esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 5 Results and Discussion  
### OH-PLIF Image and Processing Method  
Figure 4 displays typical single-shot OH-PLIF images of turbulent premixed flames at varying pressures. The images clearly indicate that the flame front wrinkles are finer at high pressure. Furthermore, at high pressure, small scale wrinkled structures superimpose on the large scale convex structure, which is a general characteristic of high-pressure flames [19; 29]. However, due to the high pressure and the optical window, the energy of the laser sheet sharply decreases, resulting in a very low signal-to-noise ratio OH-PLIF image. To obtain a clear and continues flame front, an adaptive threshold method was developed [30; 31] to binarize the OH-PLIF image and the processing steps are briefly summarized here. Firstly, the OH-PLIF images are transferred to 8-bit gray images, as shown in Figure 5a. Then, a median filter is applied to remove the pixel noise and a local threshold matrix is obtained, based on the local gray intensity of the image, as shown in Figure 5b,c. Once the threshold matrix is obtained, the gray image is binarized and the flame front can be extracted, as shown in Figure 5d,e. The adaptive threshold method is designed for the high-pressure OH-PLIF image with a low signal-noise ratio and has already been applied successfully. Lastly, 2D flame surface density can be calculated by boxing method [8; 15; 31] after the flame brush is obtained, which is shown in Figure 5f.  
Figure 4: Typical OH-PLIF images at \(U\) = 3.0 m/s: (**a**) 0.1 MPa; (**b**) 0.5 MPa; (**c**) 1.0 MPa.  
Figure 5: OH-PLIF image processing method for high pressure flame with a low signal-noise ratio. (**a**) Raw OH-PLIF image; (**b**) image with median filter; (**c**) the local threshold of image binarzation; (**d**) the binary image; (**e**) the flame front (border of binary image); (**f**) flame brush. The flame shown above was operated under 1.0 MPa with a bulk inlet velocity of 3 m/s.
# Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
## 3 Result
### Verification Based on In situ Data
The inversion results of the ocean waves based on the in situ data are outlined in Table 3. The inversion biases of the SWH retrieved by the Ku and Ka bands were, respectively, equal to 0.38 m and 0.27 m, the inversion biases of the MWW for the swells were 16.75 m and 3.67 m, and the inversion biases about the MWW for wind waves were 2.32 m and 0.57 m. Overall, the wind waves and swells could be effectively retrieved by the DInIRA, and the inversion accuracy (viz. the SWH and the MWW) in the Ka band outperformed the Ku band. Figure 12: (a) Data of GNSS buoy (5 Hz); (b) inversion result of 1D power spectra.
# Study of the Direct Metal Deposition of AA2024 by ElectroSpark for Coating and Reparation Scopes
## 3 Results and Discussion
### Surface Morphology  
Both the typical splash pattern appearance produced by this technique (Figure 4a) and the last splat superimposed on the previous layer is clearly visible. This splash morphology depends on  
Figure 3: Grain structure in AA2024-T4 polarized light micrographs showing (**a**) deposit cross-section (ST-LT plane); (**b**) ST-RD.  
Figure 2: Optical (**a**) and Scanning Electron Microscope (SEM) micrographs (**b**,**c**) of AA2024-T4 substrate (**a**,**b**) and electrode (**c**).  
the arc energy, which melt the electrode tip and result in the formation of the droplet. The molten droplet from the electrode is propelled towards and impinges on the melted or partial melted substrate. The liquid pool rapidly solidifies along with the associated shrinkage. Particularly, the deposit is characterized by peaks and valleys that indicate that the material is deposited in preferential spots. This is mainly due to a plasma pressure effect and irregular gap space between the electrode and substrate. The plasma pressure splashes the liquid pool generating peaks and valleys of solidified material [1,20]. Once generated, those irregularities led to irregular space [1,20] of electrode with substrate. The variation of gap space promotes a higher amount of material mainly on the peaks of the deposit where the electrostatic forces prevail. Of course, even casual local electric field oscillations due to the manual deposition, generate different amount of melted material and irregular deposition. Therefore, the deposit buildup is not uniform and the top surface is uneven.  
Cracks were observed onto the coated surface (Figure 4b). The formation of these cracks is mainly due to thermal stresses but it can also be generated by the mechanical impact of the electrode tip to the workpiece. Generally, the cracks can nucleate in the first deposited layer and/or on defects (voids) inside the deposit. Thereafter, they are usually propagated upwards by the thermal stress, which produces well-delimited regions on the coated surface (Figure 4b). Those cracks propagate perpendicular to the substrate and they can be clearly observed in the deposits cross-section (Figure 5). Some cracks propagated parallel to the substrate and to the deposit/substrate interface (delamination cracks). That was due to the cooler bulk of the substrate, which prevents the shrinkage during the solidification of the molten metal that is transferred to the substrate from the electrode [31,32].  
The deposit cross-section exhibits several discontinuities, i.e., voids. It was observed small spherical voids (gas porosity n\({}^{\circ}\)1), large, random shaped voids (bridging porosity n\({}^{\circ}\)2), and thin void layers (laminar porosity n\({}^{\circ}\)3). The formation of these discontinuities depends greatly on the amount of material that led to fusion during the process and its distribution (Figure 5).
## 5 Conclusions  
Energy harvesting is a sustainable way to prolong lifetime of a sensor network, which enable us to target various applications that are not feasible using battery-powered sensors. However, the uncertainty in the amount of available energy poses design challenges. This paper studied the effect of using adaptive transmission power control for energy harvesting sensors in both clustered and non-clustered networks. The adaptive transmission power control adjusts the transmission power of each individual node independently based on the node residual power and its neighboring nodes' energy condition. Once a node energy level goes below a predefined threshold level, the neighboring nodes increase their transmission power control based on their residual power accordingly, to reduce the  
Figure 8: Network performance in a clustered network in terms of (**a**) average remaining power per node and (**b**) average power consumed per delivered packet.  
Figure 9: Performance comparison regarding the network lifetime.  
traffic load on the low energy node. Using this method, power can be consumed more uniformly among the nodes in the network. The numerical results indicate that the proposed adaptive transmission power control improves the performance of non-clustered networks. We also have proven that a basic power control can still improve the clustered network performance further and help the network to achieve better end-to-end performance. In this paper, we have used a simple energy-based clustering, greedy routing, and relatively simple power control mechanism to prove the efficiency. However, in the future other clustering and routing algorithms are going to be considered, which we believe results in further improvement of network performance metrics.  
Conceptualization, M.Z. and C.V.-R.; methodology, M.Z., C.V.-R.; validation M.Z., C.V.-R., M.H.A.; writing--original draft preparation, M.Z.; writing--review and editing, C.V.-R., M.H.A., L.M., E.M.M., R.V.-H., S.G.; visualization, M.Z.; supervision, C.V.[PERSON]R.; funding acquisition, C.V.-R., M.H.A.  
This research was funded by the Royal Society of the UK under grant IES\(\backslash\)R3\(\backslash\)170342 and the SEP-CONACyT Research Project under grant 255387, the School of Engineering and Sciences and the Telecommunications Research Group at Tecnologico de Monterrey.  
The authors declare no conflict of interest.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 3 Design Methodology
### Resonant Combination Generation and Merging
However, as the computational burden remains high, intra-combination orbital deduplication is implemented, with the coalescence of homogeneous orbit types being achieved through type-specific clustering. The combination \(\mathbf{C}\) in \(\mathbf{\tilde{C}}\) is replaced with \(\mathbf{\widehat{C}}\), which represents a deduplicated orbital combination. Then, this is updated to obtain the resonant orbit set \(\mathbf{\tilde{C}^{\prime}}\). \[\mathbf{\tilde{C}^{\prime}}=\{p_{0 in_{1}},\mathbf{\widehat{C}}_{in_{1}}\},\{p_{0 in_{2 }},\mathbf{\widehat{C}}_{in_{2}}\},\ldots,\{p_{0 in_{r}},\mathbf{\widehat{C}}_{in_{r}}\} \tag{26}\]
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Sentinel-2 Imagery  
Sentinel-2 images were used in this study. The mission consists of a constellation of two satellites, Sentinel-2A and -2B, orbiting in a sun-synchronous polar orbit, phased 180 degrees apart from each other. Each Sentinel-2 carries onboard a MultiSpectral Instrument (MSI) with 13 spectral bands in the visible, near-infrared, and short-wave infrared wavelengths and spatial resolutions ranging from 10 m to 60 m. Sentinel-2A and -2B provide images periodically every 10 days at the equator with one satellite and every 5 days with two satellites [18].  
Sentinel-2 offers images at two distinct levels of preprocessing: Level 1C (Top-of-Atmosphere reflectance) and Level 2A (Bottom-of-Atmosphere reflectance). While Level 1C data undergo geometric and radiometric corrections, they lack atmospheric adjustments.  
Figure 2: Images of the study area at various zoom levels, highlighted with red boxes, illustrating the organ forest.  
This type of data is typically used in studies where atmospheric effects are not a primary concern or can be addressed at a later stage. On the other hand, Level 2A data are comprehensively processed, including geometric, radiometric, and atmospheric aspects. This level is preferred for tasks requiring precise surface reflectance. It is worth noting that Level-1C data consist of 13 bands (Table 3), whereas Level-2A data include only 12 bands. The difference is due to the removal of Band 10, which provides information on cirrus clouds. This band is considered unnecessary for surface reflectance analysis [58]. Therefore, it is removed or excluded from the Level-2A dataset to streamline the product and optimize it for terrestrial applications. All Sentinel-2 bands were resampled to a spatial resolution of 10 m using bilinear interpolation. Sentinel-2 data at both processing levels were used from 1 September 2017 to 31 August 2018 and from 1 September 2022 to 31 August 2023.
###### Abstract  
We reexamine the resonance enhancement of a single harmonic emission during the propagation of ultrafast pulses through atomic and nanoparticle tin-containing laser-induced plasma (LIP). We compare the single atomic Sn and Sn nanoparticle plasmas to demonstrate a distinction in the enhancement factor of the single harmonic in the case of fixed and tunable near-infrared pulses. The analysis of the dynamics of Sn LIP shows the range of optimal delays between heating and driving pulses (130-180 ns), at which the maximal harmonic yield can be achieved. The enhancements of the 17 th and 18 th harmonics of 806 nm pulses were analyzed in the case of single-color and two-color pumps of LIP, showing up to a 12-fold enhancement of even harmonics in the two-color pump case. We show the enhancement of a single harmonic in the vicinity of the 4d\({}^{105}\)s\({}^{2}\)5p\({}^{2}\)P\({}_{3/2}\)\(\rightarrow\)4d\({}^{9}\)5s\({}^{2}\)5p\({}^{2}\) transitions of Sn II ions and demonstrate how this process depends on the constituency of the plasma components at different conditions of target ablation. The application of tunable (1280-1440 nm) radiation allows for demonstrating the variations of single harmonic enhancement using a two-color pump of Sn-containing LIP.  
high-order harmonic generation; nanoparticles; tin plasma; resonance enhancement of harmonics +
Footnote †: journal: Accepted: 26 February 2021  
in previous studies that the enhancement of the resonance harmonics compared with the neighboring ones--in the case, of the nanoparticle-containing plasmas--was notably smaller than in the case of the monomer-containing plasmas [33]. Additionally, different issues require further consideration, once one considers the resonance processes as the suitable method for a single harmonic enhancement. Particularly, tin-containing plasmas, which have earlier been examined as a suitable medium for the demonstration of this effect [42; 43], allow for analyzing different processes in various constituents of LIPs. Additionally, the availability of tunable laser sources would allow for determining the influence of the detuning out of resonances on the enhancement factor of a single harmonic.  
In this paper, we reexamine the single harmonic resonance enhancement during the propagation of the ultrafast pulses through tin-containing LIP. We demonstrate the enhancement of the 17 th and 18 th harmonics of 806 nm pulses in the case of different (single-color and two-color) pumps and compare the single atomic Sn and Sn nanoparticle LIPs to demonstrate a distinction in the enhancement factor of a single harmonic in the case of tunable near-infrared (NIR) pulses. The application of tunable (1280-1440 nm) radiation allows for demonstrating the variations of single harmonic enhancement using a two-color pump of Sn-containing plasma.
## 3 Model Descriptions
### Calibration of the Microparameters  
The microparameters used in PFC2D cannot be directly acquired from experimental laboratory tests, which need to be calibrated using a trial-and-error procedure. In this procedure, the microparameters needed to be optimized continuously until the simulated macro parameters matched the experimental results. The macro parameters used for calibration generally include the elastic modulus, Poisson's ratio, uniaxial compressive strength (UCS), and tensile strength. As shown in Figure 6, the circular-shaped grain was used for microparameter calibration, and the model was 50 mm in width and 100 mm in height, with the particle radii ranging from 0.5 to 0.83 mm following a normal distribution. The walls were used to apply a stress-boundary condition, and the wall velocity was controlled by a numerical servo-control mechanism in order to maintain a specified wall stress.  
In this study, the microparameters were calibrated to match the macroproperties of the uniaxial compression test of Lac du Bonnet (LDB) granite, including the elastic modulus, peak strength, and Poisson's ratio, which were 70 GPa, 224 MPa, and 0.26, respectively [9]. LDB granite is a representative brittle rock. Because of its comprehensive rock mechanics parameters, it is often used for model validation [9; 18; 30]. After a trial-and-error process, the elastic modulus, peak strength, and Poisson's ratio of the calibration model were determined to be 70 GPa, 207 MPa, and 0.26, respectively, which were consistent with the corresponding experimental results. The mesoscopic physical and mechanical parameters of the model are shown in Table 1. As can be seen, it includes the micromechanical parameters of the particles and the parallel bond between them. According to the existing literature [31; 32] and our calibration process, the macro elastic modulus of the model was controlled by the contact modulus, and Poisson's ratio was affected by the ratio of normal stiffness to shear stiffness. The strength of the model was mainly decided by the tensile and shear strengths of the bond.  
Figure 5: Shape factor variation of three representative grain groups: (**a**) flatness; (**b**) roughness.  
Figure 6: Numerical model for the uniaxial compression simulation.
# Invisible Threats in the Data: A Study on Data Poisoning Attacks in Deep Generative Models
## 4 Experiments
### Results  
#### 4.2.1 Model Fidelity  
Table 3 presents an evaluation of our invisible backdoor attack method's fidelity, comparing the performance of the poisoned StyleGAN3 model with a clean StyleGAN3 model. We utilized three metrics to assess fidelity: KID, FID and EQ_T. These metrics reflect the generated images' quality and the model's ability to perform integer translations, a crucial aspect of the StyleGAN3 model's functionality.  
As demonstrated in Table 3, our method is capable of successfully implementing an invisible attack while maintaining model fidelity. In particular, the KID and FID values of the poisoned model are higher than those of the clean StyleGAN3 model, indicating that the generated images from the poisoned model are of comparable quality to those from the clean model. Importantly, the EQ_T of the poisoned model is less than 2%, demonstrating a minimal loss in the model's ability to perform integer translations, a key aspect of the StyleGAN3 model's functionality. These findings demonstrate that our approach is capable of achieving a balance between attack effectiveness and model fidelity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**LSUN**} \\ \hline Metrics & KID & EQ\_T & FID \\ Clean StyleGAN3 & 0.0093 & **54.35** & 18.91 \\ Poison StyleGAN3 & **0.0059** & 52.78 & **18.70** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of the Clean StyleGAN3 and the Poison StyleGAN3 on 3 key parameters (KID, EQ_T, and FID ), using the LSUN dataset.
## 4 Analysis of Evaluation Results
### Results of the Vulnerability Evaluation
The landslide susceptibility maps of the PSO-SVM, BO-RF, and BO-CNN models are shown as (I), (II), and (III) in Figure 6, respectively. By observing the landslide susceptibility maps predicted by the three models, all of them have similar spatial distributions, with high susceptibility zones mainly distributed in the southwestern part of the study area and low susceptibility zones mainly distributed in the northwestern part of the study area.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 7 Trade-Off Analysis
### RCS vs. Q-Factor  
The RCS of a chipless RFID tag determines its scattering characteristics when illuminated by incident electromagnetic waves. Therefore, larger arrays show a higher RCS, indicating a more substantial scattering effect, which improves tag detection and identification in radar systems. However, it is essential to note that larger arrays also increase structural mode RCS, which may lead to false tag identification in crowded RFID environments. On the other hand, the Q-factor reflects the quality of resonance in the chipless RFID tag. Smaller arrays, such as a unit cell or 2 \(\times\) 2 array, show a higher Q-factor, which signifies a narrower resonance bandwidth and a sharper resonance peak, leading to improved frequency stability and discrimination against noise and interference. However, achieving a higher Q-factor usually requires careful design considerations and higher dielectric substrate materials, which may result in more significant manufacturing costs.
## 5 Implementation
### Event Oriented Task Assignment Control  
The algorithm which is shown in Figure 9 describes the control mechanism executed by the SH to assign the batch orders to the existing operational resources. When the SH starts, it runs four parallel behaviours. The first behaviour is responsible for continuously discovering and updating the batch order list, this behaviour is always waiting to be triggered by an arrival of a new batch order event. The second behaviour is responsible for continuously discovering and updating the availability of the exciting workers, this behaviour is always waiting to be triggered by an arrival of a new worker registration/deregistration event. The third behaviour is responsible for continuously discovering and updating the availability of the exciting robots, this behaviour is always waiting to be triggered by an arrival of a new robot registration/deregistration event.  
Figure 8: (a) SH (Supervisor Holon) User Interface; (b) SH State-Machine.  
The fourth behaviour is responsible for continuously pairing between a free worker and a free robot to assign a batch order for both of them. At the beginning, the behaviour checks start and stop production conditions. If the previous condonations has been achieved, the behaviour checks if there is at least one batch order at the order list. The behaviour uses First Come First Served (FCFS) policy to select the target batch orders. If previous condition has been achieved, the behaviour checks if at least one robot is free. In case if there are more than one free robot at the workcell, the behaviour selects one of them randomly as the target robot. If the previous condition has been achieved, the behaviour checks if at least one worker is free. In case if there are more than one free worker at the workcell, the behaviour selects one of them randomly as the target worker. After achieving all the previous conditions, the behaviour pairs between the target robot and the target worker. After pairing a robot with a worker the algorithm changes their status and then it assigns the task for both of them. The task pairing and assignment will be discussed in details in Section 6.2.  
The task assignment event is responsible for triggering this behaviour again. Eventually, all the exciting operational resources will be assigned to an order. Therefore, this behaviour will wait to be triggered again in case of one of the following events arrival: start event, new batch order event, new worker registration event, new robot registration event, worker task done event, and robot task done event.