esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Inversion Results of Constitutive Model Parameters  
#### 4.2.1 Multi-Parameter Optimization Analysis  
The proposed step-by-step inverse method is adopted to solve the constitutive model parameters. The first step is to determine the proportional relationship between the key parameters and the remaining important parameters of the constitutive model. The key constitutive parameters in this case study are \(E_{50}^{ref}\) and \(\gamma_{0.7}\), and the remaining important parameters are \(E_{oad}^{ref}\), \(E_{ur}^{ref}\), and \(G_{0}^{ref}\). According to an extensive indoor experimental literature review [14; 15; 16; 30], a proportional relationship exists between the modulus of compression \(E_{\text{s1-2}}\), \(E_{50}^{ref}\), \(E_{oad}^{ref}\), \(E_{ur}^{ref}\), and \(G_{0}^{ref}\), where the \(E_{\text{s1-2}}\) can be usually obtained from a soil survey report. As \(E_{50}^{ref}\) is the key parameter, the range of the ratio of \(E_{50}^{ref}\) to \(E_{\text{s1-2}}\), \(E_{oad}^{ref}\), \(E_{ur}^{ref}\), and \(G_{0}^{ref}\) are listed in Table 4. The ranges of \(E_{50}^{ref}\):\(E_{\text{s1-2}}\) and \(E_{50}^{ref}\):\(E_{oad}^{ref}\) for mucky clay and silty clay are small, and thus the average is adopted in this study.  
In order to reduce the number of numerical tests, the uniform design table \(U_{12}^{*}(12^{10})\) was adopted to determine the optimal proportional relationship for the tested soil. The seven factors of the uniform design table were selected, and the uniformity deviation was 0.2768, which met the sampling requirements. The design schemes of different ratio combinations are shown in Table 4.  
Figure 7: Correlation analysis of constitutive model parameters.  
According to the above 12 groups of proportional relationships, the finite element model was used to analyze the horizontal displacement at the fourth stage of braced excavation. At the same time, the calculated values were compared with the measured values, and the corresponding coincidence degrees (\(W\)) under various proportions were obtained. The results are shown in Table 5. As the inverse analysis at this stage is mainly to obtain the optimal ratio among the important parameters, the degree of fit between the simulated curve and the measured curve is not high, resulting in a negative value for the goodness of fit \(R^{2}\). Comparing the \(W\) values for the 12 tests in Table 4, the fourth group has the largest \(W\) value, indicating that the proportion of constitutive parameters best aligns with the measured value, considering both the shape and the maximum value of the deformation curve.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Current Amplitude Optimization
#### 4.2.3 Design 8: Side Lobe Average Power Minimization Without FNBW Constraint of a 20-Element LAA  
The MAOA algorithm is considered to optimize the current amplitude of a 20-element LAA to reduce the SLL average power. The SLL region focused here for minimization is \([0^{\circ},82^{\circ}]\) and \([98^{\circ},180^{\circ}]\). For this design, the fitness function is illustrated above in  
Figure 14: Array pattern of a 10-element LAA for design 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Normalized Excitation Current Amplitudes**} & \multirow{2}{*}{**CSLL (dB)**} \\ \cline{2-2} \cline{4-6}  & **I** & **II** & **III** & **IV** & **V** \\ \hline ULA & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & \(-12.97\) \\ \hline AOA & 1.0000 & 0.8816 & 0.5221 & 0.5221 & 0.5720 & \(-29.58\) \\ \hline DA & 1.0000 & 0.6787 & 0.5111 & 0.5111 & 0.5111 & \(-29.73\) \\ \hline BBO [27] & 1.0000 & 0.8526 & 0.6586 & 0.4601 & 0.5101 & \(-28.59\) \\ \hline MAOA & 1.0000 & 0.7296 & 0.5205 & 0.5132 & 0.5218 & \(-29.79\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimized current amplitude of positive half elements for design 7.  
Equation (27), which requires an integration of the array factor. The normalized current amplitude obtained after simulation is outlined in Table 10. These amplitudes help in plotting the array factor of the antenna array as showcased in Figure 15. Analysis of the array factor clearly demonstrates that the SLL average power is reduced drastically in this design by MAOA in comparison to ULA, AOA, and STO optimized antenna arrays. Comparison of the SLL and FNBW is outlined in Table 11. The SLL power and the FNBW are interrelated parameters--reducing SLL affects the beamwidth and vice versa.
###### Abstract  
In view of the low efficiency of traditional electrostatic precipitators in removing fine particles, acoustic and pulsed corona discharge coupling fields were proposed to increase particle size. In this paper, monodisperse particles with three different sizes (0.5 \(\upmu\)m, 2 \(\upmu\)m, and 4 \(\upmu\)m) were generated to investigate the agglomeration effect under different parameters in external fields. A larger reduction ratio of particle number concentration resulted in a higher agglomeration efficiency. Results indicated that, in the range from 800 to 2400 Hz, the acoustic agglomeration effect on 4-\(\upmu\)m particles was better than that on 0.5-\(\upmu\)m and 2-\(\upmu\)m particles. In the pulsed corona discharge field, agglomeration efficiencies of the three particle sizes were lower than those in the acoustic field. However, application of the coupling field highly improved agglomeration efficiency compared with the single field. When a pulse input voltage of 50 kV with acoustic sound pressure level (SPL) of 143 dB and frequency of 1600 Hz was selected, the corresponding number reduction ratio of 0.5-\(\upmu\)m, 2-\(\upmu\)m, and 4-\(\upmu\)m particles increased to 0.464, 0.526, and 0.918 from 0.254, 0.438, and 0.814 in the acoustic wave field and 0.226, 0.385, and 0.794 in the pulsed corona discharge field.  
Keywords:monodisperse particles; acoustic agglomeration; pulsed corona discharge; coupling field; agglomeration efficiency +
Footnote †: journal: Applied Sciences
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 3 Graph-Based App Usage Prediction
### Data Pre-Processing  
The aim of data pre-processing is to generate an attributed multiplex heterogeneous network from historical App usage records. An attributed multiplex heterogeneous network is a network \(G=(V,E,A)\) associated with a node type mapping function \(\phi:V\to O\) and an edge type mapping function \(\psi:E\to R\), where \(O\) and \(R\) represent the set of all node types and the set of all edge types, respectively, satisfying \(|O|+|R|>2\)[32]. Each node \(v\in V\) belongs to a particular node type and is associated with some types of feature vectors. \(A=\left\{x_{i}|v_{i}\in V\right\}\) is the set of node features for all nodes with attributes, where \(x_{i}\) is the associated node feature of node \(v_{i}\). Each edge is categorized into a specific edge type.  
In our scenario of App usage, typical usage records include basic information like an anonymous user ID, a connected cellular base station ID which we regard as location information, a timestamp, and applications with their names and categories. For a typical user, there are three node types in the heterogeneous network: time, location, and application. Node pairs are divided into three different groups: \"_usedTime_\" relationship between time and App, \"_usedLocation_\" relationship between location and App, \"_precede_\" relationship between the previous App and App. Every type of relationship can be regarded as one subgraph. Obviously, \"_time_\" and \"_App_\" nodes have intrinsically different properties and shall not be treated equally. Moreover, different interactions between node pairs imply different  
Figure 1: Network embedding based framework for App usage prediction.  
levels of features and should be treated differently. Otherwise, the system cannot precisely capture the user's behavioral patterns and preferences and would be insufficient for practical use. In addition, the time node and App node also take some useful attributes.  
Figure 2 shows an example of a part of an attributed heterogeneous network extracted from the historical App usage records. It should be noted that the _day of week, day of month_, and one period of one-day _interval of day_, and so on when using Apps are treated as attributes of time nodes if they are not be used to represent time nodes. For example, from 12:00 a.m. to 6:00 a.m., the value of _interval of day_ is 1. The App type and price are attributes of the App nodes. Finally, we choose just cellular base station information to represent locations temporarily.  
There will be a more detailed description of the dataset in Section 4.1.
## 4 Results and Discussion
### Challenges Impeding IoT Adoption Within the Silicon Mountain Technology Ecosystem
* Network Connectivity and Internet Data Costs: Improving network connectivity and reducing internet data costs were also deemed crucial, with 43.8% of respondents highlighting their importance. * Reduction in IoT Device Prices: Lowering the costs of IoT devices was rated as a highly influential factor by 95 respondents (65.1%), which could significantly boost IoT adoption. * Enhanced Energy Availability: Improving the availability of energy to businesses was similarly recognized as an important factor. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Improvement Strategies** & **GTE** & **GE** & **M** & **LE** & **NE** \\ \hline Improvement in tax policies to encourage the adoption of IoT businesses & 60 & 27 & 33 & 14 & 12 \\  & 41.1\% & 18.5\% & 22.6\% & 9.6\% & 8.2\% \\ \hline Enhance network connectivity and reduce internet data costs & 38 & 26 & 20 & 39 & 23 \\  & 26.0\% & 17.8\% & 13.7\% & 26.7\% & 15.8\% \\ \hline Reduction in the prices of IoT devices & 64 & 31 & 26 & 14 & 11 \\  & 43.8\% & 21.2\% & 17.8\% & 9.6\% & 7.5\% \\ \hline Enhance the availability and reliability of energy supply & 65 & 46 & 19 & 11 & 5 \\  & 44.5\% & 31.5\% & 13.0\% & 7.5\% & 3.4\% \\ \hline Enhance the security mechanisms for IoT systems & 61 & 52 & 27 & 6 & 0 \\  & 41.8\% & 35.6\% & 18.5\% & 4.1\% & 00\% \\ \hline Organize IoT workshops, seminars, and meet-ups and incorporate IoT-related courses into the curriculum to enhance skilled labor in the IoT & 62 & 47 & 26 & 9 & 2 \\  & 42.5\% & 32.2\% & 17.8\% & 6.2\% & 1.4\% \\ \hline Provide access to funding, loans, and IoT research grants for academic institutions and incubators & 57 & 37 & 40 & 12 & 0 \\  & 39.0\% & 25.3\% & 27.4\% & 8.2\% & 0.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed improvement strategies to the challenges identified. Additionally, several other strategies were proposed to improve IoT adoption:  
* Enhanced IoT Security Mechanisms: Strengthening security measures for IoT systems is considered essential for fostering adoption. * Educational Initiatives: Organizing IoT workshops, seminars, and meetups, as well as integrating IoT-related courses into academic curricula, is expected to address the skills gap and increase labor proficiency. * Access to Funding: Providing access to funding, loans, and IoT research grants for academic institutions and incubators is identified as a key strategy to support IoT development and adoption.
## 4 Results and Analysis
### Time Horizon Impact on Operations
Direct picking activities exhibit minimal variation, with a slight decrease in the 3-week horizon, indicating that the model effectively manages operations over longer periods without significant changes in the workload. Similarly, replenishment activities maintain stable sum and mean values across all planning horizons, suggesting that the optimization model ensures consistent efficiency. While buffer usage shows a minor increase over the 3-week horizon, its impact on overall performance is minimal. In summary, the results in Table 4 highlight the model's effectiveness in maintaining consistent handling activities and operational efficiency across varying planning horizons.
# The Influence of UV on the Production of Free Trepenes in _Vitis vinifera_ cv. [PERSON]
## 3 Results and Discussion
### Response of Terpene Compounds to UV-attenuated Treatments
#### 3.2.3 Effects on the Concentrations of Sesquiterpene Compounds
The current study observed higher \(\alpha\)-muurolene and \(\gamma\)-cadinene in the UV attenuation treatment from intermediate ripeness to harvest, which was consistent with our previous study where grape bunch light exclusion from intermediate ripeness to harvest resulted in a higher \(\gamma\)-cadinence concentration at harvest than light exclusion at other phenological stages [42]. This suggested that UV shading at this period may upregulate the biosynthesis of \(\alpha\)-muurolene and \(\gamma\)-cadinence or prevent them from being converted into other metabolites. Further molecular study is required to confirm the expression of relevant terpene synthase genes.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 2 Related Works
The public key of the UAV is used to execute encryption, while the private key of the IoID is used to establish a signature when transferring data from IoIDs. After decrypting the data, the UAV checks the identity of the IoID using an \(\eta\)-hash bloom filter. The MEC server verifies the data and the sender's identity after receiving it. After successful validation and after receiving approval from the validators, the data are stored in the blockchain. A security study is conducted to demonstrate the viability of the suggested secure solution. The effectiveness of the suggested strategy is assessed using MATLAB R2023b simulations and practical applications, with some extremely encouraging outcomes. Only a few research articles on blockchains for automated decision making and control in drone swarms have been published; hence, the goal of the literature review is to demonstrate that there is a gap in the literature that this work covers. Additionally, very few studies have been conducted to assess the functionalities of proposed models through real-world tests and simulations. Also, the majority of relevant publications have overlooked the importance of the leader's security in such systems. In order to address this gap, an innovative, lightweight, and easy-to-deploy smart contract is proposed in this work, in which a new leader-election mechanism is introduced. A realistic simulation that combines blockchain technology and actual drone programming tools was used to test this work's findings. The simulation results were very promising, and this work could potentially be used as a starting point to create state-of-the-art drone systems with enhanced security and an improved decision-making process.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 2 Finite Element Analysis
### Four-Point Bending Deflection Modelling and Validation
#### 2.1.2 Four-Point Bending Deflection Validation  
The accuracy and reliability of the four-point bending FEA model were validated by comparing the theoretically calculated values of tensile stress and midspan deflection of the single cast iron pipe. The elastic section modulus was utilized to evaluate the structural behavior of the circular hollow section. The elastic section modulus is a measure of a material/s resistance to bending and is calculated using the following expression.  
\[S=\frac{\pi\big{(}D_{o}^{4}-D_{i}^{4}\big{)}}{32D_{o}}, \tag{1}\]  
where \(D_{o}\) and \(D_{i}\) are the outer and inner diameters of the pipe, respectively.  
Figure 3: Meshing configurations for the single pipe deflection validation.  
Figure 2: The four-point bending FEA modeling and its components for the single pipe deflection validation.  
The applied moment, denoted as \(M\), is calculated using the following formula.  
\[M=\frac{P}{2}x_{L}, \tag{2}\]  
where \(P\) represents the applied load and \(x_{L}\) denotes the loading distance from the support, which is equal to 635 mm (25 inches) away.  
The theoretical stress, denoted as \(\sigma\), can be determined using the following formula.  
\[\sigma=\frac{M}{S}, \tag{3}\]  
where \(S\) is the cross-sectional modulus.  
The tensile stresses and deflections at the bottom-most layer located at the midspan of the pipe, obtained through FEA models, exhibit a close correlation with the predicted tensile stresses determined through theoretical analysis, which are around 2% and 7% for the deflection and stress errors, respectively, as shown in Table 2. The consistency of the agreement is further supported by the coefficients of variation (COVs), which are found to be 5.529% and 0.772% for the deflection and stress errors, respectively.
# FEM Modelling Approaches of Bolt Connections for the Dynamic Analyses of an Automotive Engine
## 4 FRF Analyses on the Simplified Engine Model but with Full 3D Bolt Modelling  
The aim of this preliminary analysis was the validation of such 3D modelling strategy for the engine dynamic analysis, in order to subsequently adopt it as a reference for benchmarking the other two considered approaches.  
An FRF analysis was run on the simplified engine model (Figure 1b) and the acceleration levels were compared with experimental acceleration measurements, obtained by an impact hammer test in-house performed on the full engine model of Figure 1a (a disassembled engine corresponding to the configuration of Figure 1b was not available in the laboratory).  
Figure 3a shows one of the experimental hammering points (in correspondence of the engine bracket), whereas Figure 3b highlights the accelerometer nearby monitoring point (yellow circle); a slight misalignment between numerical monitoring point and experimental hammering point can be expected. It is also possible noting from Figure 3a the heads of bolts explicitly modelled (brown colored elements). The cross comparison between numerical and experimental outcomes allowed the fine tuning of this modelling strategy, e.g., the damping coefficient.  
After fine tuning of this numerical model, the final numerical/experimental comparison on the inertance (accelerations/excitation force) at the hammered point, measured by an impact hammer test, is reported in Figure 4 across the whole considered frequency range (\(100\div 4000\) Hz), showing quite a satisfactory level of correlation, with perfectly overlapped eigenfrequencies. Some discrepancies can be noticed in the acceleration amplitudes and this can be attributed to two main reasons: the uncertainties on the modal damping value introduced in the dynamic simulation, equal to 3% in all the frequency range (rigorously, it should be frequency dependent) and equal for all the three different involved materials (cast iron, iron and aluminum), and to the slight mismatch between the location of the experimental hammered point and the monitoring point in the numerical model.  
Figure 3: FEM simplified model with highlight of the reference position (yellow dot) for vibration excitation (**a**) and acceleration measurement nearby location (**b**).  
In Figure 5 the eigenmode at nearly 2700 Hz (see also Figure 4) corresponding to the first flexural mode of the engine bracket is shown. The adoption of a simplified FEM engine model allowed to significantly reduce the computational burden whereas the inherent approximations were judged as acceptable in view of satisfactory matching between numerical and experimental outcomes. As said above, the \"full 3D bolt model\" was considered as the reference against which benchmarking the \"1D model\" and \"Glue surfaces\" simplified modelling approaches.
# All About Audio Equalization: Solutions and Frontiers
## 4 Graphic Equalizers  
The graphic equalizer is a tool for independently adjusting the gain of multiple frequency regions in an audio signal. Common graphic equalizer designs can provide up to about 30 controls for manipulating the frequency response of each audio channel. Structurally, a graphic EQ is a set of filters, each with a fixed center frequency and bandwidth. The only user control is the _command gain_, or the amount of boost or cut, in each frequency band, which is often controlled with vertical sliders. The gain of each frequency band can usually be adjusted within the range \(\pm 12\,\mathrm{dB}\), corresponding to approximately \(0.25<G<4\) for each filter [19,47,48,49]. The term _graphic_ refers to the fact that the position of the sliders' knobs can be understood as a graph of the equalizer's magnitude response  
Figure 6: Flow diagram of design technique for high order IIR filters. Starting with a simple \(N\)th order prototype filter, a series of transformations are made on each first order section to transform this into any of the main high order filter types.  
versus frequency, which makes the graphic equalizer intuitive to use despite the number of controls. For this reason it is a very popular device for sound enhancement, although it is more restricted than a parametric equalizer. Digital music players, such as those available in mobile phones, usually have several preset settings for the graphic equalizer for different music styles, such as pop, rock, and jazz, see e.g., [50].  
A graphic equalizer can be implemented using either a cascade of equalizing filters [21; 47; 51; 52; 53] or a parallel bank of bandpass filters [47; 54; 55; 56]. Additionally, hybrid structures have been suggested in which two or more sets of parallel filters are cascaded [57; 58]. In a cascade implementation, each equalizing filter determines the magnitude response of the system mainly around its center frequency according to its own command gain, and, ideally, the gain of each equalizing filter should be unity at all other center frequencies to allow independent control of gain on each band. In a parallel implementation, each bandpass filter produces at its center frequency a gain determined by its command gain; the magnitude response of the bandpass filters should be close to zero at all other frequencies to avoid interaction between the filters. Nonetheless, both types of graphic equalizers suffer from interaction between neighboring filters [51; 56; 59; 60]. In practice, a change in one command gain affects the magnitude response on a fairly wide frequency band, and this complicates the design of accurate graphic equalizers. This section first discusses the choice of frequency bands and then continues to the design of cascade and parallel graphic equalizers.