esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of an Optimal Operating Point  
All target parameters must be considered to determine an optimal operating point for the developed LS machine. The nozzle temperature and the nozzle feed rate settings have the opposite effect on the different target variables. A high nozzle feed rate is beneficial for the width and depth of the HAZ and the processing speed. In addition, a decreasing nozzle temperature leads to a reduction in the width and depth of the HAZ. On the other hand, a high nozzle temperature and a low nozzle feed rate are favourable for process reliability. The CCD identified a range of 335 \({}^{\circ}\)C to 342 \({}^{\circ}\)C at a nozzle feed rate of 135 mm/min to 140 mm/min for the width and a range of 335 \({}^{\circ}\)C to 345 \({}^{\circ}\)C at a nozzle feed rate of 130 to 140 mm/min for the depth as optimal. Reducing the nozzle temperature or increasing the nozzle feed rate will result in a further reduction, but these values should not be exceeded for process safety reasons. Since the nozzle feed rate increase will improve all three remaining target values, the process limit of 140 mm/min was selected to optimise this factor. The nozzle temperature does not affect the processing speed, but a reduction will also reduce the HAZ. Since the effect of the nozzle temperature seems small in the range between 335 and 345 \({}^{\circ}\)C, 340 \({}^{\circ}\)C was selected here instead as the limit value to ensure a higher process reliability. The optimised point for process speed and HAZ size, considering process reliability, is the factorial combination of a nozzle temperature of 340 \({}^{\circ}\)C and a nozzle feed rate of 140 mm/min. According to the model, the expected width and depth of the HAZ for this setting were 2638.72 \(\upmu\)m and 523.36 \(\upmu\)m, respectively. An overview of all settings for an optimum operating point is given in Table 9.  
The following approaches can be used to increase the process reliability and process time in the future:  
* For a more controlled orientation of the rovings in the part, the inner diameter can be made smaller or adapted to the shape of the roving;
* Additional twisting of the rovings before coating could produce a rounder shape and, thus, in combination with an adapted inner diameter of the fibre nozzle, result in a higher deposition accuracy in part.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method
### Frequency Selection Strategy  
It is very important to select the appropriate frequency components for frequency domain FWI. Too few frequency components will lead to instability of the inversion and it will be easy to fall into local minima. However, too many frequency components can not only increase the accuracy of inversion but also increase redundant computation and waste memory space and calculation time.  
[PERSON] [37] and [PERSON] [38] discussed several factors that affect frequency selection in waveform inversion such as wave-number, offset, and the frequency selection strategy. In order to maintain the continuity of wave-number, the frequency selection formula should be as shown below:  
\[f_{k+1}=\frac{f_{k}}{\alpha_{\text{min}}}, \tag{16}\]  
\[\Delta f_{k+1}=f_{k+1}-f_{k}=\frac{1-\alpha_{\text{min}}}{\alpha_{\text{min}}} f_{k}. \tag{17}\]  
Equations (16) and (17) show that the optimal frequency increment is not a constant but increases with the frequency increases. This section assumes that the optimal frequency selection method is obtained under the condition that the medium is in a geological model with uniform velocity in one dimension. In ahead geological prospecting in a tunnel, the selected frequency is increased appropriately according to the tunnel observation mode and the detection distance, which is beneficial to improve the detection resolution due to the limited observation method.  
The flow chart of the adjusted frequency domain FWI method, which can realize accurate detection of adverse geological bodies in front of the tunnel, is shown as Figure 2.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods  
Feature-based methods are also algorithms that extract common features or descriptors from an image that define them, being the most common features used: points, lines, edges, corners, pixels, colors, histograms, or geometric entities [61]. These are extracted from features and compared and matched to their characteristics. These methods have a significant advantage over direct pixel-by-pixel methods, in which the relation is determined by directly minimizing pixel-to-pixel dissimilarities [21]. The feature-based methods can be divided into two categories: the global single transformation, where the main processes are feature detection and registration to perform the global projective transformation, and the local hybrid transformation.
# Minimization of the Decoupling Circuit (DC-Link) in Induction Motors
###### Abstract
The main contribution of this paper is to obtain a minimum capacitance equation necessary to guarantee the minimization of the size of the decoupling system capacitor in the electric vehicle propulsion system, which performs the function of absorbing the power fluctuations of the system. The equation was obtained through an analysis of the power flow in the power electronic converters of the system. The analysis focused on identifying the critical scenario where the highest energy return occurs in the motor operation mode of the electric vehicle, considering the existence of different operation modes of the electric machine, including regenerative braking (generator mode), for which topologies are added to the system for energy management and storage, as reported in previous works [15; 37; 38]. The developed equation considers the critical case of operation to guarantee the correct  
Figure 2: Instantaneous power using the motor as a load. operation of the system in working schemes with lower demand than this, avoiding oversizing and at the same time minimizing the minimum capacitance required by the decoupling system without the need to design and implement an extra topology that performs this function in comparison with the options reported in the literature. The article is organized as follows: Section 2 presents the methodology for selecting the case study, and the mathematical procedure to obtain the minimum capacitance. Subsequently, Section 3 presents the simulation results using the capacitance equation obtained in conjunction with the power system and in addition, the analysis of the simulation results using the developed equation in comparison with existing equations in the literature is presented. Finally, Section 4 presents the conclusions reached in the development of this work.
## 2 Theoretical Description
### Rheological and Optical Characteristics of Honey  
The optical and rheological properties of honey are essential for assessing its quality and detecting adulteration. The rheological properties that describe honey's behavior as a fluid include viscosity, shear-thinning behavior, thixotropy, elasticity, temperature dependence, Newtonian behavior, and storage modulus. These characteristics are influenced by factors such as shear rate, stress, temperature, moisture content, floral source, and processing conditions. For instance, viscosity decreases with rising temperature, while shear-thinning behavior reduces apparent viscosity under increased shear rates. Understanding these properties is crucial for determining honey's quality, authenticity, and suitability for various applications in the food industry [47; 48].  
On the other hand, optical properties of honey, such as refractive index, birefringence, optical rotation, absorption and scattering, optical path length variation, and polarization properties, are critical for its interaction with light and for designing fiber optic sensors. The refractive index, which ranges from approximately 1.4815 to 1.504 [49; 50], varies with solid content, temperature, and water content, making it a vital parameter for assessing honey quality. It aids in determining moisture content, detecting adulteration, identifying floral sources, and spotting dilution or blending of honey. Furthermore, the refractive index's sensitivity to moisture content helps estimate honey's purity, while deviations from standard values indicate potential adulteration [50]. Thus, we observe that honey exhibits a complexity and whose complete description implies the incorporation of the mentioned properties. However, the perspective of our study is focused on using a multimodal interference device to describe the optical characteristics of honey, specifically for identifying adulteration with glucose and measuring moisture content through refractometric measurements. In this sense, all procedures were carefully performed to ensure accuracy and minimize errors.
## 3 Results and Discussions  
### Validation  
To verify the correctness and accuracy of the present method, this section will first take a pyramidal lattice core sandwich beam as the research object and calculate its natural frequencies under three different typical boundary conditions, including clamped ends, simply supported ends and clamped-free ends. The calculation results in this section will be then compared to the data of existing literature, which were calculated by [PERSON] et al. [19], [PERSON] et al. [21] and [PERSON] et al. [27] under typical boundary conditions. The material properties and geometric dimensions of the pyramidal lattice sandwich beam involved in the calculation are listed as: \(L=0.6364\) m, \(B=0.06364\) m, \(t=0.0005\) m, \(c=0.015\) m, \(r=0.001\) m, \(\alpha=45\), \(E_{s}=E_{x}=210\)\(GPa\), \(\
u=0.3\) and \(\rho_{f}=\rho=7930\) kg/m\({}^{3}\). In order to describe the arbitrary boundary conditions of the lattice sandwich beam, specific abbreviations are used instead of the corresponding boundary conditions. The free, clamped, simply supported and elastic boundary conditions are represented by F, C, SS and E, respectively. By combining them with each other, arbitrary boundary conditions of lattice sandwich beams can be obtained. For example, the symbol C-E can be considered a special case when the lattice sandwich beam is under lamped and elastic boundary conditions at \(x=0\) and \(x=L\), respectively. For the convenience of calculation, it is necessary to set appropriate rigidity values for these springs and torsional springs to obtain an accurate simulation of the corresponding boundary conditions. Therefore, for clamped ends, the rigidities of these translational and torsion springs at \(x=0\) and \(x=L\) are all set to a huge value, \(10^{14}\). For simply supported ends, the rigidities of the translational springs at \(x=0\) and \(x=L\) are all set to \(10^{14}\), and those of the torsion springs at \(x=0\) and \(x=L\) are all set to \(10^{0}\). For clamped-free ends, the rigidities of the translational and torsion springs at \(x=0\) are all set to \(10^{14}\), and those of the translational and torsion springs at \(x=L\) are all set to \(10^{0}\).  
According to the above calculation parameters, the first five order natural frequencies of the pyramid lattice sandwich beam under three typical boundary conditions are solved simultaneously. For direct comparison, the calculation results of the present method and the data of existing literature are all listed in Table 1, where the error value is obtained by comparing between [PERSON]'s result and the value of the present method. As shown in Table 1, the calculation results in the present method are close to those calculated by [PERSON] et al. [19], [PERSON] et al. [21] and [PERSON] et al. [27], indicating that both the theoretical modeling process mentioned in the above section and the programs in the simulation calculations are all correct, and that the present method has sufficient precision. Therefore, this method is very suitable for calculating the free vibration frequency of lattice sandwich beams with arbitrary boundary conditions.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
###### Abstract  
Watermark is the copy deterrence mechanism used in the multimedia signal that is to be protected from hacking and piracy such a way that it can later be extracted from the watermarked signal by the decoder. Watermarking can be used in various applications such as authentication, video indexing, copyright protection and access control. In this paper a new CDMA (Code Division Multiple Access) based robust watermarking algorithm using customized 8 \(\times\) 8 Walsh Hadamard Transform, is proposed for the color images and detailed performance and robustness analysis have been performed. The paper studies in detail the effect of spreading code length, number of spreading codes and type of spreading codes on the performance of the watermarking system. Compared to the existing techniques the proposed scheme is computationally more efficient and consumes much less time for execution. Furthermore, the proposed scheme is robust and survives most of the common signal processing and geometric attacks.  
spreading; hadamard; robust; attacks; computational 1
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 5 Conclusions  
Thus, this paper considers the task of improving the quality of network intrusion detections.  
The UNSW-NB15 modified dataset was used as the source data, containing information about the normal functioning of a computer network and intrusion scenarios. The dataset contains information about the flow between hosts and packet-level inspection to distinguish between nine types of attacks: Exploits, Fuzzers, and DoS.  
To balance the classes, the Undersampling, Oversampling, and their combination (Over + Undersampling) algorithms are being researched. The use of the SMOTEENN data balancing method improved the F1 score of the models by up to 31%.  
To reduce the correlation of the initial data, the combination of the special procedure SP_PCA and the VarianceThreshold method is proposed.  
To estimate the integrated use of the SMOTEENN, SP_PCA, and VarianceThreshold methods, Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, and Random  
Figure 7: The assessment of the quality of the model after the feature space reduction stage. The obtained results are validated using GradientBoostingClassifie and the kdd-cup_data_10_percent_corrected and DoHBrw-2020 datasets.  
Forest methods were used as the base classifier. The developed methods were implemented using Python and the Google Colab cloud service with Jupyter Notebook (version 7.1). The parameters of base classifiers and meta-algorithms were tuned. The best result was obtained by using the ensemble classifier based on the Random Forest algorithm. Using the proposed procedure reduced the training time of the model based on the Random Forest algorithm by 16% and improved the recognition time by 32%. Additionally, it improved the model's F1 score to 97%.  
Thus, a method based on the preprocessing of highly correlated and imbalanced data is proposed. The scientific novelty of the obtained results lies in the integrated use of the developed procedure for reducing feature correlation, the SMOTEENN data balancing method, the selection of classifier type, and the tuning of its parameters.  
Conceptualization, S.S.; methodology, S.S.; software, R.C., B.K., S.G., V.P. and Z.V.; validation, R.C., B.K., S.G., V.P. and Z.V.; formal analysis, R.C., B.K., S.G., V.P. and Z.V.; investigation, S.S.; writing--original draft preparation, S.S. and M.K.-K; writing--review and editing, S.S. and M.K.-K.; visualization, M.K.-K.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 6 Classification
### Feature selection with mutual information  
For the first stage of our classifier, we rely on features that have low computational cost, so that many candidates can be rejected quickly. To select these features from the complete set of low-cost features, we employ the maximum relevance, minimum redundancy (MRMR) technique proposed by [46].  
In information theory, mutual information measures the statistical dependency between two random variables. The problem of feature selection can be interpreted as a search for a subset of features that jointly have the largest dependency on the target class (polyps, in our case). The MRMR approach, which extends work from [PERSON] [47] and [PERSON] [48], selects a good subset of features as the one that has a large dependency on the target class. However, a difficulty of estimating multivariate densities accurately makes the computation challenging, especially for continuous features. Instead of seeking maximum dependency, MRMR seeks a subset of features that are _maximally relevant_ on the target class but for which the features within the subset are _minimally redundant_. It can be formulated as\[max\;\Phi(D,R),\;\Phi=D-R \tag{9}\] \[D=\frac{1}{|S|}\sum_{x_{i}\in S}I(x_{i};c)\] \[R=\frac{1}{|S|}\sum_{x_{i},x_{j}\in S}I(x_{i};x_{j})\]  
where \(D\) and \(R\) represent the relevance and dependence, respectively, and \(I(x_{i};c)\) measures the mutual information between feature \(x_{i}\) and the target class \(c\), and \(I(x_{i};x_{j})\) measures the mutual information between two features \(x_{i}\) and \(x_{j}\). In practice, Equation 9 can be implemented incrementally as follows:  
\[\max_{x_{i}\in X-S_{m-1}}[I(x_{j};c)-\frac{1}{m-1}\sum_{x_{i}\in S_{m-1}}I(x_{ j};x_{i})] \tag{10}\]  
The method starts by seeking a single feature \(x_{k}\) that is maximally relevant to target class, and includes \(x_{k}\) in the set \(S_{1}\). From the remaining features \(X-S_{1}\), it then seeks the next feature that is maximally relevant to target class but minimally redundant with features that have been previously selected. The selected feature, along with feature set \(S_{1}\), forms feature set \(S_{2}\). This process iterates until the number of features has reached \(m\) features, \(S_{1}\subset S_{2}\subset\ldots\subset S_{m-1}\subset S_{m}\).
## Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit  
**[PERSON] 1,2,*, [PERSON] 3 and [PERSON] 2**  
1 Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Bolan Av., Jinjiang 362200, China  
2 Institute of Micro Technology and Medical Device Technology, Technische Universitaet Muenchen, 85748 Garching, Germany; E-Mail: [EMAIL_ADDRESS]  
3 Medical Faculty, Kunming University of Science and Technology, Kunming 650500, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-595-3635-0373; Fax: +86-595-8266-8861.  
Academic Editor: [PERSON]  
_Received: 1 July 2015 / Accepted: 14 September 2015 / Published: 29 September 2015_  
**Abstract:** Quantitative assessment of parkinsonian tremor based on inertial sensors can provide reliable feedback on the effect of medication. In this regard, the features of parkinsonian tremor and its unique properties such as motor fluctuations and dyskinesia are taken into account. Least-square-estimation models are used to assess the severities of rest, postural, and action tremors. In addition, a time-frequency signal analysis algorithm for tremor state detection was also included in the tremor assessment method. This inertial sensor-based method was verified through comparison with an electromagnetic motion tracking system. Seven Parkinson's disease (PD) patients were tested using this tremor assessment system. The measured tremor amplitudes correlated well with the judgments of a neurologist (\(r=0.98\)). The systematic analysis of sensor-based tremor quantification and the corresponding experiments could be of great help in monitoring the severity of parkinsonian tremor.  
**Keywords:** Inertial measurement unit; wearable system; tremor quantification; Parkinson's disease; technical validation
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 2 Design Methodology of the UWB Antenna
### Parametric Analysis  
As stated earlier, the additional stub loaded at the top edge of the UWB radiator is responsible for the notch band behavior. Thus, to further demonstrate the findings, a parametric analysis was done by varying the length of the stub (\(\mathrm{S_{W}}\)), and the corresponding \(\left|\mathrm{S_{11}}\right|\) results are depicted in Figure 3a. It can be observed from Figure 3a that when the optimized length of the stub is increased, the notch band shifts towards the left side, while the bandwidth of the notched area remains unchanged. Similarly, when the length of the stub is reduced, the notch band shifts toward the left side. When the electrical dimensions of an antenna are reduced, it results in a frequency response shift towards the right end.  
Figure 3b,c demonstrate the \(\left|\mathrm{S_{11}}\right|\) results with respect to the variations in gap (s) and the length \(\mathrm{C_{L}}\). The characteristic impedance Zo of the coplanar waveguide varies as itsdimensions (s and C\({}_{\text{L}}\)) are varied. When the gap length is decreased, the notch band is shifted towards the lower end of the spectrum. Furthermore, it also causes a decrease in the bandwidth of the antenna due to mismatching of the impedance. However, when the gap length is increased, the bandwidth of the antenna decreases, and the notch band is shifted towards the left side. Similarly, when the length C\({}_{\text{L}}\) is increased from the optimized value, the notch band shifts towards the left side.
## 3 Results and Discussion
### Establishment of DBA-LSTM Model
#### 3.2.2 Comparison of DBA-LSTM and DBA-BPNN
In short, machine learning for displacement back-analysis can effectively invert the equivalent physical and mechanical parameters with higher accuracy, but the deep learning algorithm has better prediction performance in terms of landslide inversion.