esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Boys (M \(\pm\) SD)** & **Girls (M \(\pm\) SD)** & **Test Used** & **Test Statistic** & \(p\)**-Value (2-Tailed)** \\ \hline Age & 14.2 \(\pm\) 0.9 & 13.9 \(\pm\) 1.0 & Mann–Whitney U & U = 1029.00 & 0.120 \\ \hline Height & 162.3 \(\pm\) 7.5 & 160.0 \(\pm\) 6.8 & Independent \(t\)-test & t(101.99) = 1.688 & 0.094 \\ \hline Weight & 55.8 \(\pm\) 9.6 & 53.4 \(\pm\) 8.7 & Mann–Whitney U & U = 1122.00 & 0.373 \\ \hline BMI & 21.1 \(\pm\) 2.3 & 20.8 \(\pm\) 2.0 & Independent \(t\)-test & t(95.49) = 0.828 & 0.410 \\ \hline Power & 224.5 \(\pm\) 32.1 & 207.3 \(\pm\) 28.7 & Independent \(t\)-test & t(101.99) = 2.483 & 0.015 \\ \hline VO\({}_{2}\)max & 52.6 \(\pm\) 6.4 & 45.7 \(\pm\) 5.9 & Mann–Whitney U & U = 539.50 & \textless{}0.001 \\ \hline FPPA & 8.7 \(\pm\) 5.2 & 9.3 \(\pm\) 4.9 & Independent \(t\)-test & t(78.68) = \(-\)0.640 & 0.524 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of parameters between genders. Similarly, [PERSON]'s correlation coefficients indicated strong positive relationships: VO2 max and power (rho = 0.83, \(p\) < 0.001), VO2 max and FPPA (rho = 0.76, \(p\) < 0.001), and power and FPPA (rho = 0.80, \(p\) < 0.001). These findings confirm that higher VO2 max and power values are strongly associated with increased FPPA. A multiple linear regression analysis was conducted to predict FPPA using VO2 max and power as independent variables (Table 5). The regression model demonstrated a strong fit, with an R-squared value of 0.85, indicating that 85% of the variability in FPPA is explained by these two predictors. The adjusted R-squared value of 0.84 further supports the model's predictive strength. The ANOVA results confirmed the statistical significance of the model, F (2,101) = 150.7, \(p\) < 0.001. Both independent variables were significant predictors of FPPA, with VO2 max (B = 3.20, \(p\) < 0.001) and power (B = 1.80, \(p\) < 0.001) contributing meaningfully to the regression equation. These findings suggest that increases in VO2 max and power lead to a significant rise in FPPA values. The resulting regression equation was as follows:  
\[\text{FPPA }[^{\circ}]=120.00+3.20\times(\text{VO2 max})+1.80\times(\text{power}).\]  
These results indicate that both VO2 max and LLMP are significant predictors of FPPA in adolescent basketball athletes.
## 2 Data and Preprocessing
### MODIS Data  
Daily MODIS surface reflectance products (MCD43A4 Version 6) and 8-day composite MODIS GPP products (MOD17A2H Version 6) with 500 m resolution were used in this study. The MCD43A4 and MOD17A2H for each carbon flux site were downloaded from the NASA MODIS/VIIRS Land Product Subsets ([[https://modis.ornl.gov/globalsubset/](https://modis.ornl.gov/globalsubset/)]([https://modis.ornl.gov/globalsubset/](https://modis.ornl.gov/globalsubset/)) (accessed on 1 August 2022)). All MODIS data were filtered according to their quality label. MOD17A2H was used as one of the comparison data to evaluate the performance of PAR-LUE in GPP estimation, and MCD43A4 was used to calculate visible albedo, EVI, and land surface water index (LSWI). Albedo, EVI, and LSWI were calculated as follows:  
\[\text{Albedo}_{\text{visible}}=0.331R_{Rad}+0.424R_{Blue}+0.246R_{Green} \tag{1}\]  
\[EVI=2.5\times\frac{R_{NIR}\ -\ R_{Rad}}{R_{NIR}+6R_{Rad}\ -\ 7.5R_{Blue}+1} \tag{2}\]  
\[LSWI=\frac{R_{NIR}\ -\ R_{SWIR}}{R_{NIR}\ +\ R_{SWIR}} \tag{3}\]  
where \(R_{Blue}\), \(R_{Red}\), \(R_{NIR}\), and \(R_{SWIR}\) are the reflectances of the blue, red, near infrared (NIR), and shortwave infrared (SWIR) bands, respectively.
## 2 Methods
### Simplified Coherent Averaging Method  
The coherent averaging method is an effective way to extract periodic signals. An ECG is a typical nonlinear, nonstationary, quasi-periodic signal. Although different beats have different lengths, they have temporal correlation. The PQRST waves of different beats are similar. Therefore, coherent averaging can be used to denoise and extract ECG signals.
## 5 Conclusions  
This article concentrates on how fluid overpressure triggers the fracture slip by using the HM coupling shear test. A granite sample with a smooth fracture is adopted in this research. The injected water pressure increased cyclically to simulate water reservoir impoundment. During the test, stick-slip happens correspondingly to the inlet and outlet pressure. The HM coupling shear test shows that the fracture becomes unstable while the fluid pressure increases to a critical value. Cyclic water pressurization is introduced in this experiment. Results show that the fracture begins to slip when both the inlet and outlet pressure exceeds the theoretical water pressure and continues to stick-slip when both the inlet and outlet pressure increase. The slip distance, slip rate, and stress drop indicate that stick-slips happen periodically. The motivation for fracture sliding is the unbalance between the inlet pressure and outlet pressure, driving the fracture to become unstable. When the new balance between the inlet and outlet pressure has formed, the pressure-controlled stick-slip will turn into a stable sliding state. In addition, the fracture surfaces become deteriorated after several stick-slips for the RMS alteration after sliding. The understress is defined to explain the mechanism of fracture activation. The values of \(\Delta p/\sigma_{n}\) and \(1-r_{c}\) determine the sliding state. When \(\Delta p/\sigma n<1-rc\), the increased pressure and shear stress cannot induce slip. When \(\Delta p/\sigma n>1-rc\), the fault is transitioning from a stable state to an unstable state. Furthermore, hydraulic energy is adopted as another  
Figure 9: The injection energy is plotted against the seismic energy and moment magnitude.  
explanation as to why fluid overpressure can induce unstable sliding by establishing the relationship between the input energy and the released energy. Based on this, the seismic energy can be estimated from the input hydraulic energy, which is an effective method for evaluating reservoir-induced seismicity. Going forward, future work is required to determine why seismic turns to aseismic while the new balance is formed, which is also a complicated problem in predicting the magnitude of seismicity.  
Conceptualization, Y.Z. and C.X.; methodology, Y.Z.; validation, Y.Z. and C.X.; investigation, Y.Z. and X.L.; resources, X.L.; data curation, D.S.; writing--original draft preparation, Y.Z.; writing--review and editing, X.L. and E.W.; supervision, X.L. and E.W.; funding acquisition, Y.Z. and C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Research Fund Program of State key Laboratory of Hydroscience and Engineering (sklhse-2022-D-01, sklhse-2022-C-04), the Natural Science Foundation of Hubei Province, China (2022 CFB673).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Xps
#### 3.1.1 Dependence of XPS Spectra on Scratching Environments and Temperature
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Environments**} & \multicolumn{3}{c}{**Peak I**} & \multicolumn{3}{c}{**Peak II\({}^{\ast}\)**} & \multicolumn{3}{c}{**Peak II\({}^{\ast}\)**} & \multicolumn{3}{c}{**Peak III**} \\ \cline{2-9}  & **Temperature** & **Peak Intensity** & **Binding** & **Peak Intensity** & **Binding** & **Binding** & **Peak Intensity** & **Binding** \\  & **(\({}^{\circ}\)C)** & **(10\({}^{\circ}\) counts/s)** & **Energy (eV)** & **(10\({}^{\circ}\) counts/s)** & **Energy (eV)** & **Energy (eV)** & **Energy (eV)** & **(10\({}^{\circ}\) counts/s)** & **Energy (eV)** \\ \hline \multirow{3}{*}{Air} & 25 & 0.80 & 720.9 & 8.24 & 712.0 & \multirow{3}{*}{5.37} & 2.40 & 708.0 \\  & 200 & 0.98 & 720.0 & 11.99 & & & 712.0 & \\  & 339 & 1.82 & 720.8 & 24.00 & & 712.0 & \\  & 25 & 1.53 & 720.8 & 12.01 & 712.0 & & \\ Benzene & 200 & 1.08 & 720.5 & 12.99 & & & 5.37 & 708.1 \\  & 339 & 2.03 & 720.6 & 24.00 & & 711.9 & & \\  & 25 & 1.18 & 720.5 & 12.01 & 711.9 & & & \\ Cyclohexane & 200 & 1.85 & 720.5 & 20.10 & & 4.90 & 708.0 \\  & 339 & 1.66 & 720.1 & 22.19 & & 712.0 & \\  & 25 & 2.01 & 721.0 & 18.08 & 712.9 & & 1.48 & 708.0 \\ Water & 200 & 2.06 & 720.6 & 18.00 & & 712.4 & \\  & 339 & 2.45 & 720.8 & 24.02 & & 712.5 & \\  & 25 & 2.49 & 721.6 & 18.09 & 712.3 & & 7.46 & 708.6 \\ Methanol & 200 & 2.77 & 720.3 & 28.50 & & 712.3 & \\  & 339 & 3.34 & 720.5 & 25.90 & & 713.1 & & \\  & 25 & 2.80 & 721.4 & 18.01 & 712.1 & & 107.7 & 708.6 \\ Ethanol & 200 & 1.67 & 720.9 & 26.02 & & 712.4 & & \\  & 339 & 2.31 & 721.3 & 29.00 & & 712.5 & & \\  & 25 & 1.92 & 720.6 & 17.85 & 712.1 & & 6.88 & 708.1 \\ Acetone & 200 & 1.67 & 720.8 & 22.18 & & 712.1 & & \\  & 339 & 2.62 & 720.8 & 27.92 & & 712.4 & & \\ \hline Average & & 720.7 & & 712.3 & & 708.1 \\ \multicolumn{9}{c}{\({}^{\ast}\) Peak II at 25 \({}^{\circ}\)C is assigned to FeOOH, and Peak II at 200 and 339 \({}^{\circ}\)C is assigned to Fe\({}_{3}\)O\({}_{4}\).} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The intensities and binding energies of Peaks I, II, and III observed in Fe 2\(p\) spectra at three temperatures for iron surfaces scratched in different environments. Figure 2: _Cont._Additionally, a main peak observed in the Fe 3\(p\) spectra, whose binding energy was almost temperature-independent, appeared at approximately 55 eV. This peak is considered to have originated from a mixed trilayer of Fe\({}_{\text{x}}\)O, Fe\({}_{3}\)O\({}_{4}\), and FeOOH, and the binding energy can be assigned to both Fe 3\(p\)3/2 and Fe 3\(p\)1/2 [17]. In Table 2, the intensities and binding energies of Peaks 1 and 2 observed in the Fe 3\(p\) spectra are listed. It is confirmed that the emission intensities of Peak 1 at 339 \({}^{\circ}\)C and of Peak 2 at 25 \({}^{\circ}\)C for the environments decreased in approximately the same order of the environments as those of Peak II at 339 \({}^{\circ}\)C and of Peak III at 25 \({}^{\circ}\)C, as shown in Table 1, respectively.
## 2 Materials and Methods
### Evaluation Metrics  
Our fine-tuning models were evaluated through macro and micro F1 scores. The macro F1 score is reported to be a good metric when there is an imbalanced dataset where all classes are equally important [33]. For our case, we found that most token-level annotations, using the BIO tagging scheme, were labeled as \"O\" (outside), resulting in a class imbalance where non-entity tokens are overrepresented in the dataset.  
The micro F1 score was particularly useful for comparing the performance of our fine-tuned models with other related works in the literature [30,34].  
Furthermore, we computed the exact and partial boundaries for precision and recall using _nervaluate_ ([[https://github.com/MantisAI/nervaluate](https://github.com/MantisAI/nervaluate)]([https://github.com/MantisAI/nervaluate](https://github.com/MantisAI/nervaluate)) accessed on 7 November 2024), which is a Python library for NER tasks based on the metrics from [35]. The exact and partial boundaries give information on the performance of the model where the predictions given to an entity are only a part (partial) or all the boundaries of the given entity [36].  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{1}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
\[\text{F1-score}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{3}\]  
where \(TP\) means true positives, \(FP\) false positives, and \(FN\) false negatives.  
To minimize the influence of random initialization of network parameters, we assessed each model five times with different seeds and averaged the performances obtained for the recall (R) (Equation (1)), precision (P) (Equation (2)), and F1 score (F1) (Equation (3)) measurements.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results  
This section thoroughly tests our proposed method in terms of robustness to the roll angle, numerical stability, noise sensitivity, and computational speed, compared with several state-of-the-art orientation estimation solvers (i.e., RPnP solver [53] and P3P solver [5]) in synthetic data. Then, we indirectly test our proposed method in real images, in terms of reprojection error and position error. This shows our proposed method is feasible in real scenarios.
## 3 Method
### Data Pre-Processing  
#### 3.1.1 SSL/TLS Certificate  
The digital certificate is an unencrypted file with a public encryption key that contains organizational details about the certificate owner and the encryption key. The format of the certificate is defined by the X.509 standard. X.509 certificates play an important role in encrypting data transmission between two parties under the HTTPS protocol. X.509 is a signed data structure that binds a public key to a person, computer, or organization and is used in many Internet protocols, including SSL/TLS [30; 31]. X.509 is also complex in terms of structure and syntax. Each certificate consists of a sequence of three required fields: tbcertificate, SignatureAlgorithm, and SignatureValue. The first part, the tbcertificate, contains the subject, the publisher, and other basic information. Compared to Version 1 certificates, Version 2 certificates have added the SubjectUniqueID (subject unique identifier) and trusteduniqueid (issuer unique identifier) fields. In addition, extended fields have been added to the Version 3 certificate. The second part, SignatureAlgorithm, contains the identifier of the signature algorithm used by the certificate authority (CA) to sign the certificate. The third part, SignatureValue, records the digital signature calculated on TBcertificate. In a Public Key Infrastructure (PKI), certificates are usually organized together with their issuer into a certificate chain. The structure of the X.509 certificate is shown in Figure 1.
## 6 Conclusions  
The problem of concept drift in the area of RSs is of increasing importance as more and more data are been added to the system on a daily basis, in contrast to a static data repository. As such, it is relatively uncommon that the target concepts remain unchanged over a long period of time, which necessitates updating the models to cope with dynamic changes.  
The previous sections described the problem of concept drift and its relevance to the recommendation system. It provides the classification of concept drift problems and works related to each sphere of dynamic recommendation systems. Many achievements have been recorded towards implementing collaborative filtering models for tracking concept drifts, mostly the concept drift of user preferences. Some of the authors assume the items property to be stable over time and thus ignore considering item dynamicity. According to [158], this is open to debate and worth exploring to check whether or not such consideration has a positive influence on the overall system accuracy.  
Another key aspect that is worth exploring in RS is developing the techniques for detecting the major changes and allow adapting the model only when it is needed. The current approaches suggested by past researchers are not detective-based and cannot make a distinction between different patterns of drifts and noise levels for each type of drifts. Furthermore, it is noteworthy that other than a single rating score, other successful approaches that use review ratings or multidimensional ratings feedbacks can be considered to properly model user preferences and changes, since those feedbacks contain more fine-grained information that can facilitate change tracking about user interest, sentiment, reasons as well as fundamental properties of items.  
Additionally, the unique fundamental problem on modeling concept drifts in RSs is the appropriate selection between the methods that seek global concept drifts and those that seek to model individual drifting concepts separately. The best solution is to provide a learning algorithm that can keep track of multiple changes and as well adapt to different rates of change patterns. All these suggest that some more future works need to be undertaken within the realm of concept drifts to ensure the improved quality of recommendations.  
All authors ([PERSON], N.S., A.D. and A.O.) have made significant contributions right from planning of the review, conducting of the review, writing and revising of the manuscript. The final version of this manuscript has also been approved by all the authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Universiti Teknologi Malaysia, through the grant number: Q.J13000.2551.21H38-Novel Deep Learning, Concept Drift, and Hybrid Models Research Funding Program.  
The authors wish to express their gratitude to Universiti Teknologi Malaysia (UTM), the Ministry of Higher Education Malaysia (MOHE) and Ibram Badamasi Babangida University, Lapai, Niger State, Nigeria for their financial support.  
The authors declare no conflict of interest.
###### Abstract  
Spatially referenced and geometrically accurate laser scanning is essential for the safety monitoring of an underground mine. However, the spatial inconsistency of point clouds collected by heterogeneous platforms presents challenges in achieving seamless fusion. In our study, the terrestrial and handheld laser scanning (TLS and HLS) point cloud registration method based on the coarse-to-fine strategy is proposed. Firstly, the point features (e.g., target spheres) are extracted from TLS and HLS point clouds to provide the coarse transform parameters. Then, the fine registration algorithm based on identical area extraction and improved 3D normal distribution transform (3D-NDT) is adopted, which achieves the datum unification of the TLS and HLS point cloud. Finally, the roughness is calculated to downsample the fusion point cloud. The proposed method has been successfully tested on two cases (simulated and real coal mine point cloud). Experimental results showed that the registration accuracy of the TLS and HLS point cloud is 4.3 cm for the simulated mine, which demonstrates the method can capture accurate and complete spatial information about underground mines.  
underground mine; overall registration; terrestrial laser scanning; handheld laser scanning; point cloud +
Footnote †: journal: Applied sciences
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
#### 4.3.2 Comparison and Analysis for Data Streams with Variable Class Proportions  
This section presents the experiment's results for data streams with dynamically changing class proportions. As shown in Tables 2-6, the AdaAL-MID also ranked first overall, except on the Var_Mix_ISSI data stream with mixed concept drift, for which ROSE achieved the best performance, with the AdaAL-MID achieving the best performance on other data streams. Moreover, the performance of the AdaAL-MID was superior to other active learning algorithms. On the Var_Mix_IGGI data stream, the AdaAL-MID improved the recall, G-mean, and F1-score by 2.31%, 2.70%, and 3.51%, respectively, compared to the second-ranked CALMID. Overall, the AdaAL-MID can adapt well to various types of concept drift and handle changes in class proportions.  
Similarly, Figure 5 shows the recall curves of all comparison algorithms. As shown in Figure 5a,b, the AdaAL-MID was less affected by virtual drift compared to other algorithms and could quickly recover to a high level of performance. For data streams with real concept drift, as shown in Figure 5c-f, the AdaAL-MID performed better on gradual drift data streams Var_Grad_1 and Var_Grad_2 than on abrupt drift. Not only does it suffer less performance loss at drift points, but it also maintains the highest recall level during the stable phase of the data stream. Although AdaAL-MID's performance was slightly lower than CALMID and MOOB for some stages of the abrupt drift data streams Var_Sudd_1 and Var_Sudd_2, overall, the AdaAL-MID still outperformed other algorithms on data streams with mixed concept drift types, as shown in Figure 5g-i. Similar to its performance on data streams with fixed class proportions, the AdaAL-MID's performance was relatively poor when incremental drift occurred, which may be an area for improvement in the future. On the Var_Mix_SIG data stream, which contains three types of drift, the AdaAL-MID demonstrated excellent performance. When abrupt drift occurred at instance 150k, it quickly recovered its performance, and its recall rate ranked first among all algorithms after recovery.  
Based on the above analysis, the AdaAL-MID also demonstrated excellent performance in scenarios in which class imbalances may change or even reverse.  
Figure 4: The recall curves of the comparison algorithms on fixed class proportion data streams.
## 2 Analysis of Sag Effect Influence
\tag{6}\]  
By substituting Equation (6) into Equation (5), the following can be obtained:  
\[k=\frac{12 EC\beta^{2}\gamma^{3}}{L^{2}\Big{(}t(\alpha^{2}+\beta^{2})^{1/2}+ \Big{(}\alpha^{2}+(1-\beta)^{2}\Big{)}^{1/2}\Big{)}\Big{(}\Big{(}\alpha^{2}+( 1-\beta)^{2}\Big{)}^{3/2}\Big{(}12\gamma^{3}+\gamma^{2}EL^{2}(1-\beta)^{2} \Big{)}+\frac{(\alpha^{2}+\beta^{2})^{1/2}(12\gamma^{3}+\gamma^{2}E\beta^{2} \gamma^{2})}{l}\Big{)}} \tag{7}\]  
Based on Equation (7), the relationship between the ratio \(t\) of crossing cable area and its constraint stiffness \(k\) is discussed. Let the steel volume of a pair of crossing cables  
Figure 4: The relationship between \(k\) and the main span length \(L\) under different anchor span ratios. Figure 5: The relationship between \(k\) and the anchor span ratio \(\beta\) under different initial stresses. \(C\) = 8.90 m\({}^{3}\). Figures 6 and 7 show the relationship between the ratio \(t\) of crossing cable area and \(k\), for main span lengths of 1000 m and 1500 m, respectively. It can be seen from Figures 6 and 7 that when the steel volume of the crossing cable is constant, there is an optimal ratio of crossing cable area in theory, which maximizes its constraint stiffness. When the anchor span ratio \(\beta\) is 0.5, 0.6, and 0.7, the crossing cable area ratios at the maximum constraint stiffness under the main span of 1000 m are 1.02, 1.43, and 2.04, respectively. At the main span of 1500 m, the ratios are 1.02, 1.53, and 2.25. When the crossing cable is anchored at the mid span, the constraint stiffness is the largest when the crossing cable of the side tower and the middle tower adopts the same cross-sectional area. When the anchorage position of the crossing cable is biased towards the side tower, increasing the ratio of the crossing cable area between the mid tower and the side tower can increase its constraint stiffness.