esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To address this issue, we introduce the change point detection method to divide the data stream adaptively, the change point refers to the time point that can be identified in which the characteristic of a model is subject to sudden change at previous time interval. Secondly, the single classifier has limited ability to classify the instances for all activity classes. Therefore, combining the proper various base classifiers can make use of their own strengths and improve the classification performance. In this paper, a heterogeneous ensemble approach by combining various base classifiers is proposed to improve the activity recognition performance. The main contributions of this paper are:  
* Design an experiment protocol to collect data of static activities, dynamic activities as well as transitional activities by using a small, low-cost, and non-intrusive wrist-worn accelerometer. The data are collected from a group of participants in a real home setting scenario. * Propose a change point-based segmentation method to dynamically identify a window's starting point and end point such that it contains the same activity samples. * Build an ensemble classification model by combining various base classifiers using majority voting combination function. The selection of base classifiers is based on the analysis of the classification performance on activity classes. The rest of the paper is organized as follows: Section 2 introduces the related work in activity recognition area. Section 3 describes the experiment protocol and data collection. Section 4 concentrates the methods adopted in the development of the heterogeneous ensemble activity recognition approach including signal preprocessing, feature extraction, and ensemble classifier construction. Section 5 discusses experimental results and performance on activity recognition. Section 6 provides the conclusions and future work.
## 2 Results
### DisA Limits RecU-Mediated HJ Cleavage  
The effect of DisA on RecU-mediated HJ cleavage was then analyzed. A large excess of DisA did not catalyze HJ cleavage (Figure 5D, lane 2). In the presence of 10 mM Mg\({}^{2+}\), RecU cleaved the HJ-J3 to yield two naked duplex products (Figure 5D, lane 3) [46; 48]. When RecU was pre-incubated with HJ-J3 DNA (5 min 37 \({}^{\circ}\)C), the addition of a stoichiometric concentration of apo DisA only marginally affected RecU-mediated HJ-J3 cleavage (\(p>0.1\)) (Figure 5D, lane 3 vs. 6). In sharp contrast, when apo DisA was pre-incubated with HJ-J3 DNA, and then RecU was added, HJ cleavage was strongly inhibited even at a molar DisA:RecU ratio of 0.2:1 (Figure 5D, lane 7), and it was abolished in the presence of substochiometric DisA concentrations (Figure 5D, lanes 8 and 9). Since the inhibition exerted by DisA was dependent on the order of addition, we assumed that it was genuine. Here, ATP was not present in the reaction mixture, thus DisA-mediated c-di-AMP synthesis plays no role.
## 4 Radiometric Processing Workflow
### Scene Reflectance Correction
#### 4.4.2 Topographic Correction  
The topography can have a large influence on the local illumination within an image [170]. The radiance of the same material varies if it is located on a slope oriented toward or away from the sunlight incidence. For a correction, a DSM and the Sun's elevation and azimuth angles at the time of acquisition are needed. Several correction methods exist [170]. [PERSON] et al. [68] implemented and tested some of the common topographic correction methods with UAV-based imagery for geological applications. The methods comprised Lambertian, such as the cosine method [171], gamma method [172] or percent method, as well as non-Lambertian methods, such as the Minnaert method [173] or the c-factor method by [PERSON] et al. [171]. They recommended the c-factor method.
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 2 KSTAR ECE Imaging System  
A block diagram of the W-band ECE imaging system installed at KSTAR is shown in Figure 2. The ECE signals emitted from each radially localized point at the tokamak plasma pass through a series of optical lenses to form parallel Gaussian beams, which radiate uniformly upon the elliptical mini-lenses attached to the antenna/detectors [14]. For the polarization alignment and filtering of the incoming ECE signals, three FSS (frequency selective surface) layers (a half-wave plate, notch filter, and dichroic filter) are placed in front of the array box. In details, the half-wave plate rotates the polarization of the incident signal by 90\({}^{\circ}\) (used only for the O-mode wave), the notch filter is used to block the strayed ECE heating signals, and the dichroic filter works to block low-frequency components. Then, the plasma-radiated ECE signal impinge upon a tilted beam splitter, which divides the signal into two paths. The tilted beam splitter also allows a perpendicularly fed LO signal to be split so that the ECE (RF) and LO signals are spatially combined at each path. These RF-LO combined signals at each path radiate uniformly over the 12 elliptical mini-lenses attached to the dual-dipole antennas. Then, each combined RF-LO signal, passing through the elliptical mini-lens and dual dipole antenna, is mixed in a Schottky diode, which is located between the two dipole radiators, to produce an IF signal with a frequency range of 2 to 9 GHz.  
Using an external electronic module (video module), this IF signal is split into 8 frequency channels, and each channel is down-converted again to produce a video signal to monitor the amplitude and fluctuation characteristics.
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 1 Introduction
The authors conducted studies on tonal languages, for which they achieved significant improvement. In this study, we have shown how low-level features describing fundamental frequency contour and the vocal tract's resonances can improve the convolutional neural network's embedded space and the final classification accuracy. The main contribution of our work include:  
* Improvement of classification accuracy by using modified embedded space in ResNet architecture with prosody features. * Confirmation that the Constant-Q transform (CQT) spectrograms better describe emotional speech than Mel spectrograms, thus more suitable for emotional speech classification tasks. * An analysis of negative and positive influences of the injection of low-level prosody features of speech signals to classify emotional states. Our motivation to perform such experiments was to determine how low-level speech features can influence the embedded space obtained in the learning process in an unsupervised manner and how it can improve discriminative power in the classification of emotional speech. The paper is organized as follows: the methodology, audio representations and brief description of deep neural network architecture are presented in the next section. Section 3 describes the dataset used, experimental setup, evaluation approach, and the result. Finally, the last section concludes the work.
# Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables
## 1 Introduction
### Electrical Properties
#### 2.3.2 Dielectric AC Breakdown Test  
According to previous research, dielectric AC breakdown voltage is one of the most important parameters for investigating the electrical strength of XLPE cables. In this paper, the test power was provided by a TQSB 3 kVA/50 kV AC transformer, which can meet the power requirements for the AC breakdown test. Six samples with the same operating time were cut into square shapes with a size of 2 cm \(\times\) 2 cm. Moreover, the sample placed between the high-voltage electrode and ground electrode is shown in Figure 4 and flashover is produced between two electrodes. The wiring diagram of the test principle is shown in Figure 5.  
In Figure 5, T is the AC transformer, \(R_{0}\) is a current-limiting resistor, \(R_{1}\), \(R_{2}\) are resistors of the resistor divider D (500:1), DAS (an intelligent device system which can measure voltage) is the voltage measuring system.  
Before the test, all samples were cleaned with anhydrous ethanol and water carefully, and then left to dry indoors for 24 h to avoid dust.  
According to GB/T 1048.1-2006, all samples and electrodes were soaked in transformer oil (#25). When testing, the uniform boosting method was adopted, where the applied voltage increases by 1 kV/s uniformly until breakdown occurs. To enhance the accuracy of the test results, six pieces of samples with the same operating time were selected, and the average value of the breakdown voltage  
Figure 4: The electrodes on Dielectric AC breakdown test.  
Figure 5: Schematic diagram of dielectric AC breakdown test circuit.  
was obtained to analyze the changes in the electrical properties through the aging process. The average breakdown strength can be obtained as follows:  
\[E_{b}=(\sum_{i=1}^{m}\frac{U_{bi}}{d_{i}})/m \tag{5}\]  
where \(U_{bi}\) is the breakdown voltage for \(i\)th sample, kV; \(d_{i}\) is the thickness for \(i\)th sample, mm; \(m\) is total number of tests, \(m=6\); \(E_{b}\) is the average breakdown strength of samples, kV/mm.  
The test results for the dielectric AC breakdown test for samples with different operating times, along with the standard deviation of results, denoted as \(a_{E}\), are listed in Table 5.  
According to the test results obtained in Table 5, with the increase in aging time, the breakdown strength gradually decreases. For samples aged for 3, 6 and 8 years, the breakdown strength decreases by 0.14%, 2.10% and 6.02%, respectively, compared with a sample aged for 1 year. The reason may be that, as the operating time increases, interface polarization loss of samples increases, as mentioned in Section 2.3.2, which would lead to an increase in the insulation resistivity of the sample. In this case, the breakdown strength of samples decreases as the operating time increases.
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
## 3 Results  
This section presents the performance of the proposed SSD scheme using the theoretical and numerical results. In Section 3.1, the theoretical results from the BER expressions in Section 2.3 are compared with the results from numerical simulation. In addition, the SSD scheme is theoretically analyzed under the conditions of different channel gain distributions together with different signal constellation rotation angles in Section 3.2.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 1 Introduction
### Research Status
#### 1.1.1 Hysteresis Modeling  
Piezoelectric actuators have several advantages, including their small size, light weight, strong driving force, low noise, nanometer-level resolution, ultra-high repeatable positioning accuracy, and excellent dynamic characteristics. As a result, they find wide application in fields such as precision manufacturing [6], biomedicine [7], ultra-precision micro- and nano-processing [8; 9], aerospace [10], robotics [11], and precision optics [12]. PZT FSM offers advantages such as fast response speed, high pointing accuracy, a high resonant frequency, and strong resistance to electromagnetic interference. As a result, it finds wide application in systems such as laser communication [13; 14], adaptive optics [15], laser beam machining [16], and three-dimensional microscopy [17].  
The hysteresis characteristics of piezoelectric actuators make their inputs and outputs nonlinear. This nonlinearity increases with the frequency of the input signal. This characteristic, known as rate dependency, can impact the closed-loop control performance of the fine tracking system [18; 19; 20; 21]. The traditional approach involves adding a closed-loop control within the piezoelectric actuator to achieve linearization. However, this can significantly reduce the bandwidth of the fine tracking system. Researchers have recently proposed numerous hysteresis models for piezoelectric actuators. These models primarily include the Preisach model [22; 23; 24], the Prandtl-Ishlinskii (P-I) model [25], the Krasonselskii-Pokrovskii (K-P) model [26], and the Bouc-Wen (B-W) model [27]. However, the models mentioned above are static models, and their accuracy tends to decrease as the input signal frequency increases. Therefore, researchers have explored the relationship between input signal frequency and hysteresis characteristics, and propose a rate-dependent hysteresis model based on a Hammerstein structure [28].  
The models mentioned above can describe hysteresis characteristics effectively. However, for the fine tracking control system, the P-I model is a more suitable choice due to its fast calculation speed and simple structure. Consequently, the rate-dependent hysteresis model of PZT FSM is established using a Hammerstein structure composed of a P-I model and a dynamic linear mode.
## 2 Theoretical Background and Preliminaries
### Fractional Calculus and Fractional-Order Systems
Figure 1: Implementation of the retuning fractional-order proportional integral derivative (FOPID) system by using the PID control loop [9].
## 3 Materials and Methods  
### Soils and Amendments  
Two coarse textured soils were sampled in the Ionian-Metapontino coastal area, namely at Taranto Castellaneta (CAS) and Taranto Ginosa (GIN), in southern Italy. Soil use at the selected field sites was orchard. For each of the two sites, about 20 kg of soil were taken from the upper 20 cm of the soil profile and stored in the laboratory, where the soil was air-dried and sieved to a diameter of 2 mm. Soils were characterized by standard laboratory techniques to determine the particle size distribution and the main chemical properties, including organic matter content, pH, electrical conductivity, cation exchange capacity, and nitrogen content [4]. According to the USDA soil texture classification, both soils were sandy-loam, with clay, silt, and sand content equal to 6%, 21%, and 73%, respectively, at CAS, and 16%, 13%, and 71%, respectively, at GIN. According to the FAO-WRB soil classification, GIN was Chromic Cambisol (Loamic), while CAS was Terric Chromic Cambisol (Loamic). The soil organic matter content was equal to 0.60% at CAS and 1.55% at GIN. Other physical and chemical properties of these two soils have been reported by [PERSON] et al. [4].  
Three soil amendments were used in this investigation, i.e., compost (COM), vermicompost (VER), and biochar (BIO). The COM was a commercial organic amendment obtained by a controlled process of transformation and stabilization of renewable organic matrices such as manure and animal and vegetable residues. The VER was a by-product of the digestion of cow manure by earthworms (_Eisenia foetida_) manufactured by the Italian Lombrichi Breeding Consortium (CONITALO, Italy). Finally, the BIO was a derivative of a pyro-gasification process of wood obtained from forest cutting (pine, oak, holm oak, chestnut, fir), obtained during a thermal degradation process at a temperature of about 700-800 \({}^{\circ}\)C. The COM, VER, and BIO amendments had pH of 8.0, 7.5, and 9.6, respectively, electrical conductivity, _EC_, equal to 2.2, 3.1, and 0.5 dS/m, respectively, and total organic carbon content, _TOC_, of 200, 197, and 882 g/kg, respectively. Other information on the main chemical properties of the considered soil amendments have been reported by [PERSON] et al. [4] for the VER and by [PERSON] et al. [28] for the COM and the BIO.
## 5 Numerical Examples
### The 25-Bar Planar Truss  
The third comparison problem revolves around optimizing the sizing and layout of a spatial 25-bar tower, as illustrated in Figure 12. The material density and the modulus of elasticity are considered to be 10,000 ksi and 0.1 lb/in\({}^{3}\). Concentrated loads are applied to the tower truss, creating a unique load scenario. At node 1, forces of 1000 lbf in the positive \(X\)-direction, 10,000 lbf in the negative \(Y\)-direction, and 10,000 lbf in the negative \(Z\)-direction are exerted. Similarly, node 2 encounters a downward load of 10,000 lbf in the \(Y\)-direction and 10,000 lbf in the negative \(Z\)-direction. Moving to node 3, a load of 500 lbf in the positive \(X\)-direction is present, while node 6 bears a load of 600 lbf in the positive \(X\)-direction. For optimization purposes, the cross-sectional areas of the truss's bars are organized into eight discrete groups, resulting in eight sizing variables. In parallel, the layout variables are divided into five distinct groups, culminating in 13 design variables under consideration. \[ \begin{split}\text{\it Sizing variables}\end{split} \]  
Figure 12: The 25-bar planar truss. \[X=\left[ \begin{array}{c}x_{4}=x_{5}=-x_{3}=-x_{6}\\ y_{3}=y_{4}=-y_{5}=-y_{6}\\ z_{3}=z_{4}=z_{5}=z_{6}\\ x_{8}=x_{9}=-x_{7}=-x_{10}\\ y_{7}=y_{8}=-y_{9}=-y_{10}\end{array} \right]_{nshape\times 1}\]  
The limit values of layout variables are defined as given below:  
\[ \begin{array}{c}20\leq x_{4}=x_{5}=-x_{3}=-x_{6}\leq 60\ (in)\\ 40\leq y_{3}=y_{4}=-y_{5}=-y_{6}\leq 80\ (in)\\ 90\leq z_{3}=z_{4}=z_{5}=z_{6}\leq 130\ (in)\\ 40\leq x_{8}=x_{9}=-x_{7}=-x_{10}\leq 100\ (in)\\ 100\leq y_{7}=y_{8}=-y_{9}=-y_{10}\leq 140\ (in)\end{array} \]  
The sizing variables are restricted to be selected in the following discrete profile set, S, containing 30 sections: S = {0.1, 0.2, 0.3...2.6, 2.8, 3, 3.2} (in2). The stress constraint for members has been set at \(\pm\)40 ksi. In contrast to the previous two examples, nodal displacement constraints are also considered, and displacements of all nodes in the \(x\), \(y\), and \(z\) directions are limited to within \(\pm\)0.35 in. Optimum designs obtained by CSBO and NCSBO and various methods reported in the literature are compared in Table 7. The optimum design obtained by NCSBO, weighing 117.257 lb after 4830 structural analyses, is the lightest design reported in Table 7. NCSBO is also the most powerful method in terms of convergence speed since it requires only 4830 structural analyses to find the optimum design. It is worth mentioning that the standard deviation of 20 independent runs with NCSBO is 0.147 lb, as indicated in Table 7. This observation supports the robustness/consistency of the proposed algorithm. Meanwhile, CSBO produces a similarly competitive design with a weight of 117.264 lb after 5310 structural analyses and a comparatively low standard deviation of 0.352 lb. After reviewing the literature, it was found that designs generated by SCPSO, GA, and D-ICDE violate the displacement constraints by margins ranging from 0.008% to 0.527%.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 3 Results  
As previously mentioned, the threshold is determined by comparing the anomaly detection performance metrics on the test set. The test set consists of full time series data from 15 different locations, totaling over 58,000 h. These data have been meticulously labeled to distinguish various anomaly types. Their respective quantities and categories are depicted in the accompanying Figure 5.  
We categorized anomalies into two main classes:  
Single anomalies: These are isolated incidents where data at a specific time point deviate significantly from normal values, often due to momentary equipment faults or external interference.  
Continuous anomalies: These anomalies manifest over multiple consecutive time points, possibly arising from equipment issues or external events. They can be further divided into:  
Short-term: Spanning two consecutive time points, indicating brief yet noticeable disturbances.  
Long-term: Persisting for more than two consecutive time points. These anomalies are less frequent, as they are more likely to have been removed in earlier quality control procedures.  
As illustrated in Figure 5, subplot (a) displays the different types of anomalies within a specific time frame. The marked points on this plot provide a clear distinction between the different anomaly classifications. Meanwhile, subplot (b) demonstrates the frequency of each anomaly type. Single anomalies occurred 947 times, making them the most commonly observed type. Short-term continuous anomalies were noted at 194 distinct time points, while long-term continuous anomalies were observed across 102 different time points.  
Despite the chosen locations being in areas with high data coverage, evident anomalies were observed in these core regions. This underscores the central role of anomaly detection in data analysis. Particularly in the edge regions probed by the radar station, which are low data coverage areas, the data not only face significant continuity issues but are also more susceptible to various interferences and synthetic problems, exacerbating the anomaly conditions. Hence, the precise detection and handling of these anomalies become particularly vital to ensure data quality and accuracy, laying a solid foundation for subsequent data analysis.  
In the ensuing discussions, we will delve into the anomaly detection methods and results for each of these three anomaly types.
## 3 Decomposition of the Diametrical Mode Shapes for Disk-Blade-Disk Configuration
### Application of the Decomposition in a Real Prototype Disk-Blades-Disk Structure
\begin{array}{ll}w_{p,i-stat}(\beta,t)=&\sum\limits_{n=0}^{\infty}\frac{A_{ n,i-rot}}{2}\cdot\cos((\omega_{i-rot}+n\Omega)t-\varphi_{n})\\ &+\sum\limits_{n=0}^{\infty}\frac{A_{n,i-rot}}{2}\cdot\cos((\omega_{i-rot}-n \Omega)t+\varphi_{n})\end{array}  \tag{12}\]  
From Equation (12) it can be extracted that the frequencies that will detect a sensor on the stationary frame are:  
\[\omega_{i-stat}=\sum_{n=0}^{\infty}\omega_{i-rot}\pm n\Omega \tag{13}\]  
And each of these frequencies will appear with an amplitude:  
\[A_{n,i-stat}=\frac{A_{n,i-rot}}{2}\ for\ n\
eq 0\ and\ A_{n,i-stat}=A_{n,i-rot}\ for\ n=0 \tag{14}\]  
It should be noted that Equation (13) does not impose a restriction on \(n\). Nevertheless, only the components (\(n\)), that appear with a relevant amplitude (\(A_{n,i-rot}\)), will really produce a peak-frequency in the stationary sensor. In the particular case of a simple disk, with only one component for each diametrical mode shape, only two peaks will be observed with a sensor on the stationary frame [17; 19]. However, for the analyzed structures in this paper, two frequencies for each component of the mode-shape are expected to be detected from the stationary frame, according to Equation (13). The mode shape decomposition proposed in the previous section gives the relative importance that each component \(n\) has on the analyzed diametrical mode shape. This means that the different \(A_{n,i-rot}\) participating in the mode shape can be obtained with the proposed decomposition. Hence, the relation to the components obtained with the mode shape decomposition and the frequencies seen from a sensor on the stationary frame is proven in this section. Similar equations to describe the periphery of blade-disk configurations as a superposition of diametrical modes have also been used in the past [46].