esa-sceva/satcom-chunk-collection huggingface dataset:
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 3 Simulation Results
### Received Signal Strength  
Figure 8 illustrates the RSS and coverage map of TX1 and TX2 at z-plane of 1.5m, the estimated RX antenna height of the passenger cars. Figure 8a,b show an RSS surf-plot of TX1 and TX2 respectively. Areas such as the roundabout, AV3, AV4, AV5 and the lower-area of ST2 and ST3 have RSS values below the RST. The dispersive effect caused by the trees, is not clearly defined in this view. Figure 8c depicts the jointly TX1 and TX2 contour-plot where RSS values above the RST are observed along AV1, AV2, ST1 and upper sector of ST2 and ST3 making feasible the V2I communication. The blocking of the signal due to the buildings is notorious (e.g., the building B4 causes the NLOS condition at ST2 and ST3).  
Coverage map for the joint configuration of TX1 and TX2. The coverage map was based on the selection of the highest TX1 or TX2 RSS at each point of the scenario when both antennas are transmitting. RSS values below the RST were identified with \"None\".  
Figure 8: Spatial RSS and coverage map: (**a**) TX1 RSS; (**b**) TX2 RSS; (**c**) TX1&TX2 RSS  
Figure 9a shows that the TX1 coverage encompasses AV1-AV2 (left-segment), ST1, ST2 (LoS) and ST3 (LoS), while the TX2 coverage embrace AV1-AV2 (right-segment). Figure 9b illustrates the AV2-RSS as function of the coverage-distance (x-axis) for each TX and, the transition region where the RX will define the point-to-point link either with TX1 or TX2, based on the sensing of the highest power received. Each TX has an approximate total coverage distance of 170m (asymmetric). The transition point is located approximately at the half distance between the TXs. The roundabout, AV3, AV4, AV5, ST2-ST3 (NLoS) is out of coverage.  
The effectiveness of cooperative systems using RSUs for V2I communications is dependent on how efficiently RSUs are being deployed according [PERSON] et al. in [20]. In this way, based on the simulated results, different TX placement configurations could be suggested to achieve RSS coverage levels above the RST; one of these configurations could be as follows:  
* TX1 and TX2 in the same simulated placement to give coverage to AV1, AV2, ST1.
* Tree additional TXs: one for coverage of ST2, one for coverage of ST3 and, one for coverage the roundabout, AV3, AV4 and AV5 areas.
# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
## 4 Case Study: A Simply Supported RC Girder Bridge
### Determination of Optimal Sensor Positions  
In the case study under investigation, three monitoring strategies are considered, as summarized in Section 4.3. In the following, the optimal sensor locations are calculated for monitoring strategy 1. The optimal sensor locations for updating of the corrosion variables based on the mode shapes are given in Figure 5. The horizontal axis on this figure represents the total number of sensors placed on the structure, and the vertical axis represents the location along the length of the structure. The different girders are represented by the different markers. Hence, when \(x\) sensors are placed on the structure, the markers corresponding to that value on the \(x\)-axis represent the locations of the sensors, i.e., on which girder and the position along the girder. For example, in case one sensor is placed on the bridge, x = 1 should be considered. A vertical line through x = 1 crosses a single purple pentagon at y = 1.5 m. The purple pentagons correspond to sensors placed on girder 5. Therefore, in case one sensor is placed on the structure, it should be located at girder 5 at 1.5 m from the support. Similarly, when placing five sensors on the structure (x = 5), a vertical line at x = 5 crosses the markers of girders 1-5, all with a y-coordinate of about 1.5 m. Hence, when five sensors are placed on the structure, each girder should be equipped with one sensor, each at about 1.5 m from the support. A similar procedure can be repeated to determine optimized sensor locations for strategies 2 and 3.  
Figure 4: Posterior distributions of the corrosion rate based on stain mode shapes at all elements. The dashed areas correspond to the 68% confidence intervals.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 2 System Model  
For the cognitive LEO satellite system, there are two types of networks: the legacy satellite network and the IoT network. The LEO satellite base station serves the legacy satellite users and the cognitive IoT users simultaneously in the CDMA manner. Meanwhile, IoT users share the same spectrum as legacy satellite users. The two kinds of systems also share the same LEO satellite receiving antenna. In this setup, the cross-channel state information (C-CSI) among the two systems is easy to obtain. We assume that the satellite and all user terminals are deployed with a single antenna. In the cognitive LEO satellite system, we consider one uplink case, as shown in Figure 1. Specifically, there are \(U\) primary users in the legacy satellite network and \(K\) secondary users in the IoT network. Each user in both the legacy satellite system and the IoT system is assigned a specific random spreading code with spreading gain \(N\). In this way, the symbol duration of IoT is the same as that of the primary satellite.  
In what follows, we will illustrate the channel model of the cognitive LEO satellite system and then the signal model followed by the achievable rates of both primary and secondary systems.
## 4 Submitted Systems
### Multimodal Diarization  
A total of 10 different systems from four participating teams were submitted.  
System Descriptions  
* G1-GTM-UVIGO [36]. Multimedia Technologies Group, Universidad de Vigo, Spain. The proposed system used state-of-the-art algorithms based on deep neural networks for face detection and identification and speaker diarization and identification. Monomodal systems were used for faces and speaker, and the results of each system were fused to better adjust the speech of speakers.
* G2-GPS-UPC [37]. Signal processing group, Universidad Politecnica de Catalunya, Spain. The submitted system consisted of two monomodal systems and a fusion block that combined the outputs of the monomodal systems to refine the final result. The audio and video signal were processed independently, and they were merged assuming there was a temporal correlation between the speaker's speech and face, that there were talking characters, his/her face did not appear, and faces that appeared, but did not speak.
* G9-PLUMCOT [38]. LIMSI, CNRS, France. The submitted systems (primary and two contrastive) made use of technologies based on monomodal neural networks: segmentation of the speaker, embeddings of speakers, embeddingsof faces, and detection of talking faces. The PLUMCOT system tried to optimize various hyperparameters of the algorithms by jointly using visual and audio information.
* G11-ODESSA [38]. EURECOM, LIMSI, CNRS, France, IDIAP, Switzerland. The system submitted by ODESSA was the same as the one presented by PLUMCOT, with the difference that in this case, the diarization systems were totally independent, and each one was optimized in a monomodal way.
# An SQP Algorithm for Structural Topology Optimization Based on Majorization-Minimization Method
## 4 Numerical Examples
### MBB Beam
#### 4.1.3 Calculation Cost  
As can be seen from Table 1, the single-step computation time of OC and QPOC was the same, about one-fifth of the single-step computation time of the IPC method. The reason for the basic agreement between the two computational costs is that both update formulas can be expressed explicitly and both use the dichotomous method to compute Lagrange multipliers. The QPOC method transforms the QP subproblem into a root-finding problem for a single-dimensional function, which is much simpler than solving a system of nonlinear equations using the Newton-Raphson method in the IPC method. This is the reason why the computational cost of QPOC is much cheaper than that of the IPC method.  
The comparison between different methods of compliance and calculation time and discrete indices can be found in Figure 5, where the data in the figure are all normalized. In general, the OC and QPOC methods had an advantage over the IPC method in terms of computation time, and the QPOC could be adjusted by the curvature parameter (\(q=2\)) such that its computation time was less than that of the OC method. IPC method could obtain better optimized structures, and it could be used to solve multi-constrained problems.  
Figure 4: Convergence curves for the MBB beam using different methods and parameters: (**a**) the changes in the compliance; (**b**) average iteration step size using different curvature parameters.  
Figure 5: Comparison of optimization indices obtained using different methods.
## 4 Conclusions  
In this study, the reliability of the GPR algorithm was evaluated for the prediction of rutting and fatigue cracking in the binder. For this aim, six SBS and two latex modified binders were used to build the study database. The SBS modified binders were used to train the model, while latex modified binders were reserved for simulation of the models. Three case studies were evaluated using unaged and short-term aged input variables to predict rutting and fatigue parameters. The motivation was to provide a predictive model to support pavement designers in tropical regions with limited highway laboratory equipment. The designers may rely on manufacturer's specifications and basic binder test to evaluate the performance of the selected binder at intermediate and high temperatures. The performance of the GPR model was verified against the traditional ANN, SVM and RNN models. The proposed GPR model showed comparable performance with ANN, SVM and RNN models. Thus, the study draws the following conclusions:  
1. Prediction of rutting parameter with unaged variables yielded a significant higher accuracy of 97% correlation with measure values with the GPR model on the simulation dataset.  
Figure 6: Sobol indices obtained for fatigue model using (**a**) unaged parameters and (**b**) RTFOT parameters.  
The selected input variables and database was not sufficient to predict fatigue parameters at intermediate temperature. This resulted to underestimation of the fatigue parameter in Case Studies 2 and 3.
3. The results further indicated that the unaged input variables have higher reliability in the prediction of fatigue parameters.
4. The phase angle, temperature, viscosity and softening point variables have a significant effect on the model output variance.
5. The limitation of the proposed model is the need for large and more comprehensive database to adjust its predictive accuracy.  
Conceptualization, I.D.U., P.A. and S.I.A.A.; methodology; I.D.U., M.R.M.H., A.S. and K.A.S.; formal analysis, I.D.U.; writing--original draft preparation, I.D.U., P.A., A.S., supervision, P.A., M.R.M.H. and S.I.A.A. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Universiti Sains Malaysia (USM) via Short-Term Research Grant Scheme (304/PAWAM/60313048) and the Ministry of Higher Education Malaysia for the Fundamental Research Grant Scheme (203/PAWAM/6071358).  
The authors wish to thank the Kraton(r) Polymers, Nederland B.V for supplying the polymers used to conduct this research.  
The authors declare no conflict of interest.
# Dual-Frequency Linear-to-Circular Polarization Converter for Ka-Band Applications
## 3 Simulated Results  
The initial dimensions of the converter were obtained using the proposed equivalent circuit model. These values have then been used as a starting point for a final optimization of the unit cell in Ansys High Frequency Simulation Software (HFSS) [35].
# Metabolism and Regulation of Ascorbic Acid in Fruits
## 6 Regulation of Ascorbic Acid Accumulation by Environmental Factors  
Environmental factors, such as temperature, light, and water, profoundly impact fruit development and nutritional quality. The accumulation of AsA in the fruit helps to improve the nutritional value of the fruit and improve the resistance of the fruit to various environmental stresses. Studying the effect of environmental factors on the accumulation of AsA in fruits has essential theoretical and practical significance for cultivating high-quality fruits rich in AsA (Figure 3). Light and AsA accumulation in fruit are closely related. After shading treatment of tomatoes, fruit ripening is delayed and AsA content is also significantly decreased [150; 151]. Similarly, light affects the AsA content of citrus fruits [152; 121]. The research on the mechanism of light-induced AsA synthesis is still very preliminary. The D-mannose/L-galactose pathway produces a majority of AsA in plants. However, although light induces tomato fruit ripening and AsA accumulation, the carbohydrate levels in the fruit did not change significantly. Changes in carbohydrate content in fruit also did not affect the light-induced AsA synthesis, indicating that light-induced AsA synthesis in tomato fruit is independent of carbohydrates in vivo [153]. In addition, light induces the expression of critical genes in the D-mannose/L-galactose pathway, while inhibiting the expression of genes related to AsA degradation, thereby enhancing the synthesis of AsA in tomato fruit [154].  
Long-term low-temperature storage can damage the quality of the fruit. With the increase in low-temperature storage time, low-temperature damage occurs in mango fruit, and the content of antioxidants, such as AsA, increased [155]. However, a recent study showed that a 1-min heat treatment at 55 \({}^{\circ}\)C can significantly reduce the damage of red bell pepper fruits during low-temperature storage. Preheating can substantially increase the content of AsA and glutathione during refrigeration [156]. Mild drought and salt stress can also increase the content of anthocyanin and AsA in strawberry fruit without affecting the yield [157]. These studies show that moderate environmental stress stimulation has a good application prospect in improving fruit quality.
###### Abstract  
A new high-efficiency method based on a particle swarm optimization and long short-term memory network is proposed in this study to predict the aerodynamic forces in an unsteady state. Based on the predicted aerodynamic forces, the dynamic derivative is further calculated. Using particle swarm optimization to optimize the hyper-parameters of a neural network, the long short-term memory network prediction model can be constructed according to the known simulating aerodynamic data to predict the aerodynamic performance of aircraft in unknown states. By coupling the least-squares method in an aerodynamic derivative model, the dynamic derivative can be quickly obtained. Unsteady motion of NACA 0012 airfoil was taken as the research example to verify this method, and its longitudinal combined dynamic derivatives were predicted and compared with CFD simulation results. The results show that the dynamic derivatives predicted by the PSO-LSTM method have high accuracy, with an error of no more than 1% compared to CFD, and a 70% improvement in efficiency. The method proposed in this study has good generalization ability and can realize fast and accurate prediction of dynamic derivatives with a small number of samples.  
particle swarm optimization; long short-term memory network; dynamic stability derivative model; high-efficiency simulation 2023
## 2 Design Approach of Deployable Reflector Antenna  
In this study, the design specifications for the deployable antenna are identified based on the performance and functional requirements of representative SAR satellites. Considering the RF (radio frequency) performance of the satellite antenna, the diameter of the main reflector should be over 5000 mm. The total mass of the DR-A in the antenna should be under 110 kg considering the SAR satellite system of a 500 kg weight. Because of the dynamic loads during the launch process, it is important to prevent the interaction of structural resonance between the launch vehicle and the satellite. For this reason, there is a design requirement for the structural mode frequency of the satellite system. In order to design the primary payload such as an SAR antenna, the frequency requirement for primary payload should be satisfied. By these stiffness requirements, the mode frequency  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Requirement** & & **Value** \\ \hline Mass & Antenna assembly & \(\leq\)110 kg \\ Diameter & Deployed main reflector & \(\geq\)5000 mm \\  & (Stowed condition) & \\  & Lateral direction & \(\geq\)33 Hz \\ Fundamental frequency & Longitudinal direction & \(\geq\)80 Hz \\  & (Deployed condition) & \\  & Bending mode & \(\geq\)3 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design requirements of the deployable reflector [13; 14].  
of the antenna should be 33 Hz, which is determined by the user manual of overseas launch vehicles [15; 16]. In order to evaluate the structural stability of the antenna under the launch environment, it is assumed that this antenna is subjected to the quasi-static load condition of 15 g for the directions of the x, y, and z axes. For the on-orbit condition, the structural-thermal coupled analysis is investigated to calculate the temperature distribution of the main reflector and the transient vibration analysis is examined using the pre-image maneuver of the satellite antenna. As mentioned above, the DR-A has a parabolic-shaped reflecting surface composed of 24 unit structures.  
As shown in Figure 1, the deployable antenna is stowed in a launch vehicle. In this regard, the main reflector of the DR-A was designed based on the sandwich composite consisting of the high-stiffness CFRP skin and honeycomb core. In order to achieve the lightweight design with high stiffness, the honeycomb sandwich composite structure in this paper is laminated by the space-grade CFRP facesheet of YSH70A which has a high strength of over 500 GPa, and the honeycomb core of ECK 4.8 with the low-density aramid. First of all, the stacking sequence of the CFRP facesheet and the distribution of ECK 4.8 core are sequentially designed using modal analysis of the unit structure of main reflector. Applying the layout of the unit structure designed in this paper, the finite element analyses of the unit structure and satellite level are implemented to evaluate structural and thermal performance.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 6 Simulation Environment and Performance Metrics
### Performance Metrics  
#### 6.2.1 Deep Learning Metrics  
For this work, the _learning time_ and _prediction accuracy_ of the proposed DL LSTM can be evaluated by varying two key parameters:  
* _Number of hidden units_: This parameter can correlate to the computational latency of the network. The key reason behind this parameter being varied is to find a balance between the number of hidden units and computational speed;
* _Number of epochs_: This parameter is varied to determine the optimal time required for training the system.  
For both of these parameters, a range of values is considered to ensure overfitting and underfitting are effectively captured. These concepts are further discussed in Section 7.
## 2 Materials and Methods
### Monitoring of Sugar Consumption and Metabolite Production  
Samples taken during fermentation were immediately centrifuged at 5000\(\times\)_g_ for 20 min at 4 \({}^{\circ}\)C and stored at \(-\)20 \({}^{\circ}\)C until analysis.  
1. Determination of sugars, organic acids and ethanol  
Lactose, acetate, ethanol, and lactate concentrations were determined through HPLC with a Waters chromatograph (Waters, Milford, MA, USA), equipped with a Hitachi (Foster City California, USA) differential refractive index (RI) detector (L-7490), 600E controller, column oven (40 \({}^{\circ}\)C), and cooled 717 Plus autosampler. An ICSep ION-300 column (Transgenomic, Omaha, NE, USA) was used with an 8.5-mM H\({}_{2}\)SO\({}_{4}\) solution as the mobile phase at a flow rate of 0.4 mL/min. Samples were diluted two-thirds with Milli-Q water, centrifuged at 20,000 x \(g\) for 15 min at 4 \({}^{\circ}\)C, and filtered (pore size of 0.45 \(\upmu\)m; Chromspec Syringe Filter; Chromatographic Specialties, Brockville, ON, Canada) before injection (15 \(\upmu\)L). A mixture of acetate, lactate (Sigma-Aldrich), and ethanol (JT Baker Chemical) was used as an external standard to perform quantifications. All samples were analyzed in triplicate.
2. Exopolysaccharide determination  
EPS isolation and purification were carried out by ethanol precipitation, as described previously [30] with a few modifications. Samples were heated for 15 min at 100 \({}^{\circ}\)C, then cells were eliminated by centrifugation at 12,000\(\times\)_g_ for 45 min at 4 \({}^{\circ}\)C. EPS were precipitated from the supernatant with 3 volumes 95% ethanol at 4 \({}^{\circ}\)C for 16 h, and collected by centrifugation at 12,000\(\times\)_g_ for 20 min. EPS pellets were dissolved in deionized water and dialyzed over a period of 2 days at 4 \({}^{\circ}\)C, with two water changes per day, and the dialysate was lyophilized. The crude EPS was further purified by dissolving it in 10% trichloroacetic acid, and the precipitate was washed twice in order to recover the total polymer. After dialysis for 3 days, with two changes of water per day, the solution was freeze-dried. Total sugars were measured by the phenol/sulfuric acid method [31], with glucose as a standard, and the results are expressed in mg glucose L-1. The SWP medium without inoculation was used as control. The blank value of unfermented SWP medium was subtracted from the EPS concentrations.
## 4 Conclusions  
A developed coded excitation method, which combined the phase coded excitation, is proposed in this paper. The method is successfully used for the improvement of the SNR and PSL of the oil and gas pipeline inspection system. The signal performance of the echo BCG coded method has been validated in experiments.  
From experimental studies, some summaries are noted: the proposed method is a feasible method to excite the T(0,1) wave mode propagation with its significant purity for ultrasonic guided wave testing on pipes. This method can effectively improve the echo SNR when the inspection system device transmits the wave mode T(0,1). The increasing SNR of the BCG code has significant improvement and the PSL of the developed BCG code is smaller than Barker code. The performance of BCG code is much better than the traditional coded excitation without changing the code length. Therefore, the proposed coded method, as a developed technique, is able to be used in guided wave testing and many other potential applications for improving the echo SNR about the coherent or random noise level.  
Figure 10: Comparison of SNR and PSL (dB) results **(a)** Comparison of the theoretical SNR results from Figure 5, front side SNR results and back side SNR results **(b)** Comparison of theoretical PSL results from Figure 4, front side PSL results and back side PSL results.  
**Author Contributions:** Conceptualization, Z.F.; Data curation, X.N.; Funding acquisition, B.M.; Methodology, Z.F. and X.N.; Project administration, H.M.; Resources, X.N.; Software, Z.F.; Supervision, H.M.; Validation, Z.F. and X.N.; Writing--original draft, Z.F. and X.N.; Writing--review & editing, X.N., B.M. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This publication was made possible by the sponsorship and support of TWI Ltd. and Brunel University London. The work was enabled through, and undertaken at, the National Structural Integrity Research Centre (NSIRC), a postgraduate engineering facility for industry-led research into structural integrity established and managed by TWI through a network of both national and international Universities.  
The authors declare no conflict of interest.