esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Terpenoids  
The dominant terpenoid compound among all cultivars was linalool (T3). Linalool accounted for over 79% of the total terpenoids (Figure 6) and ranged from traces to 40.8 ng\(\cdot\)g\({}^{-1}\)  
Figure 5: Relative concentration per cultivar (ng\(\cdot\)g\({}^{-1}\)) of lactones in 42 peach cultivars. Mean values of 3 fruit pure\(\%\) samples per cultivar. Standard error (SE) bars are reported.  
This result was consistent with previous investigations where linalool was identified to be the most-abundant terpene in nine different peach accessions [8]. Linalool was detected in 15 out of the 42 cultivars that were analyzed. The \"fresh floral\" and \"fruity\" attributes of peaches are known to be the result of the contribution from terpenoids [19]. Linalool is characterized by \"floral\" and \"lavender\" odors (Flavornet and Good Scents Company databases) and \"citrus\" flavor (Good Scents Company database). In mature peaches, one of the most abundant compounds is linalool, and its level typically increases as fruit reaches maturity [4; 8]. In this study, the cultivars with the highest linalool content were \"Springprince\", followed by \"Caroking\" and \"Fireprince\" with 40.8 ng:g\({}^{-1}\), 34.6 ng:g\({}^{-1}\), and 26.9 ng:g\({}^{-1}\), respectively (Tables 4 and S1). Early-season cultivars had a higher relative concentration of linalool (T3) and geranyl acetone (T4) in comparison to mid- and late-season cultivars (Table 3). Geranyl acetone is characterized by \"magnolia\" and \"green\" odors (Flavornet database) and \"floral\" odor and flavor (Good Scents Company database).  
Compounds' contribution to aroma does not only depend on their amount, but also on their odor-detection-threshold values [19]. The linalool content in this study was above its odor-threshold levels from the literature (6 ng:g\({}^{-1}\) or 6 ppb) [21], which may have resulted in a contribution to the peach aroma of the cultivars evaluated. This result was consistent with previous studies where linalool was suggested to contribute to the floral aroma attribute of the samples evaluated [22]. Similar results were also found in the study by [PERSON] and [PERSON] [7], where linalool was suggested to be a key odorant of peach after it had an odor-active-value greater than 1 (OAV > 1).
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 2 Materials and Methods
### Methodology and Implementation
#### 2.2.4 PPI and NDVI Time-Series for Different Vegetation Classes  
As noted by [53], time-series of vegetation indices derived from satellite spectral data are instrumental in monitoring seasonal vegetation development both within a single season and across multiple years. To facilitate this analysis, they developed the TIMESAT software package [46; 53; 54]. TIMESAT is particularly effective for analyzing satellite sensor data time-series, enabling detailed exploration and retrieval of seasonality parameters such as the start, peak, and end of the growing season.  
To obtain the PPI and NDVI values for each vegetation class and date, we first re-sampled the vegetation cover map using the nearest neighbor method to match the 10 m resolution and extent of PPI and NDVI images. Subsequently, we employed the zonal function of the terra R package [55] to extract the mean PPI and NDVI values for each vegetation type across all observations.  
To further analyze vegetation indices (VIs), we constructed time-series and fitted double logistic functions using the TIMESAT 3.3 software [46; 47]. Following the methodologies outlined by [53] and other studies employing TIMESAT [37; 56], we first removed spikes and outliers by applying a median filter with a window size of 1.5. Next, we applied consistent data ranges for each VI: \(-1\) to 1 for NDVI and 0 to 3 for PPI. Given that noise in VI data from remote sensing is typically negatively biased, we adapted the upper envelope with a strength of 3 to reduce the influence of low data values. Finally, we set the season parameter to 1 to fit one season per year. As [53] noted, the success of fitting functions to time-series is as much an art as a science, relying heavily on visual examination and the specific nature of noise and disturbances in the data.
## 3 Results and Discussion
### Incident Energy  
Incident energy of the sputtered copper atoms depends on the power of RF generator, which generates plasma and ionized argon to sputter the copper target. Incident energy values of 1 ev, 3 ev, 5 ev, 7 ev, and 10 ev were discussed in this study. To study the effect of incident energy, tantalum was selected as the substrate material, while the other parameters were set as follows: deposition height = 5000 A, incident angle = 30\({}^{\circ}\), substrate temperature = 600 K, and deposition rate = 10 atoms/ps. The resulting copper seed layers with different incident energies are shown in Figure 11.  
Figure 11: Copper seed layer with different incident energy.  
Figure 9: Bottom height and necking with different substrate temperatures.  
Copper atoms with higher incident energy transferred more kinetic energy to the deposited atoms and substrate. This gave atoms more energy to break through the diffusion barrier energy. The overall distribution of the deposited copper atoms with respect to incident energy is shown in Figure 12. The additional energy given by the depositing atoms led the deposited atoms to flow into the trench and accumulate at the top sidewall, which caused the necking to decrease. The smaller the necking, the fewer atoms reaching the mid sidewall. Around the lower sidewall, with decreasing necking, atoms reaching the lower sidewall decreased. However, with increasing diffusion ability caused by the increasing energy, atoms from the island at the bottom diffused to the lower sidewall. Figure 13 shows that the necking of the copper seed layer decreased as the incident energy increased. The step coverage of all regions increased as the incident energy increased, as shown in Figure 14. The effect of diffusivity dominated when the incident energy was smaller than 5 ev. As energy exceeded 5 ev, the shadowing effect became dominant. It is noted that in the case of 1 ev, since the incident energy was low, atoms were more likely to be captured at the top sidewall rather than depositing further into the trench; consequently, this resulted in poor uniformity, as shown in Figure 15.  
Figure 12: Overall distribution of the deposited copper atoms with respect to incident energy.  
Figure 13: Bottom height and necking with different incident energy.  
To the bottom of the trench, the increasing incident energy gave the atoms on the center island more energy to break through the diffusion barrier energy and diffuse to the two sides of the trench. This led to a decrease in number of atoms at the bottom center of the trench and an increase at the bottom sides of the trench. For the substrate atoms, the increase in incident kinetic energy caused them to flow into the copper seed layer, which increased the alloy percentage, as shown in Figure 16.  
Figure 16: Alloy percentage with different incident energy.  
Figure 14: Step coverage with different incident energy.  
Figure 15: Uniformity with different incident energy.
# Efficiency Optimization of the Main Operating Points of an EV Traction Motor
## 2 Selection of Main Operating Points
### EV Simulation for Operating Point Analysis  
The process of the EV simulation is shown in Figure 2. The torque characteristics of the initial IPMSM motor model were analyzed using finite element analysis (FEA) performed with Ansys Maxwell. Subsequently, MATLAB 2024 was used to model the EV, and simulations were performed based on the selected driving cycle. The motor's operating points derived from the simulation results were analyzed to choose the main operating points.  
The EV modeling was conducted using MATLAB 2024 through a one-degree-of-freedom (1 DOF) vehicle model. The 1 DOF model represents the longitudinal motion of a vehicle with a single degree of freedom, considering only forward and backward movements. This allows the analysis of the vehicle's longitudinal dynamics, as shown in Figure 3, and the calculation of the torque required by the motor during vehicle operation, as expressed in Equation (1). In Figure 3, the red text represents the resistance components acting against the vehicle's motion, while the blue text indicates the traction force that propels the vehicle forward. Here, F\({}_{\text{traction}}\) is the Traction force, F\({}_{\text{air}}\) is the air resistance, F\({}_{\text{grade}}\) is the grade resistance and F\({}_{\text{Roll}}\) is the rolling resistance. In Equation (1), \(T\) represents the motor torque, \(r\) denotes the wheel radius, \(\eta\) is the gear efficiency, \(G\) is the gear ratio, \(M\) is the vehicle mass, \(a\) is the vehicle acceleration, and \(\rho\) is the air density. Parameter \(C_{d}\) is the drag coefficient, \(A_{f}\) is the vehicle's frontal area, \(V\) is the vehicle speed, \(f_{r}\) is the rolling resistance coefficient, \(L\) is the wheelbase, \(h_{cg}\) is the height of the center of gravity, \(g\) is the gravitational acceleration, and \(\gamma\) is the road gradient angle.  
\[T=\frac{r}{\eta G}(Ma+\frac{1}{2}\rho C_{d}A_{f}V^{2}+f_{r}\frac{L}{h_{cg}}Mg \ cos\gamma+\frac{L}{h_{cg}}Mg\ sin\gamma) \tag{1}\]  
Previous optimal design studies using driving cycles have predominantly employed a single urban driving cycle to conduct simulations and determine the motor's main operating points. In the EV driving range certification tests conducted by the Ministry of Environment in Korea, the Urban Dynamometer Driving Schedule (UDDS), which is an urban driving cycle involving frequent stops and accelerations over short distances, and the Highway Fuel Economy Driving Schedule (HWFET), a highway driving cycle involving  
Figure 2: Main operating point selection process.  
relatively constant speeds at higher velocities, are used for the multi-cycle test (MCT); therefore, in this study, simulations were conducted using two driving cycles, UDDS and HWFET, based on the MCT test, as shown in Figure 4.
## 4 Fatigue Damage Analysis
### Finite Element Analysis
#### 4.1.1 Model Establishment and Loading  
[PERSON] [31] suggested that the wind load resistance performance of a profiled metal roof can be studied through two-span roof panels. Moreover, [PERSON] [32] pointed out that when the roof is large, the lap joints between the roof panels have little effect on its wind resistance performance, and the roof can be treated as an infinite continuous plate. [PERSON] [14] proposed that the wind resistance behavior of a profiled roof panel system can simulate the effect of wind load by uniformly distributed pressure.  
Finite element software ANSYS 14.5 was used to establish a one-span model with YX35-760 self-drilling screw-fastened metal roof claddings as the prototypes. The dimensions of the roof panel are shown in Figure 6. The material parameters of the roof panel are shown in Table 2 according to the tensile test results of the specimens, and the graded loading method was adopted for finite element analysis.  
In the finite element model, the metal roof panel selects the shell181 element. Meanwhile, the washer and screw head select the solid185 element. The metal roof panel material is assumed to be a type of perfectly elastic plastic material, and the bilinear model was adopted. The rubber washer adopts the Mooney-Rivlin model. The parameters were set referring to document [33].  
A fine mesh was used in the vicinity of the fastener hole. The contacts between the washer and the roof panel and between the washer and the screw heads were bonded. Given that the roof claddings were screw-fastened at each crest, symmetry conditions were assumed along each crest. Figure 7 shows the boundary conditions used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cladding** & **Young’s** & **Poisson’s Ratio** & **Yield Strength** & **Tangent** \\
**Thickness (mm)** & **Modulus (GPa)** & **Poisson’s Ratio** & **(MPa)** & **Modulus (MPa)** \\ \hline
0.426 & 205 & 0.3 & 349 & 4100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material information of roof panels.  
Figure 6: Dimensions of the YX35-760 metal roof panels.  
Figure 7: Boundary condition setting.
## 2 Geological Setting and Reservoir Characteristics
### Reservoir Characteristics  
The SaPu interbedded reservoir has similar petrological characteristics to the adjacent Saertu and Putaohua formations, with a low rock maturity, a high clast and feldspar content, and a low quartz content, as shown in Figure 1. Most of the parent rock compositions of such reservoirs are close to volcanic sedimentary areas, so the plasticity of these minerals is high, while their physical properties are generally poor after transportation and deposition [8].  
The core analysis data show that the porosity of the Saertu oil formation is generally between 9.0 and 23.0%, with an average of 17.2%; the air permeability is generally distributed between 0.1 and 100 mD, with an average of 0.5 mD; and it is a medium-porous and low-permeability reservoir, as shown in Figure 2.  
The SaPu intercalation elicits intricate well-logging responses. In comparison to the Sartu and Putaohua formations within the Saertu-Putaohua oil-bearing group, it has a smaller absolute value, but a larger relative value of natural gamma; a lower absolute value of spontaneous potential (SPmax-SP); a higher relative value; a sonic transit time  
Figure 1: Statistical chart of the characteristics of sandstone petrography.  
Figure 2: Histogram of porosity and permeability distribution.  
ranging between 75 and 85 us/ft; a compensated density between 2.2 and 2.5 g/cm\({}^{3}\); and a compensated neutron porosity between 0.15 and 0.25. The deep and shallow lateral ratios are inferior to those of the adjacent Saertu-Putachua reservoirs, and the oil layer resistivity is low (less than 10 ohm.m), with certain water layers demonstrating resistivity significantly surpassing that of the oil layer [9].  
As illustrated in Figure 3, using T1702 as an exemplar, the natural gamma, one of the lithological curves, exhibits a sawtooth distribution, the spontaneous potential closely aligns with the mudstone baseline, the neutron porosity and density curves indicate a high clay content, and the resistivity is below 10 \(\Omega\cdot\)m, which indicates clear low-resistivity characteristics. Sedimentologically, rhythmic bedding is observable, denoting subaqueous distributary channel deposition. The upper mud content is substantial, and the bound water saturation is elevated, underscoring distinct sedimentary control. The testing results for Layers 33 and 34 indicate that they are industrial oil and gas reservoirs with a daily oil production of 10.1 t and a daily water production of 5.4 m\({}^{3}\)[10].
## 2 UTM Monitoring Framework
### Flight Data Measurement  
The concepts of the inverted teardrop shape for conflict detection volume and its algorithm were implemented in our UTM monitoring application. The application is developed based on the network Remote ID with the architecture as shown in Figure 4. The UAV flight information is transmitted via a network remote ID to a cloud database; then, our UTM application makes a data query via an internet protocol to obtain the data in real time. After the monitoring processes, the information and its warning are displayed to the users.  
The transmitted UAV flight parameters are measured from sensors included in the Remote ID module, which are a GNSS, a barometer, and accelerometers [35]. The lateral  
Figure 4: UTM monitoring application based on the network Remote ID architecture.  
position of the UAV in coordinate format is measured from GNSS sensors in reference to a GPS, GLONASS, or GALILEO satellite constellation. Besides the position, lateral accuracy information is also provided. The barometer sensor is used to measure the height of a UAV flight with reference to the take-off point and the vertical accuracy information. The last sensors are accelerometers, which are used to measure the acceleration with reference to the north-east-down (NED) coordinates. However, the transmitted parameters is the speeds in the NED format, which are integrated from the accelerations.  
Besides the flight parameters, the time of the transmitted data and received time in the cloud database are recorded. The time required for the data to travel from the UAV until it is displayed in the UTM application is called the latency time. It consists of the broadcast time, query time, and process time. First, the broadcast time is calculated from the UAV transmitting the information until it reaches the cloud server. Second, the query time is the time from the cloud database until it is received by the UTM application. Last is the processing time, which is the time from receiving the data in the UTM application until it is streamed to the display. In addition, the interval time is also calculated based on the difference in time between data displayed. Since the UTM monitoring application uses one thread of computing calculation, the interval time is affected by the latency time.  
Moreover, due to the wireless 4G signal quality, internet connection quality, and computing capability, data latency varies over time in the UTM application. This data latency is crucial for UTM operation, especially in conflict detection and resolution, which involve wireless communication [36].
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
## 4 Simulation and Discussions
### Flowchart of Dead-Beat Control for \(\delta\) Control  
In the block of Matlab function, the program flowchart for dead-beat control and \(\delta\) control is depicted as shown in Figure 5 which can be summarized as follows.  
* Execute \(v_{i}(k-1)\) to the VSI within single-phase ES
* Measure grid voltage \(v_{G}\) and PCC voltage \(v_{C}\)
* Predict grid current \(i(k)\), inductor current \(i_{L}(k)\), and capacitor grid \(v_{ES}(k)\)
* Calculate the reference of CL voltage based on the detected instantaneous phase and RMS value of grid voltage, and also based on circuit parameters
* Calculate the modulation signal \(v_{i}(k)\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Units** \\ \hline Regulated SL voltage (\(V_{S}\)) & 110 & V \\ ES DC bus voltage (\(V_{dc}\)) & 200 & V \\ Line resistance (\(R_{1}\)) & 1.64 & \(\Omega\) \\ Line inductance (\(L_{1}\)) & 30.4 & \(\mathrm{mH}\) \\ CL (\(R_{2}\)) & 1603.4 & \(\Omega\) \\ NCL (\(R_{3}\)) & 51.05 & \(\Omega\) \\ ES inductance (\(L\)) & 2.3 & \(\mathrm{mH}\) \\ ES capacitance (C) & 26.11 & \(\mathrm{\mu F}\) \\ Switching frequency (\(f_{S}\)) & 10 & kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: The study case data. SL: smart load; ES: Electric springs; CL: critical load; NCL: non-critical load; DC: direct current.
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 5 Simulation Validations and Discussion  
### Simulation Setup  
This experiment uses Python and NS-3 (Network Simulator-3) to verify the performance of the proposed algorithm and system model, respectively. All balloon base stationsand users are initially randomly distributed, and all data are taken as the final value of the algorithm after the completion of the current iteration. The values of primary parameters are listed in Table 2.
## 4 Optimization Process  
### Dynamic Model  
For the purposes of the simulation, a 3-DOF planar robotic manipulator shown in Figure 1 is used, set on a vertical plane. The robotic manipulator consists of three revolute joints and structural properties of the robot are listed in Table 1[11].  
Figure 1: Scheme of 3-DOF serial robotic manipulator.  
Furthermore, the following friction model [21] is used to provide a more realistic model for the optimization:  
\[\mathrm{F}(\dot{q}_{i})=\ \gamma_{1}(\tanh(\gamma_{2}\dot{q}_{i})-\tanh(\gamma_{3} \dot{q}_{i}))+\gamma_{4}\tanh(\gamma_{5}\dot{q}_{i})+\gamma_{6}\dot{q}_{i} \tag{16}\]  
with the following properties:  
* \(\gamma_{1}+\gamma_{4}\) act as an approximation of the static coeffient of friction
* \(\tanh(\gamma_{2}\dot{q}_{i})-\tanh(\gamma_{3}\dot{q}_{i})\) is the equivalent of the Stribeck friction effect
* \(\gamma_{4}\tanh(\gamma_{5}\dot{q}_{i})\) is the term representing Coulomb friction
* \(\gamma_{6}\dot{q}_{i}\) is the viscous dissipation term [21].  
The friction parameters \(\gamma_{1}\) to \(\gamma_{6}\) are chosen as described in Table 2.  
Finally, three actuators of the robotic manipulator are assumed to produce finite torque with the maximum allowable torque being \(|\tau_{max}|=15\)\([Nm]\).
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
###### Abstract  
Photo response non-uniformity (PRNU) is a widely accepted inherent fingerprint that has been used in source camera identification (SCI). However, the reference PRNU noise is limited by the performance of PRNU noise extraction techniques and is easily contaminated by interfering noise from image content. The existing methods mainly suppressed the interference noise of the reference PRNU noise in the spectral domain, but there was still interference noise related to the image content in the low-frequency region. We considered that this interference noise of the reference PRNU noise could be removed by further operations in the spatial domain. In this paper, we proposed a scheme to distil the reference PRNU by removing the interference noise with the help of principal component analysis (PCA) technology. Specifically, the reference PRNU noise was modelled as white Gaussian noise, whereas the interfering noise caused correlation between pixels and their neighbourhoods in the reference PRNU noise. In the local pixel area, we modelled a pixel and its neighbours as a vector and used block matching to select PCA training samples with similar contents. Next, PCA transformation estimated the interference noise in the local pixel area, and we performed coefficient shrinkage in the PCA domain to better estimate interference noise. The experimental results on the \"Dresden\" and \"VISION\" datasets showed that the proposed scheme achieved better receiver operating characteristic curves and the Kappa statistic than state-of-the-art works.  
digital forensics; source camera identification; sensor pattern noise; photo response non-uniformity noise; principal component analysis Article  
A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification. April. Sci.2023  
3
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 2 Research Significance  
To date, much of the research on recycled aggregate concrete had focused on utilising coarse aggregate from construction and demolition waste. However, only very few studies have focused on extracting aggregate from fresh wet concrete. This study investigates the performance of concrete utilising patented [44] manufactured coarse aggregate recycled directly from excess fresh waste concrete produced in batching plants or construction yards. Utilising this manufactured aggregate in concrete production could reduce the demand for quarried aggregate as well as utilizing a waste product from concrete production, minimizing the environmental impact. Hence, the research can be highly beneficial in identifying the possible application of manufactured coarse aggregate and the associated engineering performance. The material could be a commercially viable and sustainable alternative to the current aggregates used in the construction industry.