esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 5 Hardware Implementation of Modulation Recognition Algorithm Based on Deep Learning
It can be seen that it is feasible to use a hardware development board such as FPGA to implement a modulation recognizer composed of a deep neural network. In recent years, FPGA has become an important hardware platform for realizing modulation recognizers composed of deep neural networks [113]. It brings hope to the hardware implementation of the modulation recognition algorithm based on deep learning. However, since the hardware resources of FPGA are limited, the size, type, and number of layers of the neural network are important factors that affect the performance of hardware recognition. Some studies have proved that the FPGA is the preferred platform for implementing quantitative CNN [114] and has good classification accuracy in the field of modulation recognition, so the modulation recognizer composed of CNN can be the first choice to complete the hardware implementation. On the other hand, due to the limited hardware resources of FPGA, when designing CNN, it is necessary to consider the depth of the network and the size of the convolutional layer and try to design a lightweight neural network [115].
## 2 Materials and Methods
### Test Methodology and Fuel Characteristics  
A set of four-engine partial load points were chosen for the setup of the experimental campaign. They were inside the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) operating area, and therefore usable as an indicator for efficiency and emission performance at partial load conditions. In this regard, they were performed, applying a reference engine parameter calibration (rail pressure, intake pressure, swirl, exhaust gas recirculation (EGR), etc.) derived from a real four-cylinder engine with the same engine architecture of the SCE. The key-points are schematically reported in Figure 3 and named synthetically as engine speed per brake mean effective pressure (BMEP), e.g., 1500 \(\times\) 5 indicates 1500 rpm per 5 bar BMEP. For this work, four operating points were selected, two speeds (1500 and 2000 rpm), and two loads (2 and 5 bar of BMEP) points at constant engine out NOx level. To better explore and analyze the LPG blends potential, a conventional pattern characterized by a pilot-main injection was employed. The start of injection (SOI) of the pulses and the main energizing time (ET) were adjusted to achieve the reference diesel combustion barycentre (MBF50) and load values; also, the EGR rate was controlled to achieve the constant NOx target. All the additional injection parameters (ET pilot, dwell time, rail pressure, etc.) were maintained constant and equal to the reference values. As regards the hydraulic characterization, both pilot and main injection, the mass flow profiles for different fuels were performed under the same injection conditions tested on the SCE.  
In this regard, the following fuels were considered in this study:  
* Reference EN590 European commercial diesel;
* Blend of 20%wt of LPG in diesel (DLPG20);
* Blend of 35%wt of LPG in diesel (DLPG35).  
Table 2 reports the main chemical and physical characteristics of the reference diesel and LPG, and the roughly estimated ones, obtained by linear interpolation.  
Figure 3: Engine operating points in the area of interest for this test campaign.  
For the sake of completeness, the EGR sweep results are presented in terms of the NOx-particulate matter (PM) trade-offs. The analysis was very effective and is a general approach employed for evaluating the potential of fuel formulations, technologies, etc. fuel-engine interaction performance and, in particular, for the critical engine control conditions in terms of NOx and PM.
# Text Detection and Recognition for X-ray Weld Seam Images
## 1 Introduction
Additionally, factors like irregular engineering operations may cause the text in weld seam images to appear blurred, distorted, and with varying lengths of text lines. To achieve good detection and recognition results under these interferences, we first constructed two datasets for the text detection and recognition tasks. Then, through a series of experimental analyses, we employed the DBNet model for text detection tasks and improved the post-processing methods to enhance the text detection accuracy. For text recognition tasks, we adopted the MATRN model and enhanced the model performance by introducing the CA [17], CBAM [18], and HFA [19] modules, thus improving the text recognition accuracy. Finally, the experimental results demonstrate that the proposed method performs excellently in adapting to the unique scenario of X-ray weld seam images. The main contributions of this paper are as follows:  
1. Through experimental analysis, we employed the DBNet model for weld seam image text detection and combined it with post-processing methods to improve the detection accuracy. 2. We improved the MATRN model, achieving a higher text recognition accuracy when targeting weld seam image datasets compared to other reference models. Our improved model also outperforms the original model in comparison with publicly available datasets. 3. We introduced a CA module into the backbone network to better extract visual features, thus enhancing the model performance. Additionally, we integrated the CBAM and HFA modules into the position attention module for more accurate spatial localization of characters, thereby improving the model's recognition accuracy.
## 4 Results and Discussion
### Rolling Bearings Test Results
#### 4.2.2 Wear Tests  
As previously stated, the wear tests were performed under 5 kN, 200 rpm, and at 50 \({}^{\circ}\)C over 120 h. During this time, the friction torque was measured 2 h after the start of the test and then at 5, 8, 26, and 120 h, after which the machine was stopped.  
The friction torque measured over time is shown in Figure 10. In the first hours of operation at 200 rpm, the friction torque was higher due to the grease churning. After approximately 5 h, the friction torque reduced substantially for the rest of the test. The exception was grease 1, which showed a very steep friction torque increase between 26 and 120 h. Either way, grease 1 generally showed a higher friction torque than grease 2.  
Figure 9: TBB friction torque results as a function of the entrainment speed for greases 1 and 2.  
After 120 h, both greases displayed a very dark coloration, particularly between the rolling elements and the raceway, as shown in Figure 11. This dark coloration was probably related to the presence of metallic particles originating from the wear process, but also due to some temperature-induced oxidation.  
Finally, a sample of each grease was collected at the end of the power loss tests and wear tests for particle analysis. Therometry and ferrography analysis results are shown in Table 5. The indexes of small (DS, smaller than 1-2 \(\upmu\)m) and large particles (DL, larger than 5 \(\upmu\)m) refer to the concentration and size of particles found in a given sample of grease (100 mg diluted with 20 mL of an adequate solvent), collected after the power loss and wear tests. The particle concentration index (CPUC) and the severity of wear index (ISUC) can be estimated from DS and DL follows:  
\[\text{Particle Concentration Index (CPUC) }=\text{DS}+\text{DL} \tag{10}\] \[\text{Severity of Wear Index (ISUC) }=\text{DL}^{2}-\text{DS}^{2} \tag{11}\]  
Figure 11: Remaining grease on (**a**) the rolling bearing house, lower raceway, and cage, and (**b**) the upper raceway shaft.  
Figure 10: TBB friction torque results as a function of the test time for greases 1 and 2.  
Based on these indexes and the ferrography analysis, a classification may be established according to wear severity, presence of fatigue or adhesion wear particles, and the presence of friction polymers and black oxides. More details regarding these procedures can be found elsewhere [44; 45; 46]. According to the results of the power loss tests, grease 1 showed more severe wear with a higher particle concentration index (CPUC) and severity of wear index (ISUC), suggesting once again that grease 2 performed better. Moreover, as seen in this table, the results of the wear tests were considerably higher than the results obtained for the power loss tests.
# Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
## 2 Materials and Methods
### Statistical Analysis and Feature Selection  
Feature Selection (FS) was applied on the pre-processed training dataset to identify variables that are associated with injuries. This reduction of the space dimensionality also reduces the complexity of the final ML models and thus improves their performance and generalization. The SVM-FuzCoC feature selection algorithm, that was proposed in previous studies [38; 39], was applied to select a small subset of informative as well as non-redundant (complementary) features. This method achieves a reasonable trade-off between accuracy and computational complexity.  
Statistical analysis was performed with Statistical Package for Social Sciences (SPSS), version 25.0. Quantitative variables were described as means with the standard deviations, whereas qualitative variables as percentages reporting 95% confidence intervals. Statistical comparison between risk factors and the outcome was performed using independent-samples \(t\)-test for continuous variables and Chi-squared tests for the categorical ones. Statistically significant considered all comparisons with two-tailed \(p\)-values smaller than 0.05 with confidence intervals (CI) of 95%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **C1** & **C0** & **Total** & \(p\)**-Value** \\ \hline Sex & n (\%) & n (\%) & n (\%) & 0.00 \\ \hline Female & 143 (11.68) & 300 (24.51) & 443 (36) & \\ Male & 391 (31.95) & 390 (31.86) & 781 (64) & \\ \hline Age group & & & & 0.00 \\ \hline \(<\)18 & 0 & 0 & 0 (0) & \\
18-29 & 230 (18.80) & 368 (30.07) & 598 (49) & \\
30-39 & 212 (17.32) & 236 (19.28) & 448 (37) & \\
40-49 & 84 (6.86) & 78 (6.37) & 162 (13) & \\ \(\geq\)50 & 8 (0.65) & 8 (0.65) & 16 (1) & \\ Mean \(\pm\) SD & 175.43 \(\pm\) 8.56 & 175.37 \(\pm\) 8.95 & 175.40 \(\pm\) 8.78 & \\ Median (range) & 176 (150–203) & 176 (153–198) & 176 (150–203) & \\ \hline Height & & & & 0.00 \\ \hline Mean \(\pm\) SD & 175.43 \(\pm\) 8.56 & 175.37 \(\pm\) 8.95 & 175.40 \(\pm\) 8.78 & \\ Median (range) & 176 (150–203) & 176 (153–198) & 176 (150–203) & \\ \hline Weight & & & & 0.00 \\ \hline Mean \(\pm\) SD & 77.26 \(\pm\) 13.27 & 74.84 \(\pm\) 13.23 & 75.89 \(\pm\) 13.24 & \\ Median (range) & 77 (47–120) & 77 (47–120) & 77 (47–120) & \\ \hline BMI, kg/m\({}^{2}\) & & & & 0.00 \\ \hline Mean \(\pm\) SD & 24.96 \(\pm\) 2.71 & 24.14 \(\pm\) 2.67 & 24.5 \(\pm\) 2.72 & \\ Median (range) & 24.92 (17.93–35.92) & 24.13 (17.63–35.83) & 24.54 (17.63–35.92) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic data of the participants.
## 5 Numerical Experiments
### Testing Lagrangian Relaxation Framework  
We further tested it in a randomly generated case. Specifically, we set up \(20\times 30\), \(20\times 100\) and \(20\times 200\) networks to verify the computational efficiency and solution capability of the algorithm in each scenario, in the case of handling a sparse, regular and overloaded number of tasks (due to the small size of the 20 \(\times\) 30 network, a sparse task scenario is not necessary), respectively. The parameters are present in Table 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Number of Timeslots** & **Number of Satellites** & **Number of Tasks** & **Node Capacity** & **Link Capacity** \\ \hline Random\_I & 20 & 30 & 50 & 2 & 1 \\ Random\_II & 20 & 30 & 80 & 2 & 2 \\ Random\_III & 20 & 100 & 80 & 2 & 1 \\ Random\_IV & 20 & 100 & 120 & 2 & 2 \\ Random\_V & 20 & 100 & 150 & 2 & 2 \\ Random\_VI & 20 & 100 & 180 & 2 & 2 \\ Random\_VII & 20 & 200 & 150 & 2 & 1 \\ Random\_VIII & 20 & 200 & 180 & 2 & 2 \\ Random\_IX & 20 & 200 & 240 & 2 & 2 \\ Random\_X & 20 & 200 & 300 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Random scenarios parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Iterations k** & \(\boldsymbol{LB^{*}}\) & \(\boldsymbol{UB^{*}}\) & **Gap\%** & **Violated Node Constraints** & **Violated Link Constraints** \\ \hline Scenario I: A total of 2 tasks, network node, and link capacity is upper limit 2 and 1. The time attribute of tasks is the same. \\
1 & 8.87 & 10.36 & 14.38\% & Null & [[55,69], [69,73]] \\
2 & 8.87 & 10.36 & 14.38\% & Null & [[55,69], [69,73]] \\
4 & 10.36 & 1  
We dynamically adjust the capacity of nodes and links according to the number of tasks and set the proposed algorithm to terminate when the gap value of the optimal upper bound \(UB^{*}\) and the optimal lower bound \(LB^{*}\) is less than 1%. In the small-scale cases, we keep increasing the number of tasks to test the computational efficiency and solution quality of the algorithm, in which the ratio of the number of satellites to the number of tasks for Random_II and Random_VI has reached 3:8 and 1:1.8, and the experimental results demonstrate that the proposed algorithm obtains the optimal solution in an acceptable time. In addition, we find that the running time in Random_X is much longer than in other scenarios due to the excessive number of nodes and links violating the constraints caused by these tasks.  
There are 104 nodes and links that violate the constraints at the initial iteration, but our algorithm can still obtain a solution close to the optimal solution. Therefore, the experimental results illustrate that the proposed algorithm still works well in handling overloaded tasks. The specific results are present in Table 11.
## 3 Proposed Fault Diagnosis Method
### Definition of Error Index  
Define the false alarm rate \(\gamma_{f}\), the missing reporting rate \(\gamma_{m}\), and the algorithm accuracy rate \(\gamma_{A}\). The false alarm rate describes a data that is not a fault but is misreported as a fault. The missing reporting rate describes data that should be a fault but has not been detected.The calculation method is as follows:  
\[\left\{\begin{array}{l}\gamma_{f}=\frac{n}{N},\\ \gamma_{m}=\frac{m}{N},\\ \gamma_{A}=\sqrt{\frac{\theta_{T^{2}}^{2}+\theta_{SPE}^{2}}{2}}.\end{array}\right. \tag{13}\]  
In Equation (13), \(n\) is the number of false alarm points, \(m\) is the number of missing reporting points, and \(N\) is the total number of data. \(\theta_{T^{2}}\) and \(\theta_{SPE}\) respectively indicate the accuracy of fault diagnosis when using \(T^{2}\) and \(SPE\), among which:  
\[\left\{\begin{array}{l}\theta_{T^{2}}=1-(\gamma_{f,T^{2}}+\gamma_{m,T^{2}}),\\ \theta_{SPE}=1-(\gamma_{f,SPE}+\gamma_{m,SPE}).\end{array}\right. \tag{14}\]  
In Equation (14), \(\gamma_{f,T^{2}}\) and \(\gamma_{f,SPE}\) respectively indicate the accuracy of false alarm rate when using \(T^{2}\) and \(SPE\), \(\gamma_{m,T^{2}}\) and \(\gamma_{m,SPE}\) respectively indicate the accuracy of missing reporting rate when using \(T^{2}\) and \(SPE\).
## 5 Conclusions  
In this paper, we have presented a novel CTMC-based model to analyze the performance of hybrid FSO/RF communication systems. By deriving the state probabilities, we quantified the system's behavior under varying environmental conditions. We employed a continuous-time Markov process due to its broad applicability. In particular, such a process accurately reflects changing weather conditions. If a discrete-time framework is more appropriate, the continuous-time process must be discretized, and the transition probability matrix must be computed using the fundamental properties of the Markov process. The analytical results enabled a comprehensive evaluation of key performance metrics, showcasing the model's ability to accurately predict system performance; this allows us to formulate requirements regarding the efficiency of machine learning-based algorithms for detecting the conditions required to switch between FSO and RF. This approach not only provides valuable insights into the interplay between FSO and RF links but also serves as a powerful tool for optimizing hybrid communication systems. Future work will focus on extending the model to include advanced teletraffic-engineering-based switching mechanisms and additional environmental factors for even greater applicability. We also plan to apply the proposed model to evaluate energy consumption across a wide range of scenarios, both theoretically and practically.  
Figure 6: Mean time to failure.  
**Author Contributions:** Conceptualization, V.S.; methodology, N.S.; software, V.S.; validation, V.S., N.S. and I.K.; formal analysis, V.S. and N.S.; investigation, V.S. and N.S.; resources, V.S. and I.K.; data curation, V.S., N.S. and I.K.; writing--original draft preparation, V.S. and N.S.; writing--review and editing, V.S., N.S. and I.K.; visualization, V.S.; supervision, V.S.; project administration, I.K.; funding acquisition, V.S. and I.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00250720).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article, and further inquiries can be directed to the corresponding author.  
**Acknowledgments:** We extend our heartfelt gratitude to [PERSON] for her technical support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Implementation Details of Field Arithmetic
### Field Arithmetic over \(\mathbb{F}_{p^{2}}\)  
The incomplete reduction method proposed by [PERSON] et al. [32] is one of the optimization methods in field arithmetic over \(\mathbb{F}_{p^{2}}\). Given two elements \(a,b\in[0,p-1]\), the result of operations stays in the range \([0,2^{m}-1]\), where \(p<2^{m}<2p-1\) and \(m\) is a fixed integer (in our case, \(m=128\)). Because the modulus of curve Ted127-glv4 is a Mersenne-like prime of the form \(p=2^{127}-5997\), the incomplete reduction method can be applied more advantageously.  
Let \(a=a_{0}+a_{1}i\) and \(b=b_{0}+b_{1}i\) be two arbitrary elements in a finite field \(\mathbb{F}_{p^{2}}\). Field addition and subtraction over \(\mathbb{F}_{p^{2}}\) can be computed by \(a+b=(a_{0}+b_{0})+(a_{1}+b_{1})i\) and \(a-b=(a_{0}-b_{0})+(a_{1}-b_{1})i\), respectively. Field inversion over \(\mathbb{F}_{p^{2}}\) can be computed by \(a^{-1}=(a_{0}-a_{1}i)/(a_{0}^{2}+a_{1}^{2})\).  
We utilize Karatsuba multiplication to compute field multiplication over \(\mathbb{F}_{p^{2}}\). The Karatsuba multiplication uses the fact that \(a\cdot b=(a_{0}+a_{1}i)(b_{0}+b_{1}i)=(a_{0}b_{0}-a_{1}b_{1})+\{(a_{0}+a_{1}) (b_{0}+b_{1})-a_{0}b_{0}-a_{1}b_{1}\}i\), which can be computed by \(3M_{1}+3A_{1}+2A_{i}\) operations. It requires \(1A_{1}+2A_{i}\) more operations but saves \(1M_{1}\) operations compared to general multiplication methods, which require \(4M_{1}+2A_{1}\) operations. Because field multiplication requires more computational cost than the multi-precision addition and field addition, the Karatsuba multiplication has a computational advantage. Algorithm 2 describes field multiplication over \(\mathbb{F}_{p^{2}}\) using the Karatsuba multiplication and the incomplete reduction method.  
Algorithm 3 describes field squaring over \(\mathbb{F}_{p^{2}}\) using the incomplete reduction method. Note that \(a^{2}=(a_{0}^{2}-a_{1}^{2})+2a_{0}a_{1}i=(a_{0}+a_{1})(a_{0}-a_{1})+2a_{0}a_{ 1}i\). The first representation can be computed by \(1M_{1}+2S_{1}+1A_{1}+1A_{i}\) operations, and the remaining representation can be computed by \(2M_{1}+1A_{1}+2A_{i}\) operations. Because \(1M_{1}\) operation can be implemented faster than \(2S_{2}\) operations, we use \(2M_{1}+1A_{1}+2A_{i}\) operations to compute field squaring over \(\mathbb{F}_{p^{2}}\). The results of steps 3 and 4 in Algorithm 2 and steps 1 and 3 in Algorithm 3 were represented by the incompletely reduced form.  
```
0:\(a=a_{0}+a_{1}i,b=b_{0}+b_{1}i\in\mathbb{F}_{p^{2}}\), \(p=2^{127}-5997\).
0:\(c=a\cdot b=c_{0}+c_{1}i\in\mathbb{F}_{p^{2}}\).
1:\(t_{1}\gets a_{0}\times b_{0}\pmod{p}\{M_{1}\}\)
2:\(t_{2}\gets a_{1}\times b_{1}\pmod{p}\{M_{1}\}\)
3:\(t_{3}\gets a_{0}+a_{1}\{A_{i}\}\)
4:\(c_{1}\gets b_{0}+b_{1}\{A_{i}\}\)
5:\(c_{1}\gets c_{1}\times t_{3}\pmod{p}\{M_{1}\}\)
6:\(c_{1}\gets c_{1}-t_{1}\pmod{p}\{A_{1}\}\)
7:\(c_{1}\gets c_{1}-t_{2}\pmod{p}\{A_{1}\}\)
8:\(c_{0}\gets t_{1}-t_{2}\pmod{p}\{A_{1}\}\)
9:return\(c\).
```  
**Algorithm 2**Field multiplication over \(\mathbb{F}_{p^{2}}\)[25].
# Effect of Fuel Mass Flow at the End of Injection on Cavitation and Gas Ingestion in the Nozzle
## 3 Mathematical Model
### Computational Domain and Boundary Condition
Based on the discrete points in the finite volume, the VOF phase interface was compressed by using the spatial discretization method and the compression velocity term of the phase transport equation, thus a sharp interface was obtained. The pressure-velocity equation was solved by the Pressure-Implicit with Splitting of Operators (PISO) algorithm [48]. The discrete schemes of each term in the momentum equation were as follows: the time was in Euler Implicit format, the pressure gradient term used the least square discrete scheme, and the convection term and the diffusion term were respectively the Normalized Variable Gamma scheme and the Second-order Gaussian linear discrete \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Density of diesel/\(\rho\) & 840 & kg\(\cdot\)m\({}^{-3}\) \\ Dynamic viscosity of diesel/\(\mu\) & 0.0065 & Pa\(\cdot\)s \\ Density of air/\(\rho\) & 1.21 & kg\(\cdot\)m\({}^{-3}\) \\ Dynamic viscosity of air/\(\mu\) & \(1.85\times 10^{-5}\) & Pa\(\cdot\)s \\ Density of diesel vapor/\(\rho\) & 0.14 & kg\(\cdot\)m\({}^{-3}\) \\ Dynamic viscosity of diesel vapor/\(\mu\) & \(4.25\times 10^{-5}\) & Pa\(\cdot\)s \\ Saturated vapor pressure of diesel/\(p_{v}\) & 2000 & Pa \\ Surface tension/\(\sigma\) & 0.03 & kg\(\cdot\)s\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermophysical parameters of fluids. Figure 4: Volume of gas ingestion under a different number of cells. scheme. The Courant number adaptive time step was adopted, the average time step was set to \(2\times 10^{-8}\) s, the maximum time step was \(1\times 10^{-6}\) s, the maximum Courant number (the ratio of time step to space step) was 0.3, and the maximum maxAlphaCo was also 0.3.