esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Velocity Stepping Experiments  
Representative velocity stepping data obtained from the experiments are shown in Figure 7 (see also Table 3). Before the injection, the friction coefficient of three faults decreased as the loading rate decreased from 1 \(\upmu\)m/s to 0.2 \(\upmu\)m/s, suggesting the faults were characterized by velocity-strengthening at room temperature, that is, \((a-b)>0\) (see Table 3). When 1 MPa and 15 MPa pressurized water were injected into the fault, the friction coefficient of the three faults decreased with the decreasing loading rate, suggesting that the water injection and injection pressure change did not alter the faults' velocity-dependent properties.  
The water injection and velocity stepping tests were also performed on the limestone fault at 100 \({}^{\circ}\)C for comparison. The result suggests that the limestone fault showed stick-slip (Figure 8a), and the frictional strength and stress drop increased with the decreasing loading rate (Figure 8b), that is \((a-b)=-0.01<0\), indicating that the fault was characterized by noticeable velocity weakening at 100 \({}^{\circ}\)C. Similarly to the test at room temperature, the injection of 1 MPa pressurized water hardly modified the fault shear stress and sliding rate (Figure 8(a1)). However, the 15 MPa pressurized water injection decreased the shear stress sharply and increased the sliding rate significantly (Figure 8(a2)). During the fault slip, the maximum sliding rate was 4.45 \(\upmu\)m/s, greatly exceeding the 1.7 \(\upmu\)m/s in the room temperature test. A stress drop with \(\sim\)11.5 MPa was observed when the fault reached the new steady state. The sliding displacement and time were \(\sim\)0.07 mm and \(\sim\)6 s, respectively. The decreasing rate of the shear stress was higher than that at room temperature, showing a sudden instability (Figure 8).  
Figure 7: Velocity stepping data were obtained on (**a**) granite, (**b**) sandstone, and (**c**) limestone faults. \(v_{1}\) and \(v_{2}\) are 1 \(\upmu\)m/s and 0.2 \(\upmu\)m/s, respectively. Subplots (**a1–a3,b1–b3,c1–c3**) denote the velocity stepping test performed in dry and wet samples with 1 MPa and 15 MPa pressurized water injection. The blue dotted lines denote the locations for changing loading velocity. The red dotted lines denote the steady-state friction strength (\(\mu_{\text{ss}}\)) in different loading velocity.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 3 Error Bound Analysis
### Metric of Double Measurements
#### 3.2.3 Metric of TOF/AOA  
When the metrics of TOF and AOA are selected to conduct the indoor localization, the metric of TOF/AOA follows the bivariate normal distribution. The PDF of the metric of TOF/AOA with respect to \(\theta_{i}\) is:  
\[f_{\theta_{i}}\left(\text{T/A}\right)=\prod\limits_{k=1}^{m}\frac{1}{2\pi \sigma_{2}\sigma_{3}\sqrt{1-\rho_{23}{}^{2}}}\exp\left(-\frac{\sigma_{23}}{2(1- \rho_{23}{}^{2})}\right) \tag{28}\]  
where \(\rho_{23}\) is the correlation coefficient with respect to \(\mathbf{d}_{ik}\) and \(\mathbf{\alpha}_{ik}\), \(\sigma_{23}=\frac{\xi_{32}{}^{2}}{\sigma_{2}{}^{2}}+\frac{\xi_{32}{}^{2}}{ \sigma_{3}{}^{2}}-2\rho_{23}\frac{\xi_{32}}{\sigma_{2}}\frac{\xi_{32}}{\sigma_{3}}\). From (5), we can obtain:\[\left\{\begin{array}{l}J_{xx}\left(\mathrm{R/T}\right)=\frac{I_{xx}\left( \mathrm{RSS}\right)}{1-\rho_{12}}+\frac{I_{xx}\left(\mathrm{TOF}\right)}{1- \rho_{12}}-\frac{2\rho_{12}\sqrt{\rho_{1}\rho_{2}}}{1-\rho_{12}}\sum\limits_{ k=1}^{m}\frac{\cos^{2}\alpha_{ik}}{d_{ik}}\\ J_{xy}\left(\mathrm{R/T}\right)=\frac{I_{xy}\left(\mathrm{RSS}\right)}{1-\rho_{1 2}}+\frac{I_{yy}\left(\mathrm{TOF}\right)}{1-\rho_{12}}^{2}-\frac{\rho_{12} \sqrt{\rho_{1}\rho_{2}}}{1-\rho_{12}}^{2}\sum\limits_{k=1}^{m}\frac{\sin \alpha_{ik}\cos\alpha_{ik}}{d_{ik}}\\ J_{yx}\left(\mathrm{R/T}\right)=\frac{I_{yx}\left(\mathrm{RSS}\right)}{1-\rho_{1 2}}^{2}+\frac{I_{yx}\left(\mathrm{TOF}\right)}{1-\rho_{12}}^{2}-\frac{\rho_{12 }\sqrt{\rho_{1}\rho_{2}}}{1-\rho_{12}}^{2}\sum\limits_{k=1}^{m}\frac{\sin \alpha_{ik}\cos\alpha_{ik}}{d_{ik}}\\ J_{yy}\left(\mathrm{R/T}\right)=\frac{I_{yy}\left(\mathrm{RSS}\right)}{1-\rho_{1 2}}^{2}+\frac{I_{yy}\left(\mathrm{TOF}\right)}{1-\rho_{12}}^{2}-\frac{2\rho_ {12}\sqrt{\rho_{1}\rho_{2}}}{1-\rho_{12}}^{2}\sum\limits_{k=1}^{m}\frac{\sin \alpha_{ik}}{d_{ik}}\end{array}\right.\right. \tag{29}\]  
Therefore, based on (8), the error bound by using the metric of TOF/AOA with respect to \(\theta_{i}\) is:  
\[V_{m}\left(\mathrm{T/A}\right)=\frac{J_{xx}\left(\mathrm{T/A}\right)+J_{yy} \left(\mathrm{T/A}\right)}{J_{xx}\left(\mathrm{T/A}\right)\cdot J_{yy}\left( \mathrm{T/A}\right)-J_{xy}\left(\mathrm{T/A}\right)^{2}} \tag{30}\]
## 3 Methodology
### Evaluation Method  
This study followed the procedure illustrated in Figure 1. First, four common question types from the official Hanja Proficiency Test, which are shared across Levels 1 through 6, were selected. Prompt templates and answer choice generation rules were then established for each question type. Subsequently, datasets specific to each question type and individual Hanja character data necessary for generating answer choices were constructed. Using the ChatGPT API, experiments were conducted based on this standardized dataset. The GPT-4o model was employed for the experiments.  
Following the prompt instructions, the GPT model was tasked with generating four choices, selecting the correct answer, and providing an explanation for its choice. All responses and explanations generated by GPT were recorded. The correctness of the selected answers and the accuracy of the explanations were reviewed by graduate students specializing in Classical Chinese. Lastly, the selection rate of each answer choice and the accuracy of the model-generated explanations were analyzed.  
This study evaluated the GPT model based on three metrics: the accuracy of the selected answers, the accuracy of the explanations, and the consistency in answer selection across different engines. For each GPT engine, the accuracy of explanations corresponding to both the generated and selected choices was assessed by question type. In all four question types, the correct answer was consistently set as choice 1, and choices 2, 3, and 4 were regarded as distractors. However, even if the model selected choice 1, the item was treated as containing an explanatory error if the accompanying explanation included inaccurate information.  
Responses and explanations were classified into the following five categories for systematic assessment:  
1. Both the answer and explanation were correct.
2. The answer was correct, but the explanation was incorrect.
3. The answer was incorrect, but the explanation was correct.
4. Both the answer and explanation were incorrect.
5. No explanation was provided, despite being prompted.  
By applying these detailed evaluation criteria, the response quality of GPT models was assessed systematically. For items with inaccurate or flawed explanations, the two experts engaged in discussion to reach a final evaluation. To assess inter-annotator agreement, the IAA was calculated using [PERSON]'s Kappa coefficient. The resulting score, denoted by \(\kappa\), was 0.607, indicating a substantial level of agreement between the two expert annotators.  
\[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{1}\]
###### Abstract
The aim of this study was to analyze the safety benefits and efficiency of expanding the scope of the system to include waterways of maritime routes for coastal passenger \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \multicolumn{3}{c}{**Scope of Projects Subject to MTSA**} \\ \hline Designation, notification, or modification of routes or & In cases of designating, notifying, or modifying waterway facilities \\ anchorage areas & such as routes or anchorage areas in waters navigated by vessels over 100 m long \\ \hline Construction, establishment, or maintenance of facilities & - & When establishing or repairing bridges or tunnels in waters \\ such as bridges, tunnels, or cables installed in waterways & navigated by vessels over 100 m long \\ \hline Development or redevelopment of ports or docks & - & The establishment or modification of berthing facilities used by \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of MTSA scope (Enforcement Decree of the Maritime Safety Act). Figure 1: Overview of MTSA. ships less than 100 m in length. In addition, the most appropriate option can be applied to the improvement of the MTSA system compared to the safety benefits derived and the administrative and economic burden on the operators. To achieve this, this study utilized ML models to derive the MTSA-related accident reduction rate (ARR) after implementing the MTSA system, focusing on accidents related to coastal passenger ships. The study is structured as follows:  
* The classification of maritime routes and MTSA-related accidents. * The prediction of the occurrence of MTSA-related accidents assuming no MTSA implementation. * The derivation of the MTSA-related ARR by comparing the predicted number of MTSA-related accident occurrences.
# Genetic Programming to Remove Impulse Noise in Color Images
## 5 Experimental Results
### Performance of the Detection Stage  
Experiments were conducted to measure the performance of the binary classification model implemented in the detection stage of the proposed GP filter. To this end, prior information about noisy pixel locations was stored by comparing the 360 test noisy images with their noiseless original counterparts. Later, the detection stage of the proposed GP was applied by classifying pixels as noisy or not noisy. Finally, a comparison of the model predictions with the stored prior information enabled the generation of a confusion matrix containing true positive (tp), true negative (tn), false positive (fp), and false negative (fn) predictions. The performance metrics applied to these predictions were the following: accuracy, precision, recall, specificity, and F1 score.  
Accuracy (Acc.) indicates the extent to which the predictions about the condition of the pixels differed from the real conditions, i.e., Acc = (tp + tn)/(tp + tn + fp + fn). Precision (Prec.) indicates how well the classifier performed concerning each pixel's noisy or noiseless status, computed by tp/(tp + fp). On the other hand, recall (or sensitivity) is the percentage  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Population size, \(\mu\) & 100 \\ Crossover rate, \(P_{c}\) & 0.9 \\ Mutation rate, \(P_{m}\) & 0.1 \\ Parent selection & Tournament (\(k=3\)) \\ Survivor selection & Fitness-based (\(\mu+\lambda\)) \\ Completion criteria & When the algorithm reaches \(\tau=100\) generations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration settings of the proposed genetic programming algorithm related to its behavior and performance during the training process.  
of noisy pixels that were correctly identified, calculated by tp/(tp + fn). Similarly, regarding the noiseless pixels, specificity (Spec.) is calculated by tn/(tn + fp). Finally, the F1 score represents the harmonic mean of precision and recall, i.e., F1 = (Prec-Recall)/(Prec + Recall).  
Table 3 shows the average results of the performance metrics regarding the proposed binary classification model. As can be observed, in general, the model increased the accuracy, precision, and F1 score according to the increase in the image's noise density, whereas the sensibility and specificity decreased in this respect. Note that the model is highly efficient in detecting noisy rather than noiseless pixels. Finally, although the model achieved a better balance between precision and recall in terms of salt-and-pepper noise, better accuracy was obtained in terms of uniform and correlated noise.  
Since the correction stage of the proposed GP filter consists of a modified version of the VMF (see Section 4), the average performance comparisons shown in the following section allow us to determine the influence of the detection stage on the efficiency of the GP filter.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 5 Analyzing the Selected Features
### Number of Bedrooms  
The number of bedrooms across different household energy-consumption profiles provides insightful correlations. In Figure 14, we categorize dwellings based on the number of bedrooms, including a category for more than four bedrooms, given the low proportion of such dwellings in the dataset.  
Dwellings with up to three bedrooms are predominantly found in the low-consumption profile, accounting for nearly 90% of households. This trend sharply decreases in the medium- and high-consumption profiles, where the prevalence of one and two-bedroom dwellings is significantly lower. Single-bedroom dwellings are virtually absent in the high-consumption profile, and two-bedroom dwellings constitute a small fraction (around 4.88% in k-means, 4.65% in AHC and 5.26% in SOM).  
Conversely, the proportion of dwellings with three or more bedrooms increases with the consumption profiles. This category represents almost half of the dwellings in the low-consumption profile, about three-quarters in the medium-consumption profile and close to 95% in the high-consumption profile, regardless of the clustering method used.  
This analysis reveals that dwellings with up to two bedrooms in the medium- and high-consumption profiles deviate from the common pattern, thus qualifying as out-profiled households.
## 2 Background Information
### Relative WSN Compression Approaches
The latter, is a significant drawback that negatively affects LEC's compression rate capabilities [12]. ALEC, on the other hand, is based on an adaptive lossless entropy compression approach also requiring low computational power. However, it uses three small dictionaries, the sizes of which are determined by the resolution of the analog-to-digital converter (ADC). Adaptive compression schemes allow the compression to dynamically adjust to the data source. The data sequences to be compressed are partitioned into blocks and for each block the optimal compression scheme is applied. Effectively, the algorithm is similar to LEC algorithm, while the main difference is that ALEC algorithm uses three Huffman coding tables instead of the one table used for the DC coefficients in JPEG algorithm. Specifically, ALEC uses adaptively two Huffman tables and three Huffman tables, respectively. It is noted that compared to LEC, ALEC exhibits an increased number of the lookup tables it employs. As a result, its compression-rate efficiency is also increased. However, each data block is passed through two lookup tables, which eventually will result in an increase of the algorithm's processing delay [12].
## 2 Results and Discussion
### Application of the Developed Method to Environmental Samples  
Water samples were analyzed using the developed HS-SPME method. Results of the analyzed waters and respective uncertainty are presented in Table 5.  
Siloxanes were not detected in both tap water and river water. In the wastewater sample, cyclic siloxanes presented higher concentrations, reaching up to 0.70 \(\upmu\)g/L, which is in accordance with literature values. Linear siloxanes were detected with concentrations up to 0.44 \(\upmu\)g/L. L3 and L4 concentrations were higher than those found in literature, while L5 is in accordance with the reported values, reaching up to 0.27 \(\upmu\)g/L in influent [12]. The wastewater sample should be further analyzed by GC-MS, following the exact same procedure, in order to confirm the results.  
In short, this methodology is suitable for the detection of VMSs in higher concentrations, such as effluents of industries that produce siloxanes or that utilize them in their working process. In addition, this methodology is particularly interesting for aqueous samples with low amounts of dissolved/suspended organic matter.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 2 Cross-Domain Fusion Constellation Design Method
### Basic Constellation Design
#### 2.2.4 Constellation Fusion Based on Collaborative Optimization
```
1:Input CA request,GDOP request,E request
2:Output C
3:Initiate design variables \(X=\{X_{1}^{\prime},X_{2}^{\prime},X_{3}^{\prime}\}\), \(\varepsilon\)
4:repeat
5:For satellite \(i=1,2,\cdots\) in C do
6: Evaluate CA request and update \(X_{2}^{\prime},X_{3}^{\prime}\)
7: Evaluate GDOP request and update \(X_{1}^{\prime},X_{3}^{\prime}\)
8: Evaluate E request and update \(X_{1}^{\prime},X_{2}^{\prime}\)
9:Until \(\varepsilon\) is satisfied
10: Use \(X\) to form C, output C
```  
**Algorithm 1** Multidisciplinary collaborative optimization.
## 4 Discussion
### Limitations from Soil Moisture and Canopy Temperature on Productivity  
With the increase in drought severity, the average values of available water and NPP together showed a downward trend (Figure 4), suggesting that seasonal drought limited forest production through decreasing soil moisture, which impacts on plant photosynthesis [85]. In the northern temperate and boreal forests of Canada and Amazon tropical forests, seasonal drought has been found to lead to a decline in vegetation productivity and change the regional carbon cycle [3; 16; 42; 48]. Water stored in trees may momentarily alleviate the negative impact of short-term drought [88; 90], but long-term drought may ultimately lead to tree hydraulic failure, which is one of the physiological reasons for explaining the death of trees [88; 91; 92]. Furthermore, available water for the soil layer can limit plant photosynthesis and productivity built into the ED-2.2 model [48]. The stomatal conductance equation [93] fails to characterize the soil moisture limitation of photosynthesis, however, that may be critical for the terrestrial ecosystems with seasonally dry stress [48]. The ED-2.2 model designed a wilting factor to restrain plant production with decreasing soil available moisture for filling in the gaps in the [PERSON] (1995) equation. The ED-2.2 model also defined soil water availability for photosynthesis, producing a gradual change from stomatal opening to closing when available water is close to the wilting point [48].  
With the increase in warming severity, the average values of canopy temperature increased, but NPP decreased (Figure 5), indicating that seasonal warming limited forest production through increasing canopy air space (CAS) temperature, which also impacts on plant photosynthesis [10]. Warming will lead to a decline in forest vegetation productivity [89; 94; 95]. Extreme temperatures can impact on various plant physiological processes (e.g., photosynthesis and respiration) [96]. Warming impacts on photosynthetic and respiratory activities of plants through increasing chlorophyllase activity and decreasing photosynthetic pigment [97; 98], resulting in limited plant production [96; 99]. Moreover, temperature for the CAS layer can limit plant photosynthesis and productivity built-in the ED-2.2 model. The maximum attainable carboxylation under ribulose-1,5-biphosphate (RuBP)-saturated conditions for CAS follows a modified Michaelis-Menten equation [48], in which the Michaelis constants depend on temperature, whereas the maximum carboxylation rate is expressed by a modified temperature-dependent function, explaining the rapid decline in production under the extreme temperatures [46; 100].
# Correlation Analysis and Its Application on an Asymmetry
## 2 Modeling Process
### The Governing Equations  
By assembling the corresponding matrices of each node, the mass matrix, the gyro matrix and the stiffness matrix of the whole system can be obtained. The assembled process is described by Figure 2b.  
Linear damping matrix is adopted as  
\[\mathbf{C}=a\cdot\mathbf{M}+b\cdot\mathbf{K} \tag{6}\]  
where coefficients \(a\) and \(b\) can be measured by experiment.  
Considering the gravity, unbalanced force and bearing force, the dynamic model of the rotor support structure shown in Figure 1 can be established into form  
\[\mathbf{M\ddot{q}}+(\mathbf{C}+\Omega\mathbf{G})\mathbf{\dot{q}}+\mathbf{Kq}+ \mathbf{F}_{n}=\mathbf{F}_{u}+\mathbf{F}_{g} \tag{7}\]  
where \(\mathbf{q}\) is the displacement coordinate vector, including the displacements of the two disks and two sliding bearings along the \(x\)-axis and \(y\)-axis, respectively; \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{G}\) and \(\mathbf{K}\) are corresponding mass matrix, damping matrix, gyro matrix and stiffness matrix. \(\mathbf{F}_{n}\) is the bearing force vector; \(\mathbf{F}_{u}\) is the unbalanced force vector and \(\mathbf{F}_{g}\) is the gravity vector. \(\Omega\) represents the rotating speed of the rotor which determines the frequency of the unbalanced force.
## 5 Limitations and Future Direction
Cultural backgrounds and social norms are another important issue that will inevitably affect the outcome of such analyses as human (non-verbal) interaction. In this respect, the scope of our study was also limited in that our participants were not multinationals. Although the fact that these individuals were total strangers to each other help validate the interpretation of our findings, this factor on its own is not sufficient for generalization of our results across cultures. Therefore, future research must take into account such differences, thereby focusing on studies that introduce such cultural variabilities. From a broader perspective, it is a well-known fact that the performance of the machine learning algorithms is highly dependent on the sample size based on which these algorithms are trained. It is also widely demonstrated that limited data can result in degradation of these models' performance and consequentially bias and/or reduce their accuracy. To (at least partially) control for such undesirable effects, we opted for a strategy in which we repeated our cross-validation for 1000 random split of our 5664 distance data points between train and test sets. We also further carried out our statistical tests of significance on these models' RMSE and \(R^{2}\) values through 1000 rounds of random sampling (without replacement). These steps enabled us to estimate these models' RMSE more reliably and \(R^{2}\) values as well as to obtain a more accurate estimate of their differences. However, to draw a more informed conclusion on our results, it is necessary to repeat these analyses with a larger sample sizes and in scenarios in which more comprehensive sets of non-verbal human behaviour are considered. Such analyses will not only let researchers realize the extent to which our results can be generalized but also will help them acquire more thorough understanding of the domains of application for these algorithms. Last, we limited our analyses of the facial pre-touch distances to GP, SVR, and Lasso regressors. It is apparent that the choice of regression models for approximating non-verbal human behaviour is not limited to these options. Future research can benefit from the use of other sophisticated predictive approaches such as linear mixed-effects models [39] and Bayesian linear regression [40], thereby further advancing the findings that are presented in the present study. Conceptualization, M.S. and T.M. ; formal analysis, S.K. ; investigation, M.S., T.M., and K.S. ; resources, H.I. ; data curation, M.S. ; writing--original draft preparation, S.K. ; writing--review and editing, S.K. ; M.S. ; project administration, M.S. and H.I. ; funding acquisition, M.S., T.M., and H.I. All authors have read and agreed to the published version of the manuscript. This research work was supported in part by Japan Science and Technology Agency (JST) CREST (Grant Number JPMJCR18A1), Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant Number JP17K00293), and JST ERATO Ishiguro Symbiotic Human-Robot Interaction Project (Grant Number: JPMJER1401). The authors declare no conflict of interest.