esa-sceva/satcom-chunk-collection huggingface dataset:
# Lattice Fracture Model for Concrete Fracture Revisited: Calibration and Validation
## 4 Validation
### Three-Point Bending Test  
#### 4.3.1 Size Effect  
Three-point bending tests performed by [PERSON] et al. [49] are simulated in this section to evaluate the LBM ability on flexural fracture analysis. Their research examines the size effect on the fracture response of concrete. These experimental samples, defined as large, medium, and small, are characterized by the span equal to 160 mm, 280 mm, and 400 mm, respectively, and the height equal to 40 mm, 70 mm, and 100 mm, respectively. All specimens have the same thickness and notch length to beam depth ratio equal to 0.4. During the experiment, the crack mouth opening displacement (CMOD) was measured to record the crack width. The mesh used in LBM is given in Figure 9a.  
Figure 8: (**a**) Comparison between numerical results and experimental results, (**b**) typical stress-displacement curve for tensile splitting tests.  
Figure 9: _Cont._The input parameters including \(E,f_{\text{c}}\), and \(f_{\text{t}}\) were calibrated by the best fitting of the Load-CMOD curve for the medium specimen and can be found in Table 6. Regarding crack patterns, a typical failure mode for a three-point bending test can be found in Figure 9b. A single localized crack started from the notch and propagated to the upper surface, which is the same as the crack pattern observed in the literature [49]. The crack pattern for the three model sizes was almost identical. This is in agreement with the experiments performed by [PERSON] et al. [52]. Also, Figure 9c shows that the numerical results agree well with the experimental ones before the peak load and are less satisfactory during the softening part. This deviation together with potential approaches are discussed in Section 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(E\) (GPa) & \(f_{\text{c}}\) (MPa) & \(f_{\text{t}}\) (MPa) & **Cell Size (mm)** & \(\alpha_{\text{N}}\) & **Poisson’s Ratio** & \(\alpha_{\text{m}}\) \\ \hline
20 & 75 & 7.5 & 2.5 & 1 & 0.2 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Values of input parameters in the numerical analyses about three-point bending test.  
Figure 9: (**a**) Geometry of three-point bending test specimen, (**b**) the comparison between experimental results with numerical results for three-point bending test, (**c**) crack pattern for the three-point bending test (red-cracked element; deformations have been scaled for clarity).
## 4 Conclusions and Further Work  
In this study, we demonstrated through empirical experimentation that wireless communication can be accomplished down to the difficult level of the 18650-cell constrained-space metal enclosure while delivering useful ranges for e-mobility platforms. The two communication frequencies of 868 MHz and 2.4 GHz are shown to support data links up to 10 m, despite the fact that the radiative structures used to demonstrate these properties were not specifically adjusted for this application. This information is fundamental for link budget calculations for a compact smart cell network implementation and deployment. This work may also serve as a reference point for applications that use embedded antennas within similar restrictive metal enclosures.  
By using the 3D MATLAB simulation, it was possible to illustrate how microwave antennas would be affected by small restrictive metal environments, such as the 18650-cell shell. Through simulation, it has been found that when radiative elements are packed together, they will couple, changing the initial individual radiative elements radiation pattern. Smart cells packed in a dense battery network may encounter the same phenomenon, resulting in a wireless-wired hybrid communication method based on both radiative and conductive propagation. The good agreement between the discrete experimental and the three-ray simulation values for the tested general radiative elements makes multi-ray models a promising choice for link budget calculations with applications in wireless battery management systems used in e-mobility.  
As this study demonstrates an available range up to 10 m for a common low power 868 MHz or 2.4 GHz RF transceiver enclosed inside a smart cell potentially used in vehicular transportation, indirect usage of the technology for traffic monitoring, parking occupancy, car classification, etc., may be explored and exploited in future applications.  
Research such as this is critical for future implementations of wireless BMS technology at the cell level, which will deliver the capability of fast charging and improved safety while providing additional indirect benefits through infrastructure sensing in the future smart cities.  
Figure 11: MATLAB signal attenuation simulation by using the Three-Ray and Free space models for 868 MHz (**A**), and 2.4 GHz (**B**), between the concrete ground.  
**Author Contributions:** Conceptualization, V.M. and J.F.; methodology, V.M.; formal analysis, V.M.; investigation, V.M.; writing--original draft preparation, V.M.; writing--review and editing, V.M., T.A., P.I., S.F. and J.F.; visualization, V.M.; supervision, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by by the Innovate UK project CALIBRE (Grant Reference Number 104176).  
Not applicable.  
**Data Availability Statement:** The datasets generated and analysed during this study are available from the corresponding author on a reasonable request, but restrictions apply to the commercially confident details.  
The authors declare no conflict of interest.
# Transient Analysis of a Selective Partial-Update LMS Algorithm
## 2 Proposed SPU-LMS-M-Min for a Single Block
### Computational Complexity  
In order to guarantee an effective reduction of the computational burden, the selection of index \(i\) required by the SPU-LMS-M-min algorithm (see Equation (12)) should be carried out by fast algorithms for running ordering and max/min calculation criteria [18]. Assuming the adoption of efficient ordering algorithms (which do not require neither multiplications nor sums, but only comparisons between numbers), it is possible to evaluate the number of additions and multiplications required per iteration by the SPU-LMS-M-min.  
Table 1 compares the computational complexity of the elements of an LMS algorithm bundle, taking into account SPU-NLMS (Norm. selective) and LMS-SPU-M-(max \(\operatorname{|}\operatorname{min}\)). It is noteworthy that update Equation (12) requires \(N+BL+1\) multiplications, whereas  
the standard LMS requires \(2N+1\) multiplications per iteration. Since the designer might enforce the condition \(BL<N\), a significant reduction in computational burden requirement can be attained. For example, for a configuration with \(N=4000\), \(M=8\) and \(B=1\), the number of required multiplications per iteration is reduced by 43.65%. Furthermore, no divisions are required, which is an advantage of the proposed non-normalized scheme. It is noteworthy that such complexity reduction may decrease the convergence rate of the adaptive algorithm, which may not be feasible in applications imposing stringent criteria on the number of iterations required for the learning process to reach a steady state.  
After the following new derivation approach of the target algorithm SPU-LMS-M-min, it is expected that presents some distinct features, such as reduction in the convergence rate, which can be predicted and elucidated by stochastic models. Some models based on conventional assumptions are described in the following sections. Note that such theoretical analysis are appreciated in the open literature due to their ability to provide performance guarantees for the algorithm designer, as well as important insights about its functioning.
###### Abstract  
After the Great East Japan Earthquake, energy security and vulnerability have become critical issues facing the Japanese energy system. The integration of renewable energy sources to meet specific regional energy demand is a promising scenario to overcome these challenges. To this aim, this paper proposes a novel hydrogen-based hybrid renewable energy system (HRES), in which hydrogen fuel can be produced using both the methods of solar electrolysis and supercritical water gasification (SCWG) of biomass feedstock. The produced hydrogen is considered to function as an energy storage medium by storing renewable energy until the fuel cell converts it to electricity. The proposed HRES is used to meet the electricity demand load requirements for a typical household in a selected residential area located in Shinchi-machi in Fukuoka prefecture, Japan. The techno-economic assessment of deploying the proposed systems was conducted, using an integrated simulation-optimization modeling framework, considering two scenarios: (1) minimization of the total cost of the system in an off-grid mode and (2) maximization of the total profit obtained from using renewable electricity and selling surplus solar electricity to the grid, considering the feed-in-tariff (FiT) scheme in a grid-tied mode. As indicated by the model results, the proposed HRES can generate about 47.3 MWh of electricity in all scenarios, which is needed to meet the external load requirement in the selected study area. The levelized cost of energy (LCOE) of the system in scenarios 1 and 2 was estimated at 55.92 JPY/kWh and 56.47 JPY/kWh, respectively.  
hybrid renewable energy system; simulation; optimization; hydrogen; supercritical water gasification; Japan
## 3 Pre-Processing
### Buffering of Grayscale Image  
We consider that a vehicle has a windscreen wiper and a camera installed inside it. Therefore, the wiper operation is considered as noise for the image acquired by the camera. In this operation case, the windscreen wiper image appears so that it is not possible to see or estimate the image across the windscreen wiper. Therefore, we proposed to apply the buffering of the image to reduce the noise generated by the windscreen wiper.  
The process of buffering an image (Figure 5) that is already in grayscale consists of acquiring frames according to the size of the buffer (input), and the output frame consists of the weighted average of pixels of all the frames stored in the buffer. For each algorithm interaction, the oldest frame is removed, and the current one is added. Indeed, we define several frames to be buffered denoted by N, calculate the sum of each pixel intensity value in grayscale, and divide by N. The average of that specific pixel provides a new pixel that contains an entire image with removed noise. For example, Figure 5 shows three frames (N = 3) and three pixels, respectively, with values of 25, 173, and 193, with an average value of 130.  
Through the buffering of the intensity, it is possible to reduce the noise. Depending on the vehicle speed, this process can attenuate or smooth some noises contained in the image. For example, this step can decrease the noise caused by the windscreen wiper (Figure 6).  
We can establish the process to cut the image without noise and maintain only a specific ROI for the image without noise. The ROI is defined by the image region below the vanishing point for our application, which comprises the road lane we wish to identify and recognize. Therefore, the ROI can be cropped after applying the buffering method to reduce memory and processing. Indeed, the next step to apply the median filter is made only in the cropped ROI.  
Figure 5: The process of buffering the intensity image.
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 4 Simulation Verification  
In order to verify the actual compensation performance of the improved PR controller proposed in Section 3, this section builds a two-level parallel APF model based on the MATLAB/Simulink 2022b simulation platform and compares and analyzes the performance of the proposed control strategy under steady-state and dynamic conditions. The controller structure and parameter settings are determined based on the design and root locus analysis results completed in the previous section. The simulation parameters are a grid voltage of 380 V rms/50 Hz, a switching frequency of 20 kHz, a sampling frequency of 20 kHz, an equivalent inductance \(L\) of 1 mH, \(U_{dc}\) = 800 V, and nonlinear loads used uncontrolled diode rectifier bridges.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 4 Results  
This section presents the results of our study, including the experimental environment, the evaluation indicators, and a comprehensive assessment of the performance of the proposed QP-DETR model. It provides a detailed analysis of the model's accuracy, precision, and detection speed, along with comparisons to baseline methods. Each subsection elaborates on specific aspects of the experiments and findings.
## 2 Experimental Section
### COMSOL Simulation Analysis  
The COMSOL Multiphysics software based on the finite element analysis method (FEM) was used to simulate the response of a QTF to sound waves. The initial pressure distribution on the QTF prongs with the elliptical spot and the circular spot was shown in Figure 3a,b. As the pressure distribution is a propagating wave, the pressure gradually decreases from the inner surface to the outer surface of the vibrating prongs.  
Figure 3: (**a**) Pressure distribution generated by a circular spot and (**b**) pressure distribution generated by elliptical spot.  
Figure 2: (**a**) Image of elliptical laser spot shaped by a Powell lens from a pyroelectric camera. (**b**) Image of circular light spot emitted by DFB laser from a pyroelectric camera.  
The geometric parameters of QTF model in this simulation are consistent with the standard commercial QTFs which has the prong length of 3.8 mm, width of 0.6 mm, thickness of 0.15 mm, and prong spacing of 0.3 mm. The acoustics module and acoustics solid interaction module was invoked to investigate the sound pressure distribution on the QTF prongs. Quartz LH (1978 IEEE) was selected as the component material of the QTF and the boundary layer of the air domain was a perfectly matched layer. In addition, the QTF bottom end was set as a fixed constraint and the QTF prongs can vibrate freely.  
The sound pressure distribution is depicted with different colors, reaching from high pressures (red) to low pressures (blue). For a circular spot, as shown in Figure 3a, the sound pressure is strongest at the optimal position of which the distance between the center of circular spot and QTF tips is 0.9 mm based on previously reported literature [32].  
The distribution of the sound field on the QTF prongs is well consistent with the expected behavior. The size of the elliptical spot shaped by the Powell lens is larger than the circular spot, resulting in an increasing area of interaction between the acoustic wave and QTF which can be observed in Figure 3b. Although partial acoustic leakage is inevitable due to the opening structure at the top of QTF prongs, sound pressure has a strong distribution in a strip region on the inner surface of QTF prongs resulting from the relatively large spot size.
# Bonded-Particle Model with Nonlinear Elastic Tensile Stiffness for Rock-Like Materials
## 3 Relationship between Micro and Macro Parameter
### Effect of Bond Stiffness on Macro Parameters  
Changing the value of \(k_{s}/k_{n}\) from 0.1 to 1.0 with \(k_{m}/(k_{n}R)\) equal to 0.064, 0.160 and 0.256, different values of the macro parameters can be observed. The relationships between the macro parameters  
Figure 8: Crack patterns in compressive and tensile tests.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Specimens** & \(E_{e}\) (GPa) & \(E_{t}\) (GPa) & \(v_{e}\) & \(v_{t}\) & **UCS (MPa)** & **TS (MPa)** & **UCS/TS** & \(E_{s}/E_{t}\) & \(v_{t}/v_{t}\) \\ \hline Specim A & 16.9 & 14.4 & 0.19 & 0.11 & 185.0 & 11.4 & 16.2 & 1.17 & 1.69 \\ Specim B & 16.9 & 15.4 & 0.19 & 0.14 & 195.2 & 9.5 & 20.5 & 1.10 & 1.29 \\ Specim C & 17.4 & 13.8 & 0.20 & 0.13 & 206.4 & 13.0 & 15.9 & 1.26 & 1.58 \\ Specim D & 15.0 & 12.5 & 0.19 & 0.11 & 164.1 & 11.3 & 14.5 & 1.20 & 1.64 \\ Specim E & 16.7 & 13.0 & 0.21 & 0.13 & 194.9 & 14.6 & 13.4 & 1.29 & 1.69 \\ Average & 16.6 & 13.8 & 0.19 & 0.12 & 189.1 & 12.0 & 16.1 & 1.20 & 1.58 \\ Stand deviation & 0.81 & 1.02 & 0.01 & 0.01 & 14.24 & 1.71 & 2.43 & 0.07 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 3: Properties of specimens; UCS: uniaxial compressive strength; TS: tensile strength.  
Figure 7: Load-displacement curves: (a) compression; (b) tension.  
and \(k_{s}/k_{n}\) at different \(k_{m}/(k_{n}R)\) are presented in Figure 9. The results show that \(E_{c}\) increases with the increasing of \(k_{s}/k_{n}\) at different values of \(k_{m}/(k_{n}R)\), and \(v_{c}\) decreases with the increasing of \(k_{s}/k_{n}\). The trends are in accord with those in [28; 29; 34]. With the values of the \(k_{s}/k_{n}\) changing from 0.1 to 1.0, Poisson's ratio decreases from 0.45 to 0. The effects of the bending stiffness on \(E_{c}\) and \(v_{c}\) are very small and can be ignored. However, increasing the bending stiffness can enhance the strength (UCS and TS) of the specimens and increase UCS/TS distinctly when \(k_{s}/k_{n}\) is higher than 0.4, as shown in Figure 9c-e. The values of \(k_{s}/k_{n}\) and \(k_{m}/(k_{n}R)\) have very small effects on the value of \(E_{c}/E_{t}\), as shown in Figure 9f, and small effects on the value of \(v_{c}/v_{t}\).
# Chemical Composition and Palatability of Nutraceuticaltical Dog Snacks
## 2 Materials and Methods
### Minerals  
The total content of potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu) was determined by wet mineralization in a mixture of nitric acid (V) and perchloric acid (VII) [18]. The analyses were performed with an atomic absorption spectrometer (Thermo Fisher Scientific iCE 3000 Series, Waltham, MA, USA). For the determination of Ca, K, Mg and Na content, the wavelengths were: K--766.5 nm; Ca--422.7 nm; Mg--285.2 nm; and Na--589.0 nm. For the determination of Fe, Mn, Zn, Cu content, the wavelengths were: Fe--248.3 nm; Mn--279.5 nm; Zn--213.9 nm; and Cu--324.8 nm. The calculation of the content of the individual elements K, Ca, Mg, Na, Fe, Mn, Zn and Cu was started with a standard curve taking into account the weight of the test portion and the dilutions used. The phosphorus content was determined by the Egner-Riehm colorimetric method, with ammonium molybdate on a Specol 221 apparatus  
\begin{table}
\begin{tabular}{l l c} \hline
**1.** & **Calculate gross energy (GE):** \\ \hline  & GE (kcal) & = (5.7 \(\times\) \%CP) + (9.4 \(\times\) \%CFAT) + [4.1 \(\times\) (\%TC + \%CF)] \\ \hline
**2.** & **Calculate energy digestibility (ED) (\%):** \\ \hline  & ED (\%) & = 91.2 \(-\) (1.43 \(\times\) \%CF in DM) \\ \hline
**3.** & **Calculate digestible energy (DE):** \\ \hline  & DE (kcal) & = (kcal GE \(\times\) ED)/100 \\ \hline
**4.** & **Calculate metabolizable energy (ME):** \\ \hline  & ME (kcal) & = \(\rm DE-(1.04\times\) \%CP) \\ \hline \end{tabular} \({\rm CP}\), crude protein; CFAT, crude fat; TC, total carbohydrates; CF, crude fiber; DM, dry matter.  
\end{table}
Table 2: Calculation of the metabolizable energy (ME, kcal) in dog foods. Adapted from [7; 17].  
spectrophotometer (Carl Zeiss, Jena, Germany) [19]. The material for P concentration analyses was subjected to mineralization in concentrated sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) and perchloric acid (HClO\({}_{4}\)). The absorbance value of the sample, determined spectrophotometrically from P\({}_{2}\)O\({}_{5}\) to total phosphorus, was calculated according to the chemical equivalent (0.436). The credibility of the method used has been confirmed by comparative studies, including the calibration curve, using the calibration series method. All chemical determinations were performed in triplicate and presented as mean values. The accuracy of
## 3 Results and Discussions  
### Effect of Process Parameter on Microstructural Evolution  
#### 3.1.1 Conventional Annealing Heat Treatment  
The effect of the soaking time on the microstructural evolution of the samples subjected to conventional annealing heat treatment is summarized in Table 3, in terms of average grain size and Vickers hardness values. Furthermore, Figure 2 shows the micrographs and the grain size distributions obtained from the statistical analysis.  
Figure 1: Example of conventional annealing treatment cycle as obtained by tubular furnace.  
Figure 2: Full thickness microstructures and grain size distribution of NGO grade after conventional annealing: (**a**) heating treatment time 10 s (maximum temperature = 630 \({}^{\circ}\)C); (**b**) heating treatment time 25 s (maximum temperature = 965 \({}^{\circ}\)C); (**c**) heating treatment time 60 s (maximum temperature = 1050 \({}^{\circ}\)C); (**d**) heating treatment time 90 s (maximum temperature = 1050 \({}^{\circ}\)C).  
Results show that for the sample with a soaking time of 10 s, in which the maximum temperature reached is 630 \({}^{\circ}\)C, the material is still strongly work-hardened.  
The scenario changes after a soaking time of 25 s and a maximum temperature reached of 965 \({}^{\circ}\)C (see Figure 2b) in which, although the temperature is not the target temperature of the desired treatment (1050 \({}^{\circ}\)C), the material presents a microstructure with equiaxed grains, an average grain size of 26.0 \(\pm\) 13.0 mm, and a grain size distribution resembling a log-normal curve typically associated with a recrystallized material. After 60 s and after 90 s of treatment at the target temperature, microstructures undergo grain growth phenomena up to a grain size of 87.9 \(\pm\) 43.6 \(\upmu\)m and 125.5 \(\pm\) 70.4 \(\upmu\)m, respectively. Furthermore, as the heat treatment temperature increases, in addition to a progressive shift towards higher values of the mode of the distribution (average grain size value), a gradual broadening of the distribution is observed. This indicates a heterogeneous dimensional variation characteristic of the various stages of grain growth.
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
###### Abstract  
Tolerance analysis is becoming increasingly important for tolerance design and optimization. When dealing with electromechanical products such as the array antenna, the form errors of the assembly must be considered. Traditional tolerance analysis with form errors relies on a double loop process, which is computationally expensive. A new tolerance analysis method is proposed in this paper, which can be achieved by a single loop process. First, a new tolerance modeling method considering form errors was proposed, it can represent the geometric error of a surface feature precisely. Then an effective sampling method was developed by introducing the variance separation method. An assembly simulation method was proposed to determine the final state of the whole assembly. Finally, the tolerance analysis was achieved based on the sufficient sample. The proposed analysis method was applied to an X-band spaceborne active-phased array antenna, numerical simulation results show the effectiveness of the method.  
tolerance analysis; tolerance model; form defect; array antenna; assembly simulation; tolerance sampling +
Footnote †: journal: Applied Sciences