esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sub-6 GHz MIMO Antenna Array for 5G Wireless Terminals
## 4 SAR Analysis for the Proposed MIMO Antenna  
The SAR analysis for the proposed MIMO array antenna is discussed in this section. The proposed model contains skin, fat and muscle with a thickness of 1 mm, 3 mm and 30 mm, respectively as shown in Figure 16. Skin is used as the topmost layer, while the fat and muscle are placed behind the skin in that order. The proposed MIMO configuration (i.e., corporate feed array) is placed at the center of the body model and the performance is analyzed. A conductivity of 3.5 S/m and relative permittivity of 35.3 have been used for the skin, while for fat and muscle, a conductivity and relative permittivity of 0.27 S/m, 4.69 S/m and 4.97, 48.7, respectively.  
Figure 15: MIMO: (a) ECC; (b) DG.  
Figure 16: Corporate array MIMO placed on belly model.  
Initially, for port-1, Figure 17a shows that the SAR is 0.0989 W/kg which is less than the value of 2 W/kg for the 10 g of tissue, as specified by IEC standards. Hence the SAR lies in the safe range, and no human tissue can be harmed. For the case of port-2 (Figure 17b), the SAR value obtained is 0.101 W/kg, which is also under the safe range, and again obeys the standard value set by the IEC (<2 W/kg). The reference power of 0.5 W has been used, and the lower value of SAR shows that the proposed antenna also qualifies for the human tissue's safety terms.  
Figure 17: SAR of Corporate Array MIMO placed on Belly Model: (**a**) Port-1; (**b**) Port-2.  
high isolation level within the operating band, which makes the proposed antenna quite competent for 5G-based communication applications.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 1 Introduction
### Multiferroicity in Thin Films  
The so called multiferroic materials are defined in the single-phase case by the simultaneous presence of two or more ferroic orders. Hence, a ferroelectric state can under certain conditions coexist with magnetic order [38]. Multiferroic materials were first investigated in the late 1950s and were the subject of an intense revival when the scientific community became aware of their high potential towards low-energy consuming electronics [39; 40].  
Taking advantage of the progress in thin film deposition techniques, the early motivation for multiferroic thin-film growth resided in reproducing multiferroic bulk properties in reduced dimensions to lower the external switching field requirements for applications. The pioneering results in the field deal with the growth of multiferroic magnetoelectric BiFeO\({}_{3}\) (BFO) thin films [41] and multiferroic hexagonal manganite thin films [42; 43]. In both cases, the coexistence of magnetic ordering and ferroelectric polarization was shown. The multiferroic thin film research field subsequently evolved and led to the development of strain engineering of multiferroic properties [44; 45; 46] and the design of artificial multiferroic heterostructures where multiferroicity is introduced as a product effect by the combination of different ferroic constituents [44].  
Following the recent development of the electrical control of magnetization using multiferroics [47], the development of experimental techniques giving access to multiferroic and magnetoelectric domain configurations has become a priority for the scientific community.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol  
In this section, an example is provided to illustrate the feasibility of the energy-efficiency protocol proposed in this paper. For a particular network, different energy saving schemes are tested in the control of a linearized model of a coupled multi-level tank.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 1 Introduction
There have been many studies on estimating the relative position, such as by seeking overlap of the local map [24; 25], adding special sensors which can make each robot see the others [22; 26], and cooperative localization methods [27; 28; 29]. Although these methods can achieve distributed indoor mapping, they depend on accurate matching algorithms to determine the relative positioning, which requires adjacent areas to have sufficient overlap, reduces mapping efficiency, and increases hardware costs. In order to improve the accuracy of SLAM, other sensors are integrated, such as GNSS in outdoor environments [30; 31] and Pseudo-GNSS/INS [32] and Wi-Fi [33] in indoor environments. However, the accuracy of indoor positioning is lower than that using GNSS. For high-accuracy indoor mapping, the NavVisM6 indoor mobile mapping system improves accuracy by using \"SLAM anchors\", which are ground control points with accurate absolute coordinates [34]. The mapping results of control-network-aided SLAM are then in an absolute coordinate system. When combining distributed mapping methods with control-network-aided SLAM, each mapping result will be in a unified coordinate system, which means that the multiple mapping results can be stitched conveniently without the need for estimating the relative positioning. In this paper, we propose a distributed indoor mapping scheme based on control-network-aided SLAM. The relative and absolute mapping accuracy of the control-network-aided SLAM are analyzed to verify the feasibility of the scheme. The main contributions of this paper are the following: 1. A distributed indoor mapping scheme based on control-network-aided SLAM is proposed to improve indoor mapping efficiency for large-scale environments; 2. The control-network-assisted SLAM is analyzed in detail from the perspectives of relative accuracy and control point density; 3. The feasibility of the distributed mapping scheme is verified through absolute accuracy analysis. The rest of the paper is organized as follows: Section 2 introduces the methods and materials utilized in this paper, including map generation, IMU (International Mathematical Union)-aided SLAM, control-network-aided SLAMback-end optimization, and distributed indoor mapping based on control-network-aided SLAM; Section 3 introduces the field test and presents the field test results; the field test results are discussed in Section 4; and Section 5 draws the conclusions.
## 2 State of the Art
### Model-Based Systems Engineering  
The effective implementation of document-based systems engineering is hindered by the complexity and volume of information as data are dispersed across multiple documents. Model-based systems engineering (MBSE) addresses these limitations by using digital models instead of documents to store information [6]. MBSE establishes a framework for effective, consistent SE application within teams while being adaptable to specific tasks [7]. Unlike traditional SE, which relies on isolated documents, MBSE uses the model as the central foundation for development and communication, offering several advantages. By capturing all relevant development information directly in the model, MBSE improves stakeholder communication and knowledge retention. It enables the management and development of complex systems by constructing models with the necessary complexity [6]. MBSE employs a consistent language for describing problems and solutions and facilitating system validation and the verification of requirements [7]. Successful MBSE implementation requires the integration of a modeling language, modeling tool, and modeling approach [8]. The spread of MBSE-based modeling in the safety and security space is shown in Figure 1 (right) where the standard ISO 26262:2018 [9] uses the SysML language to define one of its terms, in this case for the definition of the term ITEM.  
Figure 1: A possible approach to integrating the safety and security risk analysis [10]**(left)**; the definition of the term ITEM in ISO 26262:2018 using SysML nomenclature [9]**(right).
# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 3 Related Theory
### BiLSTM
The original signal directly inputs the information to the BiLSTM network layer through the input layer. The input sample signal obtains a value through the forward LSTM calculation and obtains a value through the reverse LSTM calculation. The value transmitted to the hidden layer is determined by these two values. The formula is as follows:  
Figure 1:2  
Figure 1:3  
Figure 1:4  
In the formula, \(\overset{\rightarrow}{h}_{t}\) is the output of the forward LSTM layer, \(\overset{\leftarrow}{h}_{t}\) is the output of the backward LSTM layer, the output state of BiLSTM will be spliced and output according to \(\overset{\rightarrow}{h}_{t}\) and \(\overset{\leftarrow}{h}_{t};y_{t}\) is the output of the hidden layer after the BiLSTM superposition [10; 11; 12; 13]. The original cardiac function signals are directly input into the deep learning network model without manual feature extraction. The deep information is learned through the neural network, and the final classification results are output, so as to realize the classification and diagnosis of cardiac function signals. Since the ECG and PCG signals are time series signals, the relationship between the front and back sequences of signals should be fully considered. The BiLSTM network can take into account the input of forward and backward information. Compared with the LSTM network, the BiLSTM network can make more effective use of the information contained in cardiac function signals. Since the signal values of forward and reverse transmission of different data are different, the BiLSTM  
Figure 1:5 BiLSTM structure diagram. network can use bidirectional information to learn its intrinsic features, so as to improve the classification accuracy.
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 6 Conclusions  
Although the sensor size effect is acknowledged in wave propagation studies, it is rarely quantified. Herein, the sensor size effect is numerically assessed for AE wave propagation in concrete and different angles of incidence to the surface. Simulations enable the isolation of the size effect without influence from the frequency response of the piezoelectric element. It is shown that the small physical size of sensor enables more precise representation of the actual propagating wave. Concerning Rayleigh waves that present the strongest aperture effect, the results can be expressed in terms of the sensor size over wavelength parameter. As this ratio increases from zero (the ideal case of point sensor) to 0.5, the amplitude has already lost approximately 35% and when the wavelength becomes equal to the sensor, the amplitude has lost 80% of the amplitude of a point sensor. For longer sensors relative to the wavelength, the amplitude continues to decrease with a smoother slope. Coherence analysis conducted between the received and original waves shows that waveforms received from surface propagating waves carry stronger distortion compared to longitudinal waves emitted from a source into the material standing diagonally or vertically beneath the sensor. Experimental evidence was taken using two widely applicable sensors. The smaller size sensor records the Rayleigh wave on the surface with higher amplitude than the longitudinal impinging vertically emitted by source located at the opposite side of a concrete sample and at the same distance. The result is inverse for a larger sensor, being a clear manifestation of the sensor size effect. The latter indicates that the amplitude of sources near the surface are more prone to underestimation errors when a large sensor size is used. Frequency content of the received waves is also distorted due to the sensor size, increasing the possible distortion due to scattering or reflections.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.5 Z-Dot Filter  
The Z-dot filter is an updated zero-DM filter that computes the zero-DM time series similar to the zero-DM filter, but subtracts only the corresponding contribution from each channel. This avoids over-subtraction in channels where there is a real signal and results in improved sensitivity of the search.  
Similar to the zero-DM filter, the zero-DM time series is estimated by de-dispersing the original data at zero-DM value. Let the corresponding time series be denoted by \(s_{dm=0}\). One then estimates the contribution per channel by determining the baseline \(\beta_{i}\) plus the scale factor \(\alpha_{i}\) such that the residuals for fitting the zero-DM contribution are minimised for each channel \(i\). Hence, a reduced-\(\chi^{2}\) metric is defined such that,  
\[\chi^{2}=(t_{i}-\alpha_{i}t_{dm=0}-\beta_{i})^{2}. \tag{3}\]  
where \(t_{i}\) is the time series of the \(i\)_th_ channel and  
\[\alpha_{i}=\frac{t_{dm=0}\cdot t_{i}-\frac{1}{N}\sum_{0}^{N}t_{i}\sum_{0}^{N}t _{dm=0}}{t_{dm=0}\cdot t_{dm=0}-\frac{1}{N}\sum_{0}^{N}t_{dm-0}\sum_{0}^{N}t_{ dm=0}}, \tag{4}\]  
where the summation is the sum over \(N\) time samples in the time series. The final filtered time series,  
\[t^{{}^{\prime}}=t_{i}-\alpha_{i}t_{dm=0}. \tag{5}\]  
\(\beta_{i}\) is just the DC offset in this case and does not need to be subtracted during the filtration process. A big advantage of this method is that the samples are subtracted with different weights, which allows the user to still preserve the full astrophysical signal while effectively removing most of the zero-DM RFI. The full recipe for this technique is provided in [12].
## 3 Results and Discussion  
### Fabrication of Micropillar and Microporous Structures onto PU Foil  
The fabrication procedure of the topographical micropillar structures as well as microporous structures at the cross-section of PU foil is schematically illustrated in Figure 1. Silicon templates, covered by arrays of post hole microstructures (negative to the micropillar arrays, see Figure 1g), were used to imprint micropillars (Figure 1a). Applying a temperature of 80 \({}^{\circ}\)C and a pressure of 5 MPa, PU melted and flowed into the post holes, yielding micropillar arrays on the surface (Figure 1b). Pressure-induced saturation [36]with supercritical CO2 was employed to generate microporous structures inside the PU. This method produced pores at the cross-section but left a thin free layer close to surface, which might have been caused by constraint from the post holes in the silicon template. Such constraint probably inhibited the pore growth near the pillar surface. The thickness of such a pore-free layer was about 25 \(\upmu\)m, as shown in Figure 1f. Increasing the saturation pressure up to 20 MPa and temperature to 60 \({}^{\circ}\)C led to the dissolution of CO2 within PU. After a rapid pressure quenching, CO2 nucleated into many tiny pores (Figure 1c) and the pore size gradually grew (i.e., forming process) by CO2 diffusion (Figure 1d). Finally, micropillar and microporous structures were obtained on the PU foil after cooling down the whole setup and demolding (Figure 1e). Pillar dimension such as side-to-side spacing (s), height (\(h\)), as well as diameter (\(d\)) are defined in Figure 1h. Figure 1i shows an optical photograph of the fabricated microstructured PU foil. The foil appeared translucent due to the light regulation by micropillars and pores. The foil size was around 30 mm \(\times\) 20 mm \(\times\) 1 mm.
## 2 Theoretical Derivation of PIC Model
### Contact Pressure Caused by Wire Twisting
#### 2.2.1 Diagonal Contact Points  
If the contact points between the wires are situated along the diagonal of the cable, as Figure 4(a.2) shows, the path for the inward transmission of contact pressure is distinctly clear. All the upper-layer wires transmitting contact pressure are aligned along the line connecting the upper-layer wire to the central wire. The contact pressure generated by twisting can thus be directly transmitted to the calculated contact point and can be directly computed as follows:  
\[q^{\langle k\rangle}_{1(i,1)}=\sum_{l=1}^{n}q^{tw}_{(i+l,1)} \tag{2}\]  
where \(\langle k\rangle\) represents the position of the contact point on the wire, and can take values 1, 2, 4, and 5.  
For instance, in the case of Figure 4(a.1), the calculation of the contact pressure at point \(\langle 2\rangle\) on the wire \((i,1)\) involves the superposition of all the contact pressures from the upper-layer wires. This can be expressed as follows:  
\[q^{\langle 2\rangle}_{1(i,1)}=q_{(i+1,1)}+q_{(i+2,1)}+q_{(i+3,1)} \tag{3}\]  
For contact points on different layers of wires, the contact pressure on either side of the same wire is distinct, with the difference being equal to the twisting force exerted by that wire, as shown in Figure 5b by \(q^{\langle 4\rangle}_{1(5,1)}-q^{\langle 1\rangle}_{1(5,1)}=q^{tw}_{(5,1)}\). Conversely, the contact  
Figure 4: **(a.1)** Diagonal contact points; **(a.2)** superposition law of twisting force along the diagonal; **(b.1)** non-diagonal contact points in different layers; **(b.2)** superposition law of twisting force in different layers; **(b.3)** angle between twisting force and contact point; **(c.1)** non-diagonal contact points in same layer; **(c.2)** superposition law of twisting force in the same layer. Orange circle indicate central wire.  
pressure between two adjacent wires at a contact point is identical, as shown in Figure 5 by \(q_{1(6,1)}^{\langle 4\rangle}=q_{1(5,1)}^{\langle 1\rangle}\).
## 3 Spectral Library and Dataset
### Dataset  
Generally, datasets of hyperspectral scenes contain two parts: one is HSI data cubes, and the other is label images indicating the classes of each pixel. In this section, we create an active HSI dataset, which can be used for designing and testing the active HSI classification algorithms. In the experiments, the dataset provides the testing set for the classification.  
The active HSI data cube consists of \(568\times 696\) pixels and 174 spectral bands in the wavelength range of 400-1000 nm, whose false-color map has been shown in Figure 3b. The file of the active HSI data cube is given the suffix \"active_HSI.mat\". Then, a hand-drawn label image of the active HSI data cube is delineated in details using Image Labeler app, shown in Figure 6. Similar to the method of ROIs selection, shadow areas and edge areas of objects (the black areas in Figure 6) are avoided, for the existence of degenerative or hybrid optical signatures from different materials. The label image file is given the suffix \"active_HSI_label.mat\", comprising \(568\times 696\) pixels. The pixels number of each object are listed in Table 2. The labels are consistent with that of the spectral library, for the convenience of classification model training and testing. Among them, 'Label 0' refers to the black areas, which are not grouped into meaningful classes. The information about  
various tasks including object classification [21]. At present, DenseNet is one of the popular supervised deep-learning-based models and has proved to be very effective for passive HSI classification task [22; 23; 24]. In this section, we will explain the proposed hybrid DenseNet classification model in detail.
## 2 Absorber Design
The MMA design chose the h1 = 120 nm parametric value, which completely blocked the transmission and provided a higher absorption level. The dielectric thickness (SiO\({}_{2}\)) also significantly influenced higher absorption levels. The parameters h2 represents the dielectric thickness, and the absorption at different dielectric thicknesses is presented in Figure 3b. It is shown that the absorption value was higher in the lower optical wavelength region than the upper optical wavelength region for h2 = 40 nm and 50 nm. On the other hand, for higher thicknesses, the absorption value was higher in the upper optical wavelength region than the lower optical wavelength region. The maximum average absorption level was achieved at h2 = 60 nm. The thickness of the MMA patch was also investigated for h3. A constant absorption was found for different h3 values with slight distortion in the upper optical wavelength region. For maximum absorption level, h3 = 15 nm was settled. Figure 3d shows the impact of the outer square ring resonator from 660 to 675 nm with a 5 nm interval, where a stable absorption bandwidth was realized, and very few deformations were found in the upper wavelength region. W1 = 670 nm was chosen for the higher absorption level. The absorption at different values of the inner square ring length (W2) from 310 to 390 nm is presented in Figure 3e, which shows a slight distortion in the absorption curve. Figure 3f demonstrates the effect of inner ring slot length (W3) from 130 to 160 nm, showing a stable absorption for different values. The investigation showed that the optimized top layer parameters (h3, W1, W2, and W3) of the proposed design will provide a sound absorption level. Figure 2: Absorption of (**a**) different layers and (**b**) design evolution.
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.4 Decoding Energy Reduction Performance
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Proposed L2**} & \multicolumn{2}{c}{**Proposed L2**} & \multicolumn{2}{c}{**[PERSON] et al. [20]**} & \multicolumn{2}{c}{**[PERSON] et al. [34]**} & \multicolumn{2}{c}{**[PERSON] et al. [46]**} \\  & **(Model Only)** & **(Model + LF [34])** & **(PUM + DBLK)** & \multicolumn{2}{c}{**(MC + LF)**} & \\ \cline{2-11}  & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% & \(\Delta E\)\({}^{\dagger}\) \% \\ \hline Band & \(-\)1.56 & \(-\)5.61 & \(-\)3.49 & \(-\)7.71 & \(-\)1.16 & \(-\)3.49 & \(-\)2.34 & \(-\)5.47 & \(-\)1.56 & \(-\)5.03 \\ Beergarden & \(-\)2.22 & \(-\)3.73 & \(-\)2.78 & \(-\)5.34 & 0.19 & \(-\)2.53 & \(-\)2.75 & \(-\)5.52 & \(-\)0.19 & \(-\)4.91 \\ Cafe & \(-\)6.28 & \(-\)12.61 & \(-\)9.87 & \(-\)14.26 & \(-\)5.60 & \(-\)7.41 & \(-\)8.02 & \(-\)13.83 & \(-\)8.79 & \(-\)12.38 \\ Dancer & \(-\)2.17 & \(-\)5.19 & \(-\)5.11 & \(-\)7.59 & \(-\)1.83 & \(-\)4.13 & \(-\)4.98 & \(-\)8.42 & \(-\)4.02 & \(-\)6.51 \\ GTFly & \(-\)6.76 & \(-\)11.26 & \(-\)8.79 & \(-\)13.17 & \(-\)3.20 & \(-\)5.48 & \(-\)8.39 & \(-\)11.39 & \(-\)7.11 & \(-\)10.58 \\ Kimono & \(-\)4.55 & \(-\)11.16 & \(-\)5.92 & \(-\)12.61 & \(-\)7.2 & \(-\)6.81 & \(-\)8.56 & \(-\)10.96 & \(-\)4.90 & \(-\)10.00 \\ Musicians & \(-\)2.11 & \(-\)6.24 & \(-\)4.06 & \(-\)6.86 & \(-\)1.12 & \(-\)3.64 & \(-\)1.53 & \(-\)4.85 & \(-\)2.68 & \(-\)5.56 \\ Parkscene & \(-\)3.82 & \(-\)6.74 & \(-\)4.14 & \(-\)7.33 & \(-\)1.69 & \(-\)3.52 & \(-\)5.32 & \(-\)8.82 & \(-\)3.61 & \(-\)7.79 \\ Poznan St. & \(-\)6.57 & \(-\)7.41 & \(-\)6.71 & \(-\)7.04 & \(-\)2.22 & \(-\)8.36 & \(-\)4.54 & \(-\)7.12 & \(-\)4.23 & \(-\)7.51 \\ \hline
**Average** & \(-\)**4.00** & \(-\)**7.77** & \(-\)**5.65** & \(-\)**9.10** & \(-\)**2.22** & \(-\)**5.04** & \(-\)**5.15** & \(-\)**8.48** & \(-\)**4.12** & \(-\)**7.80** \\ \hline \hline \end{tabular}  
* \(\Delta E\%\) achieved when using Linux ondemand frequency governor.
* \(\Delta E\%\) achieved when using an application-specific DVFS algorithm as the frequency governor.  
\end{table} 
Table 4: Energy reduction performance during video streaming. Figure 6: An illustration of the variation of the \(\Delta E\), \(\Delta\)PSNR ratio, i.e., \(\widehat{\Delta E}\) (%/dB), with respect to the bit rate for (**a**) Dancer, (**b**) Musicians and (**c**) Parkscene HD test sequences.