esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the Housing Shape Design for
## 1 Introduction
However, since the noise of electric vehicles has high-frequency characteristics, the resonance avoidance design is not sufficient to account for the high-frequency excitation force [11]. [PERSON] et al. [12] predicted the sound pressure level of a gearbox at a specific field point. The load was modeled as a simulated reaction force at the bearing position. Using a topology optimization technique, the sound pressure level was reduced with objective function of minimizing the housing surface velocity. The studies mentioned above have the following limitations: (i) the change in the reaction force of the internal mechanical elements, such as the gears, shafts, and bearings, according to the shape modification of a gearbox housing was not considered; (ii) model updating and validation procedures for optimization using finite element (FE) analysis were not performed; (iii) the objective function for optimization mainly focused on the natural frequencies or noise characteristic at a specific region, and (iv) the design area of the optimization was specified based on experience or without clear criteria. The objective of this study is to reduce the structural vibration of the gearbox housing owing to the gear mesh excitation force and resulting radiated noise. To achieve this purpose, the root cause of structural vibration should be identified, and noise and vibration should be reduced through an objective and reliable method. In this study, the main noise source and excitation force considering the noise characteristics of the electric vehicle gearbox were identified with the experimental measurement. Since the topology optimization technique was used to reduce the radiated noise, the FE model was updated and validated to develop a reliable analysis model. The housing shape design was optimized by redesigning the weak areas against noise and vibration through sound power analysis. Topologyoptimization technique was used to reduce the radiated noise of the gearbox. In order to optimize the material distribution in the redesign area, the equivalent radiated power with sound properties regardless of location was used as the output variable. Figure 1 shows the entire procedure for this study.
# Seismic Resilience of CRC- vs. RC-Reinforced Buildings: A Long-Term Evaluation
## 5 Case Study
### RC Structure Analysis and Results  
Pushover analysis of the RC structure at the selected ages shows the reduction in shear force capacity compared to the capacity at t = 0 years (Figure 15a-g) and highlights the impact of material degradation and corrosion over time on the structural capacity.  
The ultimate capacity ratio (UCR) is outlined in Equation (28):  
\[UCR=\frac{C_{u\ t=N}}{C_{u\ t=0}} \tag{28}\]  
where \(C_{u\ t}=N\) denotes the ultimate capacity at the tested year \(t=N\) and \(C_{u\ t}=0\) denotes the ultimate capacity at \(t=0\), prior to any corrosive activity.  
The yielding displacement ratio (\(YDR\)) is detailed in Equation (29):  
\[YDR=\frac{D_{y\ t=N}}{D_{y\ t=0}} \tag{29}\]  
where \(D_{u\ t}=N\) is the yield displacement at the tested year \(t=N\) and \(D_{y\ t}=0\) denotes the yield displacement at \(t=0\) before the onset of any corrosive activity.  
Specifically, Figure 15a captures the structure's capacity at the initial time t = 0 and again at t = 20 years; the similarity between these periods is represented by a single line. Figure 15b displays the capacity at t = 45 years compared to t = 0 and shows that the UCR decreased by 18.0% and the YDR increased by 56% compared to the structure that was not subjected to corrosion. Figure 15c shows the capacity at t = 70 years alongside the initial capacity curve, with a UCR decrease of 27.0% and a YDR increase of 87.8%. Continuing this sequence, Figure 15d shows a UCR decrease of 33.40% and a YDR increase of 106.0% within 95 years of construction. Figure 15e shows a UCR decrease of 33.10% and a YDR increase of 129.40% at t = 120 years. Figure 15f displays a UCR decrease of 41.80% and a YDR increase of 121.7% at t = 145 years. Finally, Figure 15g shows the capacity curve of the 170-year-old structure, with a UCR decrease of 50% and a YDR increase of 108.0%. Each figure demonstrates the progressive change in structural capacity over time, highlighting the long-term effects of material degradation and environmental stress. The variations in UCR and YDR over the years provide a precise quantitative measure of the degradation, presented in Figure 16.  
Figure 14: Long-term loss of strength of RC and CRC concretes.  
The curves displayed in Figure 16 demonstrate that the ultimate capacity of the structure decreases over time due to the effects of corrosion. In addition, the structure experiences larger displacements upon yielding. This trend highlights a reduction in the ductility level of the structure as well as a decrease in its elastic stiffness, indicating progressive deterioration of structural integrity and resilience under seismic loads.  
Figure 16: Trends in seismic performance of a case RC structure over time: (**a**) UCR; (**b**) YDR.  
Figure 15: Interaction of capacity curves for an RC structure over time. Comparison at CC t = 0 to (**a**) t = 20, (**b**) t = 45, (**c**) t = 70, (**d**) t = 95, (**e**) t = 120, (**f**) t = 145, and (**g**) t = 170 years.
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
###### Abstract
Hybrid approaches are attractive for binaural auralization because they can produce binaural signals in arbitrary head orientation without modeling the human head in room-acoustics simulation. Among the others, hybrid auralization with Ambisonics and HRTF [15; 16] has high compatibility with virtual reality (VR) applications [17; 18]. In Ambisonics, the sound field is expressed by the superposition of plane waves based on spherical harmonics expansion. The Ambisonics signal consists of expansion coefficients of spherical harmonics up to the desired order in the time domain at an arbitrary point in an interested sound field. Standard Ambisonics auralization is performed by assigning Ambisonics signals to an arbitrary speaker array so that the original sound field is reproduced at the center of the speaker array. For combination with HRTF, driving signals are assigned to the virtual speaker array instead of the real speaker array. Then, binaural signals are synthesized via convolution of each driving signal with head-related impulse response (HRIR) in the same direction of each virtual speaker. Ambisonics are roughly classified into first-order Ambisonics (FOA) [13] consisting of the zero-order and first-order expansion coefficients and higher-order Ambisonics (HOA) [14], with coefficients of up to the second order and higher. Although HOA sound reproduction can capture a sound field well compared to FOA reproduction, FOA is often applied to architectural acoustics problems such as acoustics evaluation of churches [19], concert halls [20], underground railway stations [21], and meeting rooms [22]. Some plugins for FOA to VR applications are available. This paper presents a proposal of a binaural room-acoustics auralization method based on FOA reproduction using a massively parallel wave-based room-acoustics solver by dispersion-optimized explicit TD-FEM, with examination of the efficiency and feasibility of the method. The proposed method first computes RIRs by the parallel wave-based solver under a cloud HPC environment. Then, BRIRs are computed using a hybrid approach with FOA and HRTF. Room acoustics auralization is presented to a listener via headphones. For the BRIR rendering, a novel computational method to compute expansion coefficientsof spherical harmonics via numerical differentiation using finite element's shape function is proposed. First, this study assesses the efficiency of the massively parallel wave-based room-acoustics solver via a scalability test on a cloud HPC environment with two realistic room-acoustics simulations in a meeting room and classroom. Then, the potential of the proposed binaural room-acoustics auralization method is shown via acoustics simulations in a large auditorium of up to 5 kHz.
# A Novel Device-Free Counting Method Based on Channel Status Information
## 4 Experiment Results and Analysis
### Relation Between Population and Amplitude (or Phase)  
Figure 8 shows the relationship between the percentage and the number of moving people on different antennas. It is observed that a monotonous relationship between them. Among them, Figure 8a reveals the percentage of CSI amplitude when 0, 1, 2 and 3 people are walking, and Figure 8b shows the percentage change in CSI phase. Although the monotonic relation exists both in amplitude and phase information, the curve track of is different. However, it can be observed that the trajectory of phase information on different antennas is more concentrated, and the curve changes more dramatically when describing the monotonic relationship.  
In this work, the CSI phase or amplitude covariance matrix is extracted to achieve crowd counting. Figure 9a compares the variance of each dimension of the amplitude and phase, that is the diagonal elements of covariance matrix. It can be observed that the amplitude information is with a large fluctuation, while the phase information fluctuate is relatively stable. Figure 9b shows the percentage of amplitude and phase crowding count, where the percentage of amplitude information increase slowly from the sixth person and phase from the ninth person. Thus, the phase information is more reliable and sensitive to environmental changes and can detect more people.  
Figure 8: The relationship between the number of people and the percentage at the different antennas. (**a**) amplitude; (**b**) phase.  
Figure 7: The experiments equipment and scenes. (**a**) receiver and router; (**b**) experiment scene.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
###### Abstract  
As outlined in the 3 Gpp Release 16, 5G satellite access is important for 5G network development in the future. A terrestrial-satellite network integrated with 5G has the characteristics of low delay, high bandwidth, and ubiquitous coverage. A few researchers have proposed integrated schemes for such a network; however, these schemes do not consider the possibility of achieving optimization of the delay characteristic by changing the computing mode of the 5G satellite network. We propose a 5G satellite edge computing framework (5 GsatEC), which aims to reduce delay and expand network coverage. This framework consists of embedded hardware platforms and edge computing microservices in satellites. To increase the flexibility of the framework in complex scenarios, we unify the resource management of the central processing unit (CPU), graphics processing unit (GPU), and field-programmable gate array (FPGA); we divide the services into three types: system services, basic services, and user services. In order to verify the performance of the framework, we carried out a series of experiments. The results show that 5 GsatEC has a broader coverage than the ground 5G network. The results also show that 5 GsatEC has lower delay, a lower packet loss rate, and lower bandwidth consumption than the 5G satellite network.  
Keywords:edge computing; on-board data processing; microservices; Integrated Terrestrial-Satellite Networks +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Experimental Design
The waveforms of each function were plotted with simultaneous changes in two process parameters, while the other parameters were constant and took on values consistent with the specified process optimum (Figures 1A-C and 2A-C). In order to obtain the most adequate mathematical description of the extraction process, three technological parameters were taken into account, whose ranges of variation were defined as follows: \(X_{1}\) (the raw material content): 2.25-7.5 g/100 mL of solvent; \(X_{2}\) (the ethanol concentration): 20-60% \(v/v\); \(X_{3}\) (the extraction time): 1-15 min. Actual input values were used for the calculations (Table 3). PRESS values were determined from the following Equation (3):  
\[PRESS=\sum_{i=1}^{n}(Y_{i}-Y_{ie})^{2} \tag{3}\]  
where  
\(Y_{i}\)--predicted value;  
\(Y_{ie}\)--experimental value. Table 2 shows the most relevant statistical parameters for optimising the extraction process using the response surface methodology (RSM): the determined coefficients of Equation (2), a\({}_{0}\), a\({}_{1}\), a\({}_{2}\), a\({}_{3}\), a\({}_{4}\), a\({}_{5}\), a\({}_{6}\), a\({}_{7}\), a\({}_{8}\), and a\({}_{9}\), for the function ranges tested, the correlation coefficients (R\({}^{2}\)), the adjusted R-square (adjR\({}^{2}\)), the standard deviation (SD), and the PRESS value. The reliability of the response surface methodology (RSM) for the optimised plant extract was confirmed experimentally. For this purpose, two independent test samples (extracts) were prepared, and each of them was tested in triplicate. The results were expressed as an arithmetic mean of six measurements (n = 6). Figure 1: Changes in (**A**) antioxidant activity (AA-DPPH), (**B**) antioxidant activity (AA-FRAP), and (**C**) total polyphenol content (TPC-F-C); relationship: raw material content—ethanol concentration. Constant parameters of the process: an extraction time of 10 min. Figure 2: Changes in (**A**) antioxidant activity (AA-DPPH), (**B**) antioxidant activity (AA-FRAP), and (**C**) total polyphenol content (TPC-F-C); relationship: ethanol concentration—extraction time. Constant parameters of the process: a raw material content of 7.5 g/100 mL of solvent.
## 2 Literature Review
### Energy Simulation Methods: Bottom-Up Approaches
[26] created an NN to assess residential building energy consumption based on the case study of the TxAIRE Research and Demonstration House. Finally, engineering physics-based methods simulate, with the help of simulation engines, the building's energy consumption through the study of the thermodynamic properties that determine how the building, as a thermodynamic system, interacts with the outdoor environment [27]. Engineering methods are the most accurate ones, but they also require an intimate knowledge of the buildings. It is necessary to collect a plethora of information about their geometry, shape, orientation, glazing, materials, infiltration and ventilation rates, occupancy, schedules, internal loads, installations, set-point temperature and several other building characteristics and features. A concrete knowledge of the climatic area in which the building is located is also required, comprising microclimatic conditions and local effects [28]. More precisely, engineering physics-based methods can be categorized into steady state building energy assessment tools, dynamic building energy simulation tools and other building energy assessment tools. Steady state building energy assessment tools are primarily used in buildings' energy certification procedures. These tools are usually based on a physical approach, modified by simplifications and assumptions that allow the user to limit the amount of input data and the calculation time. Dynamic building energy simulation tools mainly aim to perform a detailed calculation of a building's thermodynamic behavior, also simulating HVAC systems. These tools are potentially highly reliable, but they require plenty of time and accuracy in the assessment of input data. Aside from that, they also require long simulation sessions and computer processors that perform well. This category of energy assessment tools belongs to building energy modeling (BEM) techniques, which are fundamental in the field of building energy design and assessment. Thanks to BEM, any building-system configuration may be verified during feasibility analyses or design validations. Moreover, BEM allows energy experts to perform detailed energy audits and helps to derive design guidelines as well as create operation strategies. BEM's first implementation took place in the early 1970s. After that, some specific BEM tools have grown up and been consolidated while others fell out of use. Other BEM tools were also developed later, based on the extensive experience gained within their predecessors. Some examples of this last category can include software such as EnergyPlus [29] or TRNSYS [30]. Finally, the other building energy assessment tools usually rely on very simplified physical approaches with a low degree of adaptability to the specific design case. These simplified tools are therefore aimed particularly at the assessment of the building energy demand, the very first design stages or when dealing with a basic sizing of HVAC systems.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Experimental Results  
#### 3.1.1 Comparison With Other Classification Models Without Pre-Training  
To examine the performance of the proposed model, it was tested on the dataset presented in Section 2.1. and compared to several classic classification models. During the test, the hyperparameter settings, such as the learning rate, were guaranteed to be the same (as in Section 2.3.), and none of them were pre-trained. The test results are shown in Table 1.
# Discrete Lattice Element Model for Fracture Propagation with Improved Elastic Response
## 2 Novel Discrete Lattice Element Model
### Fracture Criteria  
The proposed model adopts a discrete representation of fracture, with the crack orientation perpendicular to the lattice element, aligning with the Voronoi cells. In this framework, the lattice element is allowed to undergo failure in both Mode I, representing tensile opening, and Mode II, representing shear sliding. Additionally, the model can simulate mixed-mode failure, where both criteria are simultaneously activated. The failure criteria are based on the maximum tensile stress and maximum shear stress permitted within the lattice element. This approach allows for a comprehensive simulation of various fracture mechanisms occurring under different loading conditions. The failure functions for both modes are written in the framework of damage mechanics and obtained as expression (23), while being applied to traction vectors in both directions:  
\[\begin{array}{l}\frac{\overline{\dot{\phi}}}{\phi}\Big{(}t_{u},\overline{ \dot{q}}_{u}\Big{)}=t_{u}-\Big{(}\sigma_{u}-\overline{\dot{q}}_{u}\Big{)}\\ \overline{\dot{\phi}}\Big{(}t_{v},\overline{\dot{q}}_{v}\Big{)}=|t_{v}|-\Big{(} \tau_{u}-\overline{\dot{q}}_{v}\Big{)}.\end{array} \tag{26}\]  
The failure criteria are localized within individual lattice elements, determined by the local stresses. This approach enables the activation of numerous local cracks and fractures within the lattice structure. However, the model allows for the occurrence of multiple local cracks that subsequently merge and coalesce. This process leads to the formation of new, large-scale fractures through the mechanism of internal force redistribution. Through such interactions, the model captures the global fracture mechanisms resulting from the merging of smaller-scale cracks, providing a representation of the material response to varying loading conditions.  
Figure 7: The damage constitutive law.
###### Abstract  
Dual one-way inter-satellite link (ISL) pseudoranges of BDS-3 satellites can be introduced as an additional measurement along with L-band pseudoranges and phases to improve the accuracy of precise orbit determination (POD). In the existing research, although the clock-free or geometry-free ISL observables are derived from the raw two one-way pseudoranges, only the clock-free observables are adopted for the ISL joint POD (Joint 1 POD) without considering the geometric-free observables. An improved joint (Joint 2 POD) strategy making full use of the clock-free and geometry-free observables is applied in this contribution. The orbits of ground-only POD, ISL-only POD, Joint 1 POD, and Joint 2 POD are comprehensively compared by the orbit overlapping differences, the Satellite Laser Ranging (SLR) residuals, and the characteristics of the satellite clock offsets estimated simultaneously. The comparisons prove that the performance of the Joint 2 POD strategy is better than that of the other three POD strategies regardless of the types of satellites. Moreover, this paper discusses ISL's contribution to the station selection strategy in terms of the number and distribution. The experimental results show that, when there are more than 20 stations, each additional 10 stations contributes to a maximum of 7.5%, 3.9%, and 2.8% improvement on MEO, IGSO, and GEO satellites 3D accuracy, respectively. When the number of stations reaches 50, the precise orbits achieve similar accuracy to the results using 80 stations. In addition, after adding ISL data, the orbits estimated using 10 regional stations and 10 global stations are greatly improved, and the accuracy between them is only 0.9 cm in 3D errors.  
BDS-3; precise orbit determination; inter-satellite link; ground-tracking network; satellite clock offset 27 June 2020; 14 August 2020; Published: 17 August 2020
## 3 Results and Discussion
### Free Amino Acids and Organic Acids Analysis
While undetectable in the unfermented rose beverage, significant accumulation of acetic acid was observed in the fermented samples, particularly those  
Figure 1: Analysis of free amino acids and organic acids of the rose beverages. (**A**) Free amino acid concentrations. (**B**) Taste activity values (TAVs) of free amino acids. (**C**) Organic acids concentrations. (**D**) TAVs of organic acids. TAVs of free amino acids and organic acids were calculated by the ratio of the concentration of a compound to its taste threshold. Values marked with different letters in the same color column of figures (**A,C**) were significantly different (\(p<0.05\), \(n=3\)). ferented with _L. plantarum_. This accumulation undoubtedly contributes to the enhanced nutritional properties of the fermented rose beverage. The TAVs of organic acids were calculated to evaluate their contribution to the flavor of rose beverages (Figure 1D). With the exception of citric acid and lactic acid in the SFR group (TAVs < 1), six organic acids exhibited TAVs greater than 1 in the fermented rose beverages. This suggests that these organic acids with TAVs > 1 likely contribute significantly to both the flavor and taste perception of the fermented rose beverages. Malic acid, lactic acid, acetic acid, and citric acid impart a fresh, acidic taste [50]. Succinic acid can contribute umami, sourness, and astringency. The synergistic interactions among these organic acids can enhance the body and modulate the sweetness of the rose beverage, resulting in a pleasant sweet-and-sour profile.