esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, the significance of surface roughness on ultrasonic testing results was recognized, as well as the accuracy of laser ultrasonic inspection of additive manufactured components with cylindrical defects [30]. Ultrasonic testing is also used in assessing the condition of components made of Ti6 Al4V alloy. The few scientific articles that describe ultrasonic testing of additive technology parts made of Ti6 Al4V alloy present the detection of structural defects [31]. The authors presented the possibility of using laser ultrasonic testing to detect internal holes with a diameter of 0.8 mm. Generating an ultrasonic wave using a laser beam has been used for many years and is constantly being improved [32], but this method has some limitations and is mainly used in laboratory conditions. Therefore, the authors decided to use ultrasonic testing with standard heads and a coupling agent to determine the properties of Ti6 Al4V alloy made both by additive manufacturing technology and conventional process. This kind of approach can find application in continuous condition monitoring of parts made by additive manufacturing technology from Ti6 Al4V alloy. The main objective of the research presented in this article is to determine the basic acoustic properties of Ti6 Al4V material produced by the conventional method (drawn rod) and by additive manufacturing technology. The comparison of these properties will form the basis for the study of joints that have been made using just this type of material. A novelty included in this article is the evaluation of the condition of adhesive joints of additively manufactured materials using the ultrasonic echo technique. The results of ultrasonic testing provide the basis for developing a new algorithm for evaluation of adhesive joints made with additive manufacturing technology. The new algorithm for evaluating the condition of adhesive joints can also be applied to continuously monitor the condition of the joint (structural health monitoring) and detect defects occurring at the boundary of the adhesive surface of a component made with additive manufacturing technology. A measure of the quality of the adhesive joint, which is the decibel drop between the amplitude of the first two pulses reflected from the boundary of the adhesive-substrate interface, was determined. This was possible due to the previous determination of the basic parameters of the ultrasonic longitudinal wave propagating in the material itself. The results of the study have an important practical aspect. According to the methodology proposed in the article, the evaluation of adhesive joints of materials made with different technologies can be conducted.
## 4 Conclusions  
This study demonstrates a self-focusing OPB for FSOC systems, demonstrating its efficacy in suppressing turbulence-induced intensity scintillation by numerical simulations. Comparative analysis shows that the OPB has superior ARP over conventional GB and lower SI throughout the propagation path. Further investigation indicates a unique distance-dependent characteristic: While the ARP of OPB temporarily drops below GB levels at intermediate distances (prior to achieving stabilization), its SI remains consistently suppressed compared to GB. These results underscore the critical importance of optimizing modulation depth \(C\) and shape factor \(\gamma\) for effective scintillation suppression. Through comprehensive statistical evaluation of five OPB configurations, the parameter \(\gamma\) = 1.4 and \(C\) = 0.00007 was identified as optimal. At a focal distance of 1500 m and under turbulence \(D/r_{0}\) = 10, this configuration reduced the SI degradation by 44% compared to the Gaussian beam case. The demonstrated capability of OPB to mitigate intensity fluctuations offers a viable approach for improving FSOC reliability. To realize this potential, it is essential to systematically optimize the parameters of OPB. Future studies should focus on experimental validation and multi-parameter co-design frameworks for harnessing its full potential.  
Conceptualization, R.W. and C.L.; methodology, R.W.; writing--original draft preparation, R.W.; writing--review and editing, R.W., B.L., K.Z., J.Z., X.L. and T.D.; supervision, H.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Western Light--Western Cross-Team Key Laboratory Special Fund of the Chinese Academy of Sciences, grant number xbgz-zdsys-202403 and National Key Basic Research Special Foundation of China, grant number Z23J0301.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors acknowledge the support of National Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
FSOC Free space optical communication AO Adaptive optics OPB Optical pin-like beam GB Gaussian beam SI Scintillation index ARP Average received power
## 4 Comparison of Different Wheel-Rail Contact Models
### Comparison under Ordinary Conditions  
To verify the correctness of the four models and to analyze the applicability of the wheel-rail contact algorithms under ordinary conditions, the wheel-rail contact dynamics on the left wheel of the first wheelset calculated based on the different models were compared, as shown in Figure 13.  
Figure 13a,b show that the time histories of \(F_{Y}\) and \(F_{Z}\) based on the different models are in good agreement, thus verifying the correctness of the established models. As presented in Figure 13c, \(K_{nr}\) calculated based on Model 1 is maintained at 11.52 \(\times\) 10\({}^{10}\) N/m\({}^{3/2}\). In contrast, when calculating based on Model 4, this value is maintained at 9.20 \(\times\) 10\({}^{10}\) N/m\({}^{3/2}\)  
Figure 12: Acceleration-time history of the ground motion.  
Figure 13: Time histories of wheel-rail dynamic responses based on different models: (**a**) Vertical wheel-rail force; (**b**) Lateral wheel-rail force; (**c**) Normal contact stiffness; (**d**) Normal compression amount.  
due to the straight segment of the wheel tread keeping in contact with the R300 arc segment of the rail head. Figure 13d shows a slight difference in \(\delta_{c}\) calculated based on Models 1 and 4. Moreover, although the empirical formula causes a calculation error for \(K_{nr}\) and \(\delta_{c}\), it has largely no impact on the contact force. Thus, it can be concluded that under this condition, it is applicable to adopt the normal compression algorithm based on vertical penetration, the empirical formula for calculating the normal contact stiffness, and only consider single-point contact.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 2 Separation Formulation Method
### Separation Formulation Based on Position Error
#### 2.2.4 UAV Performance  
Not only the flow data, which directly relate to the conflict frequency, but also the performance of UAVs will affect separation calculation in terms of traffic characteristics. UAV performance parameters include horizontal speed, vertical speed, takeoff weight, dimensions, load factor, wind resistance, maximum bank angle, turning radius, etc. Moreover, some UAVs and their ground control platform have collision avoidance capability, thereby detecting and avoiding obstacles through CDR technology. CDR includes air traffic control modules and flight control collision avoidance modules. Upon conflict, the CDR activates the air traffic control module (equivalent to a controller), computes and issues avoidance commands, and ultimately drives the UAV flight control module (equivalent to a pilot) to execute the command. Both command calculation and execution require response time. Simultaneously, parameters, such as the required avoidance distance, will affect separation formulation.
## 2 Materials and Methods
### Study Set-Up  
This in vitro study simulated space closure in a first premolar extraction case using a millimetre sled (Figure 1).  
Three different pins were used to support the metallic and ceramic brackets. The metallic brackets were laser-soldered to the pin, while the composite bracket was glued with cyanoacrylate adhesive. The IH3 DB was designed and successively printed directly with the pin in contact with the base area of the bracket (Figure 2).  
The configuration allowed the pins on brackets 1.2 and 1.5 to perform only horizontal movements to modify the inter-bracket distance, while the pin on tooth 1.3 could be moved to create different configurations.  
First, the pins on brackets 1.2 and 1.5 were fixed at a distance of 17 mm and 8 mm, respectively, from the 1.3 to simulate a first premolar post-extraction treatment. Next, a full-thickness archive was engaged in the brackets to be welded and inserted into the calibrated grooves of the guide, allowing the brackets to be accurately positioned on the rods. In this way, the device set-up reproduced the passive configuration, ensuring that the archive sliding would be passive and free during the tests. The archwires tested were fixed to the brackets using 3M buccal AlastiK silicone ligatures (3M Italia, Milan, 20096, Italy), which were replaced at each wire disassembly-reassembly. All the measurements were obtained after having irrigated the wire with water, using a pipette, during the traction, in order to simulate the conditions of the oral cavity. The friction tests were performed at the University of XXXXXX Engineering Department using a dynamometer (INSTRON Corp., 1011, Norwood, MA, USA), a testing machine for analysing the mechanical properties of materials using compression or tensile tests.  
Figure 1: Millimetre sled used to simulate a first premolar post-extraction treatment.  
Figure 2: IH3 DB design directly connected to the pins.  
The customized set-up was anchored to the Instron plate using a stud, and the wires were locked in the dynamometer jaws. The Instron machine applied a speed of 5 mm/min to all the tested wires [27].  
Three wires were tested: 0.016-inch NiTi, 0.019 \(\times\) 0.025-inch NiTi and 0.019 \(\times\) 0.025-inch stainless steel (SS) small UNAform SWM archives (Sweden & Martina, Due Carrare, PD, Italy). For each archive, five different configurations were explored: 0\({}^{\circ}\), 3\({}^{\circ}\), 6\({}^{\circ}\) and 10\({}^{\circ}\) of mesiodistal tip variation and 1 mm vertical translation (0\({}^{\circ}\) mesiodistal tip) of bracket 1.3.  
In each test, the maximum friction peak (N) between the wire and the brackets was recorded. The tests for each archive were repeated three times, and then the average was calculated. After 15 days (T1), all the tests were repeated using new brackets, new archives and new sets of ligatures by the same expert clinician (L.B.).
# Video Monitoring of Shoreline Positions in Hujeong Beach, Korea
###### Abstract  
Shoreline processes observed by a video monitoring system were investigated under different wave conditions. A 30 m-high tower equipped with video cameras was constructed in Hujeong Beach, South Korea, where coastal erosion was suspected to occur. Two-year shoreline data since December 2016 showed that beach area, \(A_{b}\), has decreased, but periods of rapid increase in \(A_{b}\) were also observed. Shoreline change was closely related to the wave propagation directions and bottom topography. \(A_{b}\) increased when waves approached the shore obliquely, whereas it decreased when they approached in a normal direction. The shoreline became undulated when \(A_{b}\) increased, while it became flatter when \(A_{b}\) decreased. The undulation process was influenced by nearshore bedforms because the shoreline protruded in the lee area where underwater rocks or nearshore sandbars actively developed, with a sheltering effect on waves. Specifically, the locations of shoreline accretion corresponded to the locations where the sandbar horns (location where a crescentic sandbar protrudes toward the shore) developed, confirming the out-of-phase coupling between sandbars and shoreline. When waves with higher energy approached normal to the shore, the sheltering effect of sandbars and underwater rocks became weaker and offshore sediment transport occurred uniformly along the coast, resulting in flatter shorelines.  
video monitoring; coastal erosion; shoreline change; nearshore sandbars +
Footnote †: journal: Applied
## 4 Test Simulation and Result Analysis
### Analysis of Algorithm Convergence Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**D**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{\begin{tabular}{c} **Optimum** \\ **Value** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Worst** \\ **Optimization** \\ **Value** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Optimization** \\ **Value** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Time Consumed by** \\ **10 Optimization/s** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Consumption Time** \\ **for Optimizing/s** \\ \end{tabular} } \\ \hline \multirow{3}{*}{D1} & CSO & 6.79\({}^{\text{\text{\tiny{10}}}}\)-21 & 5.03\({}^{\text{\text{\tiny{10}}}}\)-16 & 5.73\({}^{\text{\text{\tiny{10}}}}\)-17 & 7.05 & 0.71 \\  & PSO & 0.28 & 1.05 & 0.52 & 5.11 & 0.51 \\  & ICSO & 0 & 0 & 0 & 8.94 & 0.89 \\ \hline \multirow{3}{*}{D2} & CSO & 8.87\({}^{\text{\text{\tiny{10}}}}\)-17 & 3.39\({}^{\text{\text{\tiny{10}}}}\)-14 & 5.23\({}^{\text{\text{\tiny{10}}}}\)-15 & 8.73 & 0.87 \\  & PSO & 1.34 & 3.17 & 2.40 & 5.19 & 0.52 \\  & ICSO & 2.16\({}^{\text{\text{\tiny{10}}}}\)-197 & 8.56\({}^{\text{\text{\tiny{10}}}}\)-188 & 1.10\({}^{\text{\text{\tiny{10}}}}\)-188 & 9.76 & 0.98 \\ \hline \multirow{3}{*}{D3} & CSO & 0 & 0 & 0 & 7.75 & 0.78 \\  & PSO & 0 & 4 & 1.30 & 4.52 & 0.45 \\  & ICSO & 0 & 0 & 0 & 10.81 & 1.08 \\ \hline \multirow{3}{*}{D4} & CSO & 1.27\({}^{\text{\tiny{10}}}\)-2 & 0.27 & 0.04 & 8.33 & 0.83 \\  & PSO & 0.57 & 2.27 & 1.22 & 6.11 & 0.61 \\ \cline{1-1}  & ICSO & 1.08\({}^{\text{\text{\tiny{10}}}}\)-4 & 2.3\({}^{\text{\text{\tiny{10}}}}\)-3 & 8.44\({}^{\text{\text{\tiny{10}}}}\)-4 & 10.87 & 1.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimization Results. Figure 6: Convergence curves of the algorithms. The convergence effect of the three algorithms can be more clearly observed from Figure 6. Compared with CSO, the convergence performance of ICSO is significantly improved. For the D1, D3, and D4 functions, ICSO converges to the optimal value 4, 28, and 9 generations earlier than CSO, respectively, and ICSO maintains a high convergence accuracy. Comprehensive comparison of the convergence data in Table 1 and the convergence curves in Figure 6 reveals that ICSO has better convergence ability.
## 6 Conclusions  
The high energy of the FFC can hide the existence of a failure by covering the frequency components related to the failure. This situation becomes worse when the IM operates at low frequency and load. The use of a NF can partially solve the problem because it fails when both: the operating frequency and the load are low. The use of the GA for suppressing the FFC proved to be effective for the detection of the failure harmonics even when the load and frequency are low. The GA performs a better attenuation of the FFC, allowing the reduction of the spectral leakage around the FFC. In this sense, the proposed methodology becomes a powerful tool to enhance the results provided by the methodologies that aim to detect failure conditions in electric machines when they operate at steady state. In fact, the proposed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**(MCSA)**} & \multicolumn{2}{c}{**(NF-MCSA)**} & \multicolumn{2}{c}{**(GA-MCSA)**} \\  & \(D_{24}^{(inter)}\) & \(D_{46}^{(inter)}\) & \(D_{24}^{(inter)}\) & \(D_{46}^{(inter)}\) & \(D_{24}^{(inter)}\) & \(D_{46}^{(inter)}\) \\ \hline
31 Hz & \(3.3\times 10^{-2}\) & \(5.0\times 10^{-3}\) & \(2.4\times 10^{-2}\) & \(1.1\times 10^{-2}\) & \(1.4\times 10^{-2}\) & \(1.6\times 10^{-2}\) \\
20 Hz & \(7.1\times 10^{-2}\) & \(4.6\times 10^{-3}\) & \(3.0\times 10^{-3}\) & \(3.9\times 10^{-3}\) & \(1.1\times 10^{-2}\) & \(9.6\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inter-cluster distance for the amplitudes of the LSH.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Computational Cost (Seconds)** & **Acquisition Time (Seconds)** \\ \hline MCSA & 1.004 & 20 \\ NF-MCSA & 1.520 & 10 \\ GA-MCSA & 10.752 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational cost and acquisition time comparison.  
methodology allows identifying interharmonics even in the vicinity of the FFC, which is essential for accurate broken rotor bar detection in induction motors.  
Conceptualization, D.A.E.-O., R.d.J.R.-T., R.A.O.-R., and D.M.-S.; data curation, D.A.E.-O., A.L.Z.-L., and D.M.-S.; methodology, D.A.E.-O., A.L.Z.-L., and D.M.-S.; software, D.A.E.-O. and D.M.-S.; validation, R.d.J.R.-T., and R.A.O.-R.; writing--original draft, D.A.E.-O., and R.d.J.R.-T.; writing--reviewing and editing, A.L.Z.-L., and R.A.O.-R. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by CONACyT scholarship 415315; by a Mobility Grant of the University of Valladolid awarded to [PERSON]; by project FOFI-UAQ 2018 FIN201812; and by PRODEP UAQ-PTC-385 grant.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 3 Material and Methods
### Proposed Method
#### 3.2.2 Individual Encoding  
In this study, the detection problem of voice disorders is formulated as a multi-objective optimization problem, utilizing NSGA-II, SPEA-II, and MOEA/D. The objective is twofold: to minimise the number of features used for classification while simultaneously minimizing the error rate of the predictive model. The datasets employed in this work include the SLI, SVD, and VOICED datasets. Each candidate solution represents a possible subset of features in the evolutionary optimization framework. The solutions are encoded as binary vectors, where each bit corresponds to a feature. A value of '1' refers to the feature being selected, while '0' means the feature is excluded. In other words, if a dataset has 849 features, a binary string of length 849 represents one possible feature subset, where the activated (1) and deactivated (0) features indicate which features are selected for the classification process. Figure 2 illustrates a solution.
# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 4 Experiments and Results
### Analysis of Results  
#### 4.8.1 Comparison of Prediction Accuracy  
As shown in Figure 5, these are the results of the predictive evaluation metrics for the SVM model, the LSTM model and the LSTM_SVM_Stacking model. The Stacking model has the lowest RMSE (697.37), which is about 22% lower than the single model. The LSTM (854.53) is slightly better than the SVM (908.64), but the difference is not significant. The Stacking model significantly outperforms the single model in terms of overall prediction accuracy. The Stacking model has the lowest MAE (438.50), which is about 20.62% lower than the single model. The MAEs of SVM (573.75) and LSTM (552.38) are very close to each other. The trend of improvement in MAE is consistent with the RMSE, indicating consistency of results. The R\({}^{2}\) value of the Stacking model (0.559) is significantly higher than that of the single model. The LSTM (0.338) is slightly better than the SVM (0.252). The R\({}^{2}\) of the Stacking model almost doubled, indicating a significant improvement in the explanatory power of the model.  
SVM and LSTM have similar performance, suggesting that they may capture different features in the data. LSTM slightly outperforms SVM, possibly due to the ability to better handle temporal features in the data. The Stacking model significantly outperforms a single model, demonstrating the effectiveness of integrated learning. Performance improvement may come from combining the advantages of different models.  
Figure 4: Comparison of predicted values of models test data.
## 5 Targeted Acidogenic Fermentation for Bioplastics Production
Optimizing substrate composition and operational conditions not only improves bioconversion efficiency but also enables anaerobic fermentation processes to be tailored for the production of specific VFAs, which serve as key precursors for bioplastics. This highlights the critical role of advanced microbial and substrate engineering in boosting the economic feasibility of bioplastic production, bridging the gap between sustainable waste vaporization and industrial-scale application. While the strategies discussed above focus on manipulating operational conditions to influence microbial activity and VFA profiles, recent developments in synthetic and systems biology have opened up new avenues for controlling fermentation outcomes at the genetic level. In addition to the manipulation of environmental and operational parameters, advances in genetic and metabolic engineering have emerged as complementary strategies to improve the selectivity and efficiency of VFA production [5]. These approaches aim to reprogram microbial metabolism by redirecting carbon fluxes toward the synthesis of specific carboxylic acids. Gene knockouts are frequently used to suppress competing pathways, such as those involved in ethanol or lactate production [162], while the overexpression of key enzymes--such as acetyl-CoA acetyltransferase, phosphothransbutyrylase, butyrate kinase, or propionyl-CoA transferase--enhances the conversion of central intermediates toward target VFAs [163]. In parallel, CRISPR-based genome editing and synthetic regulatory circuits have enabled the dynamic control of gene expression in response to environmental cues, allowing for more precise metabolic steering [164]. These strategies have been successfully applied in model microorganisms, including _Escherichia coli_, _C. acetobutylicum_, and _Propionibacterium acidiproionici_, particularly in systems utilizing defined substrates like glucose, glycerol, or lactate under sterile conditions [165]. However, the application of these tools to VFA production from organic waste remains limited. The majority of waste valorization systems rely on undefined mixed microbial cultures, where genetic manipulation is not feasible due to the lack of genetic tractability and the complexity of community interactions. Furthermore, the non-sterile, variable nature of waste feedstocks makes it difficult to maintain the stability and dominance of engineered strains in open systems. As a result, most current research efforts still focus on tuning operational conditions to guide metabolic pathways naturally selected under selective pressures. Nevertheless, the development of engineered consortia, host strains with broad substrate utilization capabilities, and community-level synthetic biology approaches may pave the way for more controlled and efficient microbial platforms for VFA production in future applications.