esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Integrity Analysis of a Printed Circuit Heat exchanger with Channel Misalignment
## 2 Numerical Simulation
### Boundary Conditions  
A two-dimensional simulation using COMSOL Multiphysics was conducted with the proposed geometry. Table 2 lists the five different cases that were simulated to represent the possible misalignments due to a diffusion bonding discontinuity. The largest simulated misalignment was 1 mm, while the smallest was 0 mm, representing a perfect bond.  
Figure 2 shows the applied boundary conditions for the numerical simulation. The boundary pressure load was used as the boundary condition. The load-controlled stress limit was analyzed. For comparison to the construction code, only the primary stress produced by a mechanical load was considered. Boundary loads of 20 and 0.5 MPa were applied to the water and sodium channels, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Materials** & **\(\mathbf{T_{max}}\)**(\({}^{\circ}\)C)** \\ \hline
1 & Carbon steel & 370 \\
2 & Low alloy steel & 370 \\
3 & Martensitic stainless steel & 370 \\
4 & Austenitic stainless steel & 425 \\
5 & Nickel–chromium–iron & 425 \\
6 & Nickel–copper & 425 \\ \hline \end{tabular}
\end{table}
Table 1: Maximum temperature for various classes of permitted materials.  
\begin{table}
\begin{tabular}{c c c} \hline
**Case \#** & \multicolumn{2}{c}{**Misalignment (m)**} \\ \hline  & Right direction & Left direction \\ Case 1 & 0 mm & - \\ Case 2 & 0.25 mm & 0.25 mm \\ Case 3 & 0.5 mm & 0.5 mm \\ Case 4 & 0.75 mm & 0.75 mm \\ Case 5 & 0.85 mm & 0.85 mm \\ \hline \end{tabular}
\end{table}
Table 2: Five Simulated misalignment cases. Misalignment is measured as the center-to-center distance along the x direction.
## 5 Analysis and Discussion
### Analytical Hierarchy Process Modelling  
An AHP analysis was carried out on the direct causes an indirect cause separately, as shown in Table 5. The model explained in ref. [14], which was also summarized in ref. [2], was used to carry out the analysis. Appendix B shows how the AHP method works for weight allocation.  
According to ref. [2], safety impact (severity), probability of reoccurrence of the event if unsolved, and cost of devising a solution are common factors that can be considered as alternatives for the pairwise comparison. These were used in this study as alternatives for the direct causes in the pairwise comparisons. The hierarchical information was obtained from the FTA; however, the level above the basic events was considered for this study.  
Based on the AHP structure (Figure 5) in Table 4, it can be deduced that event 2 has is of the highest priority, followed by 5, 1, 4, and 3. respectively. It can also be noted that based on the criteria used, it was important to consider the safety of the impact followed by the probability of reoccurrence before we consider the cost of devising a solution.  
For the contributing causes, a different approach was considered where the HRO principles were considered as the alternatives for the pairwise comparisons, as shown in Table 6. This is to give a perspective of HRO in the analysis. Ref. [18] proposed and illustrated how the five HRO principles can be employed as alternative solution options for the pairwise comparisons.  
Based pm the AHP structure (Figure 6), in Table 4, it can be deduced that event 6 was given the highest priority, followed by 7, 8, 10, 9, 12, and 11, respectively. It can also be noted that based on the criteria used, it was important to consider preoccupancy with failure followed by sensitivity to operations and reluctance to simplify before considering commitment to resilience and deference to expertise, respectively. Generally, more importance was given to HRO, which enables anticipation of the failure before methods for containment.  
However, according to ref. [2], the weakness of this analysis is that it is subject to dependency on the judgement of the person using it. This could be the case in this study, as the analysis was based on the researcher's familiarity with the incident, although input from the previous analysis such as FTA and FMECA and reference to previous studies [2,10,18] provided guidelines to maintain objectivity. As proposed by ref. [2], using a group of experts for scoring can eliminate this aspect.  
Figure 7 provides a summary of the proposed framework and how, for illustration purposes, it was applied to the chosen case study. In practice, our approach can be applied as a stepwise application of the proposed techniques and compiled as a visual aid as part of organisational learning from incident reporting.
## 3 IMOGWO Algorithm Design
### Basic GWO Algorithm Description
#### 3.2.1 Encoding and Decoding
In summary, job 1 is processed in factory 1, job 3, 2, and 4 are processed in factory 2, which completes the first phase of the decoding operation. In the second phase of the decoding operation, the PS method determines the processing order and machine allocation strategy of assigned jobs in each factory. Figure 1(a) shows the scheduling Gantt chart of factory 1 processing job 1, and Figure 1(b) shows the scheduling Gantt chart of factory 2 processing job 3, 2, and 4. The PS decoding method means that each job is arranged to the machine, which, in the corresponding stage, can process the job earliest. As shown in Figure 1(b), the first operation of job 4 can be processed by two machines at stage 1, and the processing time is \(P_{41}^{21}=2\). The first idle state of the first machine at stage 1 is from time 2 to 4, which is just enough for processing \(O_{41}\). Since the earliest time was allowed to start processing on the second machine is \(S_{41}T_{12}=2\) (41 represents the first operation of job 4, and 12 represents the second machine of stage 1), the start \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Factory 1** & **Factory 2** \\ \hline job1 & 9 & 9.5 \\ job2 & 10 & 8.5 \\ job3 & 7.5 & 7.5 \\ job4 & 9.5 & 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average processing time of each job in two factories. processing time of \(O_{41}\) is \(S_{241}=2\). According to the PS method, the first machine with a previous serial number is selected for processing, and the end time of processing is \(C_{41}=S_{241}+P_{41}^{21}=2+2=4\). In addition, for the sixth operation of job 4, it can select two machines at stage 3 for processing. The processing time of \(O_{46}\) is \(P_{46}^{23}=1\), the completion time of the previous operation is \(C_{45}=9\). The earliest time of the first machine at stage 3, which meets the processing time constraint between operations allowed us to start processing is \(S_{46}T_{31}=10\). The earliest time of the second machine at stage 3, which meets the processing time constraint between operations allowed to start processing, is \(S_{46}T_{32}=9\). Due to \(S_{46}T_{31}>S_{46}T_{32}\), the second machine is chosen for processing. The start processing time of \(O_{46}\) is \(S_{246}=9\). The machine allocation strategy for each operation of other jobs is the same. The corresponding objective function values in factory 1 is \(C_{\max}\) = 14 min, \(TCT\) = 1.5518 kgCO\({}_{2}\), \(TDT\) = 0.8 min, and the corresponding objective function values in factory 2 is \(C_{\max}\) = 17 min, \(TCT\) = 3.6688 kgCO\({}_{2}\), \(TDT\) = 3.6 min.
## 3 Results  
### Evaluation of Spectral Optical Parameters  
Figure 3 shows the results of the spectral measurements of the samples in the range from 200 nm to 18 \(\upmu\)m. The charts in the figure represent all thermal regions of the electromagnetic spectrum. Figure 3a shows the spectral irradiance curve of the solar radiation spectrum (SRS) in the UV/VIS/NIR results (absorbance in shortwave spectrum). Figure 3b shows the curve of the spectral radiance intensity of a 353 K (80 \({}^{\circ}\)C) black body representing the weighted function for the determination of emissivity in  
Figure 2: Scheme of the P1 absorber production considered in the life cycle assessment (LCA).  
the results of the longwave IR range (longwave spectrum). Table 2 sums the results of the spectral reflectance analysis: thermal emissivity and solar absorbance of the tested absorbers. In addition, the table also shows the selectivity of the absorbers (the ratio between both parameters) and figure of merit (_FM_; calculated based on [39; 40]). This shows that all evaluated absorbers have almost the same solar absorbance (from 0.90 to 0.95), whereas their emissivity notably varies. The developed P1 absorber has emissivity of around 0.3 in the longwave radiation spectrum, which is approx. 60% lower than the S1-S3 paint-based absorbers but higher than those of the selective types. As a result of filtration of some regions' results (3.5, 7.0, and 14.0), the difference is up to 25% compared to the evaluated selective solar absorbers R1-R3. This fact is also evident for the level of selectivity. Results show that commercially available absorbers can be considered to be highly selective, with selectivity values ranging from 13.6 to 23.5. In comparison, the developed P1 absorber achieves selectivity of 3.2 and paints S1-S3 achieve selectivity of only 1.0. This means that P1 achieves _FM_ = 0.78, while R2 and R3 achieve _FM_ = 0.91 and 0.92, respectively, for the theoretical case with absorber operating temperature of 80 \({}^{\circ}\)C (the best theoretical efficiency is 1.0 for an ideal absorber with \(\alpha\) = 1 and \(\varepsilon\) = 0).
## 3 Results and Discussion
The mode at 1603 cm\({}^{-1}\) is ascribed to both phenylalanine and tyrosine amino acid residues [23]. The modes at 1344, 1515 and 1573 cm\({}^{-1}\) have been associated with the VDK pigment. The band at 961 cm\({}^{-1}\) can be assigned to the stretching vibration of the P-O bond in \(PO_{4}^{3-}\) and it is ascribed to the presence of organic materials belonging to animal residues [25]. The broadband at 1823 cm\({}^{-1}\) has been assigned to humic substances [26]. In the spectrum collected on sector S12 (spectrum b), the iron oxides (hematite and \(\alpha\)-goethite) are confirmed once again, by the following modes at 295, 611, 516 cm\({}^{-1}\), and 696 cm\({}^{-1}\). Rabbit skin glue has been associated with the modes at 611, 797, and 1033 cm\({}^{-1}\). At 956 cm\({}^{-1}\), the band assigned to the \(PO_{4}^{3-}\) is found which is ascribed to the organic content of the specimen. The Raman bands associated with the VDK pigment are given by the Raman modes at about 1344 cm\({}^{-1}\) and 1583 cm\({}^{-1}\). The mode at 1820 cm\({}^{-1}\) indicates that humic substances have been found. Table 1 summarizes the main constituents of each sector of specimen N. 01.1622.
## 3 Results
### Bragg Peak Range  
Figure 3a,b demonstrates the polymer gel dosimeter irradiated with a 100.2 MeV proton beam and its MRI readout of different layers. The R\({}_{2}\) map was converted to the dose map with the aforementioned R\({}_{2}\) dose-response curve. The range measured with two gel dosimeters was compared. The discrepancy between the measurement and Monte Carlo simulation was calculated. The Bragg peak was then reconstructed for 3D visualization.  
\begin{table}
\begin{tabular}{l l c} \hline
**Parameter** & **Units** & **Value** \\ \hline Echo spacing & ms & 22.5 \\ Maximum echo time (TE) & ms & 360 \\ Repetition time (TR) & ms & 3000 \\ Field of view (FOV) & mm & 180 \\ Matrix size (MS) & pixels & 512 \(\times\) 512 \\ Slice thickness & mm & 3 \\ Number of excitations (NEX) & & 1 \\ \hline \end{tabular}
\end{table}
Table 2: MRI scanning parameters.  
Figure 1: Simplified SAPT nozzle modeling scheme.  
Table 3 demonstrates the results of the Bragg peak measured by PAGAT gel, MAGAT-f gel, and MC simulation. Peak is the highest position and Bragg peak range is the depth at the distal 90% dose level. Distal dose falloff (DDF) width is the distance where the dose was reduced from 80 to 20% [10]. It was found that there was a 0.66% variation of Bragg peak range measured with PAGAT and MAGAT-f. The deviations of Bragg peak range between the measurement and MC simulation were 0.13% and \(-\)0.53% for MAGAT-f and PAGAT gels, respectively. The DDF discrepancy between the two gels was 5.56%.  
Figure 3: (**a**) Irradiated polymer gel dosimeter and (**b**) five R\({}_{2}\) maps of the gel at different planes (100.2 MeV).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Peak/g cm -2** & \
\begin{tabular}{l} **Bragg Peak Range** \\ **(d\({}_{4}\)90)/g cm -2** \\ \end{tabular} &
\begin{tabular}{l} **Distal Dose Falloff** \\ **(d\({}_{4}\)20-d\({}_{4}\)80)/g cm -2** \\ \end{tabular} \\ \hline MAGAT-f & 7.53 & 7.59 & 0.17 \\ PAGAT & 7.49 & 7.54 & 0.18 \\ TOPAS & 7.52 & 7.58 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of 100.2 MeV proton Bragg peak between polymer gel dosimeters and MC simulation.  
Figure 2: Dose–response curve of MAGAT-f gel dosimeter.  
Two perpendicular crossed planes are shown in Figure 4a. We obtained the 3D volume rendering of the Bragg peak for 100.2 MeV measured with PAGAT (Figure 4b-d). After 3D visualization, the spot size at different depths can be measured.
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 4 Results
### Computational Efficiency  
Considering the computational efficiency of each method, the calculated residual of each step is divided by the residual of the first step to obtain the relative value of the residual. Its relationship with the number of calculation iterations is shown in Figure 14:  
Figure 12: Deformation \(Z\) close to the ground.  
Figure 13: Nodal angle \(\phi\) close to the ground.  
It can be found that the ANCF method has the fastest convergence speed and a trend of accelerating convergence, followed by LM, and the FEM is the slowest.  
As the number of iterations increases, the convergence speed of the FEM gradually slows. This is because the stiffness matrix of the system in the FEM is a constant matrix, and each iteration is updated by changing the coordinates to affect the structural force of the node. When the space coordinate change of the rope decreases, the change of the structural force will not converge quickly. However, due to the cumulative effect of the length of the rope, the small changes in each rope segment will accumulate to the change in the position of the zenith anchor, which causes the speed of convergence to decrease. This phenomenon is especially obvious when loading horizontally.  
When the LM model achieves better accuracy, the number of elements reach a certain value, which will decrease calculation efficiency. However, since the stiffness matrix of the nonlinear equation system of the LM model is not a constant matrix, the convergence speed will be faster than that of the FEM.  
The ANCF method can use a smaller number of elements to achieve the same accuracy due to the higher order of its own elements. There are also nonlinear equations with nonlinear stiffness, although it brings some difficulty to the solution, but the convergence speed is very fast.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography
### Liver
#### 5.1.2 Technical Limitations in Liver Elastography  
As mentioned before, the live imaging nature of ultrasound and US elastography calls for an easily influenced image, whether it be from the surroundings of the organ of interest, including involuntary motions and normal physiologic processes, or from nearby pathologic structures, and such findings can result in limitations and artifacts in the image, thereby causing a potential false diagnosis. Breathing, inflammation, right heart failure, and fasting are all examples of what can easily affect liver stiffness. Right heart failure, for example, can result in hepatic venous congestion, which causes an increase in liver stiffness following the increased venous pressure. Performing the Valsalva maneuver, or inspiration and expiration at an augmented rate, also affects liver stiffness by increasing it in the image. Breathing techniques should be given to the patient beforehand in order to avoid any unwanted blunders while collecting liver measurements [62; 66; 78].  
One of the most common types of limitations when it comes to liver elastography imaging is patient obesity, especially when imaging with the Fibroscan or 1D-TE technique, because it depends heavily on the excitations applied to the skin surface. The technique of 1D-TE, as mentioned previously, does not allow for the acquisition of B-mode images,which limits the sampling area of interest and results in a poor acoustic window and poor penetration. In the 2D-SWE imaging method, the most commonly observed limitations included spotted reverberations and involuntary motion due to cardiac, respiratory, and vascular pulsation. Scan protocols have been optimized to minimize the influence of these errors on liver elasticity quantification [66,79].
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 3 Construction of Mixed Variational Quantum Neural Network and Error Mitigation Model  
Initially, a classical convolutional autoencoder is used for image feature extraction. Following this, amplitude coding is applied to encode the extracted feature vectors, and the resulting quantum states are input into the quantum classifier for classification tasks. The classifier consists of a quantum neural network and a quantum self-encoder. The variational quantum circuit predicts the input quantum state, followed by error mitigation through the quantum self-encoder. The classification process is then finalized through measurement.
# Influence of Thin Fluorine Resin Film on DUV LED Packaging Devices
## 2 Materials and Methods  
A 3.5 mm \(\times\) 3.5 mm \(\times\) 0.5 mm AlN ceramic submount (Hunan Tongdaxin Electronic Technology Co., Ltd.) with a Au layer on the surface was used to fabricate the DUV LED packaging device. Flip-chip LEDs (Photon Wave Co., Ltd., South Korea) with AuSn layer on the electrodes were used as the DUV light sources. The average wavelength was 274.1 nm and the average voltage was 5.8 V. Three commercial types of chips were chosen, and the sizes were 0.25 mm \(\times\) 0.5 mm (Chip A), 0.5 mm \(\times\) 0.5 mm (Chip B) and 1 mm \(\times\) 1 mm (Chip C). A zener diode (chip integration) with a size of 0.16 mm \(\times\) 0.4 mm was used as a protective element. Amorphous fluorine resin with trifluoromethyl (~CF3) ends (Ningbo Jama Material Technology Co., Ltd., Ningbo, China) was used and dissolved in electronic fluoride solution (FC-40, 3M); the concentration of the fluorine resin was 8%. The manufacturing process is shown in Figure 1. Chip and zener diode were placed on the submountby using no-clean flux (WHP-002, Shenzhen Aoniu Technology Co., Ltd., Shenzhen, China), and then bonded to the submount with the eutectic temperature at 320 \({}^{\circ}\)C for 1 min. The drip coating was implemented as follows: amorphous fluorine resin solution was dripped once on the surface center of the chip by pipette, with one drop being approximately 4 \(\upmu\)L. Then, the resin was cured by stewing at room temperature for 2 h and then for 24 h. Different thicknesses of fluorine resin film were obtained by repeating the dripping and curing process for different numbers of times.  
The absorption spectra of amorphous fluorine resin film were characterized by using an ultraviolet spectrophotometer (Techcomp UV2600). The morphologies of the resin  
Figure 1: Manufacturing process of DUV LED packaging device.  
cross-section were observed by using a field-emission scanning electron microscope (SEM, Hitachi SU3500). The photoelectric characteristics were measured by using a calibrated integrating sphere (Everfine ATA-500 UV) under DC current condition. The reliability of the packaging devices was tested by using a high-power aging tester (JXS-12-500). All measurements were performed at room temperature.
## 2 Critical Line Set and Its Search Method
### The Establishment and Dimension Reduction of the Graph Model
Therefore, before searching for the CLS, the graph is first divided into multiple subgraphs according to the breakpoints, and then the hanging nodes in each subgraph and the lines connected to them are removed. Finally, multiple LE regions can be obtained, which reduces the dimension of the graph model of the power grid, significantly reducing the target range and the burden of further analysis, thus increasing the search speed. As shown in Figure 2, firstly, three subgraphs are obtained according to breakpoints three and seven, then the hanging nodes one, two, and ten in each subgraph are removed, and finally, two LE regions are obtained. After obtaining multiple LE regions, the search of the CLS only needs to be carried out from the LE region where the branch is removed, accelerating the search speed and quickly determining the range of wide-area information that needs to be collected.
## 4 Hybrid Fiber Composition
### Hybrid Combinations of Other Fiber Types
It can be concluded that the presence of fibers of varying lengths helps effectively control cracking, especially before the maximum load is reached, indicating their beneficial distribution within the mixture. Table 13 presents the analysis of the static properties of UHPC reinforced with various combinations of fibers. Figure 10a presents the compressive strength results of UHPC with different combinations of fibers. The addition of 12 mm-length basalt fibers and 12 mm-length polypropylene fibers negatively affected the strength, with an average decrease of 12% compared to the mixture without fibers. The mixtures achieved higher strengths with a higher proportion of B fibers. Despite the high tensile strength of the B fibers and the high ductility of the PP fibers, the final strength of the matrix decreased. When the content of these fibers increased from 1% to 2%, a slight decrease in strength was observed, although higher results were obtained with a higher proportion of basalt fibers. Other fiber combinations used in UHPC included half a dose of 2% of 15 mm ST fibers and 20-30 mm C fibers, or identical C fibers and 6-12 mm G fibers. The resulting UHPC strength was more than 13% higher than the strength of the control UHPC. Changing the shape of the steel fibers to crimped in UHPC with carbon fibers caused a decrease of approximately 9% in strength compared to the fiber-free mixture, due to difficulties with the uniform distribution of the crimped fibers. The flexural tensile strength is presented in Figure 10b. UHPC with 12 mm 1% B fibers and 12 mm PP fibers showed increases in strength of up to 7.5%, which increased with a larger proportion of PP fibers. When the fiber content increased from 1% to 2%, similar trends were observed. The mixtures with 2% steel-carbon or carbon-glass fibers had flexural strengths of 19.3 MPa and 17.5 MPa, respectively. The higher values were due to the longer lengths and higher tensile strength of the steel and carbon fibers, which resulted in higher resistance forces that kept the fibers in the matrix and made the fibers more difficult to remove. UHPC with 1.5% crimped 25 mm ST fibers and 1% 7 mm C fibers achieved the highest strength, with an increase of almost 23% compared to the control mixture. This