esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Activities on a Hydrogen-Fueled Spark-Ignition Engine for Light-Duty Applications
## 1 Introduction
In this context, the primary focus in hydrogen internal combustion engines (H2-ICEs) is on reducing NOx emissions [16]. The formation of nitrogen oxide (NO) can be attributed to thermal, prompt, and fuel mechanisms. The thermal NOx mechanism develops under high temperatures, the prompt NOx mechanism is activated at low temperatures in the early combustion stages, and the fuel NOx mechanism depends on the fuel composition [17]. In the case of hydrogen, the absence of nitrogen renders the fuel NOx mechanism irrelevant, with the thermal path being the most pertinent one [18; 19; 20]. The properties of hydrogen facilitate stable combustion with high dilution, leading to a decrease in the concentration of the generated NOx[21]. It is widely acknowledged that when the air-to-fuel equivalence ratio (\(\lambda\)) exceeds two [13; 22], these emissions are notably diminished. Nevertheless, the challenges associated with NOx emissions during transient operating conditions (i.e., dynamic torque build-up phases) became evident in several investigations [23]. During these transitional periods, it is common for the air path system to experience delays in the boost pressure, resulting in a reduced air-to-fuel ratio and, consequently, brief NOx emission spikes. Consequently, selective catalytic reduction (SCR) must be implemented to mitigate these emissions [24]. Recent research has explored the use of hydrogen as a reducing agent in SCR systems. The availability of onboard hydrogen represents an innovative and convenient approach to NOx reduction, eliminating the need for urea/NH3 storage in tanks and the potential event of ammonia slip at the tailpipe. The results of using H2-deNOx catalysts are promising in reducing NOx at low temperatures under lean combustion conditions [25]. Despite these advancements, there is still a significant gap to fill, such as finding the relationship between GHG emissions and pollutants (i.e., NO\({}_{x}\)), as well as establishing the limits that minimize both. This study strives to enhance the current understanding of the impacts of mixture formation by employing diverse operating strategies. Its objective is to aid in the progression of contemporary technology for the integration of hydrogen as a fuel in small road transportation solutions. An extensive experimental campaign using a single-cylinder spark-ignition (SI) research engine was conducted to evaluate performance, combustion processes, and emissions. The analysis included scenarios in which the engine was equipped with both port fuel injection (PFI) and direct injection (DI) [26] systems under varying degrees of air dilution and ignition timing.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Spectral Indices Behaviour
Generally, the whole NDRE dataset shows a higher variance than the NDVI dataset in terms of CV (Table 4). The mere structure of the dataset explains the variance difference. Because the NDVI has less sensible values close to 1 than the NDRE, the NDIV records are compressed at high values. At the same time, the NDRE maintains a normal distribution of the records. For this reason, the NDVI could have returned a smaller variance than the NDRE (data are compressed), especially by grouping the NDVI variance (NDVI_CV) of vineyards planted in flat areas compared to vineyards from sloped areas. At the same time, those vineyards planted in flat areas can reach the highest mean value (NDVI_M), as shown in Figure 11. Thus, the ANOVA performed on the topography among the NDVI_CV could be affected by a sensitivity matter of the NDVI at the end scale, while, in this case, the NDRE results could be considered more affordable. On the contrary, the same insight may not be assumed for the topography ANOVA in Table 6, because the hilly vineyards returned the lowest mean slope value but the highest slope's CV. In this case, the NDVI showed, on average, a higher variance of the regression line slope estimated between the DOY and the VI. Thus, the pixel-by-pixel resolution could have decreased the NDRE accuracy in those cases by smoothing and losing some information from extreme pixels. The NDRE has a smaller spatial resolution and a lower sensitivity of the red edge band at lower values than the NIR used in the NDVI, especially for delicate and unpredictable areas such as vineyards planted in the hilly landscape. Concluding the above-mentioned insights may prove the disparity resulted in some ANOVA.
## 2 Method  
The principle of the proposed method relies mainly on computational geometry algorithms, aiming to assist the LMD technique in forming thin-walled overhanging structures on non-planar surfaces through operations between geometries. The process planning flow of LMD is similar to that of the AM generalization as shown in Figure 1, which outlines how the method transforms the numerical model into a solid part according to the idea of input-output.  
The computer represents the virtual digital space, and the machine tool represents the actual physical space. The input and output that pass between them consist of the STL model, the path data, and the physical workpiece. In the whole process, the most critical components are the path data, which are generated by the computer and guide the machine tool. Thus, path generation is the bridge between the digital space and the physical space. As shown in Figure 2, the path generation method mainly consists of three phases sequentially, namely, non-planar slicing, contour offset, and helical path generation. Each phase faces different geometric challenges and most of them are solved using intersection operations.  
The inputs to the method are two STL models that represent the base surface to be deposited and the part to be formed. In the slicing stage, the surface model is offset by a given layer thickness and then intersected with the part model to obtain the deposition contour for each layer. In the contour offset stage, the curve contracts or expands on the surface by a given offset distance to affect the boundary dimensions of the part to be formed. In the helical path generation phase, the contours of each layer are connected in a helical upward trend. To introduce a multi-axis strategy to cope with non-planar and overhanging features, the coordinates of points on the path are matched to vectors that fit the surface of the model.  
Figure 1: LMD process planning. In order to make the planning process clearer, the key players in it are divided into two categories: input–output and device.
## 4 Discussion
### Research Implications  
We believe that this work has implications both for satellite-derived coastal bathymmetry estimation and deep learning for Earth observation. We highlight the possibility to integrate wave and color information for SDB and the application of deep learning to physical modeling.  
Color information is often used to estimate coastal bathymetry [20; 21; 22; 25; 26; 27], but these methods can be sensitive to site and/or season-specific features such as turbidity and bottom reflectance. In this work, we observed a high sensitivity of our color-based model C-DSPEB to the background color, which led to high uncertainty in the estimated profiles. We propose that a wave kinematics-based method would have the potential for global application, which would be difficult with color-based estimation.  
This work demonstrates that physical information, i.e., wave kinematics, can be used by deep neural networks for estimation. This follows a recent trend in machine learning for Earth system science where machine learning models use information from existing physical models [49]. W-DSPEB is a deep learning regression model based on physical information, which is a relatively unexplored model type as deep learning is more often used in classification tasks [50].
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 5 Analysis of Node Transmission Capacity
### Calculation of Capacity of Nodes for Different Values of Capacity  
The location of own ship is \(X_{OS}=2,\;\;Y_{OS}=5\), set nine target ship nodes in addition to this ship, and their positions are random. The complete experimental parameters are shown in Table 5. We change the channel capacity threshold of OS and set three different values for it. Similarly, according to the difference in signal power between them, three groups of control experiments were set up for small, medium, and large respectively. The setting values are 1, 1.5, 2; 3, 6, 9; 1, 5, 9. The calculation is based on Formula (19).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Value** \\ \hline Sea area & A circular with a radius of 20 \\ The position of OS & (2, 5) \\ The channel capacity of OS & 1.5, 4.5, 7.5 \\ The number of TSs & 9 \\ The position of TSs & Random \\ The power of TSs & Random from 1 to 10 \\ Number of experiments & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental parameters and value of Section 5.3.  
Figure 11: The capacity of nodes for fixed capacity.  
The experimental results shown in Figure 12, for a fixed channel capacity, the number of connectable nodes does not necessarily increase with the increase of the transmission radius of the ship, only nodes below the threshold will be connected. Additionally, the larger the difference between thresholds, the greater the difference in the number of nodes that can be connected.  
Figure 12: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Value** \\ \hline Sea area & A circular with a radius of 20 \\ The position of OS & (2, 5) \\ The channel capacity of OS & 1, 1.5, 2; 3, 6, 9; 1, 5, 9 \\ The number of TSs & 9 \\ Number of experiments & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental parameters and value of Section 5.4.
# Research on Grooved Concrete pavement Based on the Durability of Its Anti-Skid Performance
## 2 Materials and Methods
### Skid Resistance Test
#### 2.5.2 Evaluation of Samples under Different Curing Methods  
The various curing methods have different effects on the abrasion resistance of concrete pavement. In this study, a composite curing agent, containing Na\({}_{2}\)SiO\({}_{3}\) and paraffin as the primary ingredients, was self-developed to improve the concrete performance [20]. This agent has the properties of high water retention, strength, and abrasion resistance, as listed in Table 5. This paper investigated the abrasion resistance of grooved concrete under different curing methods.  
The authors prepared eight accordant samples with the sample dimensions (300 mm \(\times\) 300 mm \(\times\) 50 mm) that offered the best skid resistance (the two best-performing T and LT samples) in the above tests, and then divided them into two groups. One group was adopted as the control group (without any curing agent), cured in standard curing box (20 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C, relative humidity >90%, maintenance water 20 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C) for 28 days [25]. The other group was sprayed with the curing agent at a spraying dose of 0.22 kg/m\({}^{2}\), with an inorganic to organic curing ratio of 4:6. The samples were sprayed for a second time with the same preparation 30 min later. After spraying twice, the samples were cured for 28 days under conditions identical to those of the control group. Subsequently, all samples were dried at room temperature, and surface dust was brushed away. The abrasion tester shown in Figure 5 was used to conduct abrasion resistance tests. And the abrasion loss per sample was measured and recorded; the mean value of four samples was calculated and adopted as the final result.  
After the cement concrete mixture was poured, the inorganic ingredient was sprayed into the mold approximately 4 to 6 h after surface exudation. Thirty minutes later, the organic ingredient was applied by spraying between 20 and 30 \({}^{\circ}\)C (room temperature). The curing agent applied to the sample's surface was sprayed uniformly, and the dose was strictly controlled. Figure 6 shows the sample after spraying the curing agent.  
The samples were cured, after demolding, for 24 h under standard conditions. A curing film was applied to each surface except the grooved surface. Three or four days after grooving, the grooved surface was sprayed with the curing agent, while the other surfaces were still undergoing the film curing processing. Figure 7 shows a sample sprayed with the composite curing agent after grooving.  
Figure 6: Appearance after spraying the composite curing agent.
# Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance
## 4 Key Challenges for Designing Antifragile Self-Improving Systems
### Distributed System Architecture  
Two architectural solutions are mainly considered for the development of distributed and autonomous complex systems: service-oriented architectures (SOAs) and multiagent systems (MASs) [13]. SOAs are a mature technology, which have proven their advantages in any application that integrates both distributed or separated software components, being \"enabled by technologies and standards that facilitate components communication and cooperation over a network\" [14]. However, the design of a service-oriented architecture is of a top-down type, which implies, on the one hand, a meticulous stage of study of as many evolutionary scenarios as possible and, on the other hand, the exclusion of \"surprises\", i.e., uncertain events, as much as possible. Unlike SOAs, the MAS architecture design, a specific structure of artificial intelligence, is based on a bottom-up approach in which the collaboration of the agent-type entities located at the lowest level of the hierarchy is essential to cope with the unpredictable changes in the context. MASs also provide various elements of structural organization which can form holons, groups, teams, hierarchies, etc. Being more natural and intuitive, MASs have been the subject of numerous works that highlight the propensity for self-organization and self-improvement [15; 16; 17]. However, the use of MASs is subject to restrictions particularly due to the limited support for ensuring MAS scalability and modularity.  
For this reason, we recommend as the best solution for the design of self-improving systems, the combination of the MAS and SOA styles proposed in [18] and defined as \"agent-services\". This solution will maintain all the valuable characteristics of the agents, including various cooperation and coordination mechanisms, goal-based planning, and flexible organization and, at the same time, will highlight the main SOA facilities for software engineering based on modularity, reusability, and interoperability. In our vision, a self-improving system should be a set of agent-service teams (ASTs), each with different specific strengths and capabilities. An AST comprises many agent-service entities (ASEs) that cooperate to reach a common target. The use of ASTs provides a way for distributed problem-solving that will lead to a faster and more strongly argued decision-making process, considering that ASEs can also benefit from collective learning procedures.
# Lightweight Domestic Pig Behavior Detection Based on YOLOv8
## 4 Experiments and Analysis
### Statistical Analysis  
To evaluate the statistical significance of the performance enhancement, we executed five separate training and testing sessions for each model using the Comp division dataset. The results of the statistical analysis show that the precision, recall, and mAP measures follow a normal distribution; therefore, we conducted paired t-tests for these metrics [29]. The \(t\)-test is a commonly used statistical significance test designed to evaluate whether the difference between the means of two groups is statistically significant. In significance testing, the \(p\)-value represents the probability of observing the current result, or a more extreme one, under the null hypothesis (i.e., no difference between the means of the two groups). Typically, the significance level (\(\alpha\)) is set at 0.05, serving as the threshold for determining statistical significance. A \(p\)-value greater than 0.05 indicates that the observed difference is not statistically significant.  
Table 3 compares the performance metrics of YOLOv8n and the improved model YOLOv8-PigLite, including precision, recall, and mAP@50%, using a \(t\)-test. The results show that the mean precision of YOLOv8-PigLite increased from 96.06 \(\pm\) 0.30% to 96.38 \(\pm\) 0.19%, with a \(p\)-value of 0.0054 (\(p<0.05\)), indicating that this improvement is statistically significant. Conversely, the recall decreased from 94.60 \(\pm\) 0.34% to 94.38 \(\pm\) 0.33%, with a \(p\)-value of 0.0402 (\(p<0.05\)), suggesting that this decline is also statistically significant. For the mAP@50% metric, the \(p\)-value of 0.6974 (\(>\)0.05) for both models indicates no significant difference. Additionally, YOLOv8-PigLite reduced the parameter count from 3.01 M to 1.21 M and GFLOPs from 8.1 to 4.9, markedly enhancing resource efficiency. In summary, the improved model demonstrates a significant increase in precision, a significant decrease in recall, and a notable advantage in resource efficiency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **P \%** & **R \%** & **mAP@50** & **Params/M** & **GFLOPs** \\ \hline Faster R-CNN & 44.0 & 99.6 & 97.3 & 136.75 & 369.79 \\ SSD & 72.9 & 99.2 & 97.7 & 24.01 & 61.06 \\ YOLOv8n & 96.0 & 95.5 & 98.6 & 3.01 & 8.1 \\ YOLOv5n & 92.6 & 90.4 & 96.1 & 2.50 & 7.1 \\ YOLOv3-tiny & 89.4 & 93.0 & 95.7 & 12.13 & 18.9 \\ YOLOv9s & 94.3 & 95.0 & 97.9 & 6.20 & 22.1 \\ YOLOv8-PigLite & 96.3 & 95.2 & 98.5 & 1.21 & 4.9 \\ \hline \end{tabular}
\end{table}
Table 2: Comparative tests of different algorithms.
## Materials and Methods
### Contribution of Climate Variability and the VGC Effect on Vegetation Greening  
We quantified the individual contributions of climate factors (TEM, PRE, and SSD of the present and preceding season) and VGC effect (preceding-season NDVI) to the observed NDVI trend of each season during 2000-2019, so as to decompose the linear trend of NDVI (dNDVI/dt) for each season over a 20-year period into the additive contribution of eight components:  
\[\begin{array}{l}\frac{\delta NDVI}{dt}=\frac{\delta NDVI}{\delta NDVI_{\text{ ps}}}\frac{dNDVI_{\text{ps}}}{dt}+\frac{\delta NDVI}{\delta TEM_{\text{ps}}} \frac{dTEA_{\text{ps}}}{dt}+\frac{\delta NDVI}{\delta PE_{\text{ps}}}\frac{ dPRE_{\text{ps}}}{dt}\\ +\frac{\delta NDVI}{\delta 5 SD_{\text{ps}}}\frac{dSSD_{\text{ps}}}{dt}+\frac{ \delta NDVI}{\delta TEM}\frac{dTEA}{dt}+\frac{\delta NDVI}{\delta PRE}\frac{ dPRE}{dt}+\frac{\delta NDVI}{\delta SSD}\frac{dSSD}{dt}+\sigma\\ =\Delta NDVI_{\text{ps}}+\Delta TEM_{\text{ps}}+\Delta PRE_{\text{ps}}+\Delta SSD _{\text{ps}}+\Delta TEM+\Delta PRE+\Delta SSD+\sigma\\ =\Delta NDVI_{\text{ps}}+\Deltaclimate_{\text{ps}}+\Deltaclimate+\sigma\end{array} \tag{6}\]where \(\Delta NDVI_{\text{ps}}\), \(\Deltaclimate_{\text{ps}}\), \(\DeltaTEM_{\text{ps}}\), \(\DeltaPRE_{\text{ps}}\), \(\DeltaSSD_{\text{ps}}\), \(\Deltaclimate\), \(\DeltaTEM\), \(\DeltaPRE\), and \(\DeltaSSD_{\text{ps}}\) represent the individual contributions to the interannual NDVI changes, respectively. \(\Deltaclimate_{\text{ps}}\) (preceding-season climate) is the sum of \(\Delta TEM_{\text{ps}}\), \(\DeltaPRE_{\text{ps}}\), and \(\Delta SSD_{\text{ps}}\). _Calimate_ is the sum of \(\Delta TEM\), \(\DeltaPRE\), and \(\Delta SSD\). Moreover, \(\partial NDVI/\partial NDVI_{\text{ps}}\) is equal to the partial correlation coefficient between NDVI and \(\text{NDVI}_{\text{ps}}\) when removing the disturbances of the concurrent and preceding season for temperature, precipitation, and sunshine duration; \(\text{dNDVI}_{\text{ps}}/\text{dt}\) is the inter-annual variation rate (slope) of \(\text{NDVI}_{\text{ps}}\); \(\Delta NDVI_{\text{ps}}\) is calculated as the product of \(\partial NDVI/\partial NDVI_{\text{ps}}\) and \(dNDVI_{\text{ps}}/\text{dt}\). A similar method is also suitable for other variables. Before quantification analysis, we detrended all variables to transform the time series to Z-score standardized anomalies:  
\[Z=\frac{x-\overline{x}}{\omega} \tag{7}\]  
where \(\overline{x}\) and \(\omega\) are the mean and standard deviation of study variables in each active season, respectively. \(x\) is the value of the study variable.
## 3 Datasets and Methodology  
### Datasets  
#### 3.1.1 Sentinel-1  
This study utilized 207 ascending-pass Sentinel-1A scenes acquired between January 2018 and December 2024, including both vertical-vertical (VV) and vertical-horizontal (VH) polarization modes. The minimum temporal baseline between adjacent scenes was 60 days [18]. All datasets were obtained in interferometric wide swath (IW) mode as single-look complex (SLC) products (Table 1).
## 3 Results and Discussion
### Filling Model Fitting and Verification  
Using historical landfall typhoon observations, the filling model for Guangzhou typhoons was calibrated by excluding rapidly transiting and short-duration landfall events (typhoons with fewer than three valid observation points over land), resulting in 58 qualified typhoon samples. The normality test for the error term \(\varepsilon\) yielded a \(p\) value of 0.1283, indicating statistical validity. Representative typhoon samples were selected to plot their central pressure deficit (\(\Delta p\)) exponential decay curves, as shown in Figure 6, which demonstrated satisfactory goodness-of-fit for the exponential model. Figure 7 shows the linear regression between the decay constant (\(a\)) and initial central pressure deficit (\(\Delta p_{0}\)) at landfall, with the observed data points evenly distributed around the regression line, confirming the effectiveness of the regression analysis. The final coefficients of Guangzhou's filling model are presented in Table 2.
# Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses
## 4 Results  
### Coherence Changes  
Figure 5a shows spatiotemporal changes of the coherence histograms of four time periods, in which the corresponding time span is nearly one year: time span 1 (365 days), time span 2 (351 days), time span 3 (323 days), and time span 4 (365 days). The mean coherence values of time spans 1, 2, 3, and 4 are 0.559, 0.548, 0.563, and 0.528 and the standard deviations are not small regarding the calculated mean values. The standard deviations are 0.182, 0.186, 0.173, and 0.183, respectively (Figure 5b). Although the general behavior of the coherence maps is similar, the peak position of the third period (18 November 2017 to 6 October 2018) is higher than the others, probably because of its shorter temporal gap (Figure 5a). The mean value of time spans shows that as the temporal gap between two images increases, the mean coherence value decreases. Figure 6 shows a cross comparison of time span 1 with time spans 2, 3, and 4.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### FATT Fairness-Aware Tree Training Method  
The authors of [8] proposed a genetic optimization-based algorithm that trains evolving Decision Trees using crossover and mutation strategies, prioritizing both individual fairness and accuracy. The algorithm ultimately produces a single model and not an ensemble one, thereby reducing complexity while maintaining fairness. The experimental evaluation of this method was conducted on five benchmark datasets: Adult, COMPAS, Communities and Crime, German Credit, and the Heritage Health dataset. FATT-trained models exhibited a 35-45% improvement in fairness, with only a minor accuracy reduction (3.6% on average) compared to Random Forests and CARTs.