esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
## 4 Conclusions  
Based on the direct laser triangulation method of stepped-shaft runout detection systems, this paper improves the imaging optical system in the runout error detection system of a stepped shaft with a large size of 500-700 mm. The coaxiality error that may occur during the stepped-shaft detection process is corrected by adding an optical plane  
Figure 14: End-face runout detection result graph: (**a**) KEYENCE without tilt; (**b**) runout detection optical system without tilt; (**c**) KEYENCE with tilt; (**d**) runout detection optical system with tilt.  
mirror group, the imaging optical system is simulated by Zamax 19.4, and the simulation results show that the total length of the structure of the imaging optical system is 50 mm and that the MTF function of each field of view is greater than 0.3 at a spatial frequency of 42 lp/mm. By building a test platform for the runout detection optical system, the system is compared with that of the Keyence displacement sensor. By building the test platform of the runout detection optical system and comparing its output with the measurement results of KEYENCE displacement sensor, the experimental results prove that the improved runout detection optical system is able to measure the runout error of step shafts in the range of 65-750 mm and correct the coaxiality error within 5\({}^{\circ}\) at the same time, so that it achieves the purpose of the high-precision on-line detection of the runout error of step shafts of large sizes with diameters in the range of 500-700 mm. This method provides a theoretical and experimental basis for the on-line detection of runout errors of large-sized step shafts. The design of aspheric imaging system can also be used to achieve higher measurement accuracy and better correction effects in subsequent research.  
Conceptualization, Y.Z. and J.D.; methodology, Y.Z. and H.Z.; software, Y.Z. and H.Z.; validation, Y.Z., Y.L. and J.L.; formal analysis, J.D.; investigation, Y.Z. and H.Z.; resources, J.D.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.D.; visualization, Y.Z.; supervision, Y.L.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Zhongshan Institute of Changchun University of Science and Technology introduced innovative research team project; Project Number: CXTD2023006. And the Department of Science and Technology of Jilin Province under Grant 20210201088 GX.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The author would like to thank Changchun University of Science and Technology and Jilin Province photoelectric measurement and control Institute.  
The authors declare no conflicts of interest.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
###### Abstract  
This study addresses the challenge of accurately correlating detailed and reduced thermal models in aerospace applications by using heuristic global optimization methods. In the context of increasingly complex thermal systems, traditional manual correlation methods are usually a time-consuming task. This research employs a series of numerical simulations using methods such as Genetic Algorithms, Cultural Algorithms, and Artificial Immune Systems, with an emphasis on parameter tuning to optimize the reduced thermal model correlation. Results indicate that these heuristic methods can achieve high-accuracy correlations, with transient simulations exhibiting temperature differences below 3 \({}^{\circ}\)C, thereby validating the hypothesis that heuristic methods can effectively navigate complex parameter optimizations. Moreover, a comparative analysis of fitness function performance across various optimization methods underscores both the potential and computational challenges inherent in these approaches. The findings suggest that while heuristic global optimization provides a robust framework for thermal model reduction and correlation, further refinement--particularly in scaling to larger, more complex models and adaptive parameter tuning--is necessary. Overall, this work contributes to the theoretical understanding and practical application of advanced optimization strategies in aerospace thermal analysis, paving the way for improved predictive reliability and more efficient engineering processes.  
heuristic global optimization; thermal model reduction; thermal model correlation; genetic algorithms; artificial intelligence; aerospace thermal analysis; optimization parameter tuning; decision support algorithms
# Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
## 3 Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering  
The performance of the seismic signal compression method based on offline dictionary learning highly depends on the correlation between the offline training data and online testing data. However, the correlation is not always high. In this section, we will introduce a seismic signal compression method using nonparametric Bayesian dictionary learning via clustering. In a typical seismic survey, seismic waves are usually generated by special vibrators mounted on trucks. The seismic waves are reflected by subsurface formations, and return to the surface, where they are recorded by seismic sensors. The trucks are always moved to different locations, where different shots are generated by the vibrators. Obviously, seismic signals from the same seismic wave are highly correlated. If these correlated signals are clustered into the same group and each group learns its dictionary, the representation of these signals could be very sparse. This is useful for compression. Furthermore, we use the online seismic signals to train the dictionaries, which are updated synchronously in the coder and decoder. Meanwhile, the correlation of the online training data and testing data is high. Similar to other compression methods, it is typical to divide seismic signals into small patches for transmission.  
Figure 1: Diagram of seismic signal compression based on offline dictionary learning.  
The seismic signals recorded by one sensor corresponding to a single shot are always called a trace. In our method, traces of each patch are divided into small segments, which are placed as columns for dictionary learning. Suppose seismic signals of current patch are denoted as \(\mathbf{X}_{P}=[\mathbf{x}_{1},...,\mathbf{x}_{i},...,\mathbf{x}_{N}]\), and seismic signals of previous \(L\) patches are denoted as \(\mathbf{X}_{P-L}\) to \(\mathbf{X}_{P-1}\). \(\mathbf{x}_{i}\) is the \(i^{th}\) segment in \(\mathbf{X}_{P}\), the dimension of which is \(M\times 1\). As seismic signals of the previous \(L\) patches are transmitted to the decoder and the seismic signals from adjacent patches are highly correlated, we could use these signals (both existing in the coder and decoder) to learn the dictionaries for the sparse representation of seismic signals from the current patch. The diagram of the proposed method is shown in Figure 2. As mentioned above, clustering is introduced for dictionary learning. A set of dictionaries is generated. Seismic signals of the current patch are sparsely represented according to their cluster's dictionaries. This includes the following steps: (1) Online training--transmitted seismic signals are used to learn the parameters of clustering and the dictionary of each cluster; (2) Online testing--firstly, seismic signals are clustered with the parameters generated from the online training step; secondly, they are sparsely represented by the corresponding dictionary of their clusters. Furthermore, the sparse coefficients are quantized and coded for transmission.
## 3 Results and Discussion
### MBN versus Tensile Stresses  
The determination of the stress state on the real components is a significant task of the MBN technique. Figure 15 depicts that the remnant magnetization value affects the obtained amplitude of the stress and also affects the sensitivity of this technique expressed in terms of standard deviations, which are remarkably higher for the magnetized state. Figure 15 clearly demonstrates that the influence of the remnant magnetization on the evolution of MBN versus tensile stress is nearly none for the regions emitting high MBN. On the other hand, the regions of lower MBN exhibit remarkably lower MBN in the magnetized state, and the whole evolution is shifted to the lower MBN values. Furthermore, it should be noted that the evolutions in Figure 15 are very different, especially at the lower tensile stresses. The regions of high MBN exhibit an initial decreasing tendency, which indicates that the matrix and the corresponding DWs within the MBN sensing depth are preferentially orientated in the RD. For this reason, the energy of magneto-crystalline anisotropy is low, and the magneto-elastic energy dominates. This evolution as well as the balance between the aforementioned energies are reversed in the regions emitting low MBN, and MBN grows along with the tensile stress. The saturation phase indicates that the prevailing energies at the lower stresses are fully consumed, and the sensitivity of MBN to the tensile stress is fully lost. The evolutions of MBN in the TD exhibit much lower standard deviations in the magnetized state, and the evolutions for the demagnetized and magnetized states are closer in comparison with those in the RD (see also Supplementary Materials).  
Figure 14: MBN versus magnetization voltage in the RD for the regions of high as well as low MBN before and after demagnetization.  
Figure 15: MBN versus tensile stress in the RD for the regions of high as well as low MBN before and after demagnetization.
## 3 Results and Discussions
### Collision Likelihood Modeling
In addition, when friction became a poor input variable because of low variation, a major accuracy decrease was observed, signifying the importance of the variable. Overall, the optimal segment length was between 6.5 and 8.5 km because the friction values in this region were the least homogenized and had a balanced dataset. When we evaluated the internal logic of these models, we found that 7.0, 7.5, and 8.5 km produced highly complex models. Although they showed excellent performance, it was difficult to assess whether the generalizations made were reasonable. In contrast, the 6.5 km models  
Figure 10: Model validation accuracy at various road segment lengths. were simple and intuitive (as shown in Figure 11) but had slightly lower accuracy of 76.9%. Ultimately, the choice of model depends on user preference, as there is a trade-off between model performance and intuitiveness. Herein, intuitiveness was prioritized. The model shown in Figure 11 assumes that collisions are influenced by friction and AADT. Collisions are unlikely when the coefficient of friction is above 0.408. For road segments with friction less than 0.408, a traffic flow rate below 19,000 vehicles per day is considered safer. This model aligns with the Federal Highway Administration (FHWA) mandate of including AADT as a variable [25]. Furthermore, these findings are in line with the existing literature that associates higher AADT and lower friction with more collisions [9].
# HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications
## 4 Experiments and Analysis
### Experimental Analysis
#### 4.4.2 Time Efficiency Analysis  
Given the limitations of algorithms such as AES, DES, and RSA, including challenges in key management and lower security strength, this paper selects ECC, the AES+RSA hybrid cryptographic algorithm [28], and the AES+ECC hybrid cryptographic algorithm HAE proposed herein for comparative analysis, as depicted in Figure 9.  
For the same data size, the time required for encryption and decryption by the two hybrid cryptographic algorithms is significantly less than that of the ECC algorithm. The hybrid algorithm HAE, based on AES and ECC proposed in this paper, exhibits slightly better time efficiency than the AES+RSA hybrid algorithm proposed by previous authors. Under equivalent security levels, the key length of ECC is substantially shorter than that of RSA, as shown in Table 4. This implies that ECC holds a significant advantage over RSA regarding attack resistance, resource consumption, network consumption, encryption and decryption speed, and suitability for blockchain scenarios. Consequently, the HAE  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Style of Attack** & **Breaking Time (Times)** \\ \hline exhaustive attack & \(2^{128}\) \\ recover key attack & \(2^{126}\) \\ related key attack & \(2^{99}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: AES attack methods and cracking time.  
Figure 9: Comparison of encryption and decryption time of three cryptographic algorithms.  
algorithm offers high time efficiency while ensuring security, providing algorithmic support for encrypted data storage and sharing in blockchain medical scenarios.
## 3 Results and Discussion
### Quality Analysis  
The quality of groundnuts roasted at optimum roasting conditions in CSR was analyzed to make a comprehensive quality comparison with standardized ranges. The experimental results (average of three replications) are given in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nutritional Composition**} & \multirow{2}{*}{**Raw Groundnut**} & \multicolumn{3}{c}{**Roasting Condition**} \\ \cline{3-5}  & & **Light** & **Medium** & **Dark** \\ \hline Ash content 1  & 4.68 & 3.79 & 3.47 & 2.99 \\ Moisture content 1  & 5.17 & 1.99 & 1.73 & 1.33 \\ Crude protein 1  & 24.90 & 26.80 & 27.30 & 28.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ash analysis, proximate and mineral composition of raw and roasted (light, medium and dark) groundnuts.  
Figure 6: Roasting performance of CSR.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Roasting Condition**} & \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Predicted**} & \multicolumn{3}{c}{**Experimental Results**} & **Average** & **Error** \\ \cline{3-5}  & & & **Run 1** & **Run 2** & **Run 3** & **Value** & **(\%)** \\ \hline \multirow{2}{*}{Light} & MC (\%) & 1.98 & 1.96 & 2.03 & 1.98 & 1.99 & 0.50 \\  & L* & 53 & 52.91 & 53.49 & 53.86 & 53.42 & 0.79 \\ \multirow{2}{*}{Medium} & MC (\%) & 1.72 & 1.74 & 1.71 & 1.73 & 1.73 & 0.58 \\  & L* & 48.5 & 48.27 & 48.84 & 49.21 & 48.77 & 0.56 \\ \multirow{2}{*}{Dark} & MC (\%) & 1.33 & 1.37 & 1.31 & 1.33 & 1.34 & 0.75 \\  & L* & 43 & 43.66 & 43.21 & 43.37 & 43.41 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicted and observed responses.  
It is evident from Table 7 that the total ash and MC in roasted groundnut were found to be lower than of raw groundnut samples. This trend showed consistency with the results given by other authors [40; 41; 42; 43]. The results of the proximate analysis revealed that the crude protein content was higher in roasted groundnuts than the raw groundnuts and found to be well in the range (19.7-31.3%) as given by [38]. There was slight increase in the crude carbohydrate and crude fiber levels of roasted groundnuts which were similar to the findings of [43]. The fat content of roasted groundnuts was also found higher than raw ones [44]. The results of the energy determination also showed higher energy contents of roasted groundnut than of raw kernels. The experimental results also revealed that the overall mineral contents of roasted groundnut kernels were comparatively higher than raw groundnut kernels. Potassium, phosphorus, and magnesium were found to be higher in the roasted groundnuts whereas calcium was decreased in roasted groundnut. Zinc was found almost similar both in raw and roasted groundnuts. These results were found to be consistent with the previous investigations on the mineral and nutritional analysis [42; 43; 44; 45]. Overall, CSR has shown quite satisfactory results for roasting of groundnuts as all the nutritional components were found within standardized limits.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 5 Discussion
### Complementarity of NDVI and VOD in Vegetation Monitoring
Finally, in contrast to NDVI, VOD is an all-weather parameter. This property (all-weather parameter) is particularly useful in rainy spring situations. For example, the description of the rainy spring episode of the year 2016 in the Paris region by [PERSON] et al. [40] was not possible with NDVI and LAI indices because of clouds.
## 2 Materials and Methods
### Fungal Cultivations
#### 2.4.3 Analytical Methods  
The biomass content in the fermentation samples was determined gravimetrically. The solid residue obtained after filtration through 85 \(\upmu\)m nylon filtration cloth was extensively washed with distilled water and then dried in a forced air oven at 70 \({}^{\circ}\)C for at least 24 h. The biomass content was expressed in g/L. Undissolved solids present in the fermentation media were determined in the same way as the biomass content. The total amount of dissolved solids in the fermentation samples was determined by specific gravity determination using the Densito 30 PX oscillating U-tube density meter (Mettler-Toledo, Greifensee, Switzerland) and converted to g/L using appropriate tables. The protein content in the biomass was determined by the biuret method using bovine serum albumin (Chempur) as is standard [20]. The liquid samples of the bread hydrolysate as well as the fermentation samples were analyzed for sugars (dextrin (DP4+), malottoriose, maltose and glucose), glycerol, acetic acid, lactic acid and ethanol using high-performance liquid chromatography (HPLC). For this purpose, the samples were centrifuged at 14,000 rpm (11,203\(\times\)\(g\)) for 10 min at 4 \({}^{\circ}\)C with the MPW 351R centrifuge (MPW Med Instruments, Warsaw, Poland) and filtered using a 0.22 \(\upmu\)m nylon syringe filter in the HPLC vials. The samples were analyzed using the Shimadzu Prominence HPLC system (Shimadzu Corp., Kyoto, Japan) equipped with Rezek ROA-Organic Acid H+ column (300 \(\times\) 7.6 mm) (Phenomenex, Torrance, CA, USA). The separation parameters were eluent used--0.005 M H\({}_{2}\)SO\({}_{4}\) (Chempur) solution, eluent flow rate--0.6 mL/min and column temperature--60 \({}^{\circ}\)C. The compounds were detected using the RID-20A refractive index detector (Shimadzu) maintained at 50 \({}^{\circ}\)C. The chromatograms were integrated using CHROMAX 10.0 software (Pol-Lab, Warsaw, Poland) using the external standard method based on a 5-point calibration curve. The product (ethanol, biomass) yields from the substrates were calculated according to the following equation:  
\[\text{Y}=\text{P/S}, \tag{1}\]  
where  
Y--product (ethanol or biomass) yield (g\({}_{\text{product}}\)/g\({}_{\text{substrate}}\));  
P--product concentration in the medium (g/L);  
S--substrate concentration in the medium (g/L).  
For the first cultivation stage, the S value was the initial concentration of bread residue in the medium, while for the second fermentation stage, the sum of all soluble carbohydrates determined via HPLC was used.  
All experiments and analyses were determined in duplicate. The results presented are average values with the error bars or intervals representing one standard deviation (\(n=2\)).
## 4 Experimental Results and Discussion
However, when the threshold ratio is too high, too many important weights are removed and cannot be recovered during retraining, resulting in a decline in network performance and an increase in BER. Additionally, we can observe that after pruning, the error rate for H-pol signals is slightly lower than that for V-pol signals, which corroborates the analysis in Figure 8 that horizontally polarized signals may be more susceptible to effects during wireless propagation. Figure 10 illustrates the impact of the entire pruning process on the BER, where PR_5 represents five pruning rounds, and so forth. The experimental results demonstrate that the DNN method can reduce the BER by an order of magnitude, while the pruning process further slightly reduces the BER. Specifically, through the combined application of DNN and pruning, we successfully achieved BERs below the HD-FEC threshold for signals ranging from 10 Gbaud to 30 Gbaud. This finding indicates that DNN has a significant effect in improving signal demodulation performance, and the pruning process further enhances performance by optimizing the network structure. This combined strategy provides an effective solution for high-efficiency and high-performance signal processing, especially in communication systems that require stringent BER requirements. Figure 10: Different equalization methods vs. BER. Figure 9: Neural network pruning threshold ratio vs. BER: (**a**) V-pol-10 Gbaud, (**b**) V-pol-20 Gbaud, (**c**) V-pol-30 Gbaud, (**d**) H-pol-10 Gbaud, (**e**) H-pol-20 Gbaud, and (**f**) H-pol-30 Gbaud. Tables 1 and 2 present data indicating that, after pruning, the complexity reduction for H-pol is slightly greater than that for V-pol, and the complexity reduction for the I path signals is significantly greater than that for the Q path signals. For the 10 GBaud and 20 Gbaud data, iterative pruning techniques can reduce complexity by approximately 75%; for the high-rate 30 GBaud signals, iterative pruning techniques can reduce complexity by about 50%. Through pruning, we successfully reduced the number of network weights and significantly lowered computational complexity. These results demonstrate the effectiveness of pruning techniques in reducing network complexity, particularly when applied to signal processing at different polarization directions and different rates.
###### Abstract  
Cocoa, a crop with great socioeconomic impact in Ecuador, faces continuous challenges that undermine the quality and yield of the crops. Moniliasis, a phytosanitary problem that is difficult to control, is the main limiting factor in cocoa production. This disease is caused by the phytopathogen _Moniliophthora roreri_, which causes premature maturation and necrosis of the ears. In this study, 50 strains of _Trichoderma_ spp. in two-culture media, PDA and MEA, were used to evaluate the mycelial characteristics and antagonistic capacity of the strains in individual situations and in circumstances of confrontation against the phytopathogens _Moniliophthora roreri_ (MRCP) and _Moniliophtora rooreri_ (MMCA). The data from the parameters obtained in the in vitro experimental practice were subjected to the multivariate PCA biplot method; the results indicated that five strains exhibited a notable antagonism capacity against the two specific strains of _M. roreri_: strain E22 grown on PDA medium, and the other four (E25, E29, E30, E39) cultivated on MEA medium. The open field trial showed that all treatments based on _Trichoderma_ spp. improved productive performance compared to control plantations to which no bioperoperation was applied. Significant differences (\(p<0.05\)) were reported for all treatments. The efficiency of the bioperoperations ranged between 51.26% and 72.46% with yields of 677.86 kg/ha to 976.90 kg/ha, respectively. The _Trichoderma_ strain E29 showed the greatest potential for the biological control of _M. roreri_ under field conditions. The findings validate the effect of diversified _Trichoderma_ biopreparations in the biological control of moniliasis, providing concrete data on the efficacy of the biopreparation under real cultivation conditions and supporting its practical viability.  
Thebroma cacao; Trichoderma spp.; Moniliophthora roreri; moniliasis; antagonism