esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
## 3 The Issue of 3D-Printed Specimens  
Previous studies primarily focus on the research of resin or woven composite materials. However, our research focuses on composites made by 3D-printing methods. This manufacturing method is currently being extensively studied. New methods and procedures are constantly being discovered to optimize the 3D-printing process to achieve the best possible properties of the printed product. The most commonly used 3D-printing methods are described in a paper by [PERSON] et al., which outlines the advantages, disadvantages and practical applications of each method [17]. Authors [PERSON] et al. in their study investigated various mechanical properties of composites reinforced with long fibers, such as ductility, flexibility, compressibility and impact resistance. They found significant improvements, even by multiple times, compared to non-reinforced composites. However, commercial use is very limited due to anisotropic behavior, weak interlayeradhesion and high porosity [18]. The advantage of 3D printing is that the properties of the final product can be influenced by different shapes and densities of internal infill. This printing method can save material and reduce the weight of the product. The results of a study by [PERSON] et al. show that a rectangular infill shape withstands higher loads and, as the density increases, the maximum load force increases significantly, while a triangular infill shape withstands lower loads and the increase in strength with increasing density is not as pronounced [19]. Authors [PERSON] et al. also reached similar results in their studies, where they tested the given structures for bending. They found that the hexagonal infill achieves a 13% to 25% lower bending stiffness compared to the triangular and rectangular infill. The influence of the infill density on the bending stiffness has a linear character [20]. Another study focuses on analyzing the impact of fiber placement and orientation on stress distribution in specimens of various shapes. The study results suggest that the proper selection of fiber placement strategy and optimization of specimen design are critical for maximizing mechanical properties and production efficiency in the application of 3D-printed composites [21]. In the publication by [PERSON] et al., the authors examined the impact of print setting parameters on the strength of specimens. Parameters such as specimen shape, the position of fiber reinforcement termination, reinforcement orientation, cavities in the structure, the number of layers and the effect of moisture were studied. All these parameters have a significant impact on the strength and quality of the printed material.
## 4 Results and Discussion
### Organic Matter Type  
The types of organic matter in source rocks are generally classified into Type I, Type II, and Type III. Different types of organic matter not only exhibit variations in hydrocarbon generation potential but also produce distinct hydrocarbon component characteristics [41; 42; 43; 44]. Based on the elemental analysis of organic matter in the Hongqi Depression, the H/C and O/C atomic ratios, as well as the relationship between pyrolysis Tmax and hydrogen index (IH) (Figure 2), the organic matter in the Jurassic Tamulangou Formation source rocks is primarily composed of Type II and Type III kerogen. This indicates that these source rocks contain both hydrogen-rich components derived from lower aquatic organisms and hydrogen-rich maceral components from higher plants, suggesting a favorable potential for oil and gas generation.  
In addition, kerogen carbon isotopes are stable during the diagenetic process and can reflect the original material composition of the organic matter. Therefore, the type of organic matter can be inferred based on the carbon isotope characteristics of kerogen [45; 46; 47]. The carbon isotope composition of Type I kerogen, derived from algae, is typically light, generally less than \(-28\%\), while Type III kerogen, derived from terrestrial higher plants, has a heavier carbon isotope composition, generally greater than \(-25\%\). Type II kerogen falls between these two. The carbon isotope analysis of kerogen shows that the kerogen carbon isotopes in the dark mudstones of the Tamulangou Formation range from \(-27.52\%\) to \(-21.37\%\), indicating that the organic matter is primarily Type II and Type III (Table 1, Figure 3).  
Figure 3: Kerogen carbon isotopes of the dark mudstones in the Tamulangou Formation.  
Figure 2: (**a**) Elemental composition of kerogen in the Tamulangou Formation and (**b**) kerogen type diagram.
## 3 Results
### Characteristics of Spatial Variation in Carbon Fluxes  
The spatial distribution pattern of carbon fluxes can be influenced by the environmental conditions of different regions, resulting in variations in values. In this study, the global distribution of carbon fluxes and their distribution within seven distinct climate zones were calculated, as depicted in Figure 6. This approach enables a more quantitative comparison of the spatial distribution characteristics of carbon fluxes. On a global scale, GPP (gross primary production) and RECO (ecosystem respiration) values are primarily concentrated within the range of 0-8 g C m\({}^{-2}\) d\({}^{-1}\), while NEE (net ecosystem exchange) values are mainly concentrated within the range of \(-2\)-2 g C m\({}^{-2}\) d\({}^{-1}\). The median values for GPP, RECO, and NEE are 2.6, 2.2, and \(-0.5\) g C m\({}^{-2}\) d\({}^{-1}\), respectively. GPP exhibits the largest range of variation, followed by RECO, and NEE shows the smallest range of variation. The results for different climatic zones indicate that the tropics exhibit the highest GPP among all climatic zones, with a median GPP of 5.9 g C m\({}^{-2}\) d\({}^{-1}\). This finding aligns with the tropical climatic environment, characterized by a hot and humid climate, ample sunshine, and abundant vegetation, which favor vegetation productivity. Additionally, subtropical regions with favorable climatic and vegetation conditions as well as oceanic climatic zones with mild climates also display higher GPP values, with a median of 4.6 g C m\({}^{-2}\) d\({}^{-1}\). Conversely, polar regions, characterized by cold climates and sparse vegetation, exhibit the lowest GPP values, with a median of only 1.0 g C m\({}^{-2}\) d\({}^{-1}\). Furthermore, in dry climate  
Figure 5: Seasonal average distribution of three carbon fluxes predicted by GCFD. MAM: March–May; JJA: June–August; SON: September–November; DJF: December–February.  
zones, where moisture conditions are insufficient, and in certain areas such as deserts with sparse vegetation, GPP values are relatively low, with a median of 1.7 g C m\({}^{-2}\) d\({}^{-1}\). Overall, the distribution of high and low carbon flux values is closely linked to climatic conditions, primarily temperature, moisture, and vegetation type.
## 5 Discussion  
The integrated testing apparatus for ultrasonic waves and acoustic emissions was a major advancement for the studies of rock damage. To distinguish acoustic wave signals emitted by UW-1, and AE activity, we applied a data filtering function [35] using the post-processing software (time filtering method) to eliminate the interference of acoustic wave signals in AE results. In this process, a small part of the AE signal was lost; however, it did not affect the overall pattern of AE. This defect needs to be optimized and improved in future developments. Although we did not study the coupling laws among rock cracks, ultrasonic wave and acoustic emission, the conclusions will be the basis of future studies.  
According to seismological principles, the AE activity of salt rock conformed to swarm-type features; there was no prominent main shock, and the rock energy was released through many earthquakes with similar magnitudes over a long period. In addition, AE activity was discontinuous in time. As shown in Figure 4b, the AE activity of salt rock had interval burst features. The statistical results indicate that there were AE during 1% of the testing time, but no AE at other times because energy had to accumulate during the process of crack extension. Different kinds of rocks have different AE activity types, and the AE relationship can be an important parameter to determine rock damage.  
We found that the ultrasonic velocity ratio, \(r\)-value, and \(lb\)-value can be effective predictors of failure in the samples, but it is difficult to confirm the specific threshold. Based on this study, we recommend the \(r\)-value as the priority indicator to warn of rock failure. When the \(r\)-value has a slow rising trend, serious damage has happened inside the rock. While both the ultrasonic velocity ratio and \(lb\)-value were also good representations of internal changes in the rock, they had their own deficiencies. Ultrasonic velocity, especially for the S-waves, was too sensitive to interpret accurately. Moreover, they did not have abnormal changes or inflection points before rock failure.
## 2 Data and Methodology
### Fractal Dimension
#### 2.2.1 Hall-Wood Estimator  
The HW proposed in [65] is a box-counting estimator that admits small scales. The area of the boxes covers the curve instead of just their sum. Formally, let us have a scale \(\epsilon_{l}=l/n\), where \(l=1,2,3,...n\). The aforementioned area is  
\[\widehat{A(l/n)}=(l/n)\sum_{i=1}^{[n/l]}(x_{il/n}-x_{(i-1)l/n}), \tag{6}\]  
where \([n/l]\) is the integer part of \(n/l\). The HW estimator is given by the expression  
\[\widehat{d_{A_{HW}}}=2-\left(\sum_{i=1}^{L}(s_{i}-\overline{s})\log\left( \widehat{A(l/n)}\right)\left(\sum_{i=1}^{L}(s_{i}-\overline{s})^{2}\right)^{- 1}, \tag{7}\]  
where \(L\geq 2\), \(s_{l}=\log(l/n)\) and \(\overline{s}=(1/L)\sum_{i=1}^{L}s_{i}\). Using \(L=2\), as suggested by [PERSON] to avoid bias [65], one obtains  
\[\widehat{d_{A_{HW}}}=2-\frac{\log\left(\widehat{A(2/n)}\right)-\log\left( \widehat{A(\overline{l/n})}\right)}{\log(2)}. \tag{8}\]  
Similarly to \(H\), the fractal dimension \(\widehat{d_{A_{HW}}}\) is applied as a component to calculate the \(EI\) index for the ethanol and for the cointegrated agricultural commodities.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 3 Grid and Condition  
This study used the X-51-like vehicle, with specific dimensions shown in Figure 6, which includes an inlet, an isolator, a combustion chamber, and a nozzle. The aircraft's external flow utilized a blended mesh of unstructured and structured grids, while the internal flow used a structured grid in order to capture the details accurately of fuel mixing. To acquire detailed information about the boundary layer, a denser grid is required near the wall. The size of the first layer grid is set at 0.003 mm. The grid near the shear layer and the injection hole is generated with a specific growth rate to ensure a more rational flow field and more precise computational results. To assess grid independence, numerical simulations were performed on an X-51-like vehicle using 16.81 million, 25.18 million, and 35.63 million cells as shown in Figure 7. Figure 8 illustrated the wall pressure distribution, revealing discrepancies between the pressure distribution computed with 16.81 million cells and that with 25.18 million or 35.63 million cells. Nevertheless, the results from the 25.18 million cells are almost identical to those from the 35.63 million cells. Therefore, to optimize computational resources, in the following section, computations used the 25.18 million cells.  
Figure 5: Comparison of transverse jet and calculated data.  
Figure 6: Structural diagram of an X-51-like vehicle. (**a**) 3D schematic diagram of the X-51-like vehicle. (**b**) Schematic diagram of the internal structure of the X-51-like vehicle.  
Figure 7: Scramjet grid distribution of different scales.  
To investigate the effect of isolator length variations on fuel mixing in the combustor, based on the longest isolator (504 mm), the distances from the leading edge of the cavity to the exit of the inlet were reduced by half, and one reflection period of the incident shock wave, resulting in distances of 263.56 mm and 388.15 mm, as shown in Figure 9.  
The schematic diagram of the cavity and injection is shown in Figure 10. The cavity, measuring 120.6 mm in length and 25 mm in height, was located on the lower wall of the scramjet. Along the spanwise direction, 46 mm from the leading edge of the cavity, there was one set of three injection holes, each with a diameter of 2.8 mm, spaced 85.16 mm apart. The term \"center injection\" referred to injection solely from the central hole, while \"3-injection\" represented injection from all three holes.  
In order to obtain stable combustor inlet conditions, the cruising parameters of an X-51A-like vehicle at an altitude of 26.5 km, flying at Mach 5.5 with a 1-degree angle of attack, were chosen as the integrated computational conditions. Gaseous kerosene C\({}_{10}\)H\({}_{20}\) was selected as the fuel, which was injected into the combustion chamber at an equivalent ratio of 1. The inlet and injection parameters are given in Table 1.  
Figure 8: Pressure distribution of three different scale grids.  
Figure 10: Schematic diagram of combustion chamber cavity and jet flow.  
Figure 9: Variation in isolator length.
## 3 Results
### Analysis for a Variable Weight of Fuel  
On the other hand, the second analysis is based on the assumption that the initial weight of the satellite is 15 kg, and the fuel weight ranges from 0.3 to 0.7 kg, at intervals of 0.05 kg.  
Table 8 presents the results of the analysis of the influence of variable fuel weight on the parameters of the orbital manoeuvre of a satellite of the initial weight of 15 kg. As the weight of fuel increases from 0.3 kg to 0.7 kg, an increase in the specific impulse and effective exhaust velocity is noted, which leads to a growing increase in velocity and duration of the impulse. The duration of the satellite's flight over Kampinos National  
Figure 5: Visualisation of the range of the nanosatellite observations during a single flight over Kampinos National Park. (Source: STK 12.7.1).  
Park remains constant and is 3.16 s, which suggests that the analysed parameters do not affect the geometry of the observation trajectory. More detailed analyses and the conclusions that result from the calculations performed are presented and discussed in Section 4--Discussion.
# Real-Time UAV Trash Monitoring System
###### Abstract
In addition, the proposed system used a UAV to execute the monitoring mission; the UAV can automatically fly along a preset path and transmit trash information to the control center, which can save manpower in coastal environment surveillance.
## 3 Methodology
### Testing and Optimization  
The final phase involved extensive testing and optimization to ensure the system's reliability, accuracy, and performance under real-world conditions.  
* Functional Testing: The system was tested to verify that all components, including the 3D visualization, data integration, and control functions, worked as intended [13].
* Performance Testing: Stress tests were conducted to evaluate the system's performance under high data loads and extreme conditions, such as network outages or equipment failures [8].
* User Feedback: Operators provided feedback on the system's usability, which was used to refine the interface and improve the overall user experience [15]. This feedback was based only on the operator's experience, and its redesign is a point considered as future work.
* Optimization: Based on the test results, the system was optimized to enhance its efficiency, reduce latency, and minimize energy consumption [18].
# Fast Thumbnail Extraction for H.264/AVC, HEVC and VP9
## 2 Proposed Partial Decoding of H.264/AVC, HEVC and VP9 for Thumbnail Extraction
### Partial Transformations  
The inverse transformation process is to transform frequency domain transform coefficients, obtained through the entropy decoding process, into magnitudes in the pixel domain by performing a 2D inverse discrete cosine transform (IDCT). In order to reduce computational complexity, the inverse transformation of H.264/AVC, HEVC and VP9 decoders consists of a butterfly structure in which 2D IDCTs are divided into two vertical and horizontal 1D IDCT operations, and each 1D IDCT is added and multiplied [14,15].  
The proposed transform is to inversely transform the lowermost and rightmost pixels according to reference pixels, in order to avoid error propagation. In addition, if a transform block is larger than 8 \(\times\) 8 pixels, one pixel is recovered per 8 \(\times\) 8 sub-block inside the larger block to avoid interpolation in the thumbnail. We employ the two-stage inverse transformation based on the separable characteristics. To perform the horizontal 1D transformation, the vertical 1D IDCT should be fully performed. However, the horizontal 1D transform can be partly performed for the reference pixels and one pixel per internal 8 \(\times\) 8 sub-block. As shown in Figure 4, the 16 \(\times\) 16 block is inversely transformed for the yellow pixels of the second stage.  
The partial horizontal 1D-IDCT is performed by removing some part of the butterfly structure. Depending on the transformation sizes, the 8 th, 16 th, 24 th and 32 nd pixels should be reconstructed. Figure 5 shows the operations required to restore the last 16 th pixel in the original 16-point 1D-IDCT. The computation amount required for the last pixel reconstruction of the horizontal 16-point 1D-IDCT is 15 additions and 24 multiplications. The popular butterfly structure of 1D-IDCT requires 64 additions and 72 multiplications. Table 2 shows the numbers of additions and multiplications for the proposed partial reconstruction depending on the transformation block sizes. As shown in the table, blocks that are larger and have greater horizontal lengths  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Block Size/Shape**} & \multirow{2}{*}{**\# Pixels**} & \multicolumn{2}{c}{**\# Pixels to Be Reconstructed**} & \multicolumn{1}{c}{**Total \# of Pixels to**} & \multirow{2}{*}{**Reconstruction**} \\  & & **Reference Pixels** & & **Inside Block** & **Be Reconstructed** & **Pixel Ratios** \\ \hline \(4\times 4\) & 16 & 7 & 0 & 7 & 44\% \\ \(4\times 8\) (8 \(\times\) 4) & 32 & 11 & 0 & 11 & 34\% \\ \(8\times 8\) & 64 & 15 & 0 & 15 & 23\% \\ \(8\times 16\) (16 \(\times\) 8) & 128 & 23 & 0 & 23 & 18\% \\ \(16\times 16\) & 256 & 31 & 1 & 32 & 13\% \\ \(16\times 32\) (32 \(\times\) 16) & 512 & 47 & 3 & 50 & 10\% \\ \(32\times 32\) & 1024 & 63 & 9 & 72 & 7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of pixels to be reconstructed for the block sizes.  
Figure 4: Two-stage partial reconstruction for 16 \(\times\) 16 inverse transformation.
# Guided Self-Accelerating Airy Beams--A Mini-Review
## 2 Results and Discussion
### Harmonic Potential Guidance
#### 2.2.1 One-Dimensional Airy Beams
However, the phase transition points are not the same as before; they are obtained as:  
\[z=\frac{1}{\alpha}\arctan\left(-\frac{\alpha}{2\beta}\right)+\frac{m}{2} \mathcal{D}. \tag{32}\]  
Concerning the trajectory, it is now:  
\[x=\frac{\sin^{2}(\alpha z)}{4\alpha[\alpha\cos(\alpha z)+2\beta\sin(\alpha z )]}+[\alpha\cos(\alpha z)+2\beta\sin(\alpha z)]x_{0}, \tag{33}\]  
and obviously, the influence from the quadratic chirp is not negligible. Comparing these two chirped cases, we note that in both cases the trajectories are modulated greatly. In the linear chirp case, the phase transition points and the period do not change, but the beam at the phase transition point has a transverse displacement. While in the quadratic chirp case, the phase transition point is moved, but the beam profile is not affected. By now, it is apparent that the propagation of beams according to the linear Schrodinger equation with parabolic potential is intimately connected with the self-Fourier (SF) transform. Generally, for an arbitrary \(\psi(x)\) propagating to \(\pi/4\) in a harmonic potential, an SF beam will be obtained at that point [74]. Here, when a truncated Airy beam \(\varphi(x)=Ai(x)\exp(ax)\) propagates to \(z=\pi/(4\alpha)\), we find the corresponding Fourier transform pair:  
\[\mathcal{F}[\psi(x)](k)=\sqrt{\frac{2\pi}{\alpha}}\psi\left(-\frac{k}{\alpha} \right). \tag{34}\]  
Therefore, the expression for the self-Fourier beam is:  
\[\psi(x)=-\sqrt[4]{2}\,\text{Ai}\left(\sqrt{2}x-\frac{1}{4\alpha^ {2}}+i\frac{a}{\alpha}\right)\exp\left[a\left(\sqrt{2}x-\frac{1}{2\alpha^{2}} \right)\right]\times\] \[\exp\left[-\frac{i}{2}\left(2\alpha x^{2}-\frac{\sqrt{2}}{\alpha }x+\frac{a^{2}}{\alpha}+\frac{1}{6\alpha^{3}}\right)\right]. \tag{35}\]  
In Figure 5, the intensity of the SF beam is shown by the black curve, and the corresponding intensity in Fourier space is shown by the red curve. One can see that the beam profiles are the same except for the inversion, which is in accordance with the theoretical result presented in Equation (34). This way of generating SF beams is universal, it does not depend on the form of the initial beam. Furthermore, we have recently demonstrated that the linear and nonlinear Talbot effects might be interpreted as a fractional SF or a regular SF transform phenomenon, respectively [75; 76]. Such SF beams may find potential applications in optical information processing, routing, and switching.
# Time Domain Damage Identification Approach of the Newly Hollow Floor Structure with TRMD
###### Abstract  
A tuned rolling mass damper system (TRMD) is newly combined with structural hollow slabs to act as an ensemble passive damping device to mitigate structural responses. The main advantage of this controlled configuration lies in its capacity to maintain architectural integrity. To investigate damage identification of this controlled structure with TRMD, a time domain identification approach with sensitivity-based model updating is studied in this paper. The simplified model and motion equation of a controlled structure with TRMD are derived considering the interaction force between the main structure and the damper. Furthermore, the dynamic response and dynamic response derivatives with respect to elemental variations in the controlled structure are analyzed to identify damage by minimizing the response difference between the intact state and the damaged state in the sensitivity-based updating procedure. A numerical example for a six-story frame structure with a TRMD on the top is employed to verify that not only the structural dynamic responses are controlled effectively by the rolling ball damper subjected to the earthquake excitation, but also damage location and extent can be identified accurately by the proposed method even with measurement noise considered. The shaking table test of a single-story frame equipped with TRMD is additionally carried out to validate the accuracy of the proposed structural damage identification method in the laboratory.  
hollow floor structure; tuned rolling mass damper (TRMD); time domain damage identification; sensitivity analysis +
Footnote †: journal: Applied  
csciences