess air coefficient was less than 1.  
Each NH\({}_{i}\) radical has two subsequent reactions: oxidation to NO or reaction with NO to N\({}_{2}\). The determination of the NO/N\({}_{2}\) product distribution is primarily influenced by the chemical identity of the NH\({}_{i}\) free radical, which is dependent on the equivalence ratio [36]. As the excess air coefficient decreases, the abundance of hydrogen atoms increases, resulting in a transition of the critical NH\({}_{i}\) from NH\({}_{2}\) to NH to N. This explains why the smaller the excess air coefficient, the less NO was produced by combustion.  
When the excess air coefficient \(\alpha\geq 1\), the effect of the excess air coefficient on NO production was relatively small. When the excess air coefficient was 1.1, the emission of NO increased relative to the chemical equivalent ratio, but the amplitude was small. This may be the reason that, with the increase in the excess air coefficient, the transformation of the critical amino group to NO was intensified, the concentration of hydrogen atom was smaller, and the increase in NO was smaller. However, when the excess air coefficient was 0.9, the conversion and production of NO decreased rapidly compared with the chemical equivalent ratio.  
When the excess air coefficient \(\alpha<1\), on the one hand, the methane in the fuel gas may undergo incomplete combustion to produce CO, and the reduction reaction with NO occurs; on the other hand, the concentration of O/H free radicals decreases during the combustion of ammonia under the condition of rich combustion, and the proportion of H in O/H free radicals increases, resulting in a large number of H free radicals. This promotes NH\({}_{\mathrm{x}}\) (x = 1, 2, 3) free radicals to bind to H and oxidize. The main reactions that occur during this process are as follows:  
\[\mathrm{NH}_{3}+\mathrm{H}=\mathrm{NH}_{2}+\mathrm{H}_{2} \tag{12}\]  
\[\mathrm{NH}_{2}+\mathrm{H}=\mathrm{NH}+\mathrm{H}_{2} \tag{13}\]  
\[\mathrm{NH}+\mathrm{H}=\mathrm{N}+\mathrm{H}_{2} \tag{14}\]  
The NH\({}_{\mathrm{x}}\) (x = 1, 2, 3) bonding reaction is dominant in rich combustion. Under this reaction condition, ammonia is mainly converted into N\({}_{2}\) through NNH without producing NO. Therefore, under the conditions set in this experiment, the emission of NO generated by combustion was very low when the excess air coefficient \(\alpha<1\).
## 3 Results and Discussion
### The Hydrolysates (PH and AH) Characterization
The difference in molecular weight distribution of PH and AH could be attribute to the varied specificities of Protamex and Alcalase for peptide bonds adjacent to certain amino acid residues. The results also indicated that short peptides constituted a dominant proportion in both PH and AH. The molecular weight of the hydrolysates or peptides, after being added to fish mince, showed significant impacts on the formation and growth of ice crystals in frozen mince, and on their interactions with fish myofibrillar proteins [5; 6; 21]. For example, short peptides can easily attach to the surface of ice and inhibit ice crystallization, therefore, acting as effective cryoprotective agents [22].
## 3 Result and Discussion
### Calibration
#### 3.4.3 Validation  
The CO\({}_{2}\) concentration was repeatedly measured in the validation process using similar configurations and procedures, as presented in Section 3.4, in which the sensor-node programming code was updated with the calibration correction factor. Table 5 shows the measurement result and deviation between the sensor node and validator device.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sensor Node (ppm)** & **Validator (ppm)** & **Deviation (ppm)** & **Absolute Percentage Deviation (\%)** \\ \hline
1751 & 1539 & \(-\)212 & 13.78 \\
1808 & 1597 & \(-\)211 & 13.21 \\
1853 & 1646 & \(-\)207 & 12.58 \\
1903 & 1692 & \(-\)211 & 12.47 \\
1949 & 1747 & \(-\)202 & 11.56 \\
2005 & 1804 & \(-\)201 & 11.14 \\
2051 & 1850 & \(-\)201 & 10.86 \\
2098 & 1900 & \(-\)198 & 10.42 \\
2152 & 1950 & \(-\)202 & 10.36 \\
2196 & 1999 & \(-\)197 & 9.85 \\ \hline \multicolumn{3}{c}{Average Deviation} & \(-\)204.2} \\ \multicolumn{3}{c}{Average Percentage Deviation} & \(11.62\) \\ \hline \end{tabular}
\end{table}
Table 4: CO\({}_{2}\) measurement results before calibration.  
Figure 11: CO\({}_{2}\) compensation factor calculation.  
Based on Table 5, the average percentage deviation of measurement after calibration was 0.50%. This proved the close correlation between the developed sensor-node and reference-device measurement results.
# Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
###### Abstract
Portfolio optimization is an actively researched question in scientific domains such as combinatorial optimization [13], operations research [14], data science [15], and the application of quantum computing [16]. Not only the academic community is focusing on this topic, but it is also a core business of banks and financial advisers and the combination of portfolio optimization with quantum techniques is addressed by numerous companies and consultants [17; 18]. Current approaches of solving portfolio optimization with annealing techniques (see [11; 19; 20; 5; 11]) exhibit, amongst others, the following limitations:  
* Limited amount of assets to choose from;
* Use of naive investment strategies for the calculation of future returns. These approaches also try to solve portfolio optimization as a whole, which limits the possible problem sizes significantly, as current quantum hardware is not advanced enough yet to tackle bigger problems. Thus, we present a new workflow combining a classical preprocessing step with a modified QUBO model for portfolio optimization that is able to solve significantly larger optimization problems. In our modified QUBO model, we enable an investor to place an arbitrary amount of money into an arbitrary number of assets, which was not possible previously. The solutions to our QUBO model tell investors how to split their capital amongst available strategies in order to reach their goals (i.e., achieve a certain return and minimize risk). We evaluate our QUBO model via quantum annealing (QA), simulated annealing (SA), and digital annealing (DA). This paper is organized as follows. First, we provide the reader with some preliminaries: in particular, we recall the foundations of portfolio optimization (Section 2), quantitative investing (Section 3), the QUBO model, and simulated and quantum annealing (Section 4). In Section 5 we give an overview of portfolio optimization in the field of quantum annealing. In Section 6 we present our approach to portfolio optimization followed by an evaluation in Section 7. The conclusion together with some discussion is given in Section 8.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spatial Domain-Based Methods
#### 2.1.1 Grayscale Gradient-Based Methods  
Grayscale gradient function is one of the commonly used image sharpness evaluation functions, which mainly evaluates by calculating the difference between adjacent pixels on the image to establish the evaluation function [28; 29]. Classical methods include the Brenner function [30], the energy gradient function [31], Laplacian function [32], Tenengrad function [33] and so on. In addition to the classical evaluation methods based on gray gradient mentioned above, other novel methods have also been studied. [PERSON] et al. [34] used the maximum gradient and the variability of gradients to predict the quality of blurry images in a highly consistent way with subjective scoring. [PERSON] et al. [35] proposed a blur evaluation algorithm for no-reference images based on discrete orthogonal moments, where gradient images are divided into equal-sized blocks and orthogonal moments are calculated to characterize the image shape and obtain sharpness metrics. [PERSON] et al. [36] evaluated the edge sharpness by calculating the grayscale values of the eight directions of the pixels.  
Most of the grayscale gradient-based methods are less computationally intensive and have high real-time performance but are susceptible to noise interference, which affects the accuracy of the evaluation.
# Timed-Release Silica Microcapsules for Consistent Fragrance Release in Topical Formulations
## 3 Results and Discussion  
### Synthesis of Sol-Gel Microcapsules with Cationic Emulsifier Suitable for Skin  
#### 3.1.1 Selection of Skin-Suitable Emulsifier for Synthesizing Fragrance Microcapsules  
Silica microcapsules were synthesized via a sol-gel reaction using a cationic emulsifier-mediated soft template [10]. The emulsifiers considered for the preparation of fragrance-loaded silica microcapsules included DPDAC (70 wt.% active of Microcare Quat EQG), PEG-2 dimeadowfoamamidoethylmonium methosulfate (Meadowquat HG-70), and behentrimonium methosulfate (Miconium BTMS50). These emulsifiers were evaluated as alternatives to cetyltrimethylammonium bromide, which is often used in previous studies for topical applications but is too toxic for cosmetic use on the skin [36]. To ensure the safety of these alternatives, they were selected from NIH PubChem and the Skin Deep(r) Cosmetic Database, administered by the Environmental Working Group (EWG) [37; 38; 39; 40]. The safety data for each emulsifier are collated in Table S1. Although cetyltrimethylammonium bromide and behetrimonium methosulfate facilitated encapsulation, their high toxicity to the skin precludes their use. Of the rest cationic emulsifiers, PEG-2 dimethyldoioleoylammonium methosulfate was found to be the least toxic to the skin. However, it proved unsuitable for encapsulation reaction. Therefore, DPDAC was identified as the optimal emulsifier, providing both safety and effective silica reactions for microencapsulation (Table S1).
# Finite Element Analysis of a Novel Approach for Knee and Ankle Protection during Landing
## 4 Discussion
Another important limitation in the study was that the two ends of the brace were tied to the skin around the thigh and shank, whereas, in reality, the semi-rigid brace at the ankle joint was connected to the participant's shoes, and the spring component at the knee joint was attached to the hip via rigid connecting rods.
# Adoption of Multiphase and Variable Flux Motors in Automotive Applications
## 3 Analysis of Performance of VF and MP Motors  
In this section, different electric motors designs implementing multiphase and variable flux technologies are compared by using FEM simulations. The considered motor designs are the following:  
* Three-phase motor.
* Multiphase motor: dual three-phase motor (DTP).
* Variable flux (VF) motor: variable flux memory motor (VFMM).
* Multiphase VF motor: combination of DTP winding and VFMM rotor.  
On the one hand, the DTP motor was selected among the MP motors, as it has the benefits of multiphase and three-phase motors and it is a potential candidate for EVs, as mentioned in the literature review. On the other hand, the VFMM was selected as VF motor, as it is the most similar to a conventional permanent magnet motor and is easier to make a fair comparison between them. However, the problem of large magnetising and demagnetising currents is not covered in this article.  
To calculate the current amplitude and angle that corresponds to each working point, the motor flux map was built for each motor in the MotorCAD software. The model was built with 7 current amplitude values (0:50:300 Arms), 19 current angle values (0:5:90\({}^{\circ}\)), and 180 rotor positions each electric cycle to calculate iron and magnet losses for the efficiency maps accurately.  
To perform the comparison between the four motors, the following key performance indicators were evaluated:  
* Maximum torque capability: following the MTPA curve of the motors, the maximum torque capability of each motor was calculated at 5000 rpm.
* Maximum power at maximum speed: considering the voltage and current limits, the maximum power at maximum speed of 15,000 rpm was computed.
* Torque ripple: the peak-to-peak torque ripple amplitude with respect to the average torque value was computed in percentages. The torque ripple was computed in 4 characteristic working points over the maximum envelope, which are listed below.
* Efficiency and power losses: efficiency maps were compared in the entire working region. In addition, the power loss distribution was calculated in the four selected working points.  
As mentioned above, four characteristic working points were selected to compare the torque ripple and losses of the motor (illustrated in Figure 4):  
* WP1: 5000 rpm and 300 Nm. High torque in the MTPA region.
* WP2: 5000 rpm and 90 Nm. Low torque in the MTPA region.
* WP3: 15,000 rpm and 100 Nm. High torque in the FW region.
* WP4: 15,000 rpm and 30 Nm. Low torque in the FW region.
## 3 Results  
### Validation of MODIS Biophysical Data  
The accuracy assessment of MODIS biophysical data compared ground measurements with LST, GPP, and reflectance data (Figure 3). Our results indicate that the MODIS estimates of biophysical variables were consistently accurate compared to the flux towers (LST and GPP) and ground-based reflectance (albedo and EVI). The reflectance data reached the highest correlation in both areas: JPRB (R\({}^{2}\) = 0.97) and ANP-CSP (R\({}^{2}\) = 0.91). The R\({}^{2}\) for LST (R\({}^{2}\) > 0.76) and GPP (R\({}^{2}\) > 0.82) was significant for both study areas.  
Figures 4 and 5 show the time series used for validation. In both study areas, we had more than one year data available for LST analysis (Figure 4a,b) and less than one year for the GPP (Figure 4c,d). In general, the estimated MODIS values for the two variables (LST and GPP) were slightly higher than the values measured in the field. Figure 5 compares the average reflectance between LBA field measurements and the MODIS data, considering six collections in the ANP-CSP area and three collections in the JPRB area. Unlike the LST and GPP variables, the MODIS reflectance values were lower than those measured in the field but maintained the same pattern.  
Figure 3: Validation using Large-Scale Biosphere-Atmosphere data for: (**a**) Land-Surface Temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, (**b**) Gross Primary Productivity (GPP) from the MODIS data, (**c**) MODIS reflectance in the Araguaia National Park and Cantão State Park (ANP-CSP), and (**d**) Ji-Paraná River Basin (JPRB).