esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Load Distribution and Progressive Decoding on Cell BE
### Considering Load Balancing Effects on Cell BE  
In this subsection, we propose our approach which enables an optimized workload distribution on the Cell BE. Figure 7 depicts the computational area required to process and to dynamically distribute the workload. The previous work has already considered load balancing on a general, homogeneous processor (e.g., Intel or AMD multi-core processors), however, the Cell BE is a heterogeneous processor which has an asymmetric core architecture. The SPEs have been designed to deliver higher computational power than the PPE, especially with the SIMD instruction set. As such, we must consider the difference between these two types of cores in order to achieve proper workload distribution.  
For that purpose, we first have defined a value called _ppefactor_ which decides the workload distribution ratio of the PPE _versus_ the SPE. For example, when ppefactor is set to 0.1, the PPE takes 10% of the available work, and the remainder is assigned to the SPE. Before considering load balancing on the asymmetric core architecture, the cores would have equally divided workload. In order to find the optimal workload distribution, the proposed idea is strongly dependent upon heuristic.  
Once the workload distribution over PPE and SPE is defined, the data partitioning to use the SIMD instructions should be defined. Although the data computation region is dynamically partitioned by DVP, architectural optimization can be achieved as the Cell BE processor supports those SIMD instruction set. The SIMD instructions for PPE and SPEs enables 128 bit operations. For that reason, the data are divided into chunks each of which is as large as 16 bytes. When the size of data is not a multiple of 16,  
Figure 7: Dynamic resource distribution to Cell BE.  
the remainder is assigned to PPE. For example, When a data size is 117 bytes, each chunk is constructed from the right most element in the data (the right most column). This means that there exist 7 chunks and remaining 5 bytes which are the left most 5 bytes. Then, the five bytes are assigned to one of the PPE threads and remaining 7 bytes are assigned to the other PPE thread and SPEs. This method is superior to the method in which each core has an equal number of elements.  
After addressing the workload distribution on each thread, we need to select a proper computation method between the table-based approach and the loop-based approach for Galois field multiplications. We now explain these two approaches in Section 3.3 and the selected method is then fully tested in Section 4.2.
###### Abstract  
Cryogenic turbopumps are used in high-performance, lightweight liquid rocket engines for space applications. The development of bearings and shaft seals for cryogenic turbopumps requires detailed characterization of the internal flow, taking into account the effects of boiling and multi-component two-phase flow. Here, a flow network solver was developed to analyse the secondary flow circuit of a cryogenic turbopump where the propellant is mixed with high-temperature helium after bearing cooling. The network solver is based on an extension of a classic 1D homogeneous model, originally developed for a pure substance, to the case of two-phase multi-component flow. The solver is capable of predicting pressures, temperatures, flow rates, and species concentrations in a complex two-phase flow in the presence of non-condensable gases. The unsteady mass, momentum, and energy conservation equations are implemented in conjunction with the thermodynamic equations of state using a general-purpose finite volume formulation, where the pressure drop and the heat transfer are calculated using correlations. The numerical tool was validated by comparing its predictions with experimental data obtained during tests on the secondary circuit of an oxygen turbopump developed at Avio S.p.A. A number of engine operating conditions were considered (inlet helium temperature in the range of 250-280 K, helium/liquid oxygen drain in the range of 165-230 K). The predicted temperature values showed good agreement with the experimental data in most conditions.  
heat transfer; multi-phase flows; boiling; cryogenic propellants; non-condensable gas; homogeneous model; flow network; turbopump; seal package; liquid rocket engine +
Footnote †: journal: Physics Letters B
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 2 Materials and Methods
### Experiments
#### 2.4.1 Developed Software  
A Python-based application has been developed in order to test the proposed method. In Figure 2, a range image is presented, where each pixel has a value proportional to the distance from the point to the origin of the reference system of the LiDAR sensor. A color map has been used to better highlight the distances. The developed application enables the user to manually select the calibration rectangle (by using the GUI provided in the application). Then, Algorithm 1 is used to obtain the points of the rectangle. Subsequently, once the rectangular polygon is detected, there is an option to visualize the position of the rectangle corners with the LiDAR reference system next to the set of points of the rectangle (Figure 5).  
Finally, the fisheye image is shown (Figure 6) to manually select the rectangle. In this step, the software developed in [25] is used and the position of the corners in the image is obtained (please refer to Section 2.2.1). Solving the system of equations proposed in Section 2.2.3, the 3D coordinates of the corners relative to the image are finally obtained. After calculating the transformation between camera and LiDAR in Section 2.3, the LiDAR information is projected onto the image to visually check that the calibration has been performed correctly.  
Figure 5: Set of points with LiDAR reference system belonging to the rectangular polygon in blue and estimation of the position of corners and edges in red.
# A Novel Optimization Layout Method for Clamps in a Pipeline System
## 4 Conclusions and Prospects  
In this paper, a complex pipeline model with multi-supports is established. A novel optimizing method for the design of the clamp layout is formed. Some new results are listed as follows:  
(1) For the complex pipeline system with multi-clamps, the surrogate model of clamp position and vibration output is established by back-propagation algorithm. The accuracy of the neural network agent model is verified by random selection of clamp position within the optimization range. The results show that the prediction error of the first-order frequency of surrogate model is about 0.5%, and the prediction error of the random load displacement and stress is about 2%, which meets the engineering application.  
(2) The genetic algorithm is proposed to optimize the position of clamps on a Z-shaped pipe under the surrogate model. Results show that optimization results have a large difference for different optimization goals. When maximizing frequency difference, the optimization result increases the first-order frequency difference by 42.11%; when minimizing vibration stress, the optimization result reduces the maximum vibration stress by 43.58%. In the engineering field, the specific optimization scheme should be determined on the basis of practical optimization goals. The proposed optimization method can provide a new method for the arrangement of clamps in pipeline system.  
(3) In the experiment, the strain gauge is used to measure the stress at a fixed position (a small area). This position may be slightly different from the theoretical point of stress concentration where the stress varies greatly nearby. In addition, the position of the clamps in this case may not be as accurate as they were in the finite element model, and this would influence the position of the maximum stress point as well. Thus, the measured value is much smaller than the theoretical value.  
(4) In most scientific research, the stress simulation of isotropic material is generally based on the von mises equivalent stress without consideration of specific directions. In this paper, we mainly focus on the effectiveness of this optimization method. So, in our simulation, only the magnitude of Von Mises equivalent stress was studied. Simulation under different directions could be taken into consideration in the future work.  
Conceptualization, P.G.; methodology, P.G.; software, P.G.; validation, J.L., J.Z. and Y.T.; formal analysis, J.L.; investigation, J.Z.; resources, Y.T.; data curation, J.L.; writing--original draft preparation, P.G.; writing--review and editing, J.L.; visualization, Y.T.; supervision, J.Z.; project administration, Q.H.; funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 51805462.  
The authors declare no conflict of interest.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 3 The Proposed DSAH Framework
### Hash Function Learning
#### 3.2.2 Feature Mapping  
The aim of DSAH is to project the original features to compact hash codes. In this paper, we adopt two linear projections as the hash mapping functions for image modality \(\mathbf{X}^{(1)}\) and text modality \(\mathbf{X}^{(2)}\), respectively.  
\[\begin{split} f_{1}(\mathbf{X}^{(1)})&=sgn(\mathbf{ P}_{1}\phi(\mathbf{X}^{(1)})),\\ f_{2}(\mathbf{X}^{(2)})&=sgn(\mathbf{P}_{2}\phi( \mathbf{X}^{(2)})),\end{split} \tag{2}\]  
where \(\mathbf{P}_{1}\in\mathbb{R}^{k\times q}\) and \(\mathbf{P}_{2}\in\mathbb{R}^{k\times q}\) are the hash mapping matrices, which map specific kernel features into Hamming subspace, and \(f_{1}(\cdot)\) and \(f_{2}(\cdot)\) are hash functions for image modality and text modality, respectively.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 6 Discussion  
This study highlights the broader potential of modern IT solutions in streamlining maintenance processes. By focusing on the practical application of data analysis, the research demonstrates how the precise measurement and modeling of service durations can enhance the predictability and efficiency of maintenance schedules. This approach addresses organizational challenges and provides a foundation for continuous improvement in operational performance.  
This study successfully met its main goals, but there is room for improvement. The algorithm used to find the best-fitting probability distribution, based on the coefficient of determination \(R^{2}\), provides a solid foundation. However, it does not fully capture the nuances of distributional differences, particularly in tails or extreme values. Adding other metrics or methods could give a clearer understanding of the data. Including the Kolmogorov test is a helpful step in making the analysis more thorough.  
The test environment, while ideal for validating the concept, offers a simplified representation of real-world conditions. Future efforts could focus on integrating the application into existing maintenance systems and testing its performance under more complex operational settings, such as varying network conditions or larger datasets. These steps would further enhance the applicability and scalability of the proposed solution.  
It is also worth noting that this study focused on a single type of periodic service for the M-346 aircraft, which provided a well-defined and manageable scope. Expanding the application to other types of maintenance tasks, with varying complexity and requirements, would provide valuable insights and help to generalize the findings across a broader range of scenarios.  
Lastly, while the application has proven its technical effectiveness, its success in real-world implementation will depend on its acceptance by maintenance personnel. Ensuring the tool is intuitive, user-friendly, and seamlessly integrates into existing workflows will be crucial for its long-term adoption and impact.
## 2 Methods
### TRS Measurement
derived from the analytical solution of the photon diffusion equation for a homogeneous semi-infinite medium is given by Equation (1) under the zero-boundary condition [8],  
\[R(t,\rho;\lambda)=\frac{z_{0}}{\left(4\pi Dc\right)^{3/2}t^{5/2}}\exp[-\mu_{a}( \lambda)ct]\exp(-\frac{\rho^{2}+z_{0}^{2}}{4 Dct}), \tag{1}\]  
where \(R(t,\rho;\lambda)\) is the time-resolved reflectance at time of \(t\) with the distance between the source and detector positions of \(\rho\) (= 40 mm) for the wavelength of \(\lambda\), \(c\) is the speed of light in the tissue, \(D\) is the diffusion coefficient given as 1/[3\(\mu_{\mathrm{s}}\)'(\(\lambda\))], and \(z_{0}\) = 1/\(\mu_{\mathrm{s}}\)'(\(\lambda\)). Here the wavelength dependences of \(D\) and \(z_{0}\) are omitted for simplicity. Equation (1), convoluted by the instrumental response function of TRS-20, was fitted to the measured time-resolved reflectance to estimate \(\mu_{a}(\lambda)\) and \(\mu_{\mathrm{s}}\)'(\(\lambda\)) of the medium using a non-linear least-squared technique. The absorption coefficient of tissue was assumed to be the sum of the absorption coefficients of oxy-Hb, deoxy-Hb and background tissue as Equation (2),  
\[\mu_{a}(\lambda)=\varepsilon_{oxy-Hb}(\lambda)C_{oxy-Hb}+\varepsilon_{deoxy-Hb}( \lambda)C_{deoxy-Hb}+\mu_{a,BG}(\lambda) \tag{2}\]  
where \(\varepsilon(\lambda)\) and \(C\) are the extinction (or molar absorption) coefficient and the molar concentration, respectively, with the subscript indicating oxy-Hb, deoxy-Hb or background tissue. Solving the simultaneous equations of Equation (2) for the three wavelengths (761 nm, 791 nm, and 836 nm) gave the concentrations of oxy-Hb (\(C_{oxy-Hb}\)), deoxy-Hb (\(C_{deoxy-Hb}\)) and total-Hb (\(C_{t-Hb}=C_{oxy-Hb}+C_{deoxy-Hb}\)). Then the oxygen saturation, SO\({}_{2}\) = \(C_{oxy-Hb}\)/\(C_{t-Hb}\), was calculated. The unit of the concentrations of oxy-Hb and deoxy-Hb are \(\upmu\)M. The mean total optical pathlength of the detected light for the wavelength of \(\lambda\), OPL(\(\lambda\)), was calculated by Equation (3),  
\[\mathrm{OPL}(\lambda)=\frac{c\int_{0}^{\infty}tR(t,\rho;\lambda)dt}{\int_{0}^{ \infty}R(t,\rho;\lambda)dt} \tag{3}\]  
It should be noted that the Hb concentrations and SO\({}_{2}\) obtained by Equations (2) and (3) are the averages over the whole regions interrogated by the light pulses, and for separating those of the individual layers, i.e., the scalp, CSF, gray and white matter layers, it is necessary to know the partial optical pathlengths of the individual layers. Although it is very difficult to know the partial optical pathlengths of the individual layers, from the reference [12] the partial optical pathlength of the cortex, mainly the gray matter, is estimated to be about 5 to 10 % of the OPL. Therefore, the Hb concentrations and SO\({}_{2}\) of the cortex occupy only 5 to 10% of those obtained by this study using Equations (2) and (3).
## 6 Conclusions  
In the present study, ground settlement due to tunneling was analyzed. Experimental and numerical investigations were done to analyze the effects of different shapes of tunnel linings on ground settlement. The main conclusions of the study are summarized as:  
* For laboratory simulation, a maximum ground settlement of 1.85 mm was recorded. This was 1.80 mm for the numerical analysis of laboratory simulations, so the error was only 2.7 percent.
* The vertical displacement in the linings was maximum at the crown position with a magnitude of 1.15 mm.
* Numerical analysis showed the least maximum settlement for an elliptical-shaped liner with a magnitude of 1.4 cm. However, the highest maximum settlement of 4.2 cm was recorded for square-shaped linings. The descending order of maximum settlement was elliptical-horseshoe-circular-arch-square.
* The principal stresses in the soil mass were greatest at springer locations and lowest at crown and invert locations along horizontal and vertical planes. The minimum and maximum stresses in the vertical plane were higher than those in horizontal plane.
* The horizontal-plane stresses were greatest around the invert, and the vertical-plane stresses were greatest around the springer. Horizontal-plane stresses were maximum for elliptical-shaped linings and vertical-plane stresses for circular-shaped linings. Because horseshoe and square-shaped linings have vertical boundaries, they experience fewer stresses at springer locations.
* Normal forces in the lining were found to be highest at springer locations for all shapes, and shear forces in the lining were found to be highest at the crown and lowest at springer locations. Circular- and elliptical-shaped linings have a negative bending moment around the springer and a positive bending moment around the crown and invert. Horseshoe- and arch-shaped linings have negative bending moments around the invert.  
The results suggest that elliptical-shaped linings are the most suitable for minimising ground settlement. Square-shaped linings are the least stable and hence it is recommended that they not used where ground settlement might cause severe damage.  
Conceptualization, M.R.S. and M.F.A.; methodology, M.R.S. and M.F.A.; validation, M.R.S. and A.H.A.; formal analysis, M.F.A. and S.A.; investigation, M.R.S.; resources, M.R.S. and A.H.A.; data curation, M.F.A.; writing--original draft preparation, M.F.A.; writing--review and editing, M.R.S., A.H.A., and S.A.; funding acquisition, A.H.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support provided by Researchers Supporting Project Number (RSP2022R473), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors are thankful to University Grant Commission, India for providing UGC BSR Startup to [PERSON] for carrying out this research.  
The authors declare no conflict of interest.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 5 Discussion  
This study explores the efficacy of quantum-enhanced models, specifically the quantum Long Short-Term Memory (QLSTM) and the Quantum Gated Recurrent Unit (QGRU), in predicting the dynamics of systems governed by ordinary differential equations. These systems include Van der Pol oscillators, two damped coupled oscillators, and the Lorenz system. We benchmarked the performance of these quantum models against traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models by evaluating their accuracy through MAE and RMSE, alongside observing their training and testing loss trajectories over epochs.  
The results from the three numerical experiments showcase that quantum-based models, particularly the QGRU, significantly outperform their classical counterparts in terms of predictive accuracy. This discovery highlights the capability of quantum computing techniques to improve the predictive performance of recurrent neural networks, particularly in the complex dynamics of systems represented by ordinary differential equations. However, it is important to recognize that these quantum-based models require greater computational resources, primarily because the operations of the Variational Quantum Circuit (VQC) are simulated on classical computing hardware. The same challenge is observed across numerous studies in the QRNN field, as highlighted in [50,51]. To address these computational challenges and enhance efficiency, we implemented batch sizes of 64 in this study, which significantly accelerated the processing time. Additionally, a critical aim of our ongoing research is to test the performance of these models on actual quantum hardware. This will not only confirm their theoretical benefits but also evaluate their practical utility and efficiency within a real quantum computing context.  
The example code for _classQLSTM_ and _classQGRU_ can be found in the following Github link: [[https://github.com/YuanChenmt/QRNN-Examples](https://github.com/YuanChenmt/QRNN-Examples)]([https://github.com/YuanChenmt/QRNN-Examples](https://github.com/YuanChenmt/QRNN-Examples)) (accessed on 16 April 2024).  
Figure 23: Predicted Lorenz system trajectory by QGRU model.  
Figure 24: Experiment 3: Train and test losses for the models over 20 epochs. From left to right: LSTM, QLSTM, GRU, QGRU.
## 3 Results and Discussion  
The characterized results of the new IL1 and IL2 using FTIR, NMR, HR-ESI-MS and elemental analysis showed that IL1 and IL2 have been successfully synthesized. IL1 form is brown viscous liquids with a yield of around 92%. Based on the FTIR data of IL1 showed specialty peaks (\(\
u\) (KBr)) namely those of 3500, 3000, 1990, 1680, 1470, 1400, and 790 cm\({}^{-1}\). \({}^{1}\)H-NMR (DMSO-d\({}_{6}\)) data namely \(\delta\): 7.99 (m), 7.58-7.11 (m), 7.04 (m), 6.99-6.88 (m), 4.09 (t, J = 6.6, 1.4 Hz, 2H), 1.85 (m), 1.00 (t, J = 7.5 Hz), 0.44 (t, J = 30.0, 7.5 Hz) and \({}^{13}\)C-NMR (DMSO-d\({}_{6}\)) namely \(\delta\): 10.4, 10.7, 11.9, 22.6, 25.9, 23.5, 68.9, 51.5, and 49.0 ppm. The HR-ESI-MS (M+H) analysis showed m/z 440.28. The elemental analysis data of IL1 showed the following compositions C: 64.00%, H: 6.21%, and N: 4.96%. Meanwhile, the IL2 form was brown viscous liquid with a yield of around 91%. The FTIR data of IL2 showed specialty peaks (\(\
u\) (KBr)) namely those of 3400, 2870, 1990, 1650, 1420, 700 cm\({}^{-1}\). In addition, also supported by \({}^{1}\)H-NMR (DMSO-d\({}_{6}\)), the \({}^{1}\)H NMR date (DMSO-d\({}_{6}\)): 8: 7.55-7.45 (m), 4.14 (t, J = 6.2 Hz), 3.99-3.87 (m), 1.54-1.46 (m), 1.32 - 1.24 (m), 0.97-0.83 (m), 0.51-0.43 (m) and \({}^{13}\)C NMR (DMSO-d\({}_{6}\)) namely \(\delta\): 14.1, 13.8, 13.9, 31.8, 20.7, 20.5, 31.9, 32.9, 33.8, 68.7, 49.3, and 46.5 ppm. HR-ESI-MS (M+H) analysis showed m/z: 482.32. The elemental analysis data showed that the compositions of IL2 are C: 65.11, H: 6.75, and N: 4.62. IL1 and IL2 were applied as corrosion inhibitors on carbon steel surfaces and tested by the EIS method and the Tafel method to demonstrate corrosion inhibition performance. The corrosion condition was designed to match the corrosion condition of the carbon steel pipes in a brackish water model (1% NaCl). All experiments were carried out at various temperatures and concentrations.
## 4 Results and Discussions
### Scenario II: Identification of Gap Size
#### 4.3.1 Classification Accuracy
Since each file corresponds to a different specimen, the variance among files may be larger for the structure-borne sensor signals. Moreover, during the measurement of Gap 0.2 specimens, the sensors needed to be recalibrated, which may have led to a change in signal characteristics before and after the calibration. Both suggest that a stable mounting technique should be established for structure-borne sensors to ensure consistent and reliable inspection quality.