esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Theoretical Background
\begin{bmatrix}f_{1(r1,\rho 1)}&\cdots&f_{II(r1,\theta 1)}&\ldots&1&0&-y_{1}\\ \vdots&\ddots&\vdots&\ddots&\vdots&\vdots&\vdots\\ f_{IM(rM,\theta M)}&\cdots&f_{IIM(rM,\theta M)}&\ldots&1&0&-y_{1}\\ g_{1(r1,\rho 1)}&\cdots&g_{II(r1,\rho 1)}&\ldots&0&1&x_{1}\\ \vdots&\ddots&\vdots&\ddots&\vdots&\vdots&\vdots\\ g_{1M(rM,\theta M)}&\cdots&g_{IIM(rM,\theta M)}&\ldots&0&1&x_{1}\end{bmatrix}  \tag{5}\]  
\[ \begin{array}{c}f_{In(r,\theta)}=\frac{r^{\alpha/2}}{2G}\Big{[}(k-\frac{ \pi}{2}+(-1)^{n})Cos\frac{n\theta}{2}-\frac{n}{2}Cos\theta\big{(}\frac{n}{2}-2 \big{)}\\ f_{IIn(r,\theta)}=\frac{r^{\alpha/2}}{2G}\Big{[}(k+\frac{\pi}{2}-(-1)^{n})Sin \frac{n\theta}{2}-\frac{n}{2}Sin\theta\big{(}\frac{n}{2}-2\big{)}\\ g_{In(r,\theta)}=\frac{r^{\alpha/2}}{2G}\Big{[}(k-\frac{\pi}{2}-(-1)^{n})Sin \frac{n\theta}{2}+\frac{n}{2}Sin\theta\big{(}\frac{n}{2}-2\big{)}\\ g_{IIn(r,\theta)}=\frac{r^{\alpha/2}}{2G}\Big{[}(-k+\frac{\pi}{2}-(-1)^{n})Cos \frac{n\theta}{2}-\frac{n}{2}Cos\theta\big{(}\frac{n}{2}-2\big{)}\end{array}  \tag{6}\]  
Different methods have been used to experimentally calculate SIF in LEFM [6; 13; 25] in a single or a combination of modes. The importance of including mode III has been extensively acknowledged [3; 7; 16; 24; 30], whereas leaving it out may lead to prediction errors [14], impact crack length [31], or crack path prediction [5; 32]. [PERSON] [33] combined X-FEM with a nested mesh to simulate III modes in curved crack fronts. In [12], \(K_{III}\) was calculated from the relative displacement of two points numerically calculated and compared to XFEM and experimental results, a similar approach used in [9]. [PERSON] et al. [31] discussed how toughness could be affected when there is mixed-mode loading, including mode III, and [PERSON] [34] acknowledged the effect of plasticity in CP. So, unaccounted \(K_{III}\) values might lead to leaving out plasticity effects, which could potentially underestimate crack growth. Finally, [PERSON] and [PERSON] [35] discussed the issues of several formulations for equivalent SIF but did not include mode III. The J integral [13; 36] is a viable alternative that is used in many commercial FEM software. However, in experimental work, it can be cumbersome to implement, and it also needs a mode separation method.
## 3 Proposed Approach for Addressing Problem (5)
### Deep Learning-Based Scheme for Optimizing \(\alpha_{k}\)
Therefore, the following inequality that holds for any \(\mathbf{Z}_{k}\geq 0\) is used to handle the aforementioned non-convex terms:  
\[\lambda_{H}(\mathbf{P}_{i})\geq\lambda_{H}(\mathbf{Z}_{i})+\mathbf{v}_{H, \mathbf{Z}_{i}}^{H}(\mathbf{P}_{i}-\mathbf{Z}_{i})\mathbf{v}_{H,\mathbf{Z}_{i}}, \tag{14}\]  
where \(\mathbf{v}_{H,\mathbf{Z}_{i}}\) represents the unit-norm eigenvector associated with the largest eigenvalue of \(\mathbf{Z}_{i}\). Hence, (14) is used to approximate \(\lambda_{H}(\mathbf{P}_{i})\) based on a feasible matrix \(\mathbf{P}_{i}^{(j)}\) at the \(j\)th iteration as follows:  
\[\lambda_{H}(\mathbf{P}_{i})\geq\lambda_{H}\Big{(}\mathbf{P}_{i}^{(j)}\Big{)}+ \mathbf{v}_{H,\mathbf{P}_{i}^{(j)}}^{H}\Big{(}\mathbf{P}_{i}-\mathbf{P}_{i}^{( j)}\Big{)}\mathbf{v}_{H,\mathbf{P}_{i}^{(j)}},\,\forall_{k}. \tag{15}\]  
The objective function, \(f(\mathbf{x}_{s})\), is defined by the sum transmission power (7a), obtained by solving problem (11), with \(\{\alpha_{k}\}\) being determined by the position of the \(s\)th particle, \(\mathbf{x}_{s}\). The computational complexity of the PSO-based method is given by \(\mathcal{O}(S\cdot T_{\max}\cdot\mathcal{O}_{SDR})\), where \(\mathcal{O}_{SDR}\) represents the complexity of solving problem (7) using the SDR method detailed in Section 3.2. Algorithm 3 presents the algorithm based on PSO designed to optimize \(\{\alpha_{k}\}\). ```
1:inputs: Number of particles, \(S\), maximum number of iterations, \(T_{\max}\), rate, and EH requirements. 2: Set iteration counter \(t=0\) and initialize the position of each particle and its velocity, \(\mathbf{v}_{s,t}=0\). 3: Set the initial \(\mathbf{x}_{s,t}^{l}=\mathbf{x}_{s,t}\) and \(\mathbf{x}_{g,t}=\underset{1\leq s\leq S}{\arg\min}\,f\Big{(}\mathbf{x}_{s,t} ^{l}\Big{)}\). 4:While\(t\leq T_{\max}\)do
5:for\(s=1,\ldots,S\),do
6: Update the velocity and the position based on (17) and (18) to obtain \(\mathbf{v}_{s,t+1}\) and \(\mathbf{x}_{s,t+1}\). 7: Restrict the value of the position based on (10). 8: Solve problem (11) with the common rates given by \(\mathbf{x}_{s,t+1}\) to get \(f(\mathbf{x}_{s,t+1})\). 9:if\(f(\mathbf{x}_{s,t+1})<f\Big{(}\mathbf{x}_{s,t}^{l}\Big{)}\)then \(\mathbf{x}_{s,t}^{l}=\mathbf{x}_{s,t+1}\) endif
10:endfor
11:\(\mathbf{x}_{g,t}=\underset{1\leq s\leq S}{\arg\min}\,f\Big{(}\mathbf{x}_{s,t} ^{l}\Big{)}\)
12: Increase counter \(t=t+1\). 13:endwhile
14:Output: Best common rates, \(\big{\{}\alpha_{k}^{*}\big{\}}\), defined by \(\mathbf{x}_{g}\). ```  
**Algorithm 3** Comparative scheme based on PSO for optimizing \(\{\alpha_{k}\}\)
###### Abstract  
The research investigated the influence of laser powder bed fusion (LPBF) parameters for Nickel Alloy HX, a nickel-based superalloy, to achieve high-density components with superior mechanical properties. A systematic approach was employed, involving printing 40 cylindrical specimens with varying energy densities (50-240 J/mm\({}^{3}\)) to evaluate porosity, hardness, and anisotropy. Results revealed that energy density significantly influences relative density, with optimal parameters identified at 111 J/mm\({}^{3}\) (900 mm/s scan speed, 120 W laser power). Microstructural examination revealed columnar grains aligned with the build direction in as-printed samples. The findings highlight the trade-offs between density, hardness, and microstructure in the additive manufacturing of nickel-based superalloys, providing actionable insights for industrial applications requiring specific property profiles.  
EOS HX superalloy composite; LPBF; mechanical properties; process optimization Article
## 3 Memristive Analog Design
### Memristive RF Circuits
#### 3.2.2 Tunable Inductors
Additionally, exploring different materials and structures could enhance the memristor's capabilities in terms of frequency range and power handling, allowing it to achieve higher efficiency and stability in RF applications. Furthermore, investigating novel fabrication techniques may also contribute to enhancing the overall performance of memristor-based inductors.
## 5 Simulation Results and Discussion
Self-heating induced thermal cross-talk between individual fingers is important, affecting channel temperatures, then device performance and device reliability. Heat diffusion, however, occurs not only between individual gate fingers (inter-finger) in a multi-finger HEMT, but also within each gate finger (intra-finger). Figure 11 shows the temperature distribution for uniform spacing 8-fingers HEMT. The results indicate clearly, that the heat generated during the operation mainly flows through the GaN and SiC layers to heatsink. The temperature-dependent of thermal conductivity is introduced in TLM-3D simulation. The influence on the GaN layer on channel temperature is less significant since the GaN and AlGaN layers are really thin compared to SiC's. Furthermore, the thermal conductivity of SiC plays a significant and decisive role in the thermal behavior of the AlGaN/GaN HEMT device. Simulation results obtained using temperature-dependent thermal conductivity for GaN were observed to differ only slightly (less than 5 \({}^{\circ}\)C) from the results obtained using the thermal conductivity of GaN + SiC as implemented in the current study. As a consequence, the thermal conductivities of the materials are key parameters of the thermal model behavior, and the temperature gradient in the SiC is mostly directed to the heatsink. On the other hand, introducing Nano-scale excitation heat source in TLM simulations showed that channel temperature is much more increased compared to the Micro-scale excitation heat source results. This is due to the fact that the Nano-scale hotspot heat source is considered as a pulsed wave heat source, under gate and described by a square 50 nm \(\times\) 50 nm and the heating pulses follow one another so closely that the junction AlGaN/GaN cannot cool down to its initial temperature during each cycle, adding to the self-heating induced by thermal cross-talk between individual fingers of the device. The thermal behavior of AlGaN/GaN-based devices strongly depends on the value of thermal conductivity of various materials on which its structures are grown. But diffusivity alone is not a wholly valid parameter by which to select materials. The effect of the (\(\rho\)*Cp) product (the heat necessary to raise the temperature of a unit volume of material) has a significant effect on the thermal profiles within miniaturized high-power amplifiers. So, the heat generated by the heat sources is transmitted to the edge of AlGaN/GaN interface through horizontal heat spreading and reduces the heat gathered around the heat source. The miniaturization of high power amplifiers SSPA creates problems of heat dissipation. Simulation presents peculiar challenges due to the need for the detailed modelling of devices. The TLM method is a powerful tool for analyzing thermal effects and modelling heat diffusion problems. Figure 10: _Cont._  
Figure 10: 3D-TLM simulation results through four chosen layers under excitation heat source. Figure 11: TLM-heat diffusion simulation through device layers under two types of excitation heat sources. (**a**) Nano scale hotspot; (**b**) Micro scale hotspot.
## 4 Results
### Service System  
After confirming the service's accurate operation and setting the minimum hardware requirements, the next step was to push the service to its limits by testing it with a significant number of simultaneous end users. This test did not involve incrementally increasing the number of end users. The primary goal was to determine the minimum number of parallel users that could be supported without compromising the quality of service based on the hardware specifications.  
In order to achieve this objective, a total of fourteen tests were conducted to assess the endurance and reliability of the system over a 24 h timeframe. User connections were established concurrently at three different levels: 100, 1000, and 10,000, with an average of 3200 calls per user per minute. The results of these tests are presented below. Figures 8-10 display screenshots illustrating the detailed test outcomes for 100, 1000, and 10,000 simultaneous users.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Normal** & **Execution** & **Fail to** \\
**Category** & **Operation** & **Error** & **Connect** \\ \hline Download bearer token & 100\% & & \\ Inability to receive bearer token & & 100\% & \\ Contact of Binarization Tool & 100\% & 100\% & 100\% \\ Communication of Noise Reduction Tool & 100\% & 100\% & 100\% \\ Communication of Transcription Tool & 100\% & 100\% & 100\% \\ Contact a word locator with a contextual query & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular} In this table, 100% indicates that the service performed as expected each time it was tested.  
\end{table}
Table 4: Development package service status for minimum server specifications.  
Table 5 provides a thorough overview of the results, summarizing their most important points. The primary parameters are listed in the initial column, while the average value for each parameter is displayed in the subsequent columns for varying numbers of end users. The average values were derived from a set of 12 iterations for each test conducted in every end-user scenario.  
Figure 8: Script execution results with 100 concurrent users.  
Figure 10: Script execution results with 10,000 concurrent users.  
Figure 9: Script execution results with 1000 concurrent users.  
Based on the test results, the system is expected to handle between 100 to 1000 simultaneous users per day. Testing with 10,000 users was conducted to determine the worst-case scenario, resulting in system failure when 7000 users logged in, with 72% of requests failing due to a \"Server Busy\" message. It is important to note that the \"Load Balancing\" function was not activated during these tests, impacting the accuracy of the data collected.  
These test outcomes allow for the identification of system limitations and the prediction of potential malfunctions in the future. The findings indicate that under average usage, the system requires between 302 ms to 3.7 s to process requests for 100-1000 simultaneous users daily, with a maximum of 6600 simultaneous users as a benchmark for system upgrade. Overall, the results are deemed satisfactory for the expected traffic and usage levels in the system.
## 5 Experimental Results
### Embedding Efficiency
From these tables, we can see that either estimated embedding efficiency or experimental one, the proposed method has higher embedding efficiency than those of [PERSON] et al. and [PERSON] et al. 's methods. This means that the proposed method is less detectable than [PERSON] et al. and [PERSON] et al. 's methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & \multicolumn{3}{c}{**Proposed Method**} & \multicolumn{3}{c}{**[PERSON] et al.**} & \multicolumn{3}{c}{**[PERSON] et al.**} \\ \hline
**Palette** & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**Reordering**} \\ \hline \(k\) & 0 & 3 & 3 & 3 & 0 & 3 & 3 & 3 & 0 & 3 & 3 & 3 \\ \hline \multicolumn{11}{c}{Actual embedding rate (\%)} \\ \hline  & 0 & 23 & 46 & 69 & 0 & 23 & 46 & 69 & 0 & 23 & 46 & 69 \\ \hline \multicolumn{11}{c}{Estimated embedding rate (\%)} \\ \hline Bell & \(-\)11 & \(-\)11 & \(-\)11 & \(-\)13 & \(-\)11 & \(-\)16 & \(-\)15 & \(-\)19 & \(-\)12 & \(-\)17 & \(-\)16 & \(-\)10 \\ Bricks & \(-\)27 & \(-\)39 & \(-\)55 & \(-\)39 & \(-\)27 & \(-\)28 & \(-\)31 & \(-\)46 & 5 & 0 & 1 & \(-\)9 \\ Church & 11 & 11 & 15 & 17 & 11 & 11 & 14 & 16 & 4 & 4 & 4 & \(-\)2 \\ Cliffs & \(-\)9 & \(-\)12 & \(-\)19 & \(-\)20 & \(-\)9 & \(-\)7 & \(-\)6 & \(-\)4 & 2 & 1 & 0 & 2 \\ Kangaroo & \(-\)40 & \(-\)46 & \(-\)40 & \(-\)43 & \(-\)40 & \(-\)52 & \(-\)60 & \(-\)75 & \(-\)39 & \(-\)44 & \(-\)47 & \(-\)43 \\ Kinkaku\(-\)ji & \(-\)15 & \(-\)16 & \(-\)24 & \(-\)31 & \(-\)15 & \(-\)19 & \(-\)25 & \(-\)30 & 4 & 2 & \(-\)1 & \(-\)4 \\ Temple & 4 & 5 & 5 & 8 & 4 & 0 & \(-\)6 & \(-\)9 & \(-\)2 & \(-\)2 & 2 & 3 \\ Monkeys & \(-\)15 & \(-\)11 & \(-\)10 & \(-\)8 & \(-\)15 & \(-\)14 & \(-\)17 & \(-\)19 & \(-\)2 & \(-\)2 & 0 & 8 \\ Painting & \(-\)11 & \(-\)13 & \(-\)19 & \(-\)22 & \(-\)11 & \(-\)12 & \(-\)22 & \(-\)35 & \(-\)5 & \(-\)9 & \(-\)13 & \(-\)17 \\ Rice paddies & \(-\)148 & \(-\)143 & \(-\)144 & \(-\)158 & \(-\)148 & \(-\)151 & \(-\)138 & \(-\)131 & \(-\)54 & \(-\)40 & \(-\)39 & \(-\)38 \\ Statue & 18 & 16 & 17 & 18 & 18 & 23 & 25 & 29 & \(-\)9 & \(-\)8 & \(-\)11 & \(-\)4 \\ Sydney & & & & & & & & & & & & \\ Opera & 1 & \(-\)2 & \(-\)6 & \(-\)7 & 1 & \(-\)2 & 1 & \(-\)2 & 8 & 14 & 15 & 17 \\ House & & & & & & & & & & & & \\ Temple & \(-\)18 & \(-\)16 & \(-\)14 & \(-\)17 & \(-\)18 & \(-\)48 & \(-\)50 & \(-\)45 & 6 & 11 & 15 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of RS steganalysis for [PERSON] et al.’s [17] and the proposed methods with block size \(1\times 3\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block Size** & \multicolumn{2}{c}{\(2\times 2\)} & \multicolumn{2}{c}{\(1\times 3\)} \\ \hline
**Methods** & [16] & **Proposed Method** & [17] & **Proposed Method** \\ \hline  & 1.406 & 3.428 & 1.909 & 3.428 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Estimated embedding efficiencies of [PERSON] et al.’s [16], [PERSON] et al.’s [17], and the proposed methods with \(k=3\).
## 5 Results and Analysis
### First-Stage Recognition
#### 5.2.4 The Results of Ablation Experiments
However, it can also be seen that the AP value and recall of three types of defects marked in italics decreased, which indicates that the three major improvements may interact with each other for the recognition of \"patches,\" \"pitted_surface,\" and \"rolled-in_scale.\"  
In order to make the experimental results in Table 6 more intuitive, we visualized some of the recognition effects, as shown in Figure 13.  
Figure 13: Visualized results of different schemes. It can be clearly seen from Figure 13 that:  
* From Scheme 1 to Scheme 4, more and more defects were identified in the images; there was almost no \"missed detection\" phenomenon in the six images of Scheme 4. * Scheme 1 can only detect relatively large and clear targets in the images, while the detection effect of Scheme 2 is greatly improved for Scheme 1. Scheme 2 can accurately identify relatively fuzzy and dark targets, such as \"inclusion\" defects. * It can be seen from the six images in Scheme 2 that, although the optimization effect of Scheme 2 is significant, there are obvious overlapping phenomena, such as \"patches.\" When the two defects are relatively close, Scheme 2 easily confuses them. Scheme 3 effectively improves the overlap phenomenon because it can identify and accurately locate two close targets. What is more, Scheme 3 can also identify small targets existing in large targets. * Scheme 4 further optimizes the third option. Although the optimization effect is not as great as with the previous two improvements, for insignificant and illegible defects, such as \"inclusion,\" the effect of Scheme 4 is clear; it further reduces the missed detection of defects.
## 3 Analysis and Results
### Theoretical Saturation Test  
In order to ensure the validity of the conceptual model, it is necessary to test the saturation of the theory. According to [39], when the reserved interview materials are encoded at three levels, no new categories and relationships are found, and the theoretical model can be considered to be saturated. In this study, two randomly selected interview materials are used for the saturation test, and the results of the data analysis are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Selective Coding**} & \multicolumn{2}{c}{**Axial Coding**} \\  & **(Categorization)** & **Open Coding** \\ \cline{2-4}  & **Primary Category** & **Secondary Category** & **(Conceptualization)** \\ \hline \multirow{6}{*}{Driver behavior} & \multirow{4}{*}{Unsafe behavior} & Not slowing down; talking on the phone; paying too much concern about curbside passengers. \\ \cline{3-4}  & & & Emotionally disturbed; pedestrians obstructing driving; nighttime environment. \\ \cline{3-4}  & & Intrinsic requirements & Driving skills; personal qualities. \\ \hline Driver characteristics & \multirow{4}{*}{Driver focuses} & \multirow{4}{*}{Transportation environment} & Signals, signs; road conditions; vehicle distances, traffic flow; intersections and directions; dead-ends; weather. \\ \cline{3-4}  & & Traffic participant & Motorcycles, electric bicycles; other motor vehicles; pedestrians. \\ \hline \multirow{6}{*}{Security awareness} & \multirow{4}{*}{Transportation environment} & Sufficient information sources & TV; driving test; traffic police publicity; friends, work exchange; books; Internet. \\ \cline{3-4}  & & Insufficient information sources & no exposure to such traffic safety information; little exposure to such traffic safety information. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The three-level coding (axial coding with two categories) process of interview data.  
Figure 3: Examples of pedestrian violations. (**a**) Children running; (**b**) playing and chatting; (**c**) children running; (**d**) cross before crosswalk; (**e**) swiping phone; (**f**) rushing with children.  
As can be seen from Table 5, no new important categories and relationships were found formed for any of the four main categories (pedestrian characteristics, age factors, improvement methods, and driver characteristics), and no new initial concepts were found within the four main categories. Therefore, after testing, it can be considered that the theoretical model of pedestrian crossing is saturated.
# Calibration of Mean Wind Profiles Using Wind Lidar Measurements
## 1 Introduction
To this end, and with no loss of generality, the logarithmic law has been assumed as representative of the mean wind speed profile, assuming a synoptic wind regime; for different values of \(z_{0}\) and \(z_{d}\), a set of profiles of 10-min averaged wind speeds are simulated for moderately strong winds, affected by numerical noise in the typical range of the accuracy of Lidar measurements. The pseudo-experimental wind speed profiles are then assumed as input data for a least-square fitting procedure that allows identification of the parameters \(z_{0}\) and \(z_{d}\) of the logarithmic profile. Starting from different morphometric situations, the identified values of \(z_{0}\) and \(z_{d}\) are discussed in relationship to the number of measuring points and to the height of the lowest available measuring point. As a main result, it can be pointed out that measurements at lower elevations than those provided by Lidars are required to identify the logarithmic profile properly, especially when mean wind speeds close to the ground are of interest.