esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 4 Discussion  
The accurate prediction of the external corrosion rates of buried pipelines is important in ensuring pipeline integrity and safe management. Nevertheless, the models developed currently are often limited by small sample sizes, and the accuracy is not as satisfactory as expected. To address these issues, this paper proposes a kernel principal component analysis (KPCA)-particle swarm optimization (PSO)-extreme learning machine (ELM) combination model for the prediction of the external corrosion rate of buried pipelines under conditions of scarce data.  
This study applies the KPCA algorithm to lower the dimensions of influencing factors and then extract principal components, ultimately making the model structure more concise. The PSO algorithm is utilized to optimize the parameters of the ELM model, including the number of hidden layer nodes. A total of 60 sets of field monitoring data containing 10 influencing factors were collected and divided into training and validation sets at a ratio of 4:1.  
When predicting the validation set, the R\({}^{2}\) of the proposed model reached 99.59%, and the standard deviation was close to the standard deviation of the actual values, with an average relative error of only 6.394% and a root mean square error of only 0.0029%. Compared with the ELM, KPCA-ELM, and PSO-ELM models, the proposed model performed well in all indicators. It demonstrated an assessment under the composite indicator. The proposed model had a stronger learning and generalization ability even in the case of small sample data. Despite the valuable findings, it is important to acknowledge several limitations in this article. Firstly, the external applicability of the model requires further verification due to the single source of sample data. Secondly, the selection of model parameters such as the number of principal components lacks theoretical guidance. Lastly, the risk assessment of the prediction results, such as the risk of overfitting of the model, was not considered. In the future, there is a need to continuously increase the sample size to improve the external applicability of the model on new data. More advanced automatic hyperparameter optimization algorithms should be developed to improve the generalized learning capability of the model. Quantitative analysis of the uncertainty in the prediction results should be conducted to improve the model's reliability. Different methods to combat  
Figure 15: Taylor diagram for different models predicting the test set data.  
the overfitting problem should be explored and compared, such as data augmentation, regularization, model selection, early stopping, or transfer learning.
## 3 Results and Discussion
### Velocity Profiles  
Figure 12 shows time-history curves of \(U\) with different turbulence models at y = 0.375 m and z = 0.188 m (Figure 3). In the time-history curves of \(U\) simulated by different turbulence models, a peak appears when the head and tail cars pass by, and the first peak value was found to be larger than the second peak value, in agreement with the observations of [PERSON] et al. [1]. However, the value of \(U\) is different when wake flows pass by the monitoring point. [PERSON] et al. [42] reported similar observations.  
Figure 12 also depicts that the main difference in the slipstream velocity obtained by different turbulence models is in the wake region. Hence, the velocity fields in the wake for different time periods are analysed. To facilitate the analysis, the three positions after the two MTs passed each other are defined and denoted as \(t_{1}\), \(t_{2}\), and \(t_{3}\), as shown in Figure 13. Figure 14 presents the flow fields of resultant velocity \(U\) at positions \(t_{2}\) and \(t_{3}\). As shown, the vortex structures of the intersection side interact, leading to a decrease in their intensity. Among them, the SA\(-\)IDDES and SST\(-\)IDDES methods can capture extra small-scale vortices, whereas the SST k\(-\omega\) cannot capture these small-scale vortices. For quantitative analysis, two horizontal sampling lines were extracted in the train wake at  
Figure 11: Pressure variation amplitude of measuring points (MPs) on the train surface along (**a**) the longitudinal and (**b**) vertical direction of the train.  
Figure 12: Time-history curves of resultant velocity with different turbulence models at y = 0.375 m and z = 0.188 m (namely point \(d\) in Figure 3).  
positions \(t_{2}\) and \(t_{3}\), as shown in Figure 14. Figure 15 shows the \(U\) values at the four lines (Figure 14). In contrast to the case of a single train, the \(U\) value on the intersection side is significantly smaller than that on the non-intersection side when the two MTs passing each other. Furthermore, the \(U\) value calculated with the SST k-\(\omega\) method is significantly lower than that calculated with the SA-IDDES and SST-IDDES models.
## 4 Experimental Results and Discussions
### Personalized Medication Care Module  
The capability to decode our self-generated and personalized QR barcode was tested. Figure 14(a) shows the screen capture of the mobile barcode decoder on an Android(tm) smart phone device trying to decode a barcode image. The decoded data are shown in Figure 14(b). This mobile barcode decoder is available free of charge. By utilizing this mobile barcode decoder, it is proved that the personally generated barcode image earlier is encoded correctly and that it can be decoded easily as well. The extracted information is used as guidance for the patient in his medication administration process.  
The QR barcode is adopted here as the data capacity is large and sufficient to encode a long piece of alphanumeric text on a little barcode image. The decoding time of the QR barcode is short and accurate regardless of how many characters are encoded. Table 3 shows a comparison of the average barcode decoding time between QR barcode with 170 characters and 23 characters from a real time experiment with the Samsung Galaxy S Android(tm) smart phone. The results show that decoding a barcode with 170 characters and 23 characters takes almost the same time. Thus, the QR barcode is suitable for use in ubiquitous healthcare applications. Another benefit of the QR barcode is that it can  
Figure 14: Personalized medicine care assistance: (**a**) Screen capture of mobile barcode decoder trying to decode the personalized QR barcode; (**b**) Display of decoded data on mobile phone.  
Figure 13: Screen capture of the real time ECG monitoring system.  
be decoded in every way or position regardless of how the barcode is placed with respect to the scanner.
# A Novel Method for the Mechanical Testing of Sports Mouthguards
###### Abstract  
Current mouthguard test methods require improvement, as the impact energies during traditional testing do not reflect the higher energy that can be observed in actual use during sport. A new data set of ball speeds experienced during the sport of field hockey was obtained. These ball speeds have then been used to estimate impact energies. This information was used to subsequently develop a new test method consisting of a high-speed camera and drop tower. Observations show an increase in energy experienced by the mouthguard for higher-impact energies. The work carried out showed that current testing methods for sports mouthguards use lower-energy impacts than experienced during the sport of field hockey. The new method that is proposed allows for a more realistic representation of real-world impacts.  
s +
Footnote †: journal: Applied  
hockey players, found that the maximum ball speed during a hit was 38.6 ms\({}^{-1}\) (equating to over 100 J) [6].  
However, several studies on testing the safety of sports mouthguards do not consider real-world data as a basis for the impact energies used. For example, in [PERSON]'s investigation into the behaviour of different mouthguards during an impact, [PERSON] utilises a drop tower and compares three mouthguard materials at impact speeds of 3.9 ms\({}^{-1}\), 4.8 ms\({}^{-1}\) and 5.7 ms\({}^{-1}\)[7], which corresponded to impact energies of about 10 J, 15 J and 20 J [8]. Another study, by [PERSON] et al., which compared the effectiveness of different mouthguard designs, used an impact energy of 10 J [9]. Furthermore, a paper on the design and testing of a novel mouthguard in 2013 tested the mouthguard models using an impact energy of only 1.37 J [10]. Most of the impact tests performed use either a pendulum technique or a drop tower test setup [11; 12]. Generally, a force transducer or load cell is used to measure the mechanical response to impact. These studies, along with other previous mouthguard testing, show that the mouthguards are tested at much lower impact energies than field hockey players may be exposed to, which is likely to compromise the safety of the players.  
The previous studies provide useful information on the kinematics of a field hockey ball; however, there is a gap in the literature for a large sample of real-world data. To test the safety of a mouthguard in this setting, this gap needs to be filled to ensure the mouthguard is safe in all situations; therefore, this work aimed to construct a more comprehensive impact testing process of mouthguards on the basis of real-world data. To achieve this, firstly, a representative data set of ball velocities was analysed. Secondly, a novel method for mouthguard testing was developed to contribute to the creation of representative impact energies and to differentiate between hockey ball and mouthguard performance. Thirdly, mouthguards were tested at impact energies observed during the field study. Finally, the novel method was verified and conclusions were drawn.
# Performance of Payement Temperature Prediction Models
## 2 Materials and Methods
### Literature Review
#### 2.1.2 Viljoen Model  
[PERSON] (1986) used local pavement temperature data to develop temperature prediction equations for asphalt pavements in South Africa. A difference in the final form of the equations of [PERSON] (1986) and Superpave is the inclusion of the zenith angle, in addition to the latitude. The inclusion of the zenith angle allows for seasonal, and daily, variation in solar energy potential.  
[PERSON] (1988) further validated the model using new pavement temperature data from outside the datasets with which the model was developed. It was demonstrated that both [PERSON]'s model and the SHRP Superpave model provide accurate predictions of the maximum surface temperature. The [PERSON] model, generally, had less scatter, and therefore a smaller standard deviation for the error of the maximum surface temperature prediction. The difference in consistency was attributed to the inclusion of the zenith angle in the [PERSON] model. It was concluded that the Viljoen model provides a pavement temperature prediction with an accuracy that is acceptable for use in hot mixed asphalt (HMA) design. It is important, however, to select a nearby weather station in an area that has a very similar climate.
## 5 Conclusions  
In this work, we have theoretically studied Si microrings with an effective second-order susceptibility. We have determined the geometries and the near-infrared pump wavelengths to have a large SHG efficiency where a quasi-phase matching condition is assured thanks to the 4-bar symmetry. In particular, we have concentrated our attention on the effects of uncontrolled fabrication variations. Our comprehensive study shows that it is possible to find microring geometries where the SHG efficiency is almost independent of fabrication errors. This allows relaxing the fabrication tolerances and the use of post-fabrication thermal trimming of the microring resonances. Finally, since these particular geometries rely on the knowledge of the dependence of the mode confinement on the specific geometry and not on the material itself, the method can be extended to other material systems than Si, provided they should show 4-bar symmetry.  
Conceptualization, P.G., C.V., and L.P.; modeling, P.G., C.V., C.C., S.S., and M.P.; refractive index measurements, M.G. and M.B.; writing--original draft, P.G. and C.V.; writing--review and editing, all; supervision, L.P. All authors have read and agreed to the published version of the manuscript. This work is supported by the NEMO project (PRIN 2015 KEZNYM). The authors want to thank all the past members of the Nanoscience Laboratory of the University of Trento group for the stimulating discussions, and the CNR Naples group, in particular [PERSON] and [PERSON], for the fruitful and useful collaboration.  
Figure 6: SHG conversion efficiency as functions of thickness \(e\) (**a**) and internal radius \(R_{in}\) (**b**). The blue line corresponds to the maximum of Figure 5 where the highest efficiency was obtained for \(R_{in}=60.755\) μm and \(e=330\) nm. The orange line corresponds to the point \(R_{in}=57.208\) μm and \(e=440\) nm. The vertical axis is the same in both cases. The waveguide width is \(w=800\) nm and the pump power \(P_{in}=1\) mW. The black lines indicate the ranges for which \(\eta\geq 10^{-4}\). In (**a**) \(\eta\geq 10^{-4}\) in a range of only \(1\) nm for the yellow line, and in a range of \(16\) nm for the blue one, showing a robustness against fabrication defects of this geometry. In (**b**) \(\eta\geq 10^{-4}\) in a range larger than \(40\) nm for both the geometries, showing that the conversion efficiency is not affected in a critical way by variations in the internal radius of the ring.  
**Conflicts of Interest:** 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.
# Enlarged Head Pressure-Dispersed Anchor Cable for Foundation Pit Engineering Purposes
## 4 Numerical Simulation Analysis of the Anchor-Cable Support System
### Results and Analysis of the Numerical Simulation
#### 4.2.1 Support Effect of the Enlarged Head Pressure-Dispersed Anchor Cable on Foundation Pit
Generally speaking, the axial force of an enlarged head pressure anchor cable is less than that of an enlarged head tension cable, which is caused by excavation depth, properties of surrounding soil and seepage. Figure 24 shows the change in the horizontal displacement of the pile top under different conditions for the two types of anchor cables. Generally, the horizontal displacement of the pile top did not exceed 20 mm during construction. The simulation results showed that the displacement results of both types of anchor cables in this study were within the error range, and the maximum error between the simulation results of the enlarged head tension-type anchor cables and the monitoring values of Section 2.2 was only 0.4 mm, which verified the accuracy of the simulation. After excavation, the simulation result of the enlarged head tension anchor cables was 7.55 mm, while that of the enlarged head pressure-dispersed anchor cable was reduced to 6.46 mm, and the horizontal displacement of the pile top was reduced by 14.43%, which indicates that the support effect of the enlarged head pressure-dispersed anchor cable was better than that of the enlarged head tension anchor cable. When excavated to 9.52 m below the ground, the horizontal displacement was calculated by taking the monitoring points of the support pile body. From Figure 25, it can be clearly seen that the horizontal displacement value of the pile body of the enlarged head pressure dispersion type was significantly reduced compared with that of the enlarged head tension-type anchor cable, and the displacement of the pile top of the enlarged head pressure dispersion type was 6.46 mm. Figure 17:4 Changes in the horizontal displacement of the pile top under different working conditions.
# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 3 Overall System Architecture
### Robot Sub-System Description  
As part of this work, an autonomous robot was constructed to collect all relevant WiFi-hotspot information from various heights throughout the desired indoor space. This information was collected from the broadcasted WiFi-beacon frames. The collected information includes service set identifiers (SSIDs), received-signal-strength indicator (RSSIs), encryption, and media-access-control (MAC) addresses. The details of the robot are discussed in the remainder of this section.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 5 Experimental Setup
### Evaluation Metrics  
We use four evaluation metrics to evaluate prediction performance, namely F-measure (F1), G-_measure_, area under curve (_AUC_), and Matthews correlation coefficient (_MCC_). These evaluation metrics are popular in software defect prediction and can comprehensively evaluate model capabilities.  
F-measure is the most frequently used evaluation metric in software defect prediction. It highlights the prediction results on buggy samples and balances the precision and recall. Specifically, \(F1\) is the most common form of F-measure, which takes the harmonic average of precision and recall (also _TPR_, true positive rate). \(F1\) is calculated as follows:  
\[precision=\frac{TP}{TP+FP} \tag{1}\]  
\[recall=TPR=\frac{TP}{TP+FN} \tag{2}\]  
\[F1=\frac{2*precision+recall}{precision+recall} \tag{3}\]  
G-_measure_ is the harmonic mean of recall and true negative rate (_TNR_). G-_measure_ targets the false alarm effects, which is of great importance in software defect prediction. G-_measure_ is calculated as follows:  
\[TNR=\frac{TN}{TN+FP} \tag{4}\]  
\[\text{G}-measure=\frac{2*TPR*TNR}{TPR+TNR} \tag{5}\]  
_AUC_ is a very important evaluation metric in many machine learning tasks. Unlike most evaluation metrics, _AUC_ is not sensitive to the threshold settings, which inferencing an output ranging from 0 to 1 as buggy or clean. _AUC_ is also not sensitive to class imbalance, which is often the case in software defect prediction.  
MCC is a good evaluation metric for imbalanced datasets. It focuses on buggy and clean samples equally and describes the correlation between them. MCC is calculated as follows:  
\[MCC=\frac{TPsTN-FP*FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{6}\]  
In this paper, we regard \(F1\) as the main evaluation metrics. We perform multiple experiments to find the experimental results with the best performance, i.e., best F1 values. The best performance of other metrics, including G-_measure_, _MCC_, and _AUC_ values, are also recorded and shown online as supplementary data to our experimental results.
## 3 Aeronautical Application
### CFD Solver Settings  
In the framework of the FV-based CFD code Ansys Fluent, the compressible RANS governing equations were solved employing the pressure-based coupled flow solver, where the cell face fluxes were determined adopting the Roe scheme. The pressure field was discretized with the central difference scheme. For momentum and turbulence variables, a second-order upwind technique was employed while adopting the least-squares cell-based method for computing the gradients. The resulting large sparse linear system was solved using the AmgX library, which provides a drop-in CPU/GPU acceleration of distributed algebraic multigrid and preconditioned iterative methods. The method allows the parallel solver to minimize communication costs while fully taking advantage of the available high-performance computing (HPC) resources. The interested reader can find more details, for instance, in [27]. Also, the parallel computations were realized by following a domain decomposition approach using the partitioning tool Metis.  
As to reference SBES, the corresponding unsteady solution was computed using a second-order implicit iterative dual time-advancement method, starting from the baseline SST RANS solution as initial condition. The constant time-step size was such that the cell-convective Courant number was of the order unity in the vicinity of the aircraft surfaces, while ten inner loop iterations for every time step were used to ensure the convergence of the normalized residuals. The spatial discretization of the convective terms in the momentum equation was performed through bounded central differencing, which enables low numerical diffusion but still ensures stability by blending in first and second-order upwind schemes in case the convection boundedness criterion is violated [28]. The simulation was run for ten convective flow units to achieve statistically steady flow conditions. Then, the time statistics were accumulated for a further ten units, resulting in less than a half drag count fluctuation of the varying mean drag value.
## 2 Materials and Methods
### Statistical Data Analysis  
Exploratory data analysis was performed by principal component analysis (PCA) [13] and heatmap. For PCA, data were autoscaled, whereas the heatmap was built centring the data to the median of the PE group and applying autoscaling using the Euclidean distance and the Ward's method for clustering. The data set obtained for Elide and Sabrina cvs. were independently investigated by linear mixed-effect modelling (LME) to take into account the effects of the technical replicates. Specifically, the concentration of VOCs was modelled considering the mulching film as a fixed effect and the technical replicate as a random effect. Pair comparisons were performed to discover the VOCs that showed a different content in N5 and in N18 modes with respect to PE as well as between each other. Bonferroni correction was applied to counteract the multiple testing problem, and significance level \(\alpha=0.05\) was assumed. Data analysis was performed using in-house-developed R-functions implemented by R 4.0.4 platform (R Foundation for Statistical Computing, Vienna, Austria).