esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Remote sensing of disturbance in the savannas from Brazil is challenging, especially due to confounding effects of the vegetation phenology and natural soil exposure on the detection of clearing and fire events. In this study, we investigated the detection of disturbance over this global hotspot of biodiversity using seven vegetation indices (VIs) calculated from the Landsat time series (2017-2019) and the Continuous Change Detection and Classification (CCDC) algorithm. The selected VIs represented distinct biophysical characteristics of the savannas. We evaluated the effects of disturbance on these VIs and assessed the accuracy of CCDC-detection in 2019, considering individual VIs, ensemble VIs, and the type of disturbance (savanna clearing and fire). Finally, we analyzed the possible existence of seasonal patterns of disturbance in a study area located at the new agricultural frontier of the _Cerrado_ biome. The results showed that the overall accuracy of CCDC detection of total disturbance ranged from 51.2% for the Green-Red Normalized Difference (GRND) to 65.9% for the Normalized Burn Ratio (NBR2). It increased to 71.2% for ensemble VIs, whose multivariate approach reduced the omission errors in the analysis when compared to the use of single VIs. For detecting events of savanna clearing and fire, the most important VIs used near-infrared and shortwave infrared reflectance bands on their formulations (NBR2, NBR, and Moisture Stress Index--MSI). The CCDC accuracy was generally higher for detecting clearing than for mapping burned areas. In contrast, the recorded date of disturbance occurrence was less precise for detecting clearing than for recording events caused by fire, especially due to the existence of some gradual processes of vegetation degradation until complete clearing. Our findings showed also the existence of a seasonal pattern of disturbance occurrence. Savanna clearing predominated in the transition from the rainy to the dry season (April to July) to open new areas for agriculture. It preceded most events of fire disturbance between August and October that occurred near the consolidated areas of agriculture and extended into the native vegetation areas. Results reinforce the importance of data-driven approaches for generating early warning alerts of disturbance in the _Cerrado_ to be further checked in the field.  
savannas; _Cerrado_; vegetation indices; disturbance; clearing; fire; CCDC; time series
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
###### Abstract  
In this paper, we theoretically study the achievable capacity of orthogonal and non-orthogonal multiple access (OMA and NOMA) schemes in supporting downlink satellite communication networks. Considering that various satellite applications have different delay quality-of-service (QoS) requirements, the concept of effective capacity is introduced as a delay-guaranteed capacity metric to represent users' various delay requirements. Specifically, the analytical expressions of effective capacities for each user achieved with the NOMA and OMA schemes are first studied. Then, approximated effective capacities achieved in some special cases, exact closed-form expressions of users' achievable effective capacity, and the capacity difference between NOMA and OMA schemes are derived. Simulation results are finally provided to validate the theoretical analysis and show the suitable limitations of the NOMA and OMA schemes, such as the NOMA scheme is more suitable for users with better channel quality when transmit signal-to-noise (SNR) is relatively large, while it is suitable for users with worse link gain when transmit SNR is relatively small. Moreover, the influences of delay requirements and key parameters on user selection strategy and system performance are also shown in the simulations.  
s +
Footnote †: journal: Electronics Electronics  
1
## 2 Multiple Types of Non-Probabilistic Representation Approaches for Epistemic Uncertainties
### Triangular Fuzzy Approach  
Fuzzy set theory was introduced by [PERSON] [47] and has been widely applied to various fields, such as uncertainty analysis, information fusion, decision-making, and clustering. A fuzzy set \(\Psi\) is defined as a pair \(\Psi=(x,\mu(x))\), where \(x\in\Omega\) is an element in the domain \(\Omega\), and \(\mu(x)\) is the membership function that can be defined as \(\mu(x)\in[0,1]\). The \(\alpha\) level of the fuzzy set \(\Psi\) is defined as \(\Psi^{\alpha}=\left\{x\in\Omega\big{|}\mu(x)=\alpha\right\}\), and the \(\alpha\)-cut of the fuzzy set is defined as \(\Lambda_{A}^{\alpha}=\left\{x\in\Omega\big{|}\mu(x)\geq\alpha,\alpha\in[0,1 ]\right\}\). For each \(\alpha\)-cut level, the uncertain interval of \(x\) is obtained as \(\left[x_{\alpha}^{l},x_{\alpha}^{l}\right]\). \(x_{\alpha}^{l}\) and \(x_{\alpha}^{l}\) are the lower and upper bound of \(x\), which are calculated based on the value of \(\alpha\) and the membership function of \(\mu(x)\).  
The shape of \(\mu(x)\) may be arbitrary, and many different calculation methods for the fuzzy set have been proposed, such as the L-fuzzy set, triangular fuzzy number, and fuzzy rough sets. The triangular fuzzy number is the most common representation model of the fuzzy set. The membership function \(\mu(x)\) is defined in Equation (2), where \(b\) is the most likely value, and \(a\) and \(c\) are the lower and upper bound of \(x\), respectively. For convenience, the triangular fuzzy number can be denoted by \([a,b,c]\). The lower bound \(x_{\alpha}^{L}\) and upper bound \(x_{\alpha}^{U}\) by \(\alpha\) cut of the membership function are shown in Figure 1:  
\[\mu(x)=\begin{cases}0&x<a\\ \frac{x-a}{b-a}&a\leq x\leq b\\ \frac{c-x}{c-b}&b\leq x\leq c\\ 0&x>c\end{cases}. \tag{2}\]
# Experimental Analysis of Driver Visual Characteristics in Urban Tunnels
###### Abstract  
Through an urban tunnel-driving experiment, this paper studies the changing trend of drivers' visual characteristics in tunnels. A Tobii Pro Glasses 2 wearable eye tracker was used to measure pupil diameter, scanning time, and fixation point distribution of the driver during driving. A two-step clustering algorithm and the data-fitting method were used to analyze the experimental data. The results show that the univariate clustering analysis of the pupil diameter change rate of drivers has poor discrimination because the pupil diameter change rate of drivers in the process of \"dark adaptation\" is larger, while the pupil diameter change rate of drivers in the process of \"bright adaptation\" is relatively smooth. The univariate and bivariate clustering results of drivers' pupil diameters were all placed into three categories, with reasonable distribution and suitable differentiation. The clustering results accurately corresponded to different locations of the tunnel. The clustering method proposed in this paper can identify similar behaviors of drivers at different locations in the transition section at the tunnel entrance, the inner section, and the outer area of the tunnel. Through data-fitting of drivers' visual characteristic parameters in different tunnels, it was found that a short tunnel, with a length of less than 1 km, has little influence on visual characteristics when the maximum pupil diameter is small, and the percentage of saccades is relatively low. An urban tunnel with a length between 1 and 2 km has a significant influence on visual characteristics. In this range, with the increase in tunnel length, the maximum pupil diameter increases significantly, and the percentage of saccades increases rapidly. When the tunnel length exceeds 2 km, the maximum pupil diameter does not continue to increase. The longer the urban tunnel, the more discrete the distribution of drivers' gaze points. The research results should provide a scientific basis for the design of urban tunnel traffic safety facilities and traffic organization.  
traffic safety; urban tunnel; driver behavior; visual features; cluster analysis +
Footnote †: journal: Applied Sciences
## 3 Experimental Setup and Procedure
### Instrumentations  
To measure the bending moments along the pile, the strain gauges (B \(\times\) 120-50 AA, electrical resistance type sensor with a precision of \(\pm\)1% FS, i.e., \(\pm\)1% full-scale) were symmetrically embedded in the predesigned grooves along the piles, so that its axis is parallel to the pile axis, and protected with thin coated transparent paper [42]. The half-bridge connection was adopted to decrease the damage to the pile caused by the groove and the interference between strain gauge wires, and temperature compensation was used throughout the testing. After being installed, these sensors were first inspected by the multimeter, then sealed with polished epoxy resin, and finally connected to the data collection instrument (TDS-540) (Figure 7). The correction was carried out before each measurement based on the instrument handbook. The earth pressure cells (DYB-2, electrical resistance stain type sensor with a range of 100 kPa and a precision of \(\pm\)0.5%FS) were also symmetrically installed along the piles by the double-sided foam tape (Figure 7d). The distances between the strain gauges and the pile head were 80, 180, 280, 380, 480, 580, 680, and 780 mm, respectively. The distances between the earth pressure cells and the pile head were 110, 260, 410, 560, and 710 mm, respectively; the distances between the dial indicators and the ground surface were \(-\)30 (above the ground surface), 90, 240, 390, 540, and 690 mm, respectively (Figure 5). To capture the pile deflection profile and exclude the impact of sandy soil, the extended probes of dial indicators with a span of 10 mm were first embedded in rigid \(\phi\) 20 PVC (polyvinyl chloride) pipes at the designated elevations and then encapsulated by a small flexible tent (tensioned tape) (Figure 8). Instead of directly contacting the model pile, a gap of about 15 mm (greater than the range of the dial indicators) between the piles and the PVC pipes was reserved for the flexible tent to exclude the influence of the PVC pipe encapsulating the dial indicator on the pile responses.  
Figure 7: Installation of strainometers and earth pressure sensors: (**a**) model piles with predesigned grooves; (**b**) installed strain gauges tested by multimeters; (**c**) sealed with polished epoxy resin; (**d**) earth pressure cells.
## 4 Result Analysis and Discussion
### Comparison Between Models
Figure 9 shows the final improved accuracy and error reduction in the proposed model (SwT+Resnet50V2) during the training and validation phases. Figure 10 shows the confusion matrices for the five models, where the (SwT+Resnet50V2) model outperformed the others by achieving correct predictions and a few incorrect predictions. The confusion matrix is a summary of accurate and inaccurate predictions.
## 5 Experimental Results and Analysis
### Simulation Analysis of GA-DQN
When \(k=0\), it corresponded to the traditional DQN algorithm; when \(k=10\), the GAN could not effectively learn the jamming signal characteristics, due to the excessively short generation cycle, which affected the algorithm's convergence; when \(k=30,40,50\), the longer generation cycles failed to achieve the optimal performance; when \(k=20\), the GAN could both learn the  
Figure 8: Comparison of DQN algorithm at different learning rates. Figure 7: Time–frequency schematic diagram of jamming environment. jamming signal characteristics and effectively improve the convergence speed, achieving the ideal effect. Therefore, the experience sample generation cycle was set to 20 in the subsequent experiments. Figure 10 shows the performance comparison between the GA-DQN algorithm and the three other comparative algorithms in the four types of jamming environments. It can be seen from the figure that the GA-DQN algorithm had the fastest convergence speed and the highest convergence value. In single- and composite-jamming environments, the GA-DQN algorithm outperformed the traditional Deep Reinforcement Learning algorithms such as the DQN and PPO in convergence, with significantly reduced jamming times in the early communication stage; the DQN algorithm converged slightly slower than the GA-DQN but could reach around 1; the PPO algorithm performed slightly worse than the DQN and needed about 275 time slots to converge, while the QL algorithm showed the worst performance and could only converge to about 0.65. In the dynamic-jamming environment, the GA-DQN algorithm could converge at 170 time slots and resist the last fixed multi-channel jamming; the DQN algorithm could only converge to about 0.94, and the PPO and QL algorithms had poor convergence with severe jamming. In the intelligent-jamming environment, the normalized throughput performance of the GA-DQN algorithm decreased at the beginning of communication, started to rise at 100 time slots, and converged to about 0.95 at 130 time slots; due to the intelligent jamming changing every 10 time slots, the other three algorithms did not achieve convergence within 300 time slots. Table 3 aggregates the anti-jamming performance metrics (convergence slots and throughput performance) of our proposed algorithm alongside the three benchmark algorithms across the four distinct jamming environments. The comparative analysis visually demonstrates our algorithm's superior convergence speed and enhanced throughput efficiency relative to the alternatives. Figure 9: Comparison of the proposed algorithm in different generation cycles.
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 2 Materials and Methods  
### LIG Fabrication  
An infrared (1064 nm, Nd:YvO4) fiber nanosecond laser system (Datalogic Arex 20 MW, Bologna, Italy) was used to fabricate LIG. The laser system consists of a galvanometric optical scan system coupled with an F-theta lens enabling flexible and easy fabrication of software-designed patterns using the (Lighter MARYIS software). For all the modifications performed in this study, the scan speed and pulse repetition rate were fixed at 3 mm/s and 22 kHz, respectively. For laser fabrication of the sensor, an average power of 9 W was used. A Direct Laser Writing (LDW) approach was used to generate LIG from the polyimide Kapton tape through a thermal decomposition process, as shown in Figure 1. Since the precursor is in the form of a polyimide tape, the laser-generated LIG is independent of the base substrate. To demonstrate the fabrication of LIG on various substrates, soda-lime glass, aluminum alloy (Al 6061), low density polyethylene (LDPE) and silicon were used as the base substrates.
## 6 Numerical Experiment
### Results and Discussion: Comparison of the GMM and IDNN  
The performance of the ML models that use the feature vectors \(\mathbf{f}_{k}^{\text{GMM}}\) and \(\mathbf{f}_{k}^{\text{IDNN}}\) is compared on the basis of Section 6.1.2. Table 2 shows the average and standard deviation of 20 accuracies on training and test data by month in \(\theta_{\text{th}}=1.00\). The header of the table indicates the combination of the feature extraction method and the ML model, e.g., GMM-SVC indicates that SVC trained by \(\mathbf{f}_{k}^{\text{GMM}}\). In addition, the parameters providing the highest average accuracies for each feature extraction method on the test data are indicated in parentheses. These include \((n)\), representing the number of GMD components for the GMM, and \((\lambda,n^{(l)})\) for the IDNN, which denotes the number of hidden layers and the number of neurons in each layer, respectively. These accuracies are obtained via the SVC, RF, and DNN with optimized hyperparameters in space described in Section 5.3. The highest average accuracy for each month is indicated in bold. The difference in accuracy between the training and test data serves as a critical indicator of the model's generalization performance. Specifically, when training accuracy is substantially higher than test accuracy, it indicates that the model is overfitting. Conversely, a small difference in accuracy implies that the model exhibits high generalization performance. Therefore, evaluating the accuracy difference between training and test data provides a more detailed assessment of each model's generalization capabilities. The results show that the accuracy of the GMM-SVC is the highest in January and February, whereas the GMM-RF achieves the highest accuracy from March to June. GMM-SVC and GMM-RF consistently show higher accuracy than IDNN-SVC and IDNN-RF across all months. The scores of IDNN-SVC fall within the 1-3 SD range of the scores for GMM-SVC and GMM-RF, indicating relatively small differences in accuracy. On the other hand, focusing on the accuracy differences between the training and test data, the range for GMM-SVC and GMM-RF is 0.019-0.392, whereas IDNN-SVC and IDNN-RF exhibit a wider range of 0.358-0.679. This suggests that IDNN-SVC and IDNN-RF are prone to overfitting, whereas feature extraction via the GMM demonstrates better generalization performance. Note that, except for IDNN-SVC in March and May and IDNN-DNN in March and June, all the results have \(p>0.050\), confirming that the accuracy distributions on test follow a normal distribution. This experiment was conducted with a missing ratio of \(\theta_{\text{th}}=1.00\), resulting in highly sparse trajectories. Consequently, the results indicate that the GMM-based features achieve higher accuracy and generalization performance than the IDNN does for sparse trajectories. The reliance of the IDNN on image-based trajectory transformations likely makes it more susceptible to noise, reducing its generalization performance. In other words, imputed trajectory information, when directly converted into pixel values, can lead to irregular
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 4 Experimental Validation
### Evaluation Criteria
#### 4.1.3 Score Evaluation Function  
In real-world situations, the relationship between the true value and the predicted value holds crucial significance for the maintenance strategy. Early prediction enables timely maintenance; however, overly early prediction may result in a waste of resources. On the other hand, delayed prediction can give rise to safety hazards and substantial losses. The evaluation Score function makes up for the deficiencies of RMSE by imposing more stringent penalties on predicted values that are higher than the true values, thereby accurately assessing the prediction effect. The smaller the Score value, the better the prediction performance of the model. The formula is presented as follows:  
\[Score=\begin{cases}\sum\limits_{i=1}^{n}\bigg{(}e^{-\dfrac{\widetilde{y_{i}}- y_{i}}{13}-1}\bigg{)},\widetilde{y_{i}}-y_{i}<0\\ \sum\limits_{i=1}^{n}\bigg{(}e^{-\dfrac{\widetilde{y_{i}}-y_{i}}{10}-1}\bigg{)},\widetilde{y_{i}}-y_{i}\geq 0\end{cases} \tag{9}\]  
The Score function puts more emphasis on the lagged case by assigning higher penalty weights compared to the advance prediction.
###### Abstract  
Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network's input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach's feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.  
ANN; FEM; damage assessment; structural health monitoring; steel truss bridge +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Bond Strength
This may be attributed to the fact that the crushing of waste concrete to produce RAs generally results in particles with higher angularity. This property of RCAs plays a positive role in enhancing frictional resistance between steel bars and concrete at larger slip values, resulting in improved residual bond strength. The maximum bond strength exhibited by each concrete mix presented in Table 2 is presented in Figure 11, where it is noticed that the bond strength attained by the control mix was higher compared to RAC mixes containing RBAs or RCAs. By replacing fine NAs with fine RBAs in the control mix, the bond strength dropped by 7%, while the reduction in bond strength was 6% when fine NAs were replaced with fine RCAs. Concrete mix containing 100% fine and coarse RBAs exhibited bond strength 40% less than the control mix; however, in the same mix, replacement of 100% coarse RBAs with 100% RCAs and NAs enhanced the bond strength by 15% and 54%, respectively. Similarly, the RAC mix containing 100% fine RCAs and 100% coarse RBAs exhibited a bond strength 31% less than the control mix; however, in the same mix, the replacement of 100% coarse RBAs with 100% RCAs and NAs enhanced the bond strength by 7% and 38%, respectively. Compared to RAC mix containing 100% fine and coarse RBAs (i.e., F(RBA) + C(RBA)), the replacement of coarse RBAs with 25%, 50%, and 75% coarse NAs improved the bond strength by 19%, 34%, and 45%, respectively. Similarly, compared to the RAC mix containing 100% fine and coarse RCAs (i.e., F(RCA)+C(RCA)), the replacement of coarse RCAs with 25%, 50%, and 75% coarse NAs enhanced the bond strength by 2%, 14%, and 23%, respectively.
# An Overview of Infrared Remote Sensing of Volcanic Activity
###### Abstract  
Volcanic activity consists of the transfer of heat from the interior of the Earth to the surface. The characteristics of the heat emitted relate directly to the geological processes underway and can be observed from space, using the thermal sensors present on many Earth-orbiting satellites. For over 50 years, scientists have utilised such sensors and are now able to determine the sort of volcanic activity being displayed without hazardous and costly field expeditions. This review will describe the theoretical basis of the discipline and then discuss the sensors available and the history of their use. Challenges and opportunities for future developments are then discussed.  
infrared remote sensing; volcanoes; Earth observation; satellites +
Footnote †: journal: Imaging