esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Composite-Interface Metallographic Analysis  
Figure 9 shows the metallographic organisation of the composite interface of the clad rebar. Evidently, the composite interface is well bonded and free of fine oxide distribution owing to the clean-interface assembly technique proposed in this paper. In addition, the composite interface is slightly wavy as a result of the interfacial destabilisation of the clad rebar under high temperature, high pressure, high velocity, and large deformation [23; 24].  
Figure 9a shows the metallographic organisation of the carbon steel side, where a pure ferrite region \(\approx\)50 \(\upmu\)m in width appears; this region is known as the decarburised zone. The main reason for the formation of the decarburised zone is that the carbon content of 20 MnSiV is 0.22%, while that of 316L is only 0.021%, generating a significant concentration difference between the two sides of the composite interface. Therefore, during the rolling process, carbon diffuses from the carbon steel side to the stainless steel side, resulting in a decarburisation zone on the former side.  
Careful observation also reveals that the grain size of the decarburised zone is significantly larger than that of the carbon steel matrix, as the reduction in carbon content accelerates the growth rate of austenite grains [25; 26]. In the post-rolling cooling process, the decarburised zone only forms a single ferrite, so all the ferrite grains inherit the grain size of the original austenite, which is coarser.  
Figure 9b depicts the metallographic organisation of the stainless steel side, where a grain region \(\approx\)50 \(\upmu\)m in width appears; this region is known as the composite zone. The main reason for the formation of the composite zone is atomic arrangement disorder at the grain boundaries of stainless steel and a greater number of crystal defects [27], causing the carbon to preferentially diffuse at the stainless steel grain boundaries and react with chromium to form chromium-rich carbides, resulting in chromium-poor grain boundaries. In the subsequent corrosion process, the chromium-poor region at the grain boundary acts as an anode and is corroded first. Thus, the stainless steel grain boundary in this zone was observed using electron microscopy.
# Directly Measuring the Pulse Front Distortion of High-Peak-Power Femtosecond Lasers
## 5 Conclusions  
In conclusion, our previous self-reference cross correlator was improved to directly measure the PFD values of high-peak-power femtosecond lasers. After improvement, the PFD at any position of pulse can be directly measured without the quadratic curve assumption, and meanwhile the global PFD can be achieved by the scanning measurement of the PFD at different positions. The measured PFD values of our 200 TW/27 fs laser are in good agreement with the calculated curve. In addition, the temporal intensity distribution of the pulse at focus was also investigated. Based on the measured PFD, the effective pulse duration at the far field of this 200 TW laser is broadened to \(\approx\)49 fs, instead of the generally regarded 27 fs. As a result, the actual available focused intensity of this laser is only 55% of the ideal case. It is worth noting that the degradation of focused intensity induced by PFD may be more significant than the degradation for the PW- and 10 PW-class lasers with larger beam sizes.  
Conceptualization, F.W., Y.X. and Y.L.; methodology, Z.Z.; formal analysis, J.H.; investigation, F.W. and X.Y.; writing--Original draft preparation, F.W.; writing--Review and editing, Y.X. and Y.L. All authors participated in the general discussion of the obtained results. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (11127901, 61521093, 61505234); Strategic Priority Research Program of Chinese Academy of Sciences (XDB160301); International S&T Cooperation of China Program (2016 YFE0119300); Natural Science Foundation of Shanghai (202R1464600); Science and Technology Commission of Shanghai Municipality (2017 SHZDZX02).  
The authors declare no conflict of interest.
# A Simulation of the Densification Process of NdFeB Bulks by a Modified Drucker-Prager Cap Model
## 3 Results
### Simulation Analysis of Comparative Experiments  
After preparing NdFeB magnets at a sintering temperature of 780 \({}^{\circ}\)C, NdFeB magnets were prepared at sintering temperatures of 850 \({}^{\circ}\)C and 940 \({}^{\circ}\)C under the same conditions. The temperature and stress field values were simulated and calculated to obtain cloud charts for comparative analyses.  
Figure 9 shows the temperature fields in the final stage of cooling at three different sintering temperatures. The temperature exhibits its maximum value in the central region of the section. As the sintering temperature rises, the central cross-sectional temperature during the cooling process increases from 186 \({}^{\circ}\)C to 317 \({}^{\circ}\)C. The temperature gradient intensifies progressively, with the internal-external temperature differential ranging from approximately 146 \({}^{\circ}\)C (Figure 9a) to 237 \({}^{\circ}\)C (Figure 9c). In particular, Figure 10 reveals the stress fields in the final stage of cooling at two different sintering temperatures, where a significant stress transition was observed: At sintering temperatures of 850 \({}^{\circ}\)C and 940 \({}^{\circ}\)C, the longitudinal compressive stress at the cross-sectional edge shifts to longitudinal tensile stress. The value changes from \(-\)14 MPa (Figure 8b) to 112 MPa and 172 MPa (Figure 10b,d), which is inconsistent with the 780 \({}^{\circ}\)C condition.  
Figure 8: Two stress components in the (**a**) transverse and (**b**) longitudinal cross-sections after 780 \({}^{\circ}\)C sintering.  
Figure 7: Equivalent residual stress simulation results and test measurements.  
This phenomenon arises as follows: As the sintering temperature escalates to the critical point of the material, it enters the high-temperature densification stage, wherein grain growth leads to the formation of new grain boundaries. Stress is relieved through lattice slip or dislocation movements, gradually releasing compressive stress [27]. Subsequently, the interior continues to contract during cooling to room temperature while the surface forms a hard shell. The internal contraction is constrained by the hard shell on the surface, causing the surface to transition from a compressed state to a tensile stress state. These tensile stresses reduce the compressive residual stresses resulting from the thermal expansion coefficient's mismatch during compaction cooling [28].
## 2 Research Method and Data
### Heat Transport Pipeline Data  
#### 2.2.1 Data Types  
To develop a model for predicting heat transport pipeline damage probability according to the attribute information, the location information of heat transport pipelines, components, and attribute information according to the components were collected from the operators of the target area. The components include the equipment history and maintenance history of facilities, as well as topographical information that interacts with the facilities. The attribute information according to the components refers to data that represent the characteristics of each component (e.g., location, length, and area). In this study, the equipment history, attribute information, and spatial information of heat transport pipelines were collected to develop a model for predicting the damage probability of heat transport pipelines. The geometric form of pipelines was examined using GIS location information, and the object information of heat transport pipelines was identified through their identification numbers. In addition, the regional characteristics of heat transport pipelines were investigated through the operator codes. The functions of heat transport pipelines are data generated from pipeline characteristics, which distinguish between pipes that supply heated water for thermal energy transfer and those designated for recovery. The condition of the sensor wire indicates the condition of the detection system capable of monitoring damage and leakage from heat transport pipelines. Key management sectionsrefer to sections with a high likelihood of damage to heat transport pipelines under the judgment of the operators. Table 1 shows the characteristics of heat transport pipeline attribute information.
## 2 Review: Regional-Scale Assessment of Buildings' Seismic Vulnerability
### Semi-Empirical Vulnerability Assessment  
Ideally, to achieve a holistic seismic-risk assessment for an urban area, every single building's behavior during ground shaking should be investigated via computational modeling. However, because effective computational models require complex, detailed information on the structures and materials of every building, it is not practical to apply them at the urban scale. Thus, to ensure that vulnerability assessment remains at an acceptable accuracy level, while avoiding time-consuming processes of large-scale data collection, the semi-empirical approach evaluates vulnerability based on a smaller range of selected building attributes, selected with the aid of experts and historical data. Since the aim of any large-scale vulnerability assessment is to estimate \"average\" performance among a group of buildings, the principle of the semi-empirical approach is to group buildings with similar seismic-vulnerability characteristics into a set of predefined building classes, each identified by a reasonable number of building characteristics.  
Data collection for the selected building characteristics can be performed rapidly through visual screening [14], which has been widely in semi-empirical approaches to urban risk assessment [15; 16]. Rather than seeking detailed information on structural designs or materials, visual screening involves the recording of a predefined number of visible structural parameters (usually no more than 15) observed during a sidewalk survey [17]. Vulnerability classifications or scores are then determined based on the collected building information, in combination with experts' judgments and historical earthquake-damage data. For example, Taiwan's National Center for Research on Earthquake Engineering (NCREE) [18] created an index of seismic resistance (ISR) from 0 to 100 based on the cumulative scores of 20 structural parameters, including soft-story effect, short-column effect, elevation and plan irregularity, state of maintenance, redundancy, and so on, and used it to investigate the vulnerabilities of 5400 school buildings. The ISR threshold was set at 80, based on empirical data including experiments and information on historical damage, and 1851 buildings that scored below 80 were classified as high vulnerability.  
Semi-empirical approaches have been criticized for sacrificing too much accuracy in exchange for time savings in the data-collection phase. Nevertheless, their reasonably effective use of a limited number of essential parameters in the estimation of buildings' vulnerability has paved the way for the application of machine-learning techniques to urban and regional-scale building-vulnerability assessment problems.
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion
### Frame-Based Parallel Algorithms Analysis  
So as to evaluate both the DMG-AI and the DSM-AI algorithms, we have run our experiments with 10, 16, 20, and 24 parallel processes. The hardware platform described before consists of 14 nodes (Distributed Memory (DM) architecture), with 12 cores in each one (Shared Memory (SM) platforms). Table 7 shows the different combinations tested, where \(N\) denotes the number of computing nodes (DM architecture) and \(R\) is the number of processes used in each computing node, so \(NxR\) is the total number of parallel processes. When \(N\) is equal to 1, we have a pure SM platform, and when \(R\) is equal to 1, we have a pure DM system. In this framework, the different setups tested are transparent to the DMG-AI parallel algorithm because all the processing units are MPI processes, regardless of the memory arrangement. On the contrary, in the DSM-AI algorithm, an \(NxR\) configuration means that each one of the \(N\) MPI processes generates \(R\) threads (OpenMP processes). In Table 8, we show the parallel efficiencies obtained for the DMG-AI parallel algorithm using up to 24 processes for the four QP values considered in our experiments and for BasketballDrill, Traffic, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Processes** & \multicolumn{2}{c}{**Num. of Nodes \(\times\) Num. of Cores**} \\ \hline  & \multicolumn{2}{c}{\(\mathbf{(N\times R)}\)} \\ \hline
10 & \((10\times 1)\) & \((5\times 2)\) & \((2\times 5)\) \\
16 & \((16\times 1)\) & \((8\times 2)\) & \((2\times 8)\) & \((1\times 16)\) \\
20 & \((20\times 1)\) & \((5\times 4)\) & \((4\times 5)\) & \((2\times 10)\) \\
24 & \((8\times 3)\) & \((6\times 4)\) & \((4\times 6)\) & \((3\times 8)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Arrangement of processing units. Figure 10: Tile-based BD-Rate with different number of processes and tile partitioning for all QPs. (**a**) PeopleOnStreet (2560 \(\times\) 1600); (**b**) Tennis (1920 \(\times\) 1080); (**c**) Kristen&Sara (1280 \(\times\) 720); (**d**) BasketballDrill (832 \(\times\) 480). Kristen&Sara, and Tennis video sequences. This table illustrates the good parallel performance of the DMG-AI algorithm, being even better for the lowest resolution video sequence. In the worst case, 24P (\(4\times 6\)), an average speed-up of 18.2\(x\) for Traffic video sequence is obtained, which corresponds to an efficiency of 76%. The efficiency of the DMG-AI algorithm always remains above 75% reaching a maximum efficiency of 98%. The disk access (both for reading the raw video data and for writing the compressed bitstream) is a sequential operation, so disk operations may become a bottleneck.
## 3 Results and Discussion
### Method Comparison
These parameters are cost per measurement, acquisition cost, ease of use, ease of interpretation, possibility to implement new compounds, adaptability to sample shapes and types, measurement time and need for maintenance. Figure 1 illustrates this comparison in a graphical way. It exhibits that each technique has its strengths and weaknesses and that there is not a single technique that is suitable for all applications. If only a few samples should be analysed for amphetamine, methamphetamine, or MDMA, IDTs could be the method of choice. In case that routine measurements are needed and the target compounds are limited to known compounds of a drug production, the use of IMS could be the method of choice. Assuming that complex samples need to be investigated, APLD-MS or another ambient MS technique can demonstrate its strengths. Figure 1: Visualisation of key parameters for the comparison of all three methods. Plus means positive or better, zero means neutral or in between, and minus means negative or worse, in comparison to the other techniques.
# Game-Theory-Based Multimode Routing Protocol for Internet of Things
## 3 Proposed Mechanism
### Assumptions and Features
#### 3.2.4 Classifications of Nodes  
Two groups of nodes can be made based on two classes: type of nodes and protocol-based nodes. There are four types of nodes in the node-type category: (a) source, (b) destination, (c) relay, and (d) neighbor nodes. Equations (2)-(5) are used to define these four types.  
\[Sn\in N\ :\ \{n_{i}\in N_{alive}\wedge n_{i}\in N_{ali}\} \tag{2}\]  
The source node is denoted by \(Sn\) that is the node \(n_{i}\) member of network \(N\). \(Sn\) is a member of alive nodes \(N_{alive}\) and active data initiating nodes \(N_{ali}\).  
\[Dn\in N\ :\ mi\in Nalive\wedge ni\in Nadr\ Dn\in N\ :\ \{n_{i}\in N_{alive} \wedge n_{i}\in N_{adr}\} \tag{3}\]  
In Equation (3), \(Dn\) denotes the destination nodes that are members of sets of alive and active data receiver nodes \(N_{adr}\).  
\[NEn\in N\ :\ \left\{n_{j}\in RtTble_{i}\wedge n_{j}\ is\ NextHop\right\} \tag{4}\]  
The set of neighbor nodes of node \(n_{i}\) can be represented by \(NEn\). \(NEn_{j}\) is a neighbor node of \(n_{i}\) that is present in the routing table \(RtTble\) of node \(n_{i}\), and is marked as \(NextHop\).  
\[REn\in N\ :\ \left\{n_{i}\in N_{alive}\wedge n_{i}\in N_{adr}\wedge\ n_{i} \in RtTble_{Sn}\right\} \tag{5}\]  
In Equation (5), a relay node \(REn_{i}\) is the node that is present in the routing table of source node \(Sn\).  
Different classes of routing protocols can be used for network nodes. Each node must belong to at least one class as shown in Equation (6). The entire heterogeneous network is composed of the nodes belonging to these classes.  
\[N=\{n_{i}\ \in ClassA\ \vee\ n_{i}\ \in ClassB\ \vee\ n_{i}\ \in ClassC\ldots\} \tag{6}\]  
In our case, we are using three classes: \(ClassA\) for proactive nodes, \(ClassB\) for reactive nodes, and \(ClassC\) for smart nodes. In the proposed mechanism the three classes can be further defined by Equations (7)-(9).  
\[ProN\in N:\{n_{i}\ \in ClassB\ |\ Generates\ TCM\ \cup Responds\ to\ TCM\} \tag{7}\]The proactive nodes, \(ProN\) belonging to \(ClassB\), periodically generate topological control messages (TCM) also known as HELLO messages. These nodes also respond to relevant TCM.  
\[ReaN\in N:\{n_{i}\ \in\mathit{ClassB}\ |\ \mathit{Generates}\ RPkt\ \cup\mathit{Responds}\ to\ RPkt\} \tag{8}\]  
\(ReaN\) denote the reactive nodes. These nodes belong to \(ClassB\). These nodes generate and receive routing packets i.e., route request (RREQ), route replies (RREP), and route errors (RERR).  
\[SmrtN\in N:\{n_{i}\ \in\mathit{ClassC}\ |\ \mathit{ClassC}=\mathit{ClassA}\ \cap\ \mathit{ClassB}\} \tag{9}\]  
Both reactive and proactive features are present in smart nodes \(SmrtN\). These nodes represent the coming together of reactive and proactive activities.
## 3 Results and Discussion
### SERS Performance of BP-AuNSs  
To understand the SERS activity of BP-AuNSs, Raman experiments were performed on two typical dyes using BP-AuNSs or citrate-stabilized AuNPs as the SERS substrates. As shown in Figure 2, the normal Raman signals of CV and IR-780 at the concentration of 1 mM were hardly observed under our experimental parameter (1s exposure time, four accumulations). In contrast, the molecular fingerprints of the dyes appeared in the SERS spectra using AuNPs or BP-AuNSs as the SERS substrates. In comparison with citrate-stabilized AuNPs, the traditionally used SERS substrate, the SERS signals induced by BP-AuNSs were remarkably enhanced under the NIR laser excitation. To evaluate quantitatively, the Raman intensity ratios of some characteristic peaks were calculated between the SERS spectra caused by AuNPs and BP-AuNSs. Based on the Raman bands of CV at 1174 cm\({}^{-1}\) and IR-780 at 1207 cm\({}^{-1}\), the Raman intensities increased about 8-13 times in the BP-AuNSs  
Figure 1: Transmission electron microscopic (TEM) images of black phosphorus (BP) sheet (**a**) and BP-AuNSs (**b**). (**c**) EDX pattern of BP-AuNSs. **(d**) Ultraviolet-visible-near infrared (UV-vis-NIR) absorbance spectra of the nanostructures. (**e**) Raman spectra of BP and BP-AuNSs.  
-based SERS nanoplatform compared to that in AuNPs based system. It could be reasonably explained that the Au nanoparticles were almost mono-dispersed in the solution, which resulted in few SERS hot spots. However, for the BP-Au nanohybrids, the BP sheet created a nano-island where a host of gold nanoparticles with various sizes were piled up, leading to plentiful hot spots formed in the nanoscale interstices and junctions among AuNPs. In addition, the charge transfers between BP sheet and the dye molecule may also contribute to the stronger SERS signals induced by BP-AuNSs [32].
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Methods
#### 2.3.2 Exploration of Response of Canopy Spectra to Forest Stand Age  
Spectral response characteristics refer to the different properties of a substance under different wavelengths of light. The absorption of light at different wavelengths is different in plant leaves, so there are absorption peaks in the spectrum [33; 34]. These peaks can be used to study the photosynthesis and growth of plants. Canopy spectra observed by satellite are significantly affected by leaf spectra and canopy structure. Obviously, there are differences in spectral characteristics of forest stands of different ages due to differences in canopy structure and leaf photosynthesis. This is the basis for studying the response of spectral characteristics to forest stand age. The response of spectral characteristics to forest stand age can be quantitatively evaluated by the Pearson correlation coefficient. In this study, the Pearson correlation coefficient was used to analyze the relationship between stand age and vegetation spectral parameters, and its significance was detected using a \(p\)-value [35].
## 4 Challenges and Considerations
### Metal AM Costs
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Machine Name** & **Manufacturer** & **Technology Type 1** & \begin{tabular}{c} **Build Size (mm \(\times\)** \\ **mm \(\times\) mm)** \\ \end{tabular} \\ \hline Kinetic Fusion & Titomic & DED & \(9000\times 3000\times 1500\) \\ EBAM 300 Series & Sciaky & EBAM & \(6096\times 1194\times 1524\) \\ SonicLayer 7000 & Fabrisonic & SL (UAM) & \(1828\times 1828\times 914\) \\ AddCreator & ADIRA & SLM & \(1000\times 1000\times 500\) \\ Lens 850-R & Optomec & LENS & \(900\times 1500\times 900\) \\ MX-1000 & InsTek & DED (LMD) & \(1000\times 800\times 650\) \\ Exome M-Print & ExOne & MJ & \(800\times 500\times 400\) \\ LUMEX Aware-60 & Matsuura & SLM & \(600\times 600\times 500\) \\ SLM 5000 H & SLM Solutions & SLM & \(500\times 280\times 365\) \\ RMP1 & Aurora Labs & DMLM & \(450\times 450\times 400\) \\ MetalFAB1 & Additive Industries & SLM & \(420\times 420\times 400\) \\ M400 & EOS & DMLS & \(400\times 400\times 400\) \\ Arcam Q20 & Arcam & EBM & \(350\times 380\times 180\) \\ LightSPE3D & SPEE3D & \
\begin{tabular}{c} DED (supersonic 3D \\ deposition) \\ \end{tabular} &
\begin{tabular}{c} 300 \(\times\) 300 \(\times\) 300 \\ 300 \(\times\) 300 \\ 250 \(\times\) 220 \(\times\) 200 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metal AM machines for fabrication of large products. chains, and the potential cost benefits of on demand production should also be factored into such models when assessing particular use cases [177].