esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Guided Manufacturing of Transducer Arrays Based on Single-Fibre Piezocomposites
## 2 Materials and Methods
### PZT Fibre Disc  
The centrepiece of the new TAS are single-fibre piezocomposites arranged in a polymer disc of 24.6 mm diameter and 0.75 mm thickness (see Figure 4). Each disc contains 17 fibres with a fibre diameter of 450 \(\upmu\)m. The chosen dimensions were previously evaluated to optimise the emission characteristics [10]. The fibres are pseudo-randomly distributed on the disc. Simulation  
Figure 3: Work-flow for transducer assembly, modelling and analysis. The EMI magnitude and phase was measured at four instances. A basic model was set by fitting the model response to the initial PZT fibre response. The model is then extended by adding additional layers which simulate the following assembly steps.  
Figure 2: One TAS before and after the assembly: (**a**) PCB, PZT fibre disk and matching layer in stacked view (top to bottom); and (**b**) finalised TAS before attaching the housing.  
predicted this distribution to be beneficial in reducing aliasing effects in the reconstructed image [19]. In addition, each disc features three holes which act as mounting aids (pinholes), two holes for ground connection (sickle-shaped electrodes at the rim) and one for placing a temperature sensor (bigger, more centrally aligned).  
The fibres are produced with a polysulfone spinning process from commercial PZT powder (Sonox P505, CeramTec, Plochingen, Germany) [20]. Single fibre strands are then positioned into a mask according to the required pattern. This arrangement is placed into a mould and filled with epoxy adhesive (EpoTek 301-2, Epoxy Technology Inc., Billerica, MA, USA). After curing, the fill is machined into shape and diced. More details of the PZT fibre disc manufacturing are given in [13].  
To electrically connect the transducers, one side of the disc is uniformly gold sputtered (see Figure 4a). The other side contains anchor-shaped bottom electrodes to allow separated conductive connection and acoustic backing (see Figure 4b). After surface preparation (cleaning with Isopropanol and wipe), the bottom electrodes showed abrasions, which led to conductive failures. Therefore, a 50-m conductive adhesive electrode layer (EJ2189-LV, Epoxy Technology Inc., Billerica, MA, USA) was printed to the bottom of the PZT fibre disc (see Figure 4c). To avoid twisting caused by thermal mismatch, the adhesive was cured at room temperature.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 2 Network and Channel Model  
In this section, we first introduce our target multi-hop underwater acoustic sensor network model and then present an algorithm to estimate the packet loss rate under the underwater acoustic channel.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review
### Land Cover Classification with Advanced Segmentation  
In the year 2021, ([PERSON] et al., 2021) presented an innovative approach to semantic segmentation specifically tailored for the classification of land cover. Their method incorporated a selection of six deep learning architectures, namely pyramid scene parsing, U-Net, deeplabv3, a path aggregation network, an encoder-decoder network, and a feature pyramid network. Through the application of these advanced architectures, their methodology yielded exceptional results, showcasing a significant advancement in the accuracy of land cover classification in comparison to existing techniques [21].
###### Abstract  
For precision measurement of miniature internal structures with high aspect ratios, a spherical scattering electrical field probe (SSEP) is proposed based on charge signal detection. The characteristics and laws governing surface charge distribution on the probing ball of the SSEP are analyzed, with the spherical scattering electrical field modeled using a 3D seven-point finite difference method. The model is validated with finite element simulation by comparing with the analysis results of typical situations, in which probing balls of different diameters are used to probe a grounded plane with a probing gap of 0.3 \(\upmu\)m. Results obtained with the proposed model and finite element method (FEM) simulation indicate that 31% of the total surface charge on a \(\phi\)1 mm probing ball concentrates in an area that occupies 1% of the total probing ball surface. Moreover, this surface charge concentration remains unchanged when the surface being measured varies in geometry, or when the probing gap varies in sensing range. Based on this, the SSEP has realized approximate point probing capability with a virtual \"needle\" of electrical effect. Together with its non-contact sensing characteristics and 3D isotropy, it can, therefore, be concluded that the SSEP has great potential to be an ideal solution for precision measurement of miniature internal structures with high aspect ratios.  
surface charge distribution; point probing characteristics; spherical scattering electrical field probe; miniature internal structures; high aspect ratios +
Footnote †: journal: Applied Physics Letters
## 2 Piezolectric Harvesters (PEHs)
### Piezolectric Generators--Equivalent Circuits and Basic Parameters
In energy harvesting systems, however, the generated energy is not fully used to power the load at the time of its generation, and it must also be stored. In [28], the authors describe in depth the steps for analyzing the matching of the piezoelectric source to the load in these systems. They begin by analyzing the mechanical system and deriving the equivalent electrical circuit, then analyze the load circuit with several energy conversion options with an analytical and graphical representation in the complex plane of each. In the complete electrical equivalent circuit of the piezoelectric load system shown in Figure 4, \(v_{eq}(t)\), \(X_{L}\), \(X_{C}\), and \(R\) are the source equivalent model parameters, corresponding to the mechanical parameters of velocity, mass, damping, and stiffness, and \(X_{E}\), \(R_{d}\), and \(R_{h}\) are the electrical part parameters. In it, it is noticed that the values are not only variable but also dependent on each other. \(R_{d}\) and \(R_{h}\) have the same electrical function of an active load but differ in that losses are denoted by \(R_{d}\) and harvested energy by \(R_{h}\). They propose a Formula (5) that describes the dependence of the harvested energy \(P_{h}\) on the parameters in the equivalent circuit, where \(V_{eq}\) is the magnitude of the equivalent source \(V_{eq}\), and \(Z_{sys}\) is the impedance seen by it. \[P_{h}=\frac{v_{eq}^{2}}{2}\frac{R_{h}}{\left|Z_{sys}\right|^{2}}=\frac{v_{eq} ^{2}}{2}\frac{R_{h}}{\left(X_{L}+X_{C}+X_{E}\right)^{2}+\left(R+R_{d}+R_{h} \right)^{2}}, \tag{5}\]  
where in the mechanical domain \(v_{eq}\) is the equivalent voltage source, \(X_{L}=\omega L\) (the equivalent inductor \(L=m\) takes into account the effective beam mass \(m\) [kg] or the inertia of the generator), the equivalent resistance \(R=d\) (\(d\) [kg/s]) represents the mechanical damping,  
Figure 4: Simplified circuit configuration of system piezoelectric energy harvester. Figure 3: Equivalent circuit diagram of a piezoelectric element: (**a**) circuit variant using a current source, according to the [PERSON] theorem; (**b**) circuit variant using voltage source, according to [PERSON]’s theorem. \(X_{L}=1/\omega C\) (the equivalent capacitor \(C=1/\)k represents the reciprocal mechanical stiffness \(k\) [N/m]), and \(X_{E}\) is the reactance of the electrical part of the PEH (the values of \(R_{d}\), \(R_{h}\), and \(X_{E}\) are not only variables (denoted with arrows) but also related with each other (illustrated with dot links among the three components)). As a consequence of this formula, they present the condition for achieving maximum power in the general case, presented as the Formula (6). \[R_{h}=(X_{L}+X_{C}+X_{E})^{2}+(R+R_{d})^{2}. \tag{6}\]
## 4 Numerical Model and Materials
### Refined Model of Anchor Joint  
#### 4.2.1 Refined Numerical Model of Anchor Joint  
Figure 8 shows the 3D refined numerical model and mesh of anchor joint, and two segments are also included in this model. Each segment has a width of 0.75 m, a center angle of 22.5\({}^{\circ}\), and the same thickness with that in the soil-tunnel system. The detailed dimensions of the anchor joint can be found in [40]. The solid C3D8 element is employed to simulate both the anchor joint and segment, and finer mesh is used for the anchor joint as well as the adjacent contact areas for reliable accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case No.** & \begin{tabular}{c} **Incident** \\ **Direction** \\ \end{tabular} & \begin{tabular}{c} **Transverse** \\ **Direction** \\ \end{tabular} & \begin{tabular}{c} **Peak** \\ **Acceleration** \\ \end{tabular} & \
\begin{tabular}{c} **Axial** \\ **Direction** \\ \end{tabular} &
\begin{tabular}{c} **Peak** \\ **Acceleration** \\ \end{tabular} \\ \hline
1 & Transverse & Wave 1 & 0.15 g & - & - \\
2 & Axial & - & - & Wave 1 & 0.15 g \\
3 & Two-directional & Wave 1 & 0.15 g & Wave 1 & 0.1275 g \\
4 & Transverse & Wave 2 & 0.15 g & - & - \\
5 & Axial & - & - & Wave 2 & 0.15 g \\
6 & Two-directional & Wave 2 & 0.15 g & Wave 2 & 0.1275 g \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cases for seismic response analysis.  
Figure 7: Acceleration time histories of seismic waves. (**a**) Wave 1. (**b**) Wave 2.  
From Figure 8, it can be seen that the anchor joint herein consists of three components, i.e., rod, lantern ring, and sleeve. During the construction of the shield tunnel with the anchor joint, the circumferential joint can be directly connected without extra manual operation due to the automatic self-join of the anchor joint. Under the pressure of jack, the rod will be inserted into the sleeve, while the six sleeve walls will further deform to fill the gap between the rod and lantern ring. Based on the above linkage mechanism, the assembly of anchor joint will be completed, which provides high tensile strength.
# Special Issue on Ultrasonic Modeling for Non-Destructive Testing
## 3 Acoustic/Elastic Bulk Waves Propagation and Scattering  
The main principles of elastic or ultrasonic wave propagation and scattering in solids can be found in several classical textbooks [6; 7; 8; 9]. Simulating an NDT measurement generally requires modeling the propagation and scattering of ultrasonic waves from targets/flaws/damage/interfaces [10]. Developed simulation tools may rely on different mathematical/physical theories or assumptions; for instance, semi-analytical, numerical, and hybrid models [11] may be used for direct simulation and model benchmarking to ensure their confidence level [12; 13].  
[PERSON] et al. developed a theory to obtain the Green function in a lossless layered isotropic elastic medium from two-sided data. To that aim, they introduced an alternative Marchenko equation and two other equations using both reflection and transmission data.  
Since the ultrasonic wave propagation field can be seen as spatio-temporal data, [PERSON] and [PERSON] developed two different spatio-temporal deep learning (SDL) models that simulate forward and reflected ultrasonic wave propagation in 2D. The training was ensured by using data obtained from finite element simulations.  
In order to simulate the ultrasonic inspection of embedded cracks in 3D configurations, an experimental and theoretical comparison of 3D models was performed by [PERSON], [PERSON] and [PERSON]: they notably compared the analytical Physical Theory of Diffraction to a hybrid method in terms of flaw scattering. Based on the spectral finite element method, the hybrid method can provide a finer prediction in configurations involving small flaws or head waves (first arrival waves [14] of a complex nature that are complicated to simulate analytically [15; 16]).  
For NDT applications, ultrasonic wave generation can involve physics other than piezoelectricity. In the framework of photoacoustics, [PERSON], [PERSON], [PERSON] et al. numerically investigated the efficiency of laser-excited ultrasounds (surface and bulk elastic waves) using phased arrays by comparing a finite element method to k-wave simulations and by varying the array characteristics.  
The simulation of the scattering of waves from obstacles in fluids also remains a significant issue [17]; it requires first- modeling ultrasonic wave propagation in fluids [18]. [PERSON] and [PERSON] extended the spec-radiation method based on acoustic holography to consider propagation in multiple fluid layers. This technique was experimentally validated on a wooden particleboard assumed to behave as a fluid.  
[PERSON], [PERSON] et al. developed a high-performance ultrasonic simulation tool for inhomogeneous moving fluids. The CFD simulation of a real experiment of liquid metal jet mixing was performed, and the moving fluid's temperature (varying in time) served as the input toa spectral finite element model for wave propagation. The authors showed that an ultrasonic transmission measurement system can monitor the principal flow fluctuation.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 1 Introduction
With the development of deep learning, a dense encoder-decoder network was proposed by [PERSON] et al. [17] for 3D particle field measurement. This method still suffers from a long training time, the use of a single recognition model, and the existence of a priori knowledge. In addition, entropy-minimization-based methods are gradually emerging [18; 19; 20]. These methods involve calculating the local minimum of the entropy value in a reconstructed image sequence and considering it the focused section. However, such approaches require the reconstruction of an entire image sequence, which can be computationally intensive. [PERSON] et al. also utilized correlation algorithms for cell classification and reconstruction [21]. The correlation algorithms used in this method focus on the classification of healthy and non-healthy cells, and cellular localization is determined by thresholding the resolution and thickness distribution of the system. In this article, we propose an algorithm based on image inverse cross-correlation, which is designed to realize the large-depth-of-field digital holographic reconstruction of multiplanar objects. The fundamental concept behind this algorithm is the use of adjacent reconstructed images for inverse cross-correlation computation to effectively capture the edge information of the object in an image, thus allowing one to accurately locate the edge position of the object and further segment the plane where the object is located. Through the integration of the depth estimation operator, the optimal reconstructed distances are calculated for each segmented plane, thus realizing multi-plane digital holographic imaging with a large depth of field. Additionally, the reconstruction of overlapping objects can be achieved. Compared to traditional methods, the advantages of this algorithm are its simplicity and efficiency. This approach capitalizes on the synergy of image inverse correlation and depth estimation, thus eliminating the need for intricate optical systems or mechanical devices. A potential application of the proposed method lies in a multiplanar large-depth-of-field digital holographic imaging, in which it will provide a new solution for realizing high-quality multi-object three-dimensional scene reproduction.
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attack Based on Access Level of an Intruder  
#### 5.2.1 Active  
This type of attack is performed by an adversary through modification and theft of data. In active attacks, an attacker performs some operation in order to hamper the confidentiality, availability and integrity of data. An adversary performs the attack by packet modification, false data injection, resource exhaustion and node capturing. Spoofing, jamming, wormhole, hello flood, DOS, sink hole, etc. are the examples of active attacks [72; 73; 74; 75; 76].
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 5 Regulators in Cell Fate Determination and Embryonic Development
tRNA adenosine deaminase ADAT2-ADAT3, which catalyzes the conversion of adenosine to inosine at position 34 (A-to-I) of certain tRNAs, shows higher levels in self-renewing hESCs, as well as a higher abundance of I34-modified tRNAs, while the differentiation treatment of hESCs leads to the downregulation of ADAT2-ADAT3 and I34 tRNA abundance. Additionally, growing evidence shows the crucial roles of tRNA modification enzymes in maintaining normal lineage specification during early embryogenesis. The deletion of \(NSun3\), a tRNA m\({}^{5}\)C modifier, leads to embryonic lethality between E10.5 and E12.5 in mice [163], and the deletion of elastin-like polypeptide 3 (ELP3), a tRNA U methylation enzyme, causes early embryonic lethality between E7.5 and E12.5 with severe growth retardation in mice [164]. Instead of the precise regulation of tRNA pools, the expression profiles of tsRNAs show dynamic alteration during embryonic development and stem cell fate commitment. tsRNAs derived from tRNA\({}^{\text{Gly}}\), tRNA\({}^{\text{Glu}}\), and tRNA\({}^{\text{Val}}\) show significant upregulation after fertilization and become particularly abundant in the eight-cell stage embryo in mice [165]. Notably, the inhibition of tsRNA\({}^{\text{Glu(CTC)}}\) in an in vitro conditioned medium of preimplantation embryos results in the heightened expression of actin-related genes, fostering the formation of actin-based troplectoderm projections and significantly increasing embryo hatching rates [166]. In embryoid body (EB) formation assays, the upregulation of tsRNAs derived from tRNA\({}^{\text{Ala}}\), tRNA\({}^{\text{Arg}}\), tRNA\({}^{\text{Glu}}\), tRNA\({}^{\text{His}}\), and tRNA\({}^{\text{Lys}}\) is observed during mESC differentiation. Transecting these tsRNAs into mESCs enhances lineage differentiation during EB formation, suggesting that certain tsRNAs are directly involved in the differentiation process of mESCs [133]. In differentiating mESCs, the set of 5\({}^{\prime}\)tsRNAs remains consistently enriched. The use of antisense oligonucleotides to inhibit the upregulation of 5\({}^{\prime}\)tsRNAs suppresses the differentiation process in mESCs, maintaining their pluripotency. This finding underscores the role of 5\({}^{\prime}\)tsRNAs in promoting differentiation while inhibiting self-renewal [167].
## 5 Analysis of Simulation Results
### Analysis of Characteristic Parameters  
The change of characteristic parameters with the increase of the straight pipe length under different installation conditions is shown in Figures 18 and 19.  
The Profile factor decreased with the straight pipe length increasing and met the requirements at about 10D with FC. The Symmetry and Cross-flow were close to 1 and kept relatively stable at about 10D, and there was no obvious difference between the two sections (Figure 18). Combined with Figure 16, the \(v_{x}\) of path B and C increased, and \(v_{x}\) of path A and D decreased, resulting in a Profile factor that was as high as 1.5 at 5D.  
Figure 16: Contours of velocity profiles on the different cross-sections downstream of the header with FC.  
Figure 17: Velocity distributions on individual paths with FC. (**a**) 10D; (**b**) 15D.  
However, without FC, the Profile factor increased with the straight pipe length and met the requirements at about 25D. The Symmetry and Cross-flow varied dramatically at different positions, and the value could be acceptable at about 20D. In addition, the Symmetry were obvious different in the two sections (Figure 19). Combining Figure 14 with Figure 17, it was clear that the swirl occurred in the experimental pipe section (as shown in Figure 10) due to the header effect. The characteristic parameters were significantly affected by the swirl. When the FC was installed, all the values of the above three characteristic parameters could be acceptable at about 10D.  
The variation of characteristic parameters at 19D corresponding to the experimental condition with the increase of Reynolds number or velocity is shown Figure 20. The Profile factor at 1 m/s was above the maximum allowable value both with or without FC. This was because the boundary layer effect was significant during the simulation process, which resulted in a lower velocity near the pipe wall, leading to the higher Profile factor. With FC, all the characteristic parameters met the requirements from 5 to 15 m/s. Nevertheless, the Symmetry and Cross-flow changed a little with the increasing of Reynolds number without  
Figure 19: Characteristic parameters without FC.  
Figure 18: Characteristic parameters with FC.  
FC, while the Profile factor dropped sharply, indicating that the effect of flow distortion was more significant with the increase of Reynolds number.  
When the FC was not installed, the values of the Symmetry and Cross-flow were acceptable, while the value of the Profile factor was not. So, the difference occurred with different installation condition, which was consistent very well with the experimental results. It was proved again that Profile factor was an important indicator to characterize the velocity distribution.
## 5 Numerical Results  
The efficiency of the proposed ICOA method has been simulated by using three standard tests with the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems considering three single-objective functions consisting of power-loss minimization, total-voltage deviation, and \(L\) index. The optimal solution search program of the proposed method has been coded on MATLAB program language and executed on a personal computer with a 2.4 GHz processor and a 4 GB RAM. In addition to comparisons with other methods available in previously published studies, we have also employed other state-of-the-art and popular methods such as salp swarm algorithm (SSA) [60], sunflower optimization algorithm (SFOA) [61], water cycle algorithm (WCA) [62], standard cyoote optimization algorithm (COA) [56], improved cyoote optimization algorithm with the first modification (COA1),  
Figure 4: The flowchart of solving the ORPD problem by using the proposed ICOA method.  
and improved coyote optimization algorithm with the second modification (COA2). For finding optimal solutions, each method has been run 50 times in independent trials for each objective of each system. The setting of the adjustment parameters of the proposed method and other ones as well as test cases are summarized in Table 1.  
It is noted that for each study system corresponding to each objective, the proposed ICOA has been run two different cases as mentioned in Section 2.  
Case 1: All control variables are set to continuous variables (CV).  
Case 2: Reactive power output of shunt capacitors and tap changer of transformers are set to discrete variables (DV) in which a change step of reactive power output is 0.1 MVAR and that of tap changer is 0.01 pu.  
The two cases are indicated in each comparison table because it is very important in evaluating the performance of methods. Basically, methods using continuous variables tend to obtain smaller fitness function than using discrete variables. For comparison with other implemented methods, we run ICOA and these methods with continuous variables only.