esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison and Evaluation of Prototype DPIs
#### 3.2.2 Comparison of Mie-Scattering Spray Image Results  
In this study, LED back illumination for Mie-scattering was applied to make the initial liquid spray to full development spray visible. This enabled an accurate SOI determination and simultaneously measured the initial liquid portion of the spray. Additionally, this Mie scattering technique is suited to the spray characteristics in cold conditions, where there is insignificant vaporization [8; 9]. The spray image was taken with an exposure time of 38 \(\upmu\)s at a resolution of 320 \(\times\) 320 pixels in 25,000 pps by a high-speed camera.  
Figure 14: Comparison of the maximum injection pressure for representative Type 1, 2 and 3 DPI.  
Figure 13: Comparison of the spray speed results in Type 3 DPIs.  
Figure 15 and Table 8 show the comparative results of the spray speeds with four different DPIs. The Type 1 and 2 DPI had observed-maximum speeds of 91 m/s and 104 m/s, respectively, with an average speed of 59 m/s and 67 m/s. In comparison to the conventional type, Types 1 and 2 have slower spray speeds and a slow SOI response of 200 us. It is known that the motion condition of a DPI needle is highly affected by the response parameter [9]. Generally, this shows that the use of hydraulic pressure for needle movement enhances the response performance. In this study, Type 1 and 2 DPIs with the needle only being controlled by a mechanical mechanism, without the use of hydraulic pressure, at about 80 us, would be slower than the conventional type in terms of the SOI response. In particular, by considering the internal actuation structure of Type 2 and the conventional type DPI, it can be seen that the plate spring and the pressure plate are key factors influencing the determination of the spray characteristics.  
In the case of the Type 3 DPI, the observed-maximum spray speed is 113 m/s, the average speed is 80 m/s, and the SOI response is 80 \(\upmu\)s. This value means that the Type 3 DPI has a fast needle movement, since about 40 \(\upmu\)s would be faster than the conventional type DPI. In other words, the Type 3 DPI needle is able to start fuel injection at 80 \(\upmu\)s, but the conventional type DPI is still closed, maintaining a needle state until the SOI timing is 120 \(\upmu\)s.  
As shown in Figure 16, there is an obvious difference in the spray characteristics with time after the start of the injection. As stated previously, the Type 3 DPI had a faster needle opening than the other three DPIs, so it displayed a rapid spray behavior until obtaining a full development spray. It is clear that the actuation structure of a direct needle-driven injector significantly affects the initial spray shape. This is due to hydraulic internal flow dynamics within the DPI nozzle. Therefore, when the needle response is relatively high, the spray tip fuel penetration increases in the liquid phase spray characteristics.
# Gypsum-Dependent Effect of NaCl on Strength Enhancement of CaO-Activated Slag Binders
## 3 Results and Discussion
### ANOVA Analysis  
The \(p\)-value for each factor (i.e., NaCl content, gypsum content, and gypsum-NaCl interaction) was calculated by the F-test of the ANOVA as shown in Table 3. The \(p\)-values of the gypsum content and the gypsum-NaCl interaction were smaller than 0.05 at all days, implying that these factors were significantly influential on strength at all days. However, the \(p\)-value of the NaCl content became larger than 0.05 at 28 days, unlike at 7 days, which indicates that the influence of the NaCl content on strength was significant at 7 days, but was largely decreased at 28 days.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \cline{2-3}  & **7 Days** & **28 Days** \\ \hline NaCl content & 0.000461 & 0.2349 \\ Gypsum content & \textless{}0.00001 & \textless{}0.00001 \\ Gypsum–NaCl interaction & 0.00014 & 0.00082 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(p\)-values for the compressive strengths of hardened paste samples.  
Figure 2: Compressive strengths of hardened paste samples. The error bars indicate standard deviations.
# Similarity Estimation for Large-Scale Human Action Video Data on Spark
## 3 Proposed Framework
### Feature Extraction
#### 3.2.3 Edge Based Local Pattern Descriptor  
In this section, we illustrate the proposed Edge based Local Pattern Descriptor that is applied for extracting human action features. We utilize the Sobel Operator to compute gradient magnitude, which represents the shape information of human activity. Firstly, we apply Sobel Operator to obtain the gradient magnitude. Then, on these gradient values, we apply our method to find the features. The following equations are used for the ELP. Figure 2 demonstrates the computation process of our proposed ELP:  
\[ELP(x_{c},y_{c})=\sum_{n=1}^{n-1}s(g_{p}-g_{c})\times 2^{p}, \tag{6}\]  
\[s(x)=\begin{cases}1,&\text{if }x\geq M_{c}\\ 0,&\text{otherwise}.\end{cases} \tag{7}\]  
Here, \(M_{c}\) denotes the average of \(|g_{p}-g_{c}|\) values of the local neighbor pixels around the center pixel \((x_{c},y_{c})\) and \(g_{p}\) corresponds to the gradient value of the local neighbor pixels. We compute the difference between the gradient value of each neighbor pixel and that of the center pixel sequentially. Then, we do the summation of these eight pairs of differences. After that, we can get the average of the difference value between neighbor gradient values and the center gradient value by computing the mean value of these eight pairs of differences. Thus, we get the \(M_{c}\). If the difference between a neighbor gradient value and the center gradient value is greater to or equal to \(M_{c}\), this neighbor value is transformed into 1. Otherwise, it is transformed into 0. We implement the same process to other neighbor gradient values in sequence. Therefore, we get a binary string. Next, we just need to compute the binary string to transform the binary string into decimal values and do summation. At the moment, it means that we obtain the ELP value of the center pixel. We repeat the implementation to compute all other pixels to get ELP value of all pixels of input frame. Thus, we get ELP feature vectors.
# Multiscale Damage Evolution Analysis of Aluminum Alloy Based on Defect Visualization
## 1 Introduction
[33] employed the Eshelby-Kroner scale transition law and used a double-scale model to describe the HCF (high-cycle fatigue) phenomenon in which damage occurred only at the microscopic scale. [PERSON] et al. [34] considered the phenomenon of building orientations and porosity in the additive manufacture structures and used the micro-scale damage-evolution equation to describe the damage evolution process at the macroscopic scale. However, such attempts focused on exploring the evolution process of micro-defect size and neglected the influence of microscopic defect shape and position distribution on fatigue damage. There is a paucity of modeling methods and fatigue damage evolution analyses based on micro-defects visualization processing and 3D reconstruction, considering aspects of the real random uniform micro-structural morphology. In this regard, this paper aimed to create a multiscale damage evolution model by an efficient and simple defect classification method and 3D reconstruction technology based on MCT (micro-computed tomography) scanning data. Firstly, fatigue specimens at different loading stages were scanned by MCT technology. The defect information for fatigue specimens was graded and simplified by the defect classification method, considering not only the micro-defect size but also the shape and position distribution on fatigue damage via AVIZO (3D visualization software, September 2, 2016, FEI SAS, Merignac, France) visual processing. Then, 3D reconstruction was carried out. An equivalent simplified model was established by the ABAQUS subroutine. This model provided an effective tool to build a bridge between mesoscopic damage and macroscopic fracture, using the damage variable in macro-scale to characterize the mesoscopic damage evolution indirectly. At the same time, the residual fatigue life for engineering structures under unknown loading times could be predicted easily through the microstructure by nondestructive detection based on this methodological study.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair
### Acellular Dermal Matrix (ADM)
#### 2.1.3 DermACELL(tm)  
DermACELL(tm) is a decellularized regenerative human tissue matrix allograft that contains both the reticular and papillary compartments with a basement membrane (Figure 2). Upon application, the reticular site is placed against the surgical wound. The patented preparation process for DermACELL(tm) includes the use of anionic detergents and endonucleases to eliminate more than 97% of the nucleic acids. It is preserved and stored at room temperature and thus can be delivered hydrated [40]. DermACELL(tm) is indicated for chronic non-healing wounds [34] (Table 1). In a prospective, multicenter, single-arm clinical trial for the treatment of large complex DFUs with exposed tendon or bone, DermACELL(tm) rapidly reduced the size of the wound. However, that study favored cases that required a single application of the ADM [41]. Additionally, in a case report [40], DermACELL(tm) was successfully used to treat a patient with scarring from a severe burn. More recently, a case report publication  
Figure 2: Detailed schematic histological representation of tissues used for decellularization for skin repair and regeneration. The various tissue compartments of skin (**A**), peritoneal membrane (**B**), small intestinal mucosa (**C**) and amniotic membrane (**D**) are represented by separate graphics and annotated on the side. When a tissue compartment is cellularized, cells are drawn in black and when it is vascularized, vessels are represented by red circles. The tissue layers used for decellularization and clinical applications are shown by arrows on the side indicating their average thickness.  
provided evidence that DermACELL(tm) can eventually be used to treat fingertip injuries, e.g., after amputation [42]. An ongoing open-label trial will assess the effectiveness of this ADM in patients with chronic venous ulcers.
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 4 Employing H.264-Like Compression  
The main objectives of the H.264/AVC standardization efforts were improving the compression performance, providing \"network-friendly\" video representation, and compressing the information more efficiently than in previous standards, such as H.263 [43].  
H.264/AVC represents advances in standard video encoding technology, improving encoding efficiency and flexibility for use in a wide range of networks and applications [43; 44].  
H.264 provides about 50%-bit rate savings for equal perceptual quality compared to the performance of previous standards, such as H.263 [45; 46].  
H.264 makes use of three frames (I- FRAMES, P-FRAMES and B- FRAMES) to improve error resilience, avoid failures in video streaming, and improve the efficiency of compression and the compressed stream [47; 48; 46].  
Compression can be improved by further modifications, such as by using differences between time and location data and compressing the commas by using the Huffman Code. Moreover, vehicles often get stuck in traffic jams; the more vehicles, the bigger the traffic jams will be [49]. The average speed today in big cities can be even under 30 km/h, as in New York [50] or Tel Aviv (around 15 km/h).  
Beyond the traffic jams, the vehicles idle at traffic lights or stop for various purposes. On all these occasions, the vehicle sends information about its location using the method described in [42] but with several changes.  
In autonomous vehicles, several types of connections help vehicles to receive information from the environment (other AVs) and vice versa, as with vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) [51].  
H.264 takes advantage of the property that adjacent video frames are usually very similar. Therefore, the values of the adjacent blocks' differences will be zero or close to zero. The more zeros we obtain, the better the Huffman coding efficiency. The information that GNSS generates also usually has this feature of similarity between adjacent blocks, so we adapted the idea of H.264 and achieved an effective and efficient preprocessing step.  
We analyzed the GNSS data compression and decompression, which can affect the total time and resources used by the GNSS. The differences between the consequent NMEA sentences will be compressed in the way that H.264 handles consequent frames with small differences.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 5 Classification of Efforts for Evolving the Control Plane
### Efforts to Evolve the Control Plane of Inter-Domain Routing
#### 5.2.2 Incremental Improvement
Summary: Works to incrementally improve the inter-domain routing try to add capabilities to the BGP control plane, such as exploring path diversity for resilience purposes or increasing the available bandwidth of a domain. One major limitation of the BGP that is tackled by related works with incremental improvement is searching for multipath solutions for inter-domain routing. Thus, instead of using only the one best route per prefix, the use of alternative routes has the potential to improve resilience against link failures, bandwidth availability and security [15; 74; 75].
# Fate of Soil Carbon Transported by Erosional Processes
## 3 Soil Erosion as a Source or Sink of Carbon
### Soil Attributes  
As is documented by [PERSON] et al. [18] regarding the difference in SOC loss from black soil vs. a loess soil, erosion-induced emissions of GHGs are also strongly affected by soil physical, chemical, and biological properties, which are indeed strong moderators of the rate and total magnitude of GHGs emissions. Soil hydrological properties, as affected by water repellency, also impact emissions of GHGs. In the context of the western U.S. wildfires, which have increased in intensity and scale because of the climate change, [PERSON] et al. [19] assessed the impact of the fire-induced soil water repellency on GHG emissions. The water repellency was characterized by: water drop penetration time (WDPT), effective contact angle and relative sorptivity of exposed silica sand (used as soil surrogate). All sand samples, exposed to either fire emissions or fulvic acid were characterized by WDPT \(>\) 81 s, effective contact angle of 78-87\({}^{\circ}\), and relative sorptivity between 0.31 and 0.49 compared with untreated sand samples with WDPT \(<\) 0.5 s, effective contact angle of 48\({}^{\circ}\), and relative sorptivity of 1.  
Soil attributes affect emissions of GHGs from eroding and depositional sites through their effects on SOC stability. Based on an experiment on SOC stability in sub-tropical China, [PERSON] et al. [20] observed that eroding and depositional sites had different SOC contents but had similar organic functional groups. However, SOC in eroded soils was more stable than that in depositional soils in the surface 0-5 cm and 5-10 cm layers only. Differences in SOCstability were attributed to differences in soil properties such as texture, bulk density, pH, SOC content, DOC, Fe content, soil aggregates, depth, erosion, and deposition.  
A study on the effects of soil properties on water erosion and emissions of GHGs on soil under diverse land uses in Spain by [PERSON] et al. [21] showed that soils with a higher SOC content showed proportionally lesser CO\({}_{2}\) emissions and were a C sink. In comparison, the shallowness of the soil profile, overgrazing, and frequent fire occurrence were elements that threatened soil ecosystem integrity and must be addressed.
## 3 Results and Discussion  
The subCULTron swarm has in the four years of the project's runtime been through many tests and demonstrations, including numerous field deployments and experimental trials. The docking segment in its various incarnations has been included and tested throughout. Previous work can be found in [4] in which the initial indoor experiments which served as proof of concept for the wireless charging are described, as well as some initial outdoor experiments aimed at testing the capabilities of the aMussel and aPad positioning systems.  
For a closer look at and discussion of the outputs of the image processing and filtering nodes, Figure 16 shows a comparison of the hue thresholding result, the neural network result, and the final filter output during a single successful docking attempt performed in the sea of Biograd na Moru, Croatia. Docking occurs around the 130 s mark, and undocking around 145 s. This sample collection of outputs clearly demonstrates the expected behavior of the image processing system as a whole, as the neural network successfully performs the initial agent detection over longer distances, after which hue thresholding comes into play as the aMussel approaches the camera, ultimately taking over as the aMussel enters the dock, with the filter seamlessly segueing between the two sources. Neural net measurements are largely not present while the aMussel is tightly held within the dock, as the robot is mostly out of the camera's field of view. However, hue thresholding output persists, as small segments of the red top cap remain visible.  
Presented in the rest of this section is a detailed writeup of two particular experiments: a structured experiment to determine the capability of and validate the final docking procedure undertaken at lake Jarun in Zagreb, Croatia; and a \"stress test\" in a challenging environment on-site near the island of Sant'Angelo della Polvere in the lagoon of Venice, Italy.  
Figure 16: Example of filter inputs and output during a single docking, with markers denoting individual measurements where received, horizontal (**above**) and vertical (**below**).
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.3.1 Main Challenges  
* The first challenge is that the wireless network environment changes dynamically over time [83; 84; 100]. This is because wireless channel conditions vary significantly according to configuration, and the shape of the RF signal changes according to variations in wireless channel conditions, which can affect transmitter identification.
* Differences in the relevant hardware feature devices are relatively small. In addition, noise and interference further distort feature detection, reducing the accuracy of estimating them for authentication purposes.
* Since wireless communication transmission exists in a random fading environment, imperfect estimation and incomplete measurement of wireless signals are unavoidable [84], resulting in unpredictably varying authentication systems.
* The rapid improvement in operational wireless infrastructure supported the dramatically increased traffic [100]. As a result, the complexity of wireless networks will grow, and wireless device users will have to switch between multiple base stations or access points more frequently, resulting in frequent authentication handovers.
## 6 System Evaluation
### Effectiveness  
We test the ability of our proposed scheme to retrieve the images most similar to the provided query. Figure 9 shows samples of our results. The first column represents the provided image queries. The other columns show the returned images arranged according to their similarity to the  
Figure 8: Variable dimension reductions affecting search precision for over 10,000 Images. MAP: mean average precision.  
corresponding query. The columns show that our scheme can usually retrieve images in the same category as that of the query image.
## 2 Joint Source-Channel Coding Scheme Based on Residual Distributed Compressive Video Sensing
Thus, the reconstructed picture quality at the decoder depends only on the number and correctness of the received measured values, i.e., the impact of any data loss is apportioned to each frame of video. Therefore, the loss of measured values has little impact on the reconstruction quality of the image.
## 3 Thickness Control and Static Comprehensive Analysis of Rolling Process
### Mechanism Analysis of Aluminum Alloy Strip Rolling Process
When the deformation resistance of the rolled piece is affected, the slope of the plastic curve of the rolled piece will change as shown in Figure 5. At this time, the deformation resistance of the rolled piece increases, and thus the slope of the original plastic curve increases from \(B\) to \(B^{\prime}\), and the rolling thickness obtained by intersecting with the original elastic curve \(A\) of the rolling mill increases from \(h_{0}\) to \(h_{1}\). At this time, the rolling mill increases the reduction and adjusts the elastic curve \(A\) to the position of \(A^{\prime}\), and thus the rolling thickness returns to the expected value. By adjusting the tension to change the slope of the plastic curve of the rolled piece, the intersection of the plastic curve and the elastic curve of the rolling mill can be adjusted to offset the influence of other factors on the rolling thickness. The front and back tension of the rolling mill reduce the positive pressure on the roll, resulting in the reduction in rolling force. The relationship between tension and rolling force is as follows. \[P_{q}=P[1-\frac{1}{K}(m_{1}q_{1}+m_{2}q_{2})] \tag{26}\]  
where \(m_{1}\) and \(m_{2}\) are the influence factor of tension at the entrance and exit of the rolling mill, and \(q_{1}\) and \(q_{2}\) are the strip tension at the inlet and outlet of the mill, respectively. \(P_{q}\) is the rolling pressure with tension, \(P\) is the rolling pressure without tension, and \(K\) is the deformation resistance. As shown in Figure 6, with the increase in tension, the plastic curve of the rolled piece can be adjusted from \(B\) to \(B^{\prime}\) (slope increases). Thus, the abscissa of the intersection point with the elastic curve \(A\) is adjusted from \(h_{1}\) to \(h_{2}\). The purpose of plate thickness  
Figure 4: Adjustment reduction principle under the influence of incoming material thickness. Figure 5: Adjustment reduction principle under the influence of deformation resistance. adjustment can be achieved without changing the size of the roll gap. From the practical application, the tension adjustment method has the characteristics of fast response and good effect. It is widely used in cold-rolled strips. The adjustment of rolling speed also affects the thickness of the plate at the outlet. The main influence is that the change in speed causes the fluctuation of the variables such as tension, friction coefficient, rolling temperature and bearing oil film thickness so that the rolling pressure can also be changed. Through the analysis of the above thickness control principle, it is known that the strip rolling system is a complex system with multivariable coupling, and there is a close relationship between process variables and equipment variables [36]. In order to optimize the mechanism of the data model, the plate thickness of the stable rolling process was analyzed based on the static comprehensive analysis theory.