esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Validation
### Estimation of Optimal Analysis Parameters  
In this paper, six analysis parameters that have uncertain values are considered for sensitivity analysis. However, analysis parameters that have a trivial influence on the analysis results may shift the optimal point significantly, since large changes in these parameters may cause only a small difference in the objective function described in Section 3.3. Therefore, the porosity, OPD, and absorptivity, which are identified as dominant analysis parameters, are selected as design variables for optimization. The reference data to evaluate the objective function of parameter optimization for numerical validation of the proposed method are the melt pool width and depth for all process parameter cases listed in Table 2, which were obtained from the thermal analysis model with each analysis parameter case listed in Table 5. If the melt pool width and depth from actual experimental builds are available, they can be used to evaluate the objective function to find the optimal analysis parameter values for a thermal analysis model that can predict the characteristics of actual builds accurately. The optimization results and errors for the numerical validation cases listed in Table 5 are shown in Table 7. Errors in Table 7 are the differences between the originally assumed analysis parameter values listed in Table 5 and the analysis parameter values obtained by parameter optimization. As shown in Table 7, three different optimization algorithms converge to similar values with small errors of less than 2% in all cases. Through the numerical validation process as described above, it was confirmed that the  
Figure 9: Regression model validation results (melt pool size): (**a**) analysis parameter case 1; (**b**) analysis parameter case 2; (**c**) analysis parameter case 3.  
analysis parameters of the thermal analysis model that generated the reference data could be estimated accurately using the melt pool size information given as the reference data, and this means that if proper reference data can be obtained from actual experimental builds, a thermal analysis model that behaves similarly to the actual LPBF process can be constructed by using the reference data.  
All optimization algorithms used in this study produce similar numerical results, but the number of iterations and elapsed time differ, as shown in Table 8. In this case, the gradient descent algorithm seems to successfully converge to a global optimum, and it requires a much smaller number of iterations than other algorithms. However, even for this algorithm, it may take more than 100 h to obtain converged solutions without the regression model, since it takes an average of 2.44 h to perform a thermal analysis once, and 254 evaluations of the objective function are required to complete the iterations. By utilizing the regression model, only 41 cases of thermal analysis need to be performed to complete the parameter optimization; otherwise, thermal analysis should be repeated for every iteration of the parameter optimization, which is almost infeasible.
## 2 System Structure
### User Terminal  
The user terminal includes a GPS receiver module, wireless communication module (_i.e._, Wi-Fi) and positioning calculation module, as shown in Figure 4. The GPS receiver is used to track the mimicked GPS signals from the different transmitting antennas. It is also used to estimate the pseudorange measurements rather than user position. This is because the position estimated by the GPS receiver is not the real user position. The range from the user terminal to TA, which is required in positioning estimation, is contained in the pseudorange measurement from the GPS receiver module. The wireless communication module is used to acquire the Rx and Tx information from the server, which are also used to estimate the range to TA. The positioning module receives the pseudorange measurements from GPS receiver module and wireless communication module, calculating the range to each transmitting antenna, and then estimating the user position.
## 3 Results and Discussions
### Temporal Variations and Comparison of m/z 44 Signal with COO of Total Carboxylic Acids
The more oxidized LV\(-\)OOA component showed strong correlation with sulfate [104, 105, 106]. Further, we can compare our data with those from other locations of the world. Figure S2 in the supporting information shows the relation between \(f_{44}\) (ratio of m/z 44 to total organics) and \(f_{43}\) (ratio of m/z 43 to total organics). \(f_{44}\) axis in Figure S2 can be considered as an indicator of photochemical aging, which leads to an increase in \(f_{44}\)[59, 107, 108, 109]. As seen in Figure S2,\(f_{43}\) values are lower than those from [PERSON] et al. [110], suggesting that non-acid oxygenates are less abundant in Okinawa aerosols. The m/z 44/OA (\(f_{44}\)) and m/z43/OA (\(f_{43}\)) ratios depend on the molecular structure of the precursors and types of oxidants [110, 111, 112]). The oxidation of aromatic hydrocarbons results in ring-opening [111, 113, 114, 115]. [PERSON] et al. [111] reported that the \(f_{43}\) of benzene oxidation products was very low (0.01), and \(f_{44}\) decreased with increasing numbers of alkyl substituents on aromatic rings. The \(f_{43}\) and \(f_{44}\) values of benzene-derived SOA are 0.016 and 0.17, respectively. The \(f_{43}\) and \(f_{44}\) values of ethylbenzene products (0.023 and 0.12) [111] during photooxidation are close to those in the present study (0.04 and 0.12). These results are consistent with the hypothesis that aromatic hydrocarbons are a potential source of SOA formation in Okinawa. [PERSON] et al. [112] reported \(f_{44}\) and \(f_{43}\) values for naphthalene (range: 0.11-0.17 and 0.015-0.094) and butadiene (range: 0.10-0.14 and 0.03-0.05). The \(f_{44}\) and \(f_{43}\) values from this study are within those for SOA products from naphthalene and butadiene [112]. Hence, main the SOA precursors of Okinawa aerosols may be alkyl benzenes, naphthalene, and butadiene via long-range transport (Figure S2).
## 1 Introduction
Additionally, they described the differences in energy conversion at each stage. [PERSON] [29] investigated the impact of water content on the damage and energy dissipation characteristics of coal bodies through uniaxial cyclic loading and unloading tests. He [30] explored the linear relationship between the dissipated energy coefficient and both the yield and peak strengths by conducting uniaxial compression tests on various types of rocks. In addition, some researchers have examined the energy evolution of rocks under triaxial cyclic loading conditions. [PERSON] [31] investigated the energy characteristics of marble under triaxial loading and unloading conditions, deriving the influence of confining stress and unloading rate on the energy evolution of rock samples. [PERSON] [32; 33] experimented triaxial variable upper and lower limit equal amplitude cyclic loading and unloading of natural state shale under different confining stresses and found that the stresses reaching the energy weakening stage at low confining stresses are lower than those at high confining stresses, while the stresses reaching the energy strengthening stage are higher than those at high confining stresses. [PERSON] [34] conducted triaxial cyclic loading and unloading tests on rock samples under six different confining stresses. The authors proposed a method for calculating rock energy based on the areas of the loading and unloading curves and employed characteristic energy density to characterize the energy evolution behavior of loaded rock samples, thereby elucidating the energy evolution process and the distribution pattern of the confining stress effect. Rock energy evolution under complex cyclic loading needs additional study; however, most studies focus on input, dissipation, and elastic energy rather than energy percentages and damping coefficient. This work provides a stress path that transcends prior research' unidirectional cycling, presenting a graded multiple-cycle loading and unloading stress path that integrates simultaneous fluctuations in axial and confining stress. Analyzing deformation parameters, including elastic modulus, Poisson's ratio, and irreversible deformation, alongside energy indices such as energy percentage and damping ratio, elucidates the mechanical response and energy evolution patterns of coal during this stress perturbation process. Two damage variables that reflect the development of coal damage were calculated from both deformation and energy perspectives. Additionally, the effects of the cyclic period on coal samples were compared in terms of deformation and energy. This study offers a theoretical foundation for a deeper understanding of the mechanical response, energy conversion, and damage deterioration mechanisms of coal under dynamic loading. Additionally, it holds significant reference value for evaluating the safety of coal bodies and predicting power-related disasters during the coal mining process.
###### Abstract  
Monitoring the learning process during task solving through different channels will facilitate a better understanding of the learning process. This understanding, in turn, will provide teachers with information that will help them to offer individualised education. In the present study, monitoring was carried out during the execution of a task applied in a self-regulated virtual environment. The data were also analysed using data fusion techniques. The objectives were as follows: (1) to examine whether there were significant differences between students in cognitive load (biomarkers: fixations, saccades, pupil diameter, galvanic skin response--GSR), learning outcomes and perceived student satisfaction with respect to the type of degree (health sciences vs. engineering; and (2) to determine whether there were significant differences in cognitive load metrics, learning outcomes and perceived student satisfaction with respect to task presentation (visual and auditory vs. visual). We worked with a sample of 31 university students (21 health sciences and 10 biomedical engineering). No significant differences were found in the biomarkers (fixations, saccades, pupil diameter and GSR) or in the learning outcomes with respect to the type of degree. Differences were only detected in perceived anxiety regarding the use of virtual laboratories, being higher in biomedical engineering students. Significant differences were detected in the biomarkers of the duration of use of the virtual laboratory and in some learning outcomes related to the execution and presentation of projects with respect to the variable form of the visualisation of the laboratory (visual and auditory vs. visual). Also, in general, the use of tasks presented in self-regulated virtual spaces increased learning outcomes and perceived student satisfaction. Further studies will delve into the detection of different forms of information processing depending on the form of presentation of learning tasks.  
monitoring: learning; cognitive load; eye tracking; galvanic skin response; data fusion +
Footnote †: journal: Applied Sciences  
==WARNING: Truncated because of repetitions==  
+++  
Regarding the workload according to the type of task, for example, using simulation videos with self-regulated design seems to increase information in working memory. Related cognitive biomarkers indicators are increased pupil diameter [21], increased electrodermal activity [22], changes in facial expressions [23] and variations in positive and negative valences recorded via EEG recordings [24]. However, interpreting these cognitive biomarkers is complicated, depending on the type of learning and the phase of learning [22]. Problem solving depends on several variables, such as the learner's metacognitive strategies, the degree of prior knowledge, how the problem is presented (structured vs. unstructured) and whether presentation includes self-regulated instruction [25]. However, the use of this methodology in the study of learning processes is just beginning. Therefore, further studies are needed in order to obtain data that can be used to develop personalised learning [17].
## 1 Introduction
Some studies do not even support the feasibility of earthquake prediction based on infrared remote sensing data [30]. Although some researchers obtained the PPV of 76.1% and the TPR of 67.1%, the spatiotemporal occupation of anomalies is high at 43.4%, which may cause a large prediction range and excessive public panic [31]. To explore a more valid algorithm for earthquake prediction using the data of infrared remote sensing, both data and research methods are improved. For one thing, multi-channel infrared images are used in this paper, and the four channels could provide more information about the state of the earth's surface and atmosphere. For another thing, the statistical method based on connected region recognition is proposed to analyze the correlation between infrared anomalies and earthquakes, which could recognize spatiotemporal characteristics of anomalies in the long-term and wide-region studies. In this paper, the relative power spectrum method is respectively used to extract the anomalies from data of every channel. Both statistical analysis and case study are used to compare the prediction performance of the data from any single channel. Finally, four-channel anomalies are analyzed statistically. The results show that multi-channel anomalies could provide larger PPV and probability gain. It proves the potential of multi-channel infrared data in earthquake prediction and shows that it is possible to identify anomalies associated with earthquakes using multi-dimensional or multi-source data. The statistical method based on connected region recognition could be used to analyze pre-seismic anomalies from most kinds of remote sensing data. It lays the foundation for more data to be used in earthquake prediction.
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System
### Dataset Generation
We randomly extract 15% of the data from the entire dataset and use it as a validation set, and use the remaining 85% as a training dataset. The validation set is used to measure the performance of the model built on the training set. In general, various parameters and models are used to find out which model best fits the data, and among them, the model with the best performance is selected as the validation set. For the construction of the test set, we additionally collected data on three driving behaviors of one driver each on the simulated and real roads. The test set is used to measure the performance of the model after the model to be used as the validation set is decided. The total number of data used in the test is presented in Table 3, divided by scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Defensive Behavior (m/s2)**} \\  & Acceleration X & Acceleration Y & Acceleration Z \\ \hline Median & 0.0209606 & 0.0041105 & \(-\)0.0003024 \\ Standard Deviation & 0.439897633 & 0.517115998 & 0.263576081 \\ \hline  & \multicolumn{3}{c}{**Normal Behavior (m/s2)**} \\  & Acceleration X & Acceleration Y & Acceleration Z \\ \hline Median & 0.0176545 & 0.0027567 & \(-\)0.0005219 \\ Standard Deviation & 0.519467419 & 0.876498761 & 0.373084299 \\ \hline  & \multicolumn{3}{c}{**Aggressive Behavior (m/s2)**} \\  & Acceleration X & Acceleration Y & Acceleration Z \\ \hline Median & 0.07252035 & 0.02170125 & \(-\)0.00392755 \\ Standard Deviation & 0.915436498 & 1.462502723 & 0.491320123 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three-axis acceleration standard deviation and median according to driving behavior. Figure 11: Real-world vehicle for data collection and testing.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 3 Proposed Method
### Graph-Based Multilevel Distribution Propagation
** The object loss is inherited from baseline(DPGN [26]):  
\[L_{l}^{object}=L_{CE}\Big{(}P^{object}\big{(}y_{j}|x_{i}\big{)},\;y_{j}\Big{)}, \tag{13}\]  
where \(L_{CE}\) is the cross-entropy loss function. \(P^{object}\big{(}y_{j}|x\;_{i}\big{)}\) is calculated by (11). **Distribution Loss. ** The distribution loss in the \(l\)-th iteration is calculated as follows:  
\[L_{l}^{dist_{x}}=L_{CE}\Big{(}P^{dist_{x}}\big{(}\hat{y}_{i}|x\;_{i}\big{)}, \;y_{i}\Big{)},\quad x=\{1,\;2,\;3\}, \tag{14}\]  
\[L_{l}^{dist}=\lambda_{dist_{1}}L_{l}^{dist_{1}}+\lambda_{dist_{2}}L_{l}^{dist_ {2}}+\lambda_{dist_{3}}L_{l}^{dist_{3}}/N_{\lambda},\;\;N_{\lambda}=\sum_{x=\{ 1,\;2,\;3\}}(\lambda_{dist_{x}}!=0), \tag{15}\]  
where \(P^{dist_{x}}\big{(}y_{j}|x\;_{i}\big{)}\) is calculated by (12). \(L_{l}^{dist_{1}}\), \(L_{l}^{dist_{2}}\), and \(L_{l}^{dist_{3}}\) are the three distribution losses. \(\lambda_{dist_{1}}\), \(\lambda_{dist_{2}}\), and \(\lambda_{dist_{3}}\) are the weight factors of the corresponding distribution loss. \(\big{[}\lambda_{dist_{1}}\), \(\lambda_{dist_{2}}\), \(\lambda_{dist_{3}}\big{]}=[0.0,\;0.0,\;1.0]\) in a 5-way 1-shot setting and \(\big{[}\lambda_{dist_{1}}\), \(\lambda_{dist_{2}}\), \(\lambda_{dist_{3}}\big{]}=[1.0,\;1.0,\;1.0]\) in a 5-way 5-shot setting. The reason for the setting of the weight factors will be explained in Section 4. The objective function is made up of these two losses:  
\[L=\sum_{l=1}^{l}\Big{(}\lambda_{object}L_{l}^{object}+\lambda_{dist}L_{l}^{dist }\Big{)}, \tag{16}\]  
where \(\hat{l}\) is the total number of iterations in the GMDP module. The two weight factors \(\lambda_{object}\) and \(\lambda_{dist}\) are used to measure the importance of the two losses. We follow the setting of the baseline: \(\big{[}\lambda_{object},\lambda_{dist}\big{]}=[1.0,\;0.1]\).
## 2 Background
### Energy Efficiency Design Index (EEDI)  
The EEDI regulations regulate a ship's CO\({}_{2}\) emissions by specifying its maximum allowed EEDI value determined as a function of deadweight tonnage (DWT) or gross tonnage (GT), separately for different types of ships (e.g., bulk carriers, tankers, gas carriers, roll-on/roll-off cargo ships) [15]. To stimulate continued innovation and technical development, the maximum allowed EEDI will be tightened incrementally every five years [9].  
In simplified terms, the EEDI value represents the amount of CO\({}_{2}\) generated by a ship carrying out a specific transport work [15]. Accordingly, the EEDI value can be expressed as per Equation (1) based on engine power, specific fuel consumption (SFC), an assumed amount of CO\({}_{2}\) per gram of fuel (C\({}_{\text{F}}\)), DWT, and ship speed [16].  
\[\text{EEDI}=\frac{\text{CO${}_{2}$ emissions}}{\text{transport work}}=\frac{\text{ Engine power}*\text{C${}_{\text{F}}$}}{\text{DWT}*\text{speed}} \tag{1}\]  
As per Equation (1), for a given type of engine and fuel, the EEDI regulations effectively limit the maximum installed propulsion power. To make ice-class ships comparable with open water ships, considering that they need extra propulsion power for operation in ice, the EEDI regulations include correction factors [17]. These are determined for five different types of ships: tanker, bulk, general cargo, container, gas carriers, and roll-on/roll-off ships [17]. Notwithstanding, the EEDI regulations are expected to reduce the average propulsion power, and consequently also the average ice-going capability of ice classed ships. This implies a reduction in both the maximum ice conditions in which ships can operate independently and the speed of ships in ice. For a given operating scenario, this will increase both the number of instances where a ship needs IB assistance and the duration of each instance of IB assistance. As a result, the demand for IB assistance is expected to increase [18]. Additionally, considering the maritime industry's overall efforts to optimize maritime operations, the demand for IB assistance might also be driven by non-EEDI-related cost and energy consumption reducing measures, further reducing the ice-going capability of ships.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 3 Proposed Algorithm  
The proposed algorithm makes use of CDMA based spread spectrum concept, explained earlier, for embedding and decoding the watermark. The spreading codes used are the hadamard codes which are orthogonal codes.
## 5 Comparative Analysis
### Comparative Analysis of Coherence  
Utilizing DInSAR technology, this research obtained two sets of coherence maps for SAR satellites in the study area, as illustrated in Figure 9. These maps were categorized into five coherence levels: poor (0-0.2), fair (0.2-0.4), medium (0.4-0.6), good (0.6-0.8), and high (0.8-1).  
The coherence calculations were performed using a uniform estimation window size of 5 \(\times\) 5 pixels for both Sentinel-1 and LT-1 data to ensure a fair comparison. The choice of this window size balances the need to preserve spatial resolution while achieving reliable coherence estimates. We employed bias-corrected coherence for both datasets to minimize potential estimation biases that may affect the coherence values, ensuring an accurate comparison. This method allows for a more realistic reflection of the coherence in the study area by accounting for inherent biases in the coherence estimation process [35].  
Figure 9d,e demonstrates the coherence distribution for LT-1 and Sentinel-1 satellite data in the study area, respectively. A detailed statistical analysis was conducted to quantitatively assess the coherence of both SAR datasets, with the findings presented in Figure 10a. The analysis indicates that 36.2% of LT-1 data and 11.4% of Sentinel-1 data exhibit coherence levels above 0.6, signifying superior coherence in LT-1 data. Notably, the number of coherent pixels exceeding 0.6 in coherence for LT-1 data is 121.1 times greater than that of Sentinel-1. This is despite LT-1 data having a larger spatial baseline, suggesting its advantage in maintaining high coherence across extensive spatial baselines.  
To further scrutinize coherence disparities in subsidence regions, the study compared the coherence outcomes of LT-1 and Sentinel-1 data in two distinct subsidence areas, and the results are shown in Figure 10b,c. Figure 9a-c focuses on a black-framed grassland area within area A for coherence ratio assessment. In this area, LT-1 data showed 79.2% coherence above 0.6, compared to 21.5% for Sentinel-1. Figure 9f-g analyzes a similar grassland area in area B, revealing 92.8% coherence above 0.4 for LT-1 and 27.4% for Sentinel-1. These results collectively underscore the notably higher effective coherence of LT-1 data over Sentinel-1.  
The statistical data clearly show LT-1's superior coherence relative to Sentinel-1, attributable to the longer L-band wavelength of LT-1, which ensures enhanced vegetation penetration and improved coherence. Conversely, the shorter C-band wavelength of Sentinel-1 exhibits weaker penetration and reduced coherence. Consequently, selecting LT-1 data as the SAR data source significantly optimizes coherence outcomes.  
Figure 10: (**a–c**) Statistical chart of coherence comparison in the study area, area A, and area B.  
Figure 9: (**d,e**) Coherence maps of LT-1 and Sentinel-1, respectively; (**a–c,f–h**) enlarged views of LT-1, Sentinel-1, and Google optical images in the typical subsidence areas A and B, respectively.