esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 3 Results
### Spatial and Temporal Distribution of Emissions  
The accumulated emissions of CO\({}_{2}\), CO, CH\({}_{4}\), NOx, and NH\({}_{3}\) gases and PM\({}_{2.5}\) particles for the four years in our study area were 67.5 (\(\pm\)20) Tg, over a surface of 12,898 km\({}^{2}\). The uncertainty of emissions (29%) was lower than that reported elsewhere (19) (47%). Figure 6 shows the annual spatial distribution of  
The emissions were highest in 2013 (27.5 Tg), accounting for 40.8% of the four-year total, with a burned area of 5272.75 km\({}^{2}\). The spatial distribution was more uniform in 2013 than in the other years (Figure 6C): 53.8% in the Southern Sierra Madre ecoregion (13.5); 28.1% in Plain and Hills of the Yucatan Peninsula (15.2); and 18.1% in Southern Pacific Coastal Plain and Hills (14.5). In contrast, in 2014, the emissions were 2.9 Tg, representing 4.2% of the four-year total, with a burned area of 657.25 km\({}^{2}\). Furthermore, the concentrations of gases were low (Figure 6D); 2014 was a wet year, with precipitations in May exceeding the May four-year average, with 45 mm in Oaxaca State and \(>\)93 mm in Yucatan. The emissions in the other two years were moderate: 21.9 Tg (32.4%) in 2011 with 4214.25 km\({}^{2}\) burned; 15.2 Tg (22.6%) in 2012 with 2753.75 km\({}^{2}\) burned. The emissions were concentrated in specific areas as a consequence of the drought distribution in Mexico. In 2011, 48.7% of the emissions were from Plain and Hills of the Yucatan Peninsula (ecoregion 15.2), 4.8% more than those from Southern Sierra Madre (ecoregion 13.5), where the highest percentage is usually recorded (Figure 6A). This increase was due to the low precipitation in April and May in 2011 compared with the same months in the other three years: 10 mm lower in Campeche State, 36 mm lower in Tabasco in April, and 111 mm lower in Quintana Roo in May. In this part of Mexico, 2011 was a year of extreme drought [51]. In 2012, 71.9% of emissions were in Southern Sierra Madre (ecoregion 13.5), in contrast to the 8.0% in Plain and Hills of the Yucatan Peninsula (15.2) (Figure 6B). May 2012 was the second driest May in the four years that were analyzed, with precipitations varying from 12 mm lower than the overall mean in Oaxaca to 18 mm lower in Michoacan. The year 2012 was abnormally dry in Guerrero and Oaxaca [52].
## 4 Numerical Simulation and Result Analysis
### Influence of Microchannel Hydraulic Diameter on Heat Dissipation Capacity  
In order to further optimize the structure design of the double-layer reflow microchannel heat sink, we continued to study the variation of the chip thermal resistance with the coolant flow under different microchannel hydraulic diameters.  
It can be seen from Figure 9 that with the decrease of the hydraulic diameter of the microchannel, the thermal resistance of the chip gradually decreased, and with the increase of the coolant flow rate, the thermal resistance of the chip under the conditions of different hydraulic diameters of the microchannel presented an approximately parallel downward trend, and the downward trend was slowing down. The results show that the smaller the hydraulic diameter of the microchannel, the better the heat dissipation effect of the chip. For example, when the coolant flow rate was 14 L/h, the hydraulic diameter of the microchannel was 0.4 mm compared to 0.8 mm, and the thermal resistance of the chip was 73.3% of the latter. However, the hydraulic diameter of the microchannel cannot be reduced indefinitely. The microchannel heat sink is sensitive to pipeline pressure. Large pipeline pressure may damage the microchannel structure, thus causing channel blockage [19]. Therefore, the variation of microchannel pipe pressure with coolant flow rate was simulated for different hydraulic diameters, and the curves are shown in Figure 10. In the double-layer reflow microchannel heat sink, the pipeline pressure increased greatly with the decrease of the hydraulic diameter, and it showed an obvious upward trend with the increase of the coolant flow rate. According to research, 1.3 bar was a relatively safe pipeline pressure threshold. It can be seen that when the hydraulic diameter is 0.4 mm, the coolant flow rate to keep the pressure in the pipeline lower than 1.3 bar was 10 L/h and  
Figure 8: Flow velocity cloud of pin-finned microchannel.  
Figure 7: (**a**) Temperature cloud map of double-layer reflux microchannel. (**b**) Temperature cloud map of honeycomb microchannel. (**c**) Temperature cloud map of pin-finned microchannel.  
14 L/h, when the hydraulic diameter was 0.5 mm, the cooling mass flow rate of 10 L/h, 14 L/h, 18 L/h, 22 L/h, and 24 L/h could meet the requirements. When the hydraulic diameter was greater than 0.7 mm, the coolant flow rate was in the range of 10 L/h-40 L/h, and the pressure of the pipes in the microchannel was less than 1.3 bar.
## 4 The Monte Carlo Simulation and Experimental Results
### The Monte Carlo Simulation  
A desirable and efficient solution to this classical method is to utilize the Monte Carlo simulation. Additionally, the reason why we use Monte Carlo simulation is that [PERSON] and [PERSON] [5] state that the numerically simulate stochastic processes could be done well by discretizing the process into small time steps and applying pseudo-random sequences to simulate the randomness. In addition, the standard Monte Carlo method using pseudo-random sequences can be quite convergence for \(N\) sample tests. They conclude that the range of application of Monte Carlo methods can be significantly extended by modification of the standard Monte Carlo techniques. In addition, [PERSON] et al. [6] introduce the reasons for utilizing a Monte Carlo simulation are: (a) the Monte Carlo simulation generally applies to all simulations that use stochastic methods to generate new configurations of a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & **Loading (\%)** & **Length of Vehicle** & \multicolumn{4}{c}{**Initial Seconds**} & \multicolumn{4}{c}{**Initial Queued Vehicles**} & \multicolumn{4}{c}{**Passing Time**} \\ \cline{3-14}
**Scenario** & & **Max** & **Min** & \(R_{1}\) & \(R_{2}\) & \(R_{3}\) & \(R_{1}\) & \(R_{2}\) & \(R_{3}\) & \(R_{1}\) & \(R_{2}\) & \(R_{3}\) \\ \hline
1 & 80 & 15 & 10 & 40 & 40 & 40 & 6 & 6 & 6 & 5 & 6  
system of interest, (b) the Monte Carlo move is accepted or rejected based on an acceptance criterion that guarantees that configurations are sampled in the simulation from a statistical mechanics ensemble distribution, and (c) most Monte Carlo simulation algorithms are comprised of several different Monte Carlo moves that are effective at relaxing different degrees of freedom in a molecule or system. According to its efficient and wide scope of applicability in [11], we determine to utilize the Monte Carlo simulation as informatics tools to verify the results of the proposed dynamic adjusting model. Furthermore, several studies also apply Monte Carlo in the research such as [PERSON] et al. [27] and [PERSON] et al. [28]. In the test problem, we consider five factors in Monte Carlo simulation. That is, the loading, length of vehicle, the initial seconds, the initial queued vehicles and the passing time, as shown in Table 1.  
In this study, Microsoft Excel 2000 Visual Basic Application (VBA) is applied to implement the Monte Carlo simulation because it is the most common development software used in companies and its programming language is the same as the Visual Basic which is very easy to use. In addition, we adopt an i5-6200U 2.30 GHz Note Book as our test environment.
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology
### Digital Twin  
Designing an intelligent digital twin has two parts: (a) signal approximation using a combination of mathematical-based vibration signal of bearing modeling with machine learning-based regression, and (b) signal estimation to improve the performance of signal modeling using the combination of a Kalman filter, the high-order variable structure technique, and the adaptive neural-fuzzy method (ANF).
## 3 Master-Slave Optimization Framework
### Master Stage: Multi-Vorse Optimizer (MVO)
#### 3.1.7 Algorithm Summary  
The structure of the MVO algorithm is presented in Algorithm 1.  
```
MG parameters, DER power limits (\(U_{min}\), \(U_{max}\)) for\(\mathit{iter}=1\) to \(\mathit{iter}_{\max}\)do if\(\mathit{iter}==1\)then  1. Generate the initial population of MVO (U\({}_{i}\)) randomly;  2. Evaluate the objective function for each individual in the population and store this value in OF\({}_{i}\) (Slave stage);  3. Sort U and OF\({}_{i}\) from minimum to maximum value;  4. Select the universe and its objective function located in the first position of U and OF\({}_{i}\) as incumbent (\(\mathit{Best}_{U}\) and \(\mathit{Best}_{OF}\)); else  5. Apply the exploration and exploitation strategy to universes different from the incumbent;  6. Verify the boundary values of the elements that compose the universes;  7. Evaluate the objective function for each individual in the population and store this value in OF\({}_{i}\) ;  8. Sort U\({}_{i}\) and OF\({}_{i}\) from minimum to maximum value;  9. Select the universe and its objective function located in the first position of U and OF\({}_{i}\) as incumbent (\(\mathit{Best}_{U}\) and \(\mathit{Best}_{OF}\)); endif if Any stopping criterion has been met? then  10. Finish optimization process;  11. Print \(\mathit{Best}_{U}\) and \(\mathit{Best}_{OF}\) as the solution to the problem; Break; else Continue; endif endfor
```  
**Algorithm 1** MVO (Multi-Verse Optimizer) Algorithm Pseudocode  
This algorithmic framework is suitable for high-dimensional, non-convex, and multimodal optimization problems, demonstrating strong performance across various engineering and scientific applications.
# Study on Disintegration and Infiltration Characteristics of Nanjing Jiangxinzhou Embankment Soil
###### Abstract  
Flood season embankment due to long-term high water immersion environment creates situations where its soil properties will deteriorate, quickly triggering bank slope instability and other dangerous situations. In order to study embankment slope soil in the disintegration and permeability characteristics under the long-term water immersion process, disintegration tests and variable head infiltration tests were carried out on embankment-disturbed clays in Jiangxinzhou, Nanjing, Jiangsu Province, China. The results showed that the disintegration rate decreased with increasing dry density. Moreover, the samples gained weight quickly when the water content was low. Moreover, there is a threshold water content of around 20%, and it is assumed that the disintegration pattern of the soil may be significantly different depending on the optimum moisture content. The effect of moisture content on the disintegration of the samples was more significant. There were three main typical disintegration modes of the samples. The primary influencing factors resulting in the various disintegration patterns are the soil matrix suction, the change in consistency following water immersion, and the water membrane on the surface of the clay particles. The coefficient of permeability decreases with increasing dry density, and the variation of the coefficient of permeability with dry density coincides with the exponential function relationship. Long-term immersion conditions will change the permeability of the soil. An electron microscope test of immersed and unimmersed samples shows that the internal structure change is an essential factor affecting the permeability. After water immersion, with the dissolution of cementing material between soil particles, the particles move to fill the intergranular pore space so that the permeability coefficient decreases.  
disintegration characteristics; disintegration modes; long-term immersion; permeability; slope instability +
Footnote †: journal: Applied
# Sustainable Modularity Approach to Facilities Development Based on Geothermal Energy Potential
## 3 Results and Discussion
### Available Thermal Power and Potential Balneological Utilization
#### 3.1.2 The Site of Banatsko Veliko Selo  
At the site of Banatsko Veliko Selo, two geothermal wells were drilled: VS-1/H (925 m) and VS-2/H (895 m). The maximum measured outlet temperature was 43 \({}^{\circ}\)C at VS-1/H and 54 \({}^{\circ}\)C at VS-2/H. The yield of the VS-1/H well equaled 10 l/s, whereas the VS-2/H well had a somewhat higher yield of 12 L/s. According to the temperature values, geothermal resources at Banatsko Veliko Selo could be categorized as geothermal resources with low enthalpy, and according to their chemical composition belong to the HCO\({}_{3}\)-Na water type. Table 6 shows the basic physical-chemical values of geothermal waters sampled from the borehole VS-2/H. Applying the method of D'Amore's genetic parameters, geothermal waters at Banatsko Veliko Selo belong to the elastic water type, and this confirmed that the primary geothermal reservoir was formed within the sands of Pliocene epoch (Figure 10).  
Available thermal power from the geothermal resource at B.V. Selo (VS-2/H) site is calculated for the following scenario:  
* passive geothermal system with plate heat exchanger
* covering peak-load demands
* first cascade stage (temperature reduction for heating)
* second cascade stage (for balenological purposes, for bathing)
* temperature reduction, \(\Delta\)T = 19 \({}^{\circ}\)C, which is adopted to meet temperature conditions for bathing (35 \({}^{\circ}\)C).  
The calculation of disposable thermal energy from the geothermal borehole VS-2/H was performed according to the Equation (1).  
\[\mathrm{E}=4.2\ \mathrm{kJ}/\mathrm{kg}/^{\circ}\mathrm{C}\times 12\,1/\mathrm{s} \times 19^{\circ}\mathrm{C}\]  
which brought around 950 kW of thermal power.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline T & **TDS** & **pH** & **Na** & **K** & **Ca** & **Mg** & **HCO\({}_{3}\)** & **SO\({}_{4}\)** & **Cl** \\ \hline (\({}^{\circ}\)C) & (mg/L) & & (mg/L) & (mg/L) & (mg/L) & (mg/L) & (mg/L) & (mg/L) & (mg/L) \\
54 & 219 & 7.6 & 478.5 & 4.0 & 12.0 & 11.0 & 1415.2 & 14.0 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physical-chemical features of geothermal waters VS-2/H.  
Figure 10: (a) D’Amore parameters diagram of chemical analyses from Table 3; (b) \(\alpha\): evaporative terrains; \(\beta\) circulation in limestone; \(\gamma\): circulation through a crystalline basement; \(\delta\): circulation in argillaceous rocks.  
The first cascade level of exploitation provided energy for facilities heating, and the second level was provided a warm bath for balneological purposes. After geothermal waters pass through the heat exchanger, what remained disposable was water at 12 L/s at 35 \({}^{\circ}\)C, which is the most frequent temperature of healing waters used for curative purposes.
## 4 Experiment
### Ablation
#### 4.3.1 Adjacency Pair Performance  
Considering reward shaping influence on convergence, the different adjacency pairs settings for reward shaping are compared. There are five settings: all the types of adjacency pairs, only ask-action adjacency pairs, only confirm-action adjacency pairs, only byte-action adjacency pairs and naive reward function. The training curves are shown in Figure 3. These success rate curves are obtained through testing dialogue managers with their respective learning simulator after every 300 training steps. Two settings (i.e., all the types of adjacency pairs and only ask-action adjacency pairs) achieve the best performance on speed up learning.  
As the learning from scratch may cause that learned policy deviate from normal human-human conversation, these final dialogue managers are also tested with human users to check whether they deviate from normal human-human conversation or not. The same paralleled test strategy as described in Section 4.2.1 is conducted in human users test. The success rate and average turns are shown in Table 4. Results show that only all the types of adjacency pairs outperform the Rule-M. Other settings show bad performance on human users test. There are two reason for this: slow convergence and derivation from normal human-human conversation. Above results demonstrate that all the types of adjacency pairs for reward shaping can speed learning and avoid derivation from normal human-human conversation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **M-MADM-OM** & **Rule-M** \\ \hline
1 & SYS: Hello, where is the meeting? & 1 & SYS: Hello, where is the meeting? \\
2 & USR:The location is in bupt technology building. & 2 & USR:The location is in bupt technology building. \\
3 & SYS: Please confirm, the meeting place is in bupt technology building? & 3 & SYS: Please confirm, the meeting place is in bupt technology building? \\
4 & USR:Yes. & 4 & USR:Yes. \\
5 & SYS: How many people are there? & 5 & SYS: How many people are there? \\
6 & USR:The meeting will be held at 10 a.m. on 5 & USR:The meeting will be held at 10 a.m. on 5 & April 2019. \\
7 & SYS: Will the meeting be held at 10 a.m. on 5 & USR:The meeting will be held at 10 a.m. on 5 & April 2019. \\
8 & USR:Forry, please change 2 p.m. on 5 April 2019. & 9 & SYS: How many people are there? \\
9 & SYS: Please confirm. the meeting be held at 2 p.m. on 5 April 2019? &. \\
10 & USR:Pefect. &. \\
11 & SYS: How many people are there? &. \\
12 & USR:Eight. &. \\
13 & SYS: Is there eight people to participate? & 32 & SYS: How many people are there? \\
14 & USR:For sure. & & \\
15 & SYS: Booked at 14:00 on 5 April 2019 Sixth meeting room at bupt technology building for you, if there are any questions, please call 010-5281088, good bye. & \\ \hline
**Success** & **Failure:** System does not book the right meeting room within the max turn \\ \hline \hline \end{tabular}
\end{table}
Table 3: Two sample dialogue sessions on human users comparing M-MADM-OM with Rule-M dialogue manager (SYS: system, USR: human user).
## 2 Related Works
The authors of [41] used the Explainable Graph Neural Network (GNN), and Graph Attention Network (GAT) to predict employee leave and pinpoint key variables influencing their choices. They exploited the GNN model's capacity to identify the deep-rooted structure of employee data, where linkages between coworkers can hold significant insights. By using explainable AI methods, the model highlights the most significant factors influencing employee turnover and produces interpretable predictions. In a another study, the authors of [42] introduce a novel approach to predict employee turnover by combining clustering techniques with Artificial Neural Networks (ANNs). To obtain the best ANN models, they concentrate on hyperparameter tuning with different input parameters. The study's data segmentation helps identify important turnover predictors, which enables the implementation of focused interventions to increase the efficacy and efficiency of retention strategies. Conditional Generative Adversarial Networks (CTGANs) are used to augment data on clusters that have unbalanced data. After that, these augmented clusters are subjected to the optimized ANN models, which significantly enhances model performance. The SHAP method was utilized to assess each feature's significance across various clusters in a predictive model. [PERSON] et al. [43] evaluated four machine learning models for employee attrition prediction using a real dataset obtained from an Italian financial company. They discovered that the model with the greatest performance was Random Forest. They also used the ROSE [44] technique in combination with Random Forest to address the class imbalance problem because of the very unbalanced dataset. Moreover, a SHAP (SHapley Additive exPlanation) technique was used to identify feature contributions and assess their direction. Despite considerable efforts in predicting employee attrition, this research domain continues to face significant challenges that warrant further investigation. Many existing studies predominantly rely on classical machine learning models or relatively basic deep learning architectures, which may fall short in effectively capturing the complexities of employee attrition data. To achieve more accurate predictions, the development and application of advanced, robust models are essential. Additionally, the pre-processing stage, particularly data augmentation, is often overlooked, despite its crucial role in improving model performance and reliability. Furthermore, only a few articles utilized explainable AI (XAI) methods to identify the most influential features and offer valuable insights into HR decision-making.
## 5 Experiments and Discussion
### Limitations  
For a two-axis camera, unlike the horizontal displacement of the imaging field of view during downward-viewing scanning, the imaging field of view under backward-squint conditions also rotates around the LOS due to camera roll motion, as shown in Figure 25. Since the proposed image motion compensation method is based on LOS stabilization, it cannot compensate for image rotation. Therefore, the maximum applicable rotational scanning speed is determined through calculation.  
Considering two consecutive image frames with optical axes OC and OD, the camera pitch angle is \(\theta\) and the roll variation angle is \(\Delta\phi\). The corresponding image rotation angle  
Figure 24: Computation accuracy verification: (**a**) computed results vs. reference values; (**b**) computed errors.  
\(\alpha\), represented by \(\angle\)AFB in the figure, is associated with an imaging altitude of \(H\). Based on geometric relationships, it can be derived as follows:  
\[\begin{array}{c}\text{AF}=H\tan\theta+H\cot\theta=\frac{2H}{\sin 2\beta}\\ \text{AS}=\frac{1}{2}\text{AB}=\text{OA}\sin\left(\frac{1}{2}\Delta\phi\right)= \frac{H}{\cos\theta}\sin\left(\frac{1}{2}\Delta\phi\right)\\ \sin(\frac{1}{2}\alpha)=\frac{\text{AS}}{\text{AF}}=\sin\left(\frac{1}{2} \Delta\phi\right)\sin\theta\end{array} \tag{23}\]  
Under the small-angle approximation, the image rotation angle \(\alpha\) is given by Equation (24), which is the product of the roll variation angle and the sine of the camera pitch angle.  
\[\alpha=\Delta\phi\sin\theta \tag{24}\]  
For an area-array detector with \(m\times n\) pixels, given a camera whisk-broom scanning angular velocity of \(\omega\) and an exposure time of \(t\), the image motion during exposure should be within 0.5 pixels to ensure image stability. This constraint can be expressed as follows:  
\[\begin{array}{c}\frac{1}{2}\alpha\sqrt{m^{2}+n^{2}}\leq 0.5\\ \Delta t\leq\frac{1}{\omega\sin\theta\sqrt{m^{2}+n^{2}}}\end{array} \tag{25}\]  
According to Equation (25), both the scanning speed and the pitch tilt angle jointly determine the allowable exposure time. When the scanning speed is fixed, a smaller pitch tilt angle results in a longer maximum exposure time; conversely, at a fixed pitch tilt angle, reducing the scanning speed extends the allowable exposure time. For an infrared camera equipped with a 640 \(\times\) 512 area-array detector, under a scanning speed of 40\({}^{\circ}\)/s (i.e., 0.7 rad/s) and a pitch tilt angle of 45\({}^{\circ}\), the maximum exposure time constrained by image rotation is calculated to be 2.47 ms. When the tilt angle is reduced to 20\({}^{\circ}\), the maximum allowable exposure time increases to 5.11 ms. These values closely match the simulated results presented in Section 5.2 regarding the influence of pitch tilt angle variation. Due to forward flight effects and attitude disturbances, the actual compensated exposure time is slightly reduced, although it still approaches the theoretical limit, which further validates the accuracy of the proposed compensation method.