esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Evaluation Metrics of Network Performance  
In this paper, the loss changes during network training, the mean average precision (mAP), the number of images that the algorithm can process per second (FPS), and the number of parameters and calculations are used as metrics to evaluate the improved Yolov5-s-m-l-x network. Among them, the changes in the loss and average precision are shown in Figure 7, and the statistical results of the parameters, calculations, and processing speed of the algorithm are shown in Table 3.  
It can be seen that the Yolov5-s-m-l-x network model with only the prediction network improvement shows good convergence performance without overfitting, as the changes in the training loss and validation loss during the training process are close. The accuracy evaluation index mAP0.5 reached a value of about 0.99 after about six epochs of training and stabilized with training, while the mAP0.5:0.95 reached accuracy of about 0.88 after about 30 epochs of training and grew slowly, to about 0.9, with training.  
The high accuracy of the prediction results can be attributed to the specificity of the linear detection method in this paper, i.e., the specificity of the detection target objects and the specificity of the scene: (1) there are only two detection target objects, and their shapes and sizes are relatively fixed; (2) the backgrounds of the targets are relatively singular and they do not involve complex and variable backgrounds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**OS** & **CPU** & **GPU** & **Pytorch** & **Python** \\ \hline Windows 10 & E5-2620v3*2 & RTX2060-6G & 1.7.0 & 3.6.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm operating platform.  
Figure 6: Data annotation.  
Table 3 shows the differences in the number of parameters, calculations, and inference speed of the Yolov5-s-m-l-x network model with only prediction network improvements. In particular, Yolov5-s achieves the highest prediction speed of 67 FPS with the smallest number of parameters and the smallest amount of computation, while the number of parameters and the amount of computation gradually increase as the number of layers and the complexity of the network increase, leading to a reduction in the prediction speed of Yolov5-x to around 33 FPS.  
Combined with the prediction accuracy shown in Figure 7, it can be seen that the difference in prediction speed between the four Yolov5-s-m-l-x networks with only prediction network improvements is much greater than the difference in prediction accuracy. Therefore, to meet the needs of real-time detection, the corresponding algorithm improvements and results were implemented only for the Yolov5-s network in the subsequent research.
## 3 Materials and Methods
### Similarity Measures
#### 3.2.2 The New \"Weighted and Combined\" Distance Metrics  
The AMBD metric is appropriate for measuring the similarity of rules with the same consequence, and IBDM is also proposed for that purpose (to compute the similarity of CARs). Aiming at applying these distance metrics to cluster the CARs with the same class, an efficient distance metric, Weighted and Combined Distance Metric (WCDM), is developed by merging the IBDM and AMBD distance metrics. The advantage of WCDM is that it considers the direct measure (rule items) and the indirect measure (rule coverage). The \(d^{\text{WCDM}}\) between two rules, \(rule1\) and \(rule2\), is defined below:  
\[d_{rule1,rule2}^{WCDM}=\alpha\times d_{rule1,rule2}^{IBDM}+(1-\alpha)\times d _{rule1,rule2}^{AMBD} \tag{7}\]  
where \(\alpha\) is weight parameter, \(d^{1 BSM}\) is a direct distance metric and \(d^{AMBD}\) is an indirect distance metric.  
When we apply AMBD (\(\alpha=0\)) distance metric in the proposed method, a larger average number of clusters with slightly better accuracy is obtained. When the IBDM (\(\alpha=1\)) was applied, the distance metric to the proposed method showed a smaller average number of clusters with slightly lower accuracy. After performing some experiments, different \(\alpha\) parameters were determined to show how the distance metrics affected the performance of the classifier. The following distance metrics are developed:  
Weighted Combined and Balanced (WCB): The \(\alpha=0.5\) parameter was set in the distance metric developing part; that is, the contribution of IBDM and AMBD was considered equal to make the balanced distance metric. The resulting distance metric is defined in Equation (8).  
\[d_{rule1,rule2}^{WCB}=0.5\times d_{rule1,rule2}^{IBDM}+0.5\times d_{rule1,rule2 }^{AMBD}. \tag{8}\]  
Weighted Combined and Indirect advantage (WCI): The \(\alpha=0.25\) parameter was set in the distance metric developing part; the majority part of the \"Combined\" distance metric becomes the \"Indirect\" distance metric that is shown in Equation (9).  
\[d_{rule1,rule2}^{WCI}=0.25\times d_{rule1,rule2}^{IBDM}+0.75\times d_{rule1, rule2}^{AMBD}. \tag{9}\]  
Weighted Combined and Direct Advantage (WCD): The \(\alpha=0.75\) parameter was set in the distance metric developing part, and the majority of the \"Combined\" distance metric became the \"Direct\" distance metric that is expressed in Equation (10).  
\[d_{rule1,rule2}^{WCD}=0.75\times d_{rule1,rule2}^{IBDM}+0.25\times d_{rule1, rule2}^{AMBD}. \tag{10}\]
# High-Speed Micro-Grooving of Metal by Angled Irradiation of Single-Mode CW Fiber Laser
## 3 Experimental Results and Discussion
### Effects of Thermophysical Properties on Micro-Groove Formation  
On the basis of the discussion in the previous section, the fluidity and cooling rate of the molten metal are thought to be particularly important factors for groove formation. To discuss the effects of these factors, micro-groove formation in aluminum alloy, which has different thermophysical properties from mild steel, was investigated.  
Figure 10 compares the micro-grooves formed on the surfaces of mild steel (SS400) and aluminum alloy (A1050). The major thermophysical properties are listed in the figure. The laser power, scanning speed, and incident angle were \(P\) = 200 W, \(V_{s}\) = 2 m/s, and \(\theta_{i}\) = 45\({}^{\circ}\), respectively. The spot size, \(d_{s}\), was 35 \(\upmu\)m for SS400 and 18 \(\upmu\)m for A1050. Under these experimental conditions, only shallow grooves were formed in A1050, even during the same high-speed processing.  
As in the low-speed processing of SS400, the formation of shallow grooves is attributed in part to the high fluidity of the molten aluminum resulting from its low viscosity. However, it is also necessary to consider the increase in molten volume due to the low melting point, low density, and low specific heat of aluminum and the increase in cooling rate due to its high thermal conductivity. Therefore, in micro-grooving by angled laser irradiation, it is necessary to optimize the laser irradiation conditions according to the thermophysical properties of the material. A numerical simulation of the molten metal behavior in a high-speed angled laser irradiation process is under development to qualitatively discuss the groove formation mechanism and to optimize the processing parameters.  
Figure 10: Comparison of micro-grooves and thermophysical properties of mild steel and aluminum alloy (\(P\) = 200 W, \(\theta_{i}\) = 45\({}^{\circ}\), and \(V_{s}\) = 2 m/s).
## 3 Overview of Studied Numerical Cases
### Earthquake Ground Motions  
As shown in Table 3, GMs 1 and 2 are used to generate the training data used for developing the MLMs. They are both derived from the same strong-motion record of the Hyogo-ken Nanbu earthquake at the Japan Meteorological Agency (JMA) station of Kobe city, but with different durations for reasons explained further in this paper. GMs 3, 4, and 5 are used to test the generalization capability of developed MLMs. Eventually, structural responses of the three separate building models subjected to GMs 2, 3, 4, and 5 are evaluated by the proposed hybrid seismic analysis and compared to those of conventional analysis, taking as a reference. All GMs were intentionally derived from famous earthquake records with different amplitudes, frequency contents, and durations. In the acceleration response spectra graph (Figure 3), fundamental horizontal periods of the building's superstructures (T\({}_{n,1}\), T\({}_{n,2}\), and T\({}_{n,3}\)) and the effective periods of their respective isolation layers (T\({}_{eff,1}\), T\({}_{eff,2}\), and T\({}_{eff,3}\)) are marked with vertical dashed lines, covering the common period range of based isolated structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Ground Motion** & **Earthquake Event** & **PGA** & **Duration** & **Acceleration** \\ \cline{2-5}  & **Designation** & **Occurrence Date** & **(Gal)** & **(s)** & **Sparkline** \\ \hline
1 & Kobe 1995 NS & 17 January 1995 & 818 & 5 & Tuning network architecture \\
2 & Kobe 1995 NS & 17 January 1995 & 818 & 15 & Training the MLM \\
3 & Taft 1952 EW & 21 July 1952 & 176 & 19 & Hybrid analysis \\
4 & Tohoku 1978 NS & 12 June 1978 & 258 & 23 & Testing the MLM \\
5 & El Centro 1940 NS & 18 May 1940 & 342 & 27 & Testing the MLM \\  & & & & & Hybrid analysis \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of earthquake ground motions used in this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Isolation Level** & & **Building 1** & **Building 2** & **Building 3** \\ \hline
**Effective Period** & **(s)** & **T\({}_{eff,1}\) = 2.5** & **T\({}_{eff,2}\) = 4.0** & **T\({}_{eff,3}\) = 6.0** \\ \hline
**Base Weight** & **(kN)** & **4500** & **4500** & **4500** \\ \hline \multirow{3}{*}{Oil Damper} & C\({}_{1}\) & (kN\({}_{8}\)/mm) & 2.194 & 1.617 & 1.237 \\  & C\({}_{2}\)/C\({}_{1}\) & & 0.067 & 0.067 & 0.067 \\  & V\({}_{r}\) & (mm/s) & 320 & 320 & 320 \\ \hline \multirow{3}{*}{LRB+NRB} & K\({}_{1}\) & (kN/mm) & 175.5 & 129.4 & 99 \\  & K\({}_{2}\)/K\({}_{1}\) & & 0.046 & 0.042 & 0.031 \\ \cline{1-1}  & F\({}_{y}\) & (kN) & 1755 & 1294 & 990 \\ \hline \multicolumn{3}{c}{ML model designation} & MLM1 & MLM2 & MLM3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Isolation levels properties of studied building models.
## 3 Results and Discussion
### Volatile Compound Profile  
In general, the concentration of the volatile compounds in olive oil was significantly affected by the irrigation practices, as previously reported by other authors [38; 39], but not by the factor season, with 18 compounds being identified and quantified in the volatile profile of the super-high-intensive 'Arbequina' oils under study (Table 5). These compounds provide olive oils with notes of alcohol, apple, sweet, sour, bitter, green, pungent, almond, astringent, leaves, earthy, fruity, floral and herbal, among others, all of them positive characteristics of an extra virgin olive oil, EVOO [25; 40; 41].  
No effect caused by the factor season was observed on the volatile profile of these oils and the mean values of the 2018 and 2019 seasons are summarized in Table 5. The two predominant compounds were _trans_-2-hexenal and _trans_-2-hexen-1-ol; however, the irrigation treatment only caused significant effects on the content of the first compound, with T\({}_{1}\) and T\({}_{3}\) generating equivalent contents (91.67 and 96.28, respectively). The _trans_-2-hexenal compound is one of the main compounds in the volatile fraction of olive oils, especially in extra virgin olive oils [42]. In this sense, the concentration of this compound significantly affected the final concentration of volatile compounds present in the oils, representing -46.5%. Oils obtained after T\({}_{0}\) and T\({}_{3}\) had the highest values for total volatile content (188 and 186, respectively). These results agreed with those obtained by [PERSON] et al. [43], who observed that the volatile compounds most affected by irrigation in 'Cornicabra' oils were _trans_-2-hexenal, _trans_-2-hexen-1-ol and the hexen1-ol.  
Olive oils also showed significant differences in the contents of ethanol, benzaldehyde, hexyl acetate, _trans_-8-ocimene, benzyl alcohol, acetophenone and benzoic acid. Oil samples obtained after T\({}_{1}\) had the highest values of benzaldehyde and _trans_-8-ocimene (8.14 mg 100 g\({}^{-1}\) olive oil and 2.19 mg 100 g\({}^{-1}\) olive oil, respectively). However, some aldehydes present in olive oils can provide undesirable sensory attributes such as sweaty, musty-damp or vinegary [42], although this is not the case. On the other hand, the control and confederation SDI treatments led to olive oil with the highest contents of hexyl acetate (3.54 mg 100 g\({}^{-1}\) olive oil and 4.77 mg 100 g\({}^{-1}\) olive oil, respectively). Finally, oil samples from T\({}_{0}\) and T\({}_{2}\) stood out for their contents of benzyl alcohol and benzoic acid.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Feature**}  
In previous studies, [PERSON] et al. (2019) and [PERSON] et al. (2019) demonstrated that RDI and SDI led to extra virgin olive oil with similar or even higher amounts of the main volatile. These results agreed well with those obtained in the current study in the sense that both studies demonstrated that the applications of irrigation strategies had no negative effects on the volatile profiles of olive oil.
## 4 Experimental Results
### When the ROV Is Side-On to the Current  
In this scenario, the ROV moved along the direction \([-15,\,-15,\,-60]\), while the ocean current flowed orthogonally to its trajectory. To achieve a \(90^{\circ}\) angle between the current and the path, the current vector was defined as \([1,\,-1,\,0]\), lying purely in the horizontal plane. This corresponded to an azimuth angle \(\beta_{c}=-\pi/4\) and elevation angle \(\alpha_{c}=\pi/2\).  
With the ROV's initial position at \([0,\,0,\,-40]\), target at \([-15,\,-15,\,-60]\), and target yaw set to \(0^{\circ}\), this setup allowed us to analyze how lateral wake effects influenced the vehicle. The simulation outcome under this perpendicular interaction is visualized in Figure 10.  
In this sample, the ROV started at \([0,\,0,\,-40]\) and finally reached \([-14.6195,\,-14.4217,\,-60.0334]\). The resulting Euclidean distance between the two points was approximately \(0.8221\) m. The individual absolute deviations were \(0.3805\) m in the x-direction, \(0.5783\) m in the y-direction, and \(0.0334\) m in the z-direction.
## 6 Conclusions and Discussion  
In this paper, we extend the StEM algorithm by incorporating a combination of Gibbs sampling and Metropolis-Hastings steps to address the complexity of modeling individual-specific change points in longitudinal data. Specifically, we model change points using a zero-inflated exponential (ZIE) distribution, allowing us to capture both immediate and delayed antiretroviral therapy (ART) initiation. We also generalize standard random change point models by incorporating nonlinear mixed-effects (NLME) specifications before and after the change point. Although our algorithm uses MCMC-based imputation steps reminiscent of Bayesian methods, it is embedded within a maximum likelihood framework. This hybrid structure avoids full posterior sampling--particularly of high-dimensional variance components--resulting in improved computational efficiency and estimation stability. It also preserves desirable asymptotic properties of maximum likelihood estimators while allowing for flexible inference via stochastic approximation. In addition, although our primary emphasis has been on point estimation, the framework supports approximate inference via a score-based variance-covariance estimator, enabling the construction of confidence intervals and facilitating hypothesis testing for model parameters. Our method is evaluated through simulation studies and real data analysis. Compared to empirical rule-based approaches, which often suffer from instability due to measurement error or sparse sampling, our model-based framework leverages the full structure of the data to yield more reliable estimates. Application to clinical HIV cohort data demonstrates the utility of the method, revealing that a substantial proportion of individuals initiate ART immediately upon diagnosis--a biologically meaningful finding in light of \"test-and-treat\" policies. Beyond statistical modeling, the clinical context of ART initiation is critical. While immediate initiation is now the recommended standard, delays remain common due to factors such as patient hesitancy, anticipated side effects, co-occurring mental health or substance use issues, stigma, or gaps in follow-up care. By accommodating both immediate and delayed ART initiation, the zero-inflated change point model enhances public health relevance and interpretability. On the computational side, our use of Metropolis-within-Gibbs sampling and Geweke diagnostics enables convergence within 3000 iterations, even for complex, high-dimensional missing data structures. While our framework is flexible enough to incorporate other zero-inflated distributions (e.g., gamma, Weibull, or log-normal), we limited this work to the exponential case to preserve computational tractability. Future work will explore
# Evaluation of Blue(r)m Mouthwash vs. Chlorhexidine
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Outcome** & **Time Points** & **Mouthwash** & **Median (IQR)** & **Range** & _p-Value_ \\ \hline \multirow{4}{*}{Plaque Index} & Baseline (Day 0) & Chlorhexidine & 1.13 (0.83 to 1.22) & 0.64 to 1.54 & \multirow{4}{*}{0.10} \\  & Blue®m & 1.21 (1.13 to 1.28) & 0.72 to 1.92 & & \\ \cline{2-5}  & \multirow{2}{*}{14 th day} & Chlorhexidine & 0.76 (0.68 to 1.08) & 0.49 to 1.25 & \multirow{2}{*}{0.22} \\  & Blue®m & 0.91 (0.76 to 1.08) & 0.56 to 1.60 & & \\ \cline{2-5}  & \multirow{2}{*}{30 th Day} & Chlorhexidine & 0.68 (0.53 to 0.95) & 0.41 to 1.12 & \multirow{2}{*}{0.01} \\  & Blue®m & 0.54 (0.38 to 0.69) & 0.20 to 1.03 & & \\ \hline \multirow{4}{*}{Gingival Index} & Baseline (Day 0) & Chlorhexidine & 0.82 (0.69 to 1.02) & 0.38 to 1.14 & \multirow{4}{*}{0.40} \\  & Blue®m & 0.86 (0.79 to 0.98) & 0.67 to 1.10 & & \\ \cline{2-5}  & \multirow{2}{*}{14 th day} & Chlorhexidine & 0.62 (0.52 to 0.70) & 0.34 to 0.99 & \multirow{2}{*}{0.09} \\  & Blue®m & 0.69 (0.62 to 0.79) & 0.35 to 0.96 & & \\ \cline{2-5}  & \multirow{2}{*}{30 th Day} & Chlorhexidine & 0.53 (0.46 to 0.60) & 0.28 to 0.92 & \multirow{2}{*}{0.35} \\  & Blue®m & 0.50 (0.42 to 0.62) & 0.26 to 0.79 & & \\ \hline \multirow{4}{*}{Bleeding on Probing} & Baseline (Day 0) & Chlorhexidine & 14.3 (12.5 to17.85) & 9.6 to 27.58 & \multirow{2}{*}{0.628} \\  & Blue®m & 15.7 (12.6 to 18.75) & 9.3 to 33.34 & & \\ \cline{2-5}  & \multirow{2}{*}{14 th day} & Chlorhexidine & 11.8 (9.5 to 14.28) & 6.25 to 20.68 & \multirow{2}{*}{0.607} \\  & Blue®m & 12.4 (9.3 to 13.34) & 6.67 to 17.85 & & \\ \cline{2-5}  & \multirow{2}{*}{30 th Day} & Chlorhexidine & 9.8 (8.4 to 11.2) & 6.25 to 20.68 & \multirow{2}{*}{0.001} \\  & Blue®m & 6.7 (3.57 to 8.4) & 0 to 13.2 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Intergroup comparison of PI, GI, and % BoP at different time intervals in both groups. Figure 4: Comparison of the change in percentage of bleeding on probing between the Chlorhexidine and Blue®m mouthwash group over one month. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Chlorhexidine Group** & **Blue®m Group** \\ \hline Altered Taste & 40\% & 12\% \\ Burning Sensation & 48\% & 0 \\ Staining of Teeth & 32\% & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Patient satisfaction survey (percentage of patients reporting the issue).
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 3 Results and Discussion
### Load-Displacement Curves
#### 3.2.2 Energy Absorption Capacity  
Energy absorption capacity was calculated as the area under the load-deflection curve for each specimen up to the point where the load capacity decreased to 85%. The increase or decrease percentages in energy consumption of the repaired beam specimens, compared to the damaged and undamaged reference specimens, are presented in Figure 11. In Figure 11, the positive values indicate an increase relative to the reference specimen, while the negative values denote a decrease.  
Generally, all the repaired beams resulted in energy absorption capacity values that were between 71% and 98% lower than those of the undamaged beam (RefB); however, the energy absorption capacity increased between 40% and 1401% as compared to the damaged specimen (RefB-D). The repaired specimens with steel rebar gave a better energy absorption capacity as compared to the GFRP rebar. This is mainly due to the yielding of steel rebar and splitting of GFRP rebar from mortar embedment. When the energy absorption values of the beams repaired with the SNSM and NSM methods by using GFRP rebar are compared to each other, it can be seen that the NSM method is more effective than the SNSM method. The RB2-SNSM-G specimen has an increase of 538%, while the RB3-NSM-G specimen has an increase of 1068%, in energy absorption capacity as compared to RefB-D.  
The beam repaired with the SNSM method by using GFRP rebar in Group B (RB2-SNSM-G) absorbed more energy than the beams repaired by the EBR method in Group E (RB8-EBR-G). However, when the steel rebars are used for repair of the beams (RB1-SNSM-S and RB7-EBR-S), the energy absorption capacity in the EBR method is more than that of the SNSM method.  
In the beams of Group D, the RB5-EBR-CS specimen without a U boundary sheet has an increase of 328%, while the RB6-EBR-CS-U specimen with an added U boundary sheet has an increase of 1001%, in energy absorption capacity as compared to RefB-D. The specimen having a CFRP sheet with a U boundary (RB6-EBR-CS-U) absorbed more energy than the specimens repaired by using steel and GFRP rebars in the SNSM technique (RB1-SNSM-S and RB2-SNSM-G) and by using GFRP rebars in the EBR technique (RB8-EBR-G).  
Figure 11: The percentage changes in energy consumption compared to (**a**) the undamaged reference specimen and (**b**) the damaged reference specimen.