esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Acoustic Emission Characterization
#### 3.3.2 AE Characteristics under Cyclic Loading  
Figure 18 shows the evolutions of stress, AE counts and cumulative AE counts over time for specimens under cyclic loading at different cyclic stress steps and loading-foliation  
Figure 17: Evolutions of stress, AE counts and cumulative AE counts with time for specimens under monotonic loading: (**a**) \(\beta=0^{\circ}\); (**b**) \(\beta=30^{\circ}\); (**c**) \(\beta=45^{\circ}\); and (**d**) \(\beta=90^{\circ}\).  
angles. In general, the AE counts show a similar variation trend, in that a large number of AE counts are generated during the loading process, especially in the pre-load and near-peak loading stages, while very few AE counts are generated during the unloading process. It is found that the AE counts of the specimens at \(\beta\) = 0\({}^{\circ}\) are mainly distributed in the near-peak region, which is consistent with the Kaiser effect, whereas the AE counts of the specimens at the other loading-foliation angles are mainly distributed in the early stage of each cyclic-loading process, exhibiting an obvious Felicity effect. This implies that the foliation planes in specimens can be repeatedly compacted over a low stress range, resulting in a large number of AE counts each time in the early stage of the loading process. In addition, for the specimens loaded at \(\beta\) = 0\({}^{\circ}\) and \(\beta\) = 90\({}^{\circ}\), regardless of cyclic stress step, the maximum stress level reached in the last loading process is smaller than that in the previous loading process, with a large number of AE counts occurring at the peak of the previous cycle, which indicates that the accumulated damage in these specimens before the last cycle has already reached a very high level.
# Effect of Aggregate Type on the Shear Behavior of Reinforced Lightweight Concrete Beams
## 1 Introduction
Considered test variables included concrete strength, the shear span-to-depth ratio (_a/d_), and the amount of shear reinforcement (_r\({}_{w}\)_). Their findings highlighted the pronounced effect of concrete strength on shear ductility as _a/d_ increased and the significant influence of increased _r\({}_{w}\)_ in settings with higher _a/d_[27]. [PERSON] focused on the crack patterns and shear behavior of reinforced LWAC beams, noting brittle cracking and reduced shear resistance after diagonal cracking compared to that of NWC [28]. [PERSON] et al. studied the effects of concrete strength and reinforcement ratios on the bending behavior of reinforced LWAC beams, observing that higher concrete strengths led to increased vertical displacement and curvature, especially in beams with a low tensile reinforcement ratio [29]. [PERSON] and [PERSON] analyzed a comprehensive dataset to propose a modification factor for estimating the shear capacity of reinforced LWAC beams, enhancing prediction accuracy beyond the existing ACI 318 shear provisions [30]. [PERSON] et al. investigated the flexural ductility of reinforced LWAC beams with compressive strengths ranging from 22 to 60 MPa, identifying the tensile reinforcement ratio as a crucial factor affecting flexural ductility [31]. [PERSON] et al. compared lightweight self-consolidating concrete (SCC) beams to normal SCC beams, finding that the lightweight beams exhibited lower shear strength, decreased post-cracking shear resistance, more cracks, and wider crack widths [32]. Despite extensive research focused on the shear capacity of LWAC beams, the effects of different aggregate types and properties of LWAs on shear capacity remain incompletely understood. Moreover, most studies have concentrated on the structural behavior of lightweight concrete beams prepared with a lightweight CA and a normal FA rather than on all lightweight aggregate concrete (ALWAC) beams, which utilize both types of lightweight aggregates. A comprehensive understanding of how aggregates influence the shear behavior of reinforced concrete (RC) beams is essential for more accurate shear strength prediction and effective structural design of LWAC structures. To further this understanding, experiments were conducted on a total of twelve RC beams: four made of NWC and eight of ALWAC. Additionally, concrete mix proportions were developed and introduced to produce ALWAC with a density of less than 1600 kg/m\({}^{3}\).
# Dielectric Characterization of Chinese Standard Concrete for Compressive Strength Evaluation
## 3 Experimental Results
### Compressive Strength after 28-Day Curing  
In order to obtain the average strength values, three 150-mm cubes were used for the compressive strength tests of the GB concrete: C30, C35, C40, C45, and C50. All concrete specimens were moist-cured in a curing room for 28 days. The compression test was undertaken after the measurements of complex permittivity had been recorded, wherein all surfaces of the concrete specimens were deliberately dried, as the surface moisture will sensitively affect the accuracy of the measured permittivity. Figure 4 shows the average strength of each GB concrete type with the corresponding standard deviation, where the nonlinear relationship between the compressive strength and water-to-cement (\(w/c\)) ratio, was observed. From the strength results, all fabricated GB concretes were verified as fulfilling the Chinese standard JGJ55-2011 [2]. The average 28-day compressive strengths grow nonlinearly with decreasing \(w/c\) ratios, in a way that follows a 2-term power law, as given by:  
\[f^{\prime}_{\text{cs}}(w/c)=1.57(w/c)^{-2.75}+30.5 \tag{3}\]  
Figure 4: Measured average compressive strength and standard deviation versus \(w/c\) ratio.  
Figure 3: Experimental setup of dielectric constant measurement.  
Equation (3) is obtained by the curve fitting of the six measured data, with the essential goodness-of-fit, as shown in Figure 4. One can estimate the compressive strength of the GB concrete using Equation (3), as long as the \(w/c\) is given in the range of 0.40 to 0.60.
## 3 Results
### Data Results Related to the 3 rd Set of Surveys  
Additional analysis was undertaken to visualize the spatial distribution of the IL obtained during the third set of measurements. This set of measurements is focused on two types of barriers, related to configuration 4, composed of four rows of type A diameter  
Figure 8: 3D view of the spatial distribution related to configuration 4, at 5 kHz (**a**) and 6.3 kHz (**b**). Distance between sound source and edge of barrier (_Xs_) is equal to 800 mm. Distance between receiver and edge of barrier (_Xr_) is variable and corresponds to 25 positions of a 200 \(\times\) 200 mm grid.  
sticks, and configuration 6, composed of four rows of type A diameter sticks and two rows of type B diameter sticks.  
Based on previous results, a deepening study of the sound source position 800 mm from the barrier was investigated. Acoustic maps were elaborated for the frequency bands equal to 5 kHz and 6.3 kHz, which are the most representative of higher IL values. The acoustic maps were realized with OriginPro2022 software [31], employed to plot the measured results obtained by 25 receiver positions.  
Figure 8 shows the IL spatial distribution related to configuration 4, at 5 kHz and 6.3 kHz.  
Both graphs of Figure 8 indicate a non-uniform distribution of IL values; IL was found to be lower at a short distance from the barrier and increased at the 400 mm distance, equal to 8 dB. At 6.3 kHz, the greater the distance from the barrier, the less the sound attenuation, although this trend was not recorded at 5 kHz.  
Another example of IL spatial distribution is shown in Figure 9, related to configuration 6, at 5 kHz and 6.3 kHz, by adding two rows of type B diameter sticks.  
Figure 9 indicates a better efficiency in terms of IL by adding two rows of type B diameter sticks. In particular, in relation to the first graph, IL values up to 14 dB were found at a 400 mm distance from the barrier values around 8 dB at a 600 mm distance, and even lower at 200 mm from the barrier. Regarding 6.3 kHz, the IL values are more uniform across the grid, at around 10 dB, with a slight peak equal to 13 dB recorded at 400 mm distance from the barrier.  
Considering the distance between the source and the edge of barrier (_Xs_) equal to D, this third set of measurements demonstrates that a higher IL was found at D/2, while less attenuation was found very close to the barrier.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 6 Implementation of the FPGA-Based Embedded System  
The FPGA CYCLONE IV EP4 CE115F29C7 by INTEL was adopted for system development [29]. The design has been deployed on a DE2-115 development board by TERASIC for test and debug purposes [30]. The design environment is Quartus Prime by Intel/Altera [31], but it is portable on any FPGA foundry and development environment.
# Low-Order Electrochemical State Estimation for Li-Ion Batteries
###### Abstract
The EKF uses the nonlinear model for prediction and a local linearisation about the current estimate to predict the associated error covariance in dependence of measurement and process noise covariances, to achieve a local minimization of the estimation error covariance. Kalman Filter methods have already been applied by different groups to battery models, see, e.g., [22; 25; 44]. Besides these classical Kalman Filter approaches, e.g., particle filters (PF) and Adaptative Extended Kalman filter (AEKF) have also been used to predict the SOC in data-driven models but considering only the experimental and measurable outputs obtained from physical set-ups, leaving aside the underlying electrochemical behaviour (see, e.g., [45; 46; 47]). The combination of DMDc with Kalman filtering techniques has also been discussed in several studies (see, e.g., [48; 49; 50; 51; 52; 53]), given that both are very systematic and easily implementable methods providing a high degree of precession and efficiency. Having the above mentioned studies as points of departure, the purpose of the present study is to (i) extend the ROM proposed in [39] to include thermal dynamics, (ii) use the improved ROM for time-variant adaptation of the parameters of an ECM, and (iii) combine it with an EKF for estimation of microscopic (i.e., electrochemical) and macroscopic (i.e., voltage-current, SOC) states. The performance of the proposed estimation scheme is established by simulation studies with a comparison to a high-fidelity numerical solution of a detailed electrochemical model [9; 54], in order to show the approximation quality achieved with the DMDc based ROM in combination with the EKF. The paper is organized as follows: First, in Section 2, the methodology is presented with a review of the mathematical equations of the single particle electrode model. This is followed by a derivation of the numerical model obtained by the solution through a finite difference scheme. Then DMDc is recalled and applied to the battery model to derive the ROM. In continuation, an ECM and its improvement by an adaptation scheme based on the ROM is presented. Section 3 includes simulation studies to show the performance of the proposed approach. Conclusions and an outlook are presented in Sections 4 and 5, respectively.
## 4 The Application of CNN and Other Deep Learning Algorithms
Multi-scale fusion structure, efficient communicative loss function, K-means++ clustering, and hyperparameter optimization are used to further improve the detection performance. This research solves the problem of missed detection to some extent [98]. The network structure of [98] is shown in Figure 9:8  
In addition, Table 2 summarizes the works using CNN and other deep learning methods, listing their data sources and data form. Figure 9:9 The network structure in [96]. \[Convolution(X,W)=\sum{(X*W)}+b \tag{8}\]0 The network structure in [98]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Year** & **Data Source** & **Data Form** & **Improvement** \\ \hline CNN & 2022 [98] & real data from a 5.0 & & YOLOv3-FDL, EloU \\  & 2022 [98] & khm section of a & highway (G210) in & C-scan+B-scan & loss function and K-Means++ \\  & & Zhejiang Province, China & & clustering \\ \hline Faster-RCNN & 2018 [69] & real data from several sites in & & \\ Faster-RCNN & 2018 [69] & France and synthetic data generated by gprMax & B-scan & pretraining \\ \hline Faster-RCNN & 2020 [70] & real data from four & & \\ Faster-RCNN & 2020 [70] & highways in & B-scan & \\  & & Shanai Province & & \\ \hline Faster-RCNN & 2018 [71] & real data collected by the subgrade status inspection vehicle & B-scan & feature cascade, adversarial spatial dropout network (ASDN), Soft-NMS, and data augmentation \\ \hline Faster-RCNN & 2019 [72] & synthetic data and on-site data & B-scan & data augmentation \\ \hline Faster-RCNN & 2022 [73] & real data from Zhengzhou & B-scan & Attention-guided Context Feature Pyramid Network \\ \hline Faster-RCNN & 2022 [74] & real data is measured in the field & B-scan & \\ \hline Mask-RCNN & 2020 [75] & real data from a concrete bridge deck & B-scan & a new loss function based on distance guided intersection over union. \\ \hline MS R-CNN & 2021 [76] & real data from UT & & \\ MS R-CNN & 2021 [76] & Gardens, Knoxville, USA & B-scan & customize the anchor’s aspect ratio \\ \hline Mask R-CNN & 2023 [77] & simulation data and real data obtained from S1I provincial highway measurements in Jinhua City, Zhejiang Province & & \\ \hline DBN & 2014 [78] & Eastern Test Range (ETR) and Western Test Range (WTR) & B-scan & \\ \hline DBN & 2022 [79] & the measured data of the built model & B-scan & \\ \hline convolutional autoencoder & 2018 [80] & real data from two different test sites & B-scan & \\ \hline SSD & 2020 [81] & real data from residential buildings in two construction sites & B-scan & \\ \hline SSD & 2021 [82] & simulated GPR data & B-scan & FPN, GloU \\ \hline CNN-LSTM & 2020 [83] & simulated GPR data & \\ CNN + Bi-LSTM & 2021 [84] & real data & B-scan & \\ \hline LSTM & 2021 [85] & simulated GPR data & B-scan & \\ \hline CNN+ Bi-ConvLSTM & 2022 [86] & simulation data and sandbox model & B-scan & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Parametric Analysis and Optimization of the Results of the 60-GHz Metasurface
#### 3.2.4 Thickness of the Square Loop, 'c'  
The analysis presented in Figure 8 shows that the thickness of the square loop 'c' has more impact on the resonant frequency depicted by the transmission coefficient plot than the reflection coefficient of the dual loop metasurface. From the plots in Figure 8, it can be deduced that as 'c' increases from 0.15 mm to 0.30 mm, the resonant frequency also increases, except at 0.10 mm, as shown by the transmission coefficient plot of Figure 8c. The reflection coefficient magnitudes at 60 GHz are approx. \(-\)0.2 dB at varying values of 'c' as shown in Figure 8b. The reflection phase falls within 3% of 0 deg as 'c' increases from 0.15 mm to 0.30 mm, except at 0.10 mm when it is about 10% as shown in Figure 8a. This may be due to the narrow space between the square loops which restricts the smooth flow of the surface current. A wide bandwidth that covers the desired frequency band if 60 GHz is observed at \(-\)20 dB reference.  
Figure 8: Reflection phase, reflection coefficient and transmission coefficient of the dual loop metasurface at the varied thickness of the square loop, ‘c’: (**a**) Reflection phase, (**b**) reflection coefficient and (**c**) transmission coefficient.  
Figure 7: Reflection phase, reflection coefficient and transmission coefficient of the dual loop metasurface at the varied inner radius, ‘b’: (**a**) Reflection phase, (**b**) reflection coefficient and (**c**) transmission coefficient.
## 4 Experiments and Performance Analysis
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Equation**} & \multirow{2}{*}{**Complexity**} & \multicolumn{3}{c}{**Pavia University**} & \multicolumn{2}{c}{**Moffett**} \\ \cline{3-6}  & & & **OM** & **PRT (Seconds)** & **OM** & **PRT (Seconds)** \\ \hline \multirow{2}{*}{\(s^{j+1}\)} & (17a) & \(\mathcal{O}\left(L_{b}(NL)^{2}\xi\right)\). & \(10^{16}\) & 31.225 & \(10^{16}\) & 57.537 \\  & (18) & \(\mathcal{O}((N^{2}r^{4}+NL)\xi)\) & \(10^{7}\) & 0.024 & \(10^{7}\) & 0.028 \\ \hline \multirow{2}{*}{\(u^{j+1}\)} & (17b) & \(\mathcal{O}\left(N^{3}L^{3}\right)\)) & \(10^{16}\) & 0.257 & \(10^{16}\) & 0.272 \\  & (19) & \(\mathcal{O}\left(N^{2}L\sqrt{L}\right)\) & \(10^{8}\) & 0.087 & \(10^{8}\) & 0.093 \\ \hline \multirow{2}{*}{\(a^{j+1}\)} & (21a) & \(\mathcal{O}(N^{4}M^{3}+(NM)^{2}\xi^{\prime})\) & \(10^{11}\) & 27.196 & \(10^{11}\) & 42.635 \\  & (22) & \(\mathcal{O}\left(N^{4}M^{3}+N^{2}L\right)\) & \(10^{10}\) & 0.023 & \(10^{10}\) & 0.107 \\ \hline \multirow{2}{*}{\(b^{j+1}\)} & (21b) & \(\mathcal{O}\left(N^{3}M^{3}\right)\) & \(10^{9}\) & 0.009 & \(10^{9}\) & 0.013 \\  & (23) & \(\mathcal{O}\left(M^{3}+NM^{2}\right)\) & \(10^{6}\) & 0.002 & \(10^{6}\) & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Complexity comparison of ADMM iterations in the proposed JSMV-CNMF algorithm for two datasets, in which the non-simplified (including Equations (17a), (17b), (21a) and (21b)) and simplified solutions (including Equations (18), (19), (22) and (23)) to various variables are compared for two datasets. Figure 8: The performance curves of (top row) RSNR and (bottom row) RMSE w.r.t. wavelength (i.e., spectral bands), including CNMF (black dash-dotted line), BSR (green solid line), HySure (cyan dashed line), CSTF (blue dotted line) and the proposed JSMV-CNMF (red solid line) for (**a**) Pavia University and (**b**) Moffett datasets, respectively.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 4 Building Blocks of the CPS Communication Hub
### Data Plane Operation Process  
The sharing of information using data plane negotiation capability is clarified in this section (see also the orange arrow in Figure 10). The basic operation is divided to control plane and data plane functions. It is expected that the physical resources first execute the registration of their presence into the virtual communication space of the owner as a control plane function. After that, the owners need to execute the trust checking process including mutual agreement and checking of the access control policies, otherwise no interaction can happen between the physical resources of the referred owners. These are also a control plane function. After such an agreement has been created, then any resource of the owners can interact/exchange messages between each other, as clarified earlier.  
If the information exchange via the CPS communication spaces is not enough efficient, for example, in the case of multimedia delivery or enterprise specific means is required to be used, then data plane operation can be a possibility. The data plane operation can be activated by executing so-called \"M2 MGrids negotiation\", where the applied data plane communication channel is negotiated as the control plane action first. This negotiation can happen between any two individual physical resources that have passed all the control plane actions clarified earlier. On the basis of the \"M2 MGrids negotiation\", the used end-to-end data plane format/protocol can be defined by the resources themselves. After the \"M2 MGrids negotiation\" has happened, the interaction between referred physical resources can happen, as the data plane functions directly without any involvement of the communication spaces related network/control plane processing using the agreed data plane means.  
Figure 10: Data plane of CPS communication hub.  
Figure 9: Information sharing algorithm.
## 3 Design Equations of Key Circuit Components in the Presented LED Driver  
### Design of Coupled Inductors \(L_{B11}\), \(L_{b12}\), \(L_{b21}\) and \(L_{b22}\)  
The coupled inductors (\(L_{B11}\) and \(L_{B12}\); \(L_{B21}\) and \(L_{B22}\)) are designed to be operated in DCM for naturally achieving PFC, and the design equation of them can be expressed as follows [12; 20]:  
\[L_{B11}=L_{B12}=L_{B21}=L_{B22}=\frac{\eta\cdot v_{ac-pk}^{2}\cdot D^{2}}{4 \cdot f_{s}\cdot P_{o}}, \tag{4}\]  
where \(V_{ac-pk}\) is the peak value of utility-line voltage; \(\eta\) is the estimated efficiency; \(D\) is the duty cycle of the power switches; \(f_{s}\) is the switching frequency; and \(P_{o}\) is the output rated power.  
In reference to Equation (4) with a \(\eta\) of 0.85, a \(V_{ac-pk}\) of \(220\sqrt{2}\)V, a \(D\) of 0.45, a \(P_{o}\) of 165 W and an \(f_{s}\) of 50 kHz, the coupled inductors \(L_{B11}\), \(L_{B12}\), \(L_{B21}\) and \(L_{B22}\) are given by:  
\[L_{B11}=L_{B12}=L_{B21}=L_{B22}=\frac{0.85\cdot\left(220\sqrt{2}\right)^{2} \cdot 0.45^{2}}{4\cdot 50k\cdot 165}=505\mu H\]  
Figure 6: Key waveforms of the presented LED driver for streetlight applications.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 2 System Model and Problem Formulation  
In order to study the UAV communication system, the radio propagation characteristics and LoS propagation characteristics of the UAV communication platform were studied first.