esa-sceva/satcom-chunk-collection huggingface dataset:
# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
## 3 Methodology
### Genetic Algorithm
An individual or a scenario outputted from the GA can be within one of the following categories as discussed below and shown in Figure 8.  
* Category 1: This comprises individuals whose TTC < TST. Individuals in this category require information before hand in order to avoid a collision. These individuals make up a small percentage within the large scenario space. * Category 2: This comprises individuals whose TTC is not less than TST but is within a certain safety parameter (which has been assumed to be 5 m). Individuals in this category could result in a collision and can move to category 1 if any parameter is changed negatively, for example, road surface conditions become icy. The individuals in this category require communication despite the possibility of being able to avoid a collision narrowly. * Category 3: The individuals in this category have TST < TTC and would therefore not require communication in order to avoid a collision. However, if any parameter is changed negatively, the individuals in this category can move to Category 2. * Category 4: Category 4 comprises those individuals in which no collision occurs. Since there would have been no collision that would have occurred, there would be no value for TTC. The TTC for individuals in this category is equal to the total run time of the simulation. Thus, having a larger value for TTC. The GA is used to search for solutions that lay within Category 1 and Category 2.
# Influence of Burner Nozzle Parameters Analysis on the Aluminium Melting Process
## 3 Results and Discussion  
### Results of CFD Modeling  
The influence of the air nozzle diameter on the combustion process in the physical model of the rotary furnace can be determined on the basis of the mathematical modelling results. Mathematical modelling enables investigation of the flame shape, flue gas flow, temperature field distribution, heat flow to the charge, and the distribution of concentrations of individual components under the stabilised conditions. The results of the mathematical modelling confirmed the hypothesis that the increase of oxygen concentrations in the oxidizer causes the shortening of the flame. Figure 5 (on the left) illustrates the temperature fields at the ordinary air nozzle diameter and oxygen concentrations in the oxidizer within a range of 21% to 50%. On this basis, the flame shape and the temperature distribution in the combustion chamber related to the changes of oxygen concentrations in the oxidizer can be determined. The results of the CFD simulations suggest that, for this particular type of a burner with the conventional air nozzle, the oxygen enrichment of the oxidizer cannot exceed 50% during the combustion process. When the oxidizer oxygen concentration is higher, backflash into the burner or possible thermal damage to the burner orifice can occur.  
Figure 5: The temperature fields of the experimental device using ordinary air nozzles (on the **left**) and optimized air nozzles (on the **right**) for various levels of the oxidizer oxygen enrichment.  
On the basis of the mixture combustion rate, a constant flame length can be maintained by optimizing the burner air nozzle diameter. The results of the mathematical modelling, shown in Figure 5 (on the right), confirmed the possibility of optimizing the air nozzle diameter when the oxidizer is enriched, in accordance with the Equation (1).
###### Abstract  
This paper proposes a new framework for reconfigurable intelligent surface (RIS)-equipped unmanned aerial vehicles (UAVs) in free-space optical (FSO) communication. To ensure practicality, we consider atmospheric loss caused by fog, which leads to an inhomogeneous medium for laser propagation. In addition, we incorporate the pointing error loss caused by the power fraction on the photodetector (PD) into the system and derive a closed-form expression for the elliptical beam footprint in the pointing error loss. We then propose a leading angle assisted particle swarm optimization (PSO) method to efficiently optimize the numerical results of pointing error loss. Furthermore, after obtaining these numerical results as a precondition, the UAV trajectory is optimized using the proximal policy optimization (PPO) method to achieve the maximum average capacity. Numerical simulations demonstrate that the proposed optimization method achieves greater efficiency and accuracy compared to the decode-and-forward (DF) relay and deep Q-learning (DQN) methods.  
refe space optical communications; reconfigurable intelligent surfaces; unmanned aerial vehicle; particle swarm optimization; deep reinforcement learning +
Footnote †: journal: Electronics  
Article
## 5 Case Study: A PHM System for Satellite Constellation
### Results of PHM System  
In this case, part of the results produced by the PHM system constructed based on the above framework are shown in Figure 12. The upper part shows the simulation results produced by the system at the satellite, orbit and link after injecting the data in Table 2. These results are fused with telemetry data as the input for system status monitoring. The status monitoring interface of PHM is shown in the lower left corner, which can provide a more intuitive and comprehensive display of the satellite constellation status. The curve in the lower right corner shows the prediction result of the PHM system based on the current satellite battery state of charge. Imagine that if the satellite needs to perform a high-power task after entering the shadow area, it is necessary for the O&M personnel to predict the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of orbital planes & 6 \\ Number of satellites per plane & 11 \\ Inclination & 86.4\({}^{\circ}\) \\ Eccentricity & 0.00126 \\ Argument of perigee & 90\({}^{\circ}\) \\ Ascending node & 31.6\({}^{\circ}\) \\ Mass of satellite & 900 Kg \\ Max flywheel torque & 32 N\(\cdot\)m \\ Thrust of thruster & 5 N \\ Maximum power generation & 3200 W \\ Bus voltage & 28 V \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of the satellite constellation model.  
Figure 11: Example satellite constellation system model.  
state of charge of the physical battery of the satellite in orbit during the mission planning stage. If the prediction results show that the power stored before the satellite enters the shadow area does not support the satellite to perform the mission, then the O&M personnel have sufficient time to adjust the mission plan.
## 3 Results and Discussion
### Establishing the Quality by Design Bases for Analysis
#### 3.1.3 Critical Method Parameters and Quality Risk Assessment  
The selection of the critical method parameters (CMPs) was based on the extraction efficiency of SPME fiber and the GC-MS analytical performance. The selected parameters for quality risk assessment (QRA) are presented through the Ishikawa diagram (Figure S1, Supplementary Material) and were based on scouting phase analysis. For HS-SPME, the chosen CMPs were fiber type, extraction time, extraction temperature, pH (alkalizing agent), and ionic strength (salt concentration, NaCl). The main parameter regarding HS-SPME effectiveness is the fiber type, which is greatly influenced by the selectivity of the polymer that forms the fiber and its interaction with the target analytes [39].  
In GC methods, the type of column is a relevant parameter that influences the separation performance. Optimization is achieved by selecting the column constituents according to the functional groups, bonding type, and chemistry of the stationary phase. The other parameters that can be adjustedare gas flow, initial oven temperature, ramp temperature, split method, eluent composition, and EI mode [37; 38]. For GC-MS methodology analysis, the chosen critical method parameters were gas flow, initial oven temperature, and ramp temperature. The parameters that were not chosen for investigation were fixed as in previous works [27].
## 3 Results  
### Variation in Growth  
The results showed that the _Spirulina_ strain exhibited distinct growth patterns in response to different salinity treatments (Figure 1a; Table 1). The growth at lower and moderate salinity levels (10-36 ppt) increased initially and started to slow down after Days 8-10. In contrast, growth at higher salinities (40-45 ppt) increased steadily, reaching its peak by Day 14.  
Figure 1: Effects of salinity on the growth of the _Spirulina_ strain. (**a**) Optical density (OD) at 450 nm, a proxy for biomass, was measured every 2 days over a 20-day incubation period. Each panel represents a different salinity treatment (ranging from 10 to 45 ppt), with replicate cultures indicated by different symbols (\(\bullet\), \(\blacktriangle\), \(\blacksquare\), and +). (**b**) Bar plots showing the differences in maximum specific growth rate (d\({}^{-1}\)) across salinity treatments (10–45 ppt). Error bars represent standard errors.  
A Generalized Linear Model (GLM) employing a Gaussian distribution and identity link function was applied to investigate how salinity treatment affected the maximum specific growth rate (\(r\)). The analysis of deviance showed that salinity treatment had a statistically significant effect on growth rate (F(7, 23) = 4.14, \(p\) < 0.01). Salinity as an independent variable in the model captured a significant portion of the total deviance (\(\Delta\)Deviance = 0.32), reducing the residual deviance from 0.57 to 0.25.  
The post hoc analysis using [PERSON]'s HSD test demonstrated multiple statistically significant differences in the growth rate between salinity treatments (Figure 1b). The _Spirulina_ strain grown at 10 ppt had the highest growth rate compared to cultures grown at 17 ppt (\(\Delta r\) = 0.24 d\({}^{-1}\), \(p\) < 0.05), 40 ppt (\(\Delta r\) = 0.27 d\({}^{-1}\), \(p\) < 0.01), and 45 ppt (\(\Delta r\) = 0.28 d\({}^{-1}\), \(p\) < 0.01). Also, the growth rate at 27 ppt was significantly higher compared to the rates obtained at 45 ppt (\(\Delta r\) = 0.23, \(p\) < 0.05). All other pairwise comparisons were not statistically significant, implying that intermediate salinity treatments (i.e., 22-36 ppt) did not differ significantly in their effects on the _Spirulina_ strain growth rate.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 5 Necessity and Methods to Increase the CFRP Conductivity
### Incorporation with Metal Fibers  
At the same time, highly conductive metal fibers could also be incorporated into the carbon fiber reinforced polymers to increase the conductivity of CFRP. The novel hybrid material proposed is a promising approach for advancing the mechanical properties of conventional CFRP-structure [205]. The increased density of the composite is compensated by eliminating the need for additional electrical system installation items. Electrical conductivity tests are realized on unidirectional coupons with different steel fiber volume fractions and compared with analytical values [206] and compared to conventional CFRPs, the electrical conductivity of the hybrid composites with incorporated stainless steel fibers is up to 4.2 times higher. For a hybrid composite with 18.8 vol\(\%\) integrated copper cladded low carbon steel fibers, a conductivity increase by a factor of 29.8 can be verified. C-0, CV-10, CV-20, V-60, CB-10, CB-20, and B-60 refer to seven multilayered laminates with different fiber volume fractions and a cured thickness of approximately 1 mm (Figure 8).
# Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
## 3 Materials and Methods  
### Materials  
This study used dynamic tachometer graph (DTG) data to predict the average speed on highway sections. The DTG is a device that stores vehicle operation records, including GPS location, speed, RPM, acceleration, sudden braking, and travel distance. All commercial buses, taxis, and trucks in the Republic of Korea must be equipped with a DTG. However, DTG data do not include data from private cars. The experimental data were recorded from 1-30 September 2016, and consisted of approximately 300 million records (about 800 GB). The number of buses was 1,139,865, taxis 110,116, and trucks 165,638. The system architecture of this study is represented in Figure 1.  
Figure 1: System architecture.  
All data were uploaded to the S3 bucket in Amazon Web Service (AWS). We utilized Amazon Elastic MapReduce (EMR), similar to the Hadoop system, in AWS to conduct data preprocessing. We used the r4.4 xlarge instance (i.e., 16 CPU and 128 GiB Memory), which is a memory-optimized instance to deliver fast performance for workloads that process large data sets in memory. In particular, we developed a Pig script to conduct data preprocessing (e.g., road link matching or aggregation of the data). Afterwards, deep learning was implemented using R with Keras.  
Figure 2 presents the experimental area, which consists of some upward and downward sloping parts of the Gyeongbu Expressway. The downward-sloping highway sections included the following: Seoul toll gate to Singal junction, Singal junction to Suwon Singal interchange, and Suwon Singal interchange to Gibeung interchange. The upward-sloping sections included the following: Gibeung interchange to Suwon Singal interchange, Suwon Singal interchange to Singal junction, and Singal junction to Seoul toll gate.  
The average speed during the one month period between the Singal junction and Seoul toll gate is shown in Figure 3. The vehicle operation information was recorded every 10 s. We averaged the vehicle speed of each road section every 15 min. Thus, each day was divided into 96 time intervals. Furthermore, the Gyeongbu Expressway has bus lanes. We divided the DTG data into two datasets: bus lane data and regular lane data. Remarkably, there were rare historical data for buses between 0 a.m. and 5 a.m. Thus the experimental data for bus lanes had 76 time intervals after removing non-operational time. In addition, we removed outliers (e.g., wrong coordinate position or extraordinary speed) using the standard boxplot approach based on the interquartile range. To evaluate the deep learning algorithms, we used ten days of the data as training data, ten days as validation data, and ten days as test data.  
Figure 2: Experimental area.
## 2 Decoupling Techniques (DTs) in Ultra-Wideband MIMO Antennas (UMAs)
### Slotted Edge Substrate (SES)  
In this DT, an open-end slot is cut from the substrate to improve isolation. In reference [72], the monopole AE consisted of an elliptical patch radiator, which is fed by a microstrip feed line, and second-order Koch fractal geometry was designed on the opposite edges of the ground, as shown in Figure 10. An elliptical CSRR was etched from the radiator to achieve two notched bands within the UWB range. Also, a U-shaped slot was added in the ground plane to obtain an improved matching within the UWB. A tapered conducting patch was placed between the elliptical patch and the feed line to maintain the full UWB frequency range. A slotted edge substrate was used to increase inter-element isolation. Table 2 presents an investigation of the literature review of various DTs.  
Figure 8: Parasitic element: **(a)**[68], **(b)**[69], and **(c)**[70].  
Figure 10: Slotted edge substrate [72].  
Figure 9: Carbon black film [71].  
As presented in Table 2, the 180\({}^{\circ}\) placement of AEs required a large substrate area, and metamaterial DT needed less chip area. Also, DTs such as orthogonal placement of AEs, DS, metamaterial, and CBF provided a high value of gain. Nearly all UWB MIMO  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**DT** & **Ref.** & **Ports** & \begin{tabular}{c} **Size** \\ **(mm \(\times\) mm)** \\ \end{tabular} & **Substrate Used** & **BW (GHz)** & \
\begin{tabular}{c} **Min. Gain** \\ **(dB)** \\ \end{tabular} &
\begin{tabular}{c} **Min. Isolation** \\ **(dB)** \\ \end{tabular} \\ \hline
**Placing AEs at 90\({}^{\circ}\)** & [37] & 2 & 38 \(\times\) 60 & FR4 & 2.9\(-\)15 & 4 & 19 \\ \hline  & [38] & 4 & 55 \(\times\) 25 & FR4 & 3.1\(-\)12.3 & 0.5 & 20 \\ \hline  & [39] & 4 & 81 \(\times\) 87 & FR4 & 3.03\(-\)10.74 & 0 & 20 \\ \hline  & [40] & 4 & 37 \(\times\) 46 & FR4 & 2.5\(-\)12 & 2 & 20 \\ \hline  & [41] & 2 & 34 \(\times\) 80 & FR4 & 2.84\(-\)12 & — & 22.5 \\ \hline  & [42] & 4 & 36 \(\times\) 36 & FR4 & 3.2\(-\)11 & 2.5 & 14 \\ \hline  & [43] & 4 & 50 \(\times\) 50 & Rogers RO3003 & 3.14\(-\)12.24 & — & 20 \\ \hline  & [44] & 4 & 50 \(\times\) 50 & Cotton fabric & 3.1\(-\)10.6 & 2.8 & 17 \\ \hline  & [45] & 4 & 92 \(\times\) 92 & Polyethylene foam & 2\(-\)14 & 0 & 15 \\ \hline  & [46] & 4 & 52 \(\times\) 52 & FR4 & 1.25\(-\)40 & 4 & 18 \\ \hline
**Placing AEs at 180\({}^{\circ}\)** & [47] & 2 & 52 \(\times\) 35 & FR4 & 3.1\(-\)10.6 & — & 12 \\ \hline  & [48] & 2 & 25 \(\times\) 35 & FR4 & 3\(-\)11 & — & 18 \\ \hline  & [49] & 2 & 42 \(\times\) 100.16 & FR4 & 2.6\(-\)11  
antennas were printed on FR4 substrate, except a few antennas [43; 44; 45; 63]. As compared to the orthogonal position, the 180\({}^{\circ}\) position of AEs provides high isolation. Among the various DTs, NL, PE, and SES provided high isolation because, in these DTs, there was no direct connection between the ground planes of AEs. Mostly, DTs that placed AEs orthogonally, DS, DGS, metamaterial, and PE were integrated into the UMAs.
## 3 Results and Discussion
### Water Sensitivity  
Figure 6 shows the mean measured dry (\(ITS_{d}\)) and wet (\(ITS_{w}\)) Indirect Tensile strength, as well as the Indirect Tensile Strength Ratio (\(ITSR\), on the right axis) for the specimens as a function of bitumen content for each of the studied mixes. The error bars represent \(\pm 1\) standard deviation in each of the measurements. The dotted line on the graph gives the minimum value of \(ITSR\), which corresponds to the Spanish norm [31].  
As may be seen from Figure 6, the wet tested specimens experienced a loss of indirect tensile strength when compared to their dry counterparts, due to a loss in adhesion and cohesion in the mastic bitumen. The AC-WC mixes had higher indirect tensile strength then the other mixes.  
Figure 5: Marshall quotient data and best second-order polynomial fit as a function of bitumen content in the studied mixes.  
Figure 6: Mean measured dry (\(ITS_{d}\)) and wet (\(ITS_{w}\)) Indirect Tensile strength, as well as the Indirect Tensile Strength Ratio (\(ITSR\), on the right axis) for the specimens as a function of bitumen content for each of the studied mixes. The error bars represent \(\pm 1\) standard deviation in each of the measurements. The minimum value of \(ITSR\), which corresponds to the Spanish norm, is given by the dotted line on the graph.  
The \(ITSR\) value of all of the mixes increased with increasing bitumen content, principally due to the fact that the thickness of the mastic layer is greater in mixes with a higher bitumen content [37]. As may be observed in the figure, the values of \(ITSR\) for the AC-PA control mixes exhibited \(ITSR\) values in excess of 85% in specimens with a bitumen content higher than 3.5%. The AC-WC mixes exhibit maximal \(ITSR\) values in specimens with a bitumen content higher than 4.5%. The AC-MT mixes did not comply with the minimum 85% requirement for any of the bitumen contents studied, which is an indication of low adhesivity between the binder and the aggregate. The low \(ITSR\) of the AC-MT mixes is also due to that fact that a significant part of the bitumen is absorbed by the aggregate, which lowers the effective bitumen in the mix, and consequently, the thickness of the binder film that covers the aggregate, which, in turn, promotes the disassociation of the aggregate under exposure to water.
## 2 Materials and Methods
### Phenolic Acids: Extraction, Determination, and Quantification  
The free phenolic acids extraction and quantification (i.e., caffe, rosmarinic, chicoric, p-Coumaric, and ferulic acids) were performed by High-Pressure Liquid Chromatography (HPLC) according to the method described by [PERSON] et al. (2013).  
All reagents and solvents were HPLC grade (Sigma Aldrich, Milan, Italy). Two mL of 70% aqueous methanol (_v/v_) were mixed with 100 mg of freeze-dried basil leaves and then stirred for 1 minute with a Vortex Classic stirrer (Velp(r) Scientific, Usmate Velate, Monza Brianza, Italy), sonicated for 20 min with a Q500 ultrasonic sonicator (Qsonica, Newtown, Connecticut, USA) and shaken for 10 minutes with a SSL4 see-saw rocker (Cole-Parmer(tm), Vernon Hills, IL, USA). The extracts were centrifuged at 6800 rpm for 10 minutes with a R-10M centrifuge (Remi Elektrotechnik Ltd., Mumbai, Maharashtra, India) and filtered through a 0.45 \(\upmu\)m Teflon membrane filter (Phenomenex, Torrance, CA, USA) into vials for analysis. Chromatographic separation of free phenolic compounds was performed on an Agilent Technologies Model 1100 chromatograph (Palo Alto, CA, USA) equipped with a G4225A degasser, a G1311A four-channel low-pressure gradient pump, and a G1315B diode-matrix detector, using a 20 \(\upmu\) sample injection volume. A reversed-phase Kinetex(r) C18 100 A column (5 \(\upmu\)m particle size, 150 \(\times\) 4.6 mm; Phenomenex, Torrance, CA, USA) was used. The eleuents were 0.1% (_v/v_) trichloroacetic acid in water (A) and acetonitrile (B). The gradient program was 0-50% B for 50 min with a constant flow of 1 mL min\({}^{-1}\). Detection of the individual free phenolic compounds was performed at 280 nm and shown in the representative chromatogram in Supplementary Figure S1.  
The calibration curves were constructed using seven concentration levels (0.15, 0.5, 1, 10, 20, 50, and 100 mg L\({}^{-1}\)) for each standard. The concentration of each phenolic compound was reported as mean \(\pm\) SE (standard error) expressed in mg kg\({}^{-1}\) dw, \(n=3\).
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Models and Methods for Sound Transmission Loss Calculation
#### 4.1.1 Statistical Energy Analysis  
This statistical method describes the energy flow correlation for the vibration modes of resonating DPS. It has been suitably adopted when dealing with large structures and vibro-acoustic excitation with high frequency response. The prediction of the STL of sandwich structures using the statistical energy analysis (SEA) method was studied by [PERSON] and [PERSON] [94]. In their study, the predicted results of the honeycomb sandwich structures filled with foams were compared with the measured data. The authors stated that the accuracy of the predicted solution of the STL was dependent of the accuracy in the estimation of some parameters such as the modal density, internal and coupling loss factor of the structure. The predicted and experimental solutions showed good agreement near the critical frequency when the values of the radiation loss factor were used rather than the theoretical ones used for single layered panel. In the same vein, [PERSON] et al. [95] predicted the STL sandwich structures using SEA. Their approach took into consideration antisymmetric and symmetric movement of the panels which are calculated independently. The authors' predicted solution showed improvement when compared with the conventional SEA solution and it also matched well with the measured data. Other authors [79; 96; 97; 98] have adopted this approach to predict the STL of double walls, composite and sandwich structures.