esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
## 1 Introduction
In the design process, the number of compensating elements determines the design flexibility of the system, and multiple compensating elements will make the system have higher design freedom. As typical higher-order topologies, the resonant LCC-based topologies are extensively used in the primary side of the IPT systems to supply the transmitting coil with a constant current which induces a constant voltage on the secondary side coil. Meanwhile, LCC-based compensation provides a higher degree of design freedom. However, too many compensation components would increase the cost and produce more losses. Using a single resonant element in the secondary series compensation, an LCC-S compensation network can be composed. At the resonant frequency, the resonant elements in the LCC-S topology can both be tuned to achieve constant current output [22] or constant voltage output [36]. In order to prevent excessive compensation components from deteriorating system efficiency and to provide more design freedom to implement the desired characteristics, an LCC-S compensation network is adopted in this paper. The inevitable coil misalignment is the most destabilizing factor affecting the system's performance. The purpose of this paper is to realize the stable charging outputs by the achievement of CC output or CV output. With the aid of an artificial intelligence optimization algorithm, this paper attempts to obtain well-designed compensation parameters for an LCC-S-compensated IPT system, in which the current or voltage output are insensitive to the coupling coefficient and load variation, simultaneously. Based on different modes of a loosely coupled transformer, Section 2 provides the overview of two conventional types of LCC-S compensation topologies, where the parameter resonant tuning design principles and output characteristics are presented. The detailed optimization process of multi-objective PSO is described in Section 3. The experimental verification is presented in Section 4. Finally, conclusions are outlined in Section 5.
## 6 Application to Experimental Data
### Experimental Data  
The original data sets consist of calibrated cross range and time scattering data from various simple shapes and UXO targets at nine different orientations. The frequency range of the data is 1-30 kHz and the sampling rate is 100 kHz. An understanding of the apparatus that acquired the data can be found in [25]. Among the data sets, the three measurement sets of an aluminum cylinder and two rocks (Rock 1 and Rock 2) are utilized for this investigation. The objects were located 10 m horizontally from the rail-mounted active sonar system and are proud on the sand sediment. Note that the sonar system uses a 6 ms linear frequency modulated (LFM) transmit signal. Each set consists of nine measurement groups according to object orientations from \(-80\) degree to 80 degree with 20 degree increment noting that 0 degrees is broadside in the experiment. Each group has 1600 time series of 2000 samples. Figure 14 displays 2D plots of the time series of the objects at the orientation of 0 degrees.  
In this evaluation, two measurement groups are constituted by selecting 2520 time series randomly from the 3600 cylinder measurements that are located in the cross range from \(-2.5\) to \(2.5\) m as marked in Figure 13(a).The first group is utilized for training the logistic regression model that is used to select the simulated time series while the second group is utilized for testing the model that is trained by the simulated data. In addition, 3600 measurements of Rock 2 are used to test the model while 3600 measurements of Rock 1 are employed to train the model since the clutter model cannot be built due to lack of information on the experiment environment.  
Figure 14: _Cont._A 2000-point DFT is applied to each measurement to obtain its magnitude spectrum. Considering the magnitude spectrum symmetry and the energy distribution, the spectral components between the 100 th bin (5 kHz) and the 500 th bin (27.5 kHz) are selected to make the 451-component feature vectors.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 4 Track Fault Detection Using Machine Learning
### Detection of Track Faults Using Machine Learning  
In this study, we used a support vector machine (SVM), which is a type of machine-learning algorithm created to solve two-class classification problems. An SVM identifies the most distant points (\"maximum margin\") between two groups and draws a boundary at the centre point between them. A feature of the SVM is that maximising this separation (margin) results in a high degree of generalisation. Utilising the SVM, we classified track conditions by using the following procedure.  
Figure 6: Calculated car-body vibration.  
Figure 7: Feature space of car-body vibration RMS.  
First, we classify the obtained data into two classes, \"faulty track with longitudinal-level irregularity\" or \"not.\" Next, we classify the data into \"faulty track with alignment irregularity\" or \"not.\" Subsequently, we classify the data into \"faulty track with cross-level irregularity\" or \"not.\" Finally, if the data are not classified in the faulty data group, the track is diagnosed as normal. By using the procedure above, the track is diagnosed for every 10 m section with the algorithm.  
We have not considered for optimising parameters in SVM [14] in this study, but it should be considered in the future work.
## 3 Proposed Model
### BPMR Channel  
In the system, the signal \(a[j,k]\in[-1,1]\) is stored in the BPMR channel by magnetizing the original data \(u[k]\in\{0,1\}\). In the BPMR channel, the readback signal is distorted by the interference and additive white noise as follows:  
\[y[j,k]=a[j,k]*c[j,k]+w[j,k], \tag{8}\]  
where \(d[j,k]\), \(c[j,k]\), and \(w[j,k]\) are the 2D discrete input data, 2D channel response, and the electronic noise modeled as the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\), respectively.  
The 2D channel response \(c[j,k]\) is presented in (9), which is a sampling of a 2D Gaussian pulse response in (10) and used to simulate the 2D island response [8; 25; 28].  
\[c[j,k]=P\left(jT_{x},kT_{z}\pm\Delta_{off}\right), \tag{9}\]  
\[P(x,z)=A\exp\left(-\frac{1}{2q^{2}}\left[\left(\frac{x+\Delta_{x}}{PW_{x}} \right)^{2}+\left(\frac{z+\Delta_{z}}{PW_{z}}\right)^{2}\right]\right), \tag{10}\]  
where \(x\) and \(z\) are the time indices in the down-track and cross-track directions, respectively. \(\Delta_{x}\) and \(\Delta_{z}\) are the bit location fluctuations, \(A\) is the peak amplitude of the 2D Gaussian function, \(q\) represents the relationship between the standard deviation of a Gaussian function and \(PW_{50}\), which is the pulse width at half of the peak amplitude, and \(q\) is \(1/2.3548\); and \(PW_{x}\) and \(PW_{z}\) are the \(PW_{50}\) components of the down-track and cross-track pulses, respectively; \(j\) and \(k\) represent the time indices of the isolated island in the cross-track and down-track directions, respectively. Additionally, we define the TMR for the BPMR system as  
\[\text{TMR}(\%)=\frac{\Delta_{off}}{T_{z}}. \tag{11}\]
# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 2 ML Methods Used in Structural Wind Engineering
### Ensemble Methods (EM)  
The EM methods include: (1) bagging regression tree that is also referred to as the random forest (RF) algorithm, (2) gradient boosting regression tree (GBRT) or decision tree (GBDT), and (3) extreme gradient boosting (XGB). All EM methods could be defined as a combination of different decision trees to overcome the weakness that may occur in a single tree such as sensitivity to training data and unstableness [58]. The forest generated by the RF algorithm is either trained through bagging, which was proposed by [PERSON] [59], or through bootstrap aggregating [60]. RF splits in each node n features among the total m features, where n is recommended to be \(\frac{1}{3m}\) or \(\sqrt{2m}\)[61]. It reduces the overfitting of datasets and increases precision. Overfitting is overtraining the model which causes it to be particular to certain datasets and lose the generalized aspect desired in ML models. The DR and RF methods are commonly used in classification and regression problems.  
GBRT, also known as GBDT as mentioned above, was first developed by [PERSON] [62] and is one of the most powerful ML techniques deemed successful in a broad range of applications [63; 64]. GBDT combines a set of weak learners called classification and regression tree (CART). To eliminate overfitting, each regression tree is scaled by a factor, called learning rate (Lr) which represents the contribution of each tree to the predicted values for the final model. The predicted values are computed as the sum of all trees multiplied by the learning rate [65]. Lr with maximum tree depth (Td) determines the number of regression trees for building the model [66]. Previous studies proved that smaller Lr decreases the test error but increases computational time [63; 64; 67]. A subsampling procedure was introduced by [PERSON] [60] to improve the generation capability of the model using subsampling fraction (Fs) that is chosen randomly from the full date set to fit the base learner.  
Another popular method from the EM family is the XGBoost, or XGB as defined above, which is similar to the random forest and was developed by [PERSON] and [PERSON] [68]. XGB has more enhancement compared to other ensemble methods. It can penalize more complex models by using both LASSO (L1) and Ridge (L2) regularization to avoid overfitting. It handles different types of sparsity patterns in the data, and it uses the distributed weighted quantile sketch algorithm to find split points among weighted datasets. There is no need to specify in every single run the exact number of iterations as the algorithm has built-in cross-validation that takes care of this task.
# Underground Disposal of Coal Gauge Backfill in China
## 4 Gangue Backfilling Technologies: Advantages and Deficiencies  
Mechanical backfilling has a limited coal gangue disposal ability, and it is suitable only for a few roadways. This backfilling technology has been gradually abandoned [78].  
Fully mechanized mining with solid backfilling technology has developed the key equipment, the backfilling shield and the multihole-bottom-dump chain conveyor, and formed a complete set of mature processes with a huge demand for underground coal gangue disposal. This technology reduces coal gangue discharge, alleviates the surface subsidence induced by mining, and improves the mining resource recovery rate [79; 80]. However, this technology is associated with a high investment cost and high requirements for the working face. For example, the working face is required to be straight and the roof of the coal seam has to be intact.  
The mixed workforce using a gangue backfilling and caving method mainly applies to coal mines where there is a need to protect the aboveground structures, accompanied by a working face length above 220 m and the backfilling section's maximum length of 150 m. This backfilling technology does not interfere with the mining operation and can protect the ground surface. In addition, its efficiency is 4-6 times that of the simple backfilling working face [81].  
In longwall-roadway cemented backfilling, the working face can be arranged flexibly. This backfilling technology is more favored for residual mining area where the fully mechanized working face is difficult to arrange. It can be used for large-scale disposal of solid wastes such as coal gangue and fly ash. The paste is backfilled to the goal via a pipeline pumping system from the surface backfilling stations, and the backfilled ratio can exceed 90%. This technology can effectively support the goat, control the movement of the overlying strata, and protect the environment of the mining area [82].  
Paste backfilling combines the advantages of cemented backfilling and fully mechanized mining with solid backfilling. The cemented filling materials are backfilled to the goat of the fully mechanized working face via the pipeline. This technology has the benefits of the high degree of mechanization and high production capacity in the working face, its simple process, and its high system stability. It can effectively control the movement of overlying strata in the mining area and dispose of solid wastes such as coal gangue and fly ash on a large scale, hence protecting mines environment [83; 84].  
Some typical examples of applying underground gauge backfilling in coal mines in China are listed in Table 1. They are mainly concentrated in Shandong, Hebei, Shanxi, Henan, Anhui, and other provinces which have large populations and dense buildings. Coal gangue backfilling can reduce the impact of mining on the surface. Fully mechanized mining with solid backfilling technology is the most widely used among these applied technologies.
## 3 Results and Discussion
### Pollution Assessment of HMs in Sediments
In these studies, contamination levels or risk contributions of HMs varied across study areas. For example, Cd was the largest contributor to the ecological risk of sediments [43]. Similar to the results of this study, [PERSON] et al. [31] observed that the ecological risk of Hg in the Beibu Gulf was relatively high, and [PERSON] et al. [42] found that the ecological risk of Hg and Cd in HMs in Daya Bay was also high. [PERSON] et al. [33] found that the ecological risk of Hg in the sediments of the Maowei River aquaculture area was high. Hg and Cd had stronger toxic effects on organisms [44]. The toxicity coefficients (\(T_{r}\)) of Hg and Cd were 40 and 30, respectively, much higher than those of the other four HMs, which led to a higher potential risk in sediments of the dumping area. Hg and Cd have strong biological toxicity and high bioaccumulation potential, which seriously threaten marine ecosystems and human health. Therefore, to reduce the ecological risk caused by dumping, the monitoring of Hg and Cd in dredged materials should be strengthened before ocean dumping. The \(E_{r}^{i}\) values of HMs at the different stations are shown in Figure 6. In terms of spatial distribution, the order of the potential ecological risk of each monitoring station was S2 \(>\) S3 \(>\) S4 \(>\) S1 \(>\) S5. Areas with high ecological risk were mostly located in the northwest of the dumping area, and the ecological risk in the south of the dumping area are low. In the north of the dumping area, the results of ecological risk are consistent with the concentration distribution and the Nemerow evaluation results. In areas (S1, S2, and S3) with frequent dumping, higher concentrations of HMs in sediments were detected, indicating that dumping activities had a significant impact on the marine ecological environment. Figure 6: \(RI\) of HMs in different years (**A**) and \(E_{r}^{i}\) of HMs in different stations (**B**).
# Cell-Laden Thermosensitive Chitosan Hydrogel Bioinks for 3D Bioprinting Applications
## 2 Materials and Methods
### Characterization of the Chitosan Hydrogel
#### 2.2.2 Rheological Analysis  
A rheological analysis was performed with an advanced rheometric expansion system (ARES, Rheometric Scientific, UK). Changes in the elastic (storage) modulus (G\({}^{\prime}\)) and the viscous (loss) modulus (G\({}^{\prime}\)) were recorded as a function of the temperature (25-50 \({}^{\circ}\)C) at a fixed frequency of 1 Hz. The temperature was varied with a constant heating rate of 1 \({}^{\circ}\)C/min.
## 1 Background and Introduction
[PERSON] et al., 2020, studied several variations of ISO 6330 standard washing programs using TPU encapsulation for two different conductive materials: structured copper foil (SCP) and conductive fabric (Tex-Printed Circuit Board) [14]. Both materials also laminated to the fabric. While TPU type and thickness is not mentioned, the washing program variations were thoroughly investigated, and a standardized washing test was used. The TPU film encapsulation method has proven to be beneficial in the e-textiles field to endure washing [13; 14; 15; 16; 17]. Nevertheless, to benchmark these results, it would be necessary to know the lamination parameters and the thickness of the films used. Moreover, textiles have several variables to consider while analyzing the results, e.g., knit fabric weight, the composition of the materials and their treatment procedures, end-user wearing habits, etc. This study focuses on improving the integration of flexible and stretchable electronics in knit fabrics by lamination with TPU films. The stretchability of the circuits is obtained by using extensible meander shaped Cu interconnections. The work aims to increase the washing reliability of the integrated circuit by evaluating three different copper-meander designs and three TPU film thicknesses. Additionally, two different knit fabrics are used in the first round of experiments. The prepared samples are washed according to domestic washing and drying procedures standard (ISO 6330-2012 [12]). This work aims to determine the most reliable and stretchable combination of integration materials and design, with a focus on its use in wearable sports and healthcare applications.
# Improved Simplified Engineering Fault Displacement Hazard Evaluation Method for On-Fault Sites
## 4 Discussion of the Results
### Consideration for Validation of the Method  
#### 4.2.1 Possible Elements of the Validation of the Methodology  
Table 3 summarises the elements of the validation procedure. The validation should focus on the new elements of the methodology compared to the other already implemented methodologies. These are the definitions of the fault parameters based on the parameters of the PSHA models, the screening method for the ruptures affecting the site and the calculation of the probability of intersections.  
The most crucial element of all methods is the use of scaling correlations. These are validated by the data. In our case, the PSHA modelling was validated by the seismic catalogue and related to the seismic hazard information.  
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline \multicolumn{4}{c}{**Elements of the Validation of the Proposed Methodology**} \\ \hline  & & The number of published studies is limited, and the seismotectonic conditions \\  & & radically differ from the Paks case. \\  & & Apart from the author’s studies, the only research on the fault displacement hazard for a site in the entire region (countries, Slovakia, Slovenia, Hungary) was made for a new Krsko site in Slovenia [16; 17]. The Krsko site is at the periphery of the Pannonia Basin, significantly more active than the central part of the Basin. Contrary to the Paks site, specific empirical indications of fault displacements could be identified, e.g., characteristic stratigraphic horizons could be identified contrary to the Paks case. \\  & & Principal compliance could be stated by comparing some essential steps of the analysis process and the results for the Krsko and Paks sites. \\
2 & Checking the self-confidence & It is demonstrated below. \\  & & A systematic assessment of the factors influencing the results and the effects of different modelling options. \\  & & & This is an inherent part of the methodology. \\
3 & & & Another study for the same site does not exist. \\  & & & Therefore, in the future, either the recent method should be applied to a site \\
4 & & & analysed by another method and compared, assuming the other method is validated, or other methods qualified as proven should be used for the Paks site. For example, the use of the Caltrans method [12] seems to be feasible. \\
5 & & & The predicted hazard aligns with the evidence regarding the Pannonian Basin’s considerations on the neotectonics of the area \\ \hline \hline \end{tabular}
\end{table}
Table 3: Elements of the validation.  
The entire hazard evaluation procedure is a prediction, an intellectual construct that could be validated posteriorly. It should be noted that only the consensus of the technically informed community can now verify the validity of our prediction that a natural event assessed with \(10^{-7}/\)a will happen in the coming 50-60 years (in the case of a new reactor).
# Evaluation and Comparison of Pear Flower Aroma Characteristics of Seven Cultivars
## 3 Results
### Identification of the Volatile Compounds
Anli flower contained the highest amount (691.8 \(\mathrm{\SIUnitSymbolMicro g}\) kg\({}^{-1}\)) close to Brown peel (667.2 \(\mathrm{\SIUnitSymbolMicro g}\) kg\({}^{-1}\)), while KorlaXiangli showed the lowest amount of volatiles (62.7 \(\mathrm{\SIUnitSymbolMicro g}\) kg\({}^{-1}\)), approximately the eleventh of Anli flower; the total amount of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Volatile Compounds**} & \multicolumn{1}{c}{**RT**} & \multicolumn{1}{c}{**RI**} & \multicolumn{1}{c}{**LRI**} & \multicolumn{1}{c}{**Anli**} & \multicolumn{1}{c}{**Bayuesu**} & \multicolumn{1}{c}{**Brown Peel**} & \multicolumn{1}{c}{**Golden**} & \multicolumn{1}{c}{**KorlaXiangli**} & \multicolumn{1}{c}{**Lyubaoshi**} & \multicolumn{1}{c}{**Xizilu**} \\ \hline Methyl gramate & 12.71 & 1327 & 1326 & - & 3.7 & & & & & - & - & MS, RI \\ Eugenol & 13.27 & 1364 & 1359 & - & 0.40c & 2.16b & 20.15a & - & 0.42c & - & MS, RI \\ (\(\dot{E}\))-B-Damascone & 14.11 & 1421 & 1420 & & & & & & 0.81 & - & MS, RI \\ (\(\dot{E}\))-Germany acetone & 14.60 & 1486 & 1454 & 12.00a & 2.93b & - & - & 0.63c & 2.21b & 2.29b & MS, RI \\ \(\alpha\)-Curcureme & 15.09 & 1492 & 1483 & - & - & - & - & 0.63b & 0.50b & 2.02a & MS, RI \\ \(\alpha\)-Farnese & 15.35 & 1512 & 1507 & 2.62c & - & - & 18.09a & 2.89c & 1.48d & 4.0b & MS, RI \\ Geranyllinalo & 23.85 & 2024 & 2034 & 72.36 & - & - & - & - & - & MS, RI \\ SubTOTAL & & & & 93.17a & 66.11b & 2.16e & 38.24c & 4.16e & 8.88d & 11.39d & \\  & & & & Ketones & & & & & & & \\
6-Methyl-5-heptem-2-one & 5  
Golden flower (384.8 \(\upmu\)g kg\({}^{-1}\)) was close to Xizilu (351.6 \(\upmu\)g kg\({}^{-1}\)); cultivars Bayuesu and Lyubaoshi flower were 312.8 and 185.6 \(\upmu\)g kg\({}^{-1}\), respectively. Among these volatile chemicals, terpenoids such as geranyl linalool, linalool, and \(\alpha\)-farresene had relative high amounts in Anli, Bayuesu, and Golden flower; 3-methyltetradecane and hexadecane were the major volatiles that accounted for 29.0% and 31.9% in Brown peel flower; 2-phenylethanol, 1-decanal, pentadecane, 1-nonanal were the most abundant volatiles in Anli flower (15.5%), Bayuesu (26.0%), Golden (20.3%), and Lyubaoshi (20.1%), respectively. In addition, a range of esters (fatty acid-derived volatiles) was detected in each cultivar, while the levels were relatively lower compared to other chemicals. Even where volatiles in different pear flowers involved the same chemical classes, the amount varied greatly (Figure 2): alkanes existed in almost all cultivars as the background volatiles with a remarkable amounts level; alcohols mainly existed in the flowers of Brown peel, Anli, and Lyubaoshi; arenes were the dominant volatiles in Brown peel, Anli, and Golden flower; and the highest amount of aldehyde appeared in Bayuesu, Xizilu, and Lyubaoshi flower.