esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 1 Introduction
The scope of the analysis covers:* Modelling and simulation of combustion processes,
* Applications in engines and energy systems,
* Design and optimization of alternative fuels,
* Emission control,
* Large-scale industrial combustion systems. Additionally, this article includes a geographical analysis identifying leading research centers worldwide. Significant contributions have been noted from countries such as Canada, China, India, Iran, Japan, Germany, Malaysia, Saudi Arabia, the United Kingdom, and the United States. What sets this review apart from previous publications is its breadth, methodological rigor, and unique thematic and algorithmic classification. It is the first systematic review of the past decade to encompass both physico-chemical modeling and artificial intelligence applications in the context of diverse fuels and combustion technologies. Importantly, it also considers model validation methodologies, challenges related to limited availability of experimental data, and issues concerning computational costs and model interpretability in ML. Another essential aspect of real-world deployment is the integration of sustainability-related performance indicators. These include not only improvements in fuel economy and a reduction in CO\({}_{2}\) emissions but also the ability to balance trade-offs between thermal efficiency and pollutant formation. ML and GA-based models can be instrumental in achieving such multi-objective optimization, enabling dynamic control of combustion systems under varying operating conditions while meeting environmental and economic targets. Future studies should more explicitly evaluate the environmental impact of applied algorithms to better align combustion research with sustainability goals. In light of the growing importance of efficient energy management, emission reduction, and the advancement of low-emission technologies, there is a pressing need for such a broad and integrated review. This article serves as a valuable reference point for further research and practical implementations in the field of digital optimization of combustion processes both at the laboratory scale and at the industrial scale. The goal of this review is to provide a comprehensive and structured synthesis of the application of machine learning and genetic algorithms in combustion research across a wide spectrum of fuel types. This paper seeks to identify which computational techniques are most frequently used in combustion modeling, clarify how their applications vary depending on the specific characteristics of different fuels, and highlight the most pressing methodological challenges and research gaps. By addressing these issues, the review aims to support future advancements in the development of more efficient, robust, and interpretable AI-driven solutions for sustainable combustion systems.
## 2 Model and Methodology
### Model Structure
#### 2.2.1 Data Collection
* Charging events probability distribution over the estimated range of the vehicle: Usually xEV driving range allows for more than one day of use so owners can decide to charge their vehicles when state of charge (SOC) approaches the minimum level or before. This consideration, together with the hypothesis of only complete recharges, leads to different possible energy requests for the single charging event. Thus, it was necessary to develop a methodology to distribute the probability of the charging events over the whole driving range allowed by the battery size. Since this situation is strongly related to user behavior modeling, in order to cover the various possibilities, three different scenarios have been developed, each with a specific probability distribution over the timeframe. The independent variable is represented by the time elapsed since the last charging event (normalized to the maximum autonomy) and is therefore included in an interval [0,1]; the dependent variable \(p\) is the probability of a charge event at a given time, defined as a monotonous increasing function with its values included in the interval [0,1]. Immediately after a charging event \(p=0\), while at the end of the driving range \(p=1\), thus avoiding the possibility that a xEV runs out of charge. Figure 3 shows the trends of the three functions defining the different scenarios.
## 4 Experimental Investigation of Aeroelastic Vibration and Harvesting Performance
### Experimental Validation  
To validate the effectiveness of the numerical simulation methodology presented in Section 3, a comprehensive comparison between numerical and experimental results was conducted. Figure 12 presents the comparative analysis at installation angles of 0\({}^{\circ}\) and 9\({}^{\circ}\). The results demonstrate excellent agreement between numerical predictions and experimental measurements, as shown in Figure 12a-f. However, some minor discrepancies were observed between the simulated and experimental values.  
Figure 12: Output performance of the harvester system with and without installation angle obtained theoretically and experimentally: (**a**) output voltage versus airflow velocity; (**b**) output power versus airflow velocity; (**c**) time history of the output voltage at 8 m/s; (**d**) time history of the output voltage at 11 m/s; (**e**) time history of the output power at 8 m/s; (**f**) time history of the output power at 11 m/s.  
These differences can be attributed to several factors, primarily the inherent challenges in precisely replicating experimental conditions in numerical simulations, particularly regarding environmental parameters such as airfoil-based geometry and surface roughness characteristics. Furthermore, the flow field exhibits increasingly complex behavior at higher air velocities, characterized by significant deformations and pronounced nonlinear effects. These combined factors account for the slight variations observed in Figure 12a-d. Despite these minor discrepancies, the strong correlation between numerical and experimental results substantiates the reliability and accuracy of our simulation methodology.
## 1 Introduction
[17] emphasize that practices such as supplementation, mobility, and harvesting, respectively, can mitigate these effects by optimizing forage use during both wet and dry seasons. Previous studies also examined the relationship between soil moisture and forage availability in Botswana [13; 17]. However, this approach may not fully align with the practices and realities of traditional livestock farmers. These farmers primarily rely on their intuition and historical knowledge of rainfall patterns and duration to manage their livestock rather than scientific indicators such as soil moisture levels. Additionally, traditional farmers in Botswana often lack access to advanced climate data or technologies [17], and their management decisions are based more on observations of rainfall than on an understanding of how water infiltrates and is stored in the soil. While some of the studies reviewed link rainfall to forage biomass and livestock productivity, they often lack an integrated quantitative approach that considers both the intensity and timing of rainfall events in shaping forage production and livestock dynamics. The existing mathematical models for plant-animal interaction have limitations, as they fail to capture the effect of rainfall variability (both in the timing and intensity) on livestock production. However, this issue can significantly impact agricultural planning and the livelihood of traditional farmers, more especially in arid and semi-arid environments. This study employs a mathematical model to assess how variations in rainfall influence natural forage availability and livestock populations over time. By incorporating spatial and temporal rainfall variability, the model provides insights into optimal grazing and livestock management strategies for climate adaptation. This manuscript is structured into four sections, not counting the references. In Section 2, we briefly describe the materials and methods: the study area and the formulation and analysis of the mathematical model. Section 3 presents the findings derived from numerical simulations, followed by an in-depth discussion of these results. The manuscript concludes with a set of recommendations outlined in Section 4.
## 3 Results and Discussion  
According to the trench model shown in Figure 4, the parameters applied to study the results of the deposited copper seed layer are defined as follows:  
* Step coverage is defined as the average number of atoms deposited on the target region over the average number of atoms deposited on wing of the trench.
* Uniformity is defined as the average number of atoms deposited on the top sidewall over the average number of atoms of the overhang.
* Bottom height is defined as the average height of atoms accumulated at the bottom of the trench.
* Alloy percentage is defined as the percentage of substrate atoms in the copper seed layer.
* Necking is defined as the distance between the atoms at the two trench necks.  
The influences of substrate temperature, incident energy, incident angle, crystal plane, and liner materials on the sputtering process are illustrated and discussed below.  
Figure 4: Defined regions of the trench model.  
Figure 3: Model of copper deposition into substrate trench with cosine distribution of incident copper atoms.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 3 Modeling Approaches and Frameworks in the Field of Industrial IoT  
Modeling represents the core concept in Model-Driven Engineering (MDE) and is considered an abstraction of the system under development. A modeling language definition describes the set of available concepts and well-formed rules to which a valid model must conform. A meta-model describes the set of available concepts and well-formed rules to which a valid model must conform. The heterogeneity of software and hardware is a major challenge in the world of IoT [24]. MDE can provide a unique means for the many aspects of heterogeneous systems to be represented all in one place thanks to modeling languages, specifically domain-specific ones (DSLs) [25]. Models defined by these languages are intended to be far more human-oriented than common code artifacts, which are inherently machine-oriented. This means that concepts such as software can be defined without regard to the underlying platform or technology.  
Over the last decade, there has been a growing demand for model-based solutions to address specific IoT challenges ranging from heterogeneous application development and management to real-time models and system monitoring. In this chapter, we will provide a brief overview of the various modeling approaches and frameworks used in industrial IoT, as well as their comparison and evaluation. This is, to the best of our knowledge, the first survey paper that provides a comprehensive overview of--and comparison of--modeling approaches and frameworks in the field of Industrial IoT. We contribute by identifying the main challenges in this field and comparing fully-fledged modeling frameworks based on the identified challenges and their solutions.
## 2 Algorithm Theory and Method
### Pretreatment and Feature Extraction
#### 2.1.2 Normalization  
Because of the different dimensionality levels of the data, the larger-scale dimensional data will overwhelm the smaller-scale data and affect the clustering speed. To eliminate this effect, each variable needs to be normalized such that different variables can be analyzed and compared equally. The corresponding equation is as follows:  
\[q_{i}^{q}=\frac{q_{i}-q_{min}}{q_{max}-q_{min}}\qquad i=1,2,\ldots,N \tag{5}\]  
where \(q_{i}\) is the measured value of the variable at the \(i\)-th moment, \(q_{max}\) is the maximum value of the variable in this period, \(q_{min}\) is the minimum value, and \(q_{i}\)\(q\) is the normalized value.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 6 Conclusions  
A modification of RTCP has been presented to fulfill the given requirements. Two new APP packets are added to the RTCP to communicate the synchronization information between sender and receivers. It also makes use of RR packets to provide information required by the sender application. The size of newly added APP packets is not consuming a considerable amount of bandwidth under good network conditions. Depending upon the application, sender and receivers are required to have extra buffering space. Implementation results show that the proposed algorithm achieved accurately synchronized playout on all the receivers under all the possible scenarios defined in the experimental setup.  
We are thankful to Ericsson Research Corporate Unit, Lulea, Sweden for their kind support during the implementation phases of this project. This work is financially supported by National Natural Science Foundation of China (Grant Nos. 61631018, 61461136002).  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper and [PERSON] and [PERSON] technically reviewed the paper.  
The authors declare no conflict of interest.
# Numerical and Experimental Analysis of Pressure Pulsation Attenuator Based on Helmholtz Resonator
## 6 Discussion and Conclusions  
The HTPA was designed based on the Helmholtz resonator to eliminate the vibration and noise caused by pressure pulsation in the hydraulic piping system. It was installed at the pump outlet to eliminate pump source pressure pulsation. The mathematical model of HTPA was established in the form of the transfer matrix by combining the centralized parameter method and the distributed parameter method. The insertion loss and pulsation attenuation rate are used to evaluate the attenuation effect. The attenuation characteristics of the HTPA were analyzed in three aspects: numerical simulation, Fluent simulation, and experiment. The following conclusions are given:  
1. The pressure pulsation decay characteristics of HTPA were compared and analyzed by numerical simulation and fluent simulation. The trend of the attenuation characteristic line between Fluent simulation and numerical simulation is the same in 0-500 Hz. The insertion loss in the frequency range of 375-500 Hz is above 10 dB, which has a good attenuation effect.
2. By designing and manufacturing the HTPA, the pressure pulsation passive attenuation experiment was carried out under different load pressures. The experiment results show that the device has a good attenuation effect under different load pressures. The pulsation attenuation rate can reach 40% at 21 MPa, which is consistent with the results of Fluent simulation, verifying the correctness and validity of the simulation model.
3. For the pressure pulsation at the outlet of the commonly used nine-plunger pump, the HTPA designed in this paper can effectively eliminate the pressure pulsation around 450 Hz and greatly reduce the amplitude of the pressure pulsation. It also verifies the correctness and validity of the mathematical model and simulation model.  
Conceptualization, S.Z.; methodology, S.Z.; software, H.J.; validation, H.J.; formal analysis, H.J. and D.L.; investigation, S.Z.; resources, S.Z. and W.L.; data curation, H.J.; writing--original draft preparation, H.J.; writing--review and editing, S.Z.; visualization, H.J.; supervision, Z.L. and Q.H.; project administration, J.L. and Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Major Project, grant number: J2019-I-0008-0008.  
Not applicable.  
The authors declare no conflict of interest.
## 2 System Dynamic Description
### Dead Zone Non-Linearity Description  
The dead zone is usually described as a static non-linear function that indicates the system's insensitivity to small signals. For non-linear systems, the dead zone prevents the system from responding to some small signals and, finally, leads to significant tracking errors.  
Generally, a dead zone's non-symmetric non-linearity can be depicted as in Figure 2, and its mathematical model can be described as follows [10; 36]:  
\[u_{D}=D(u_{C})=\begin{cases}n(u_{C})&u_{C}\leq d^{-}\\ 0&d^{-}<u_{C}<d^{+},\\ m(u_{C})&u_{C}\geq d^{+}\end{cases} \tag{3}\]  
where \(u_{C}\) and \(u_{D}\) denote the input signal and the dead zone output signal, respectively. \(m(u_{C})\) and \(n(u_{C})\) are smooth non-linear unknown functions. \(d^{+}\) and \(d^{-}\) are also unknown boundaries and represent most of the dead zone phenomena.  
**Assumption 1**.: \(m(u_{C})\) _and \(n(u_{C})\) are all monotonically increasing, smooth, and invertible [10]._  
Figure 2: Non-symmetric dead zone non-linearity.  
Then, the inverse of \(D(u_{C})\) can be obtained as:  
\[D^{-1}(u)=\begin{cases}n^{-1}(u)&u<0\\ 0&u=0\\ m^{-1}(u)&u>0\end{cases} \tag{4}\]  
and depicted as in Figure 3.  
Thus, the following property is satisfied:  
\[D\Big{(}D^{-1}(u)\Big{)}=u. \tag{5}\]
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Dataset Sources and Relevance  
This study exploited synthetic datasets designed to enable the controlled experimental performance of Apache Spark operators, which were used in this work. These datasetswere of particular importance, which allowed for strict control of parameters--size, feature dimensionality, and complexity--and permitted a comprehensive study of computational performance and scalability.
* **Synthetic Datasets**: Generated using Python's 3.10 version, scikit-learn library for key MLlib operators:
* make_blobs for K-Means Clustering: Configured to test scalability across varying numbers of clusters and data dimensions.
* make_regression for Random Forest: Designed to simulate regression problems with tunable complexity.
* Custom sentence generators for Word2 Vec: Created sequences of words with varying sentence lengths, mimicking natural language text.  
The systematic exploration of operator behavior over multiple configurations was possible using synthetic datasets. While we did not use real-world datasets directly, the synthetic datasets were generated to resemble real-world behavior in the machine learning workflows, including clustering, regression, and text processing. The approach offers a solid basis for understanding operator performance in controlled situations, extending to actual use in real-world situations in the future.
# A SAR-Based Index for Landscape Changes in African Savannas
## 3 Results
### Overall Change and Variability of Natural Resources  
As indicated in Section 2.5, attentive interpretation of change intensities requires information on the variability of the different grid cells. If an area shows moderate change between 2007 and 2017, but a high variability, as derived according to Figure 5, this area is either subject to higher intra-annual or inter-annual seasonal changes or to generally observable instabilities regarding the interrelations between related types of land cover (such as shrubland or grassland). In turn, if a low variability (smaller than 1.5) can be observed, changes are rather of long-term nature, such as trends in vegetation cover or transitions between types of land-use (bare soil to built-up areas). Figure 7 shows the changes as assessed by the \(\mathrm{NRD_{w}}\) between 2007 and 2017 in combination with the variability \(\mathrm{v}\) per grid cell. While colors indicate the direction and severity of changes, line signatures within the grid cells indicate their variability. Developments can now be interpreted regarding their long-term information content and the affinity of an area towards changes. The indications from Section 3.1.2 can be affirmed and specified: There is no general loss of natural resources in the study area but certain areas are more prone to change than others. Areas around the settlements are almost stable whereas grids of both positive and negative development are accumulated at the South East of the study area. These result from the transition between forest and shrubland. Variability values of mostly above 2.00 indicate that this area is not gradually changing but generally prone to changes. Similar developments can be attached to Wadi Aouada where forest, shrubland, and grassland underlie both seasonal and inter-annual changes.  
General conclusions about the study area can be drawn based on the statistical distribution of the different variability values compared to their \(\mathrm{NRD_{w}}\) value as shown in Figure 8. It clearly demonstrates that areas of smaller variability (\(\mathrm{v}<2.00\)) form the majority in the study area whereas areas with distinctively high variability (\(\mathrm{v}>4.00\)) are very rare. It furthermore shows that areas with slight changes (\(\pm 2\%\)) are more frequent than areas with no change at all, especially for higher variabilities. This indicates that many areas within the study area are affected by smaller changes  
Figure 7: Weighted Natural Resource Depletion Index (\(\mathrm{NRD_{w}}\)) and variability for the study area between 2007 and 2017.  
and show a relatively unsteady development. It is additionally interesting that areas of highest positive development (>8%) increase with larger variability. This leads to the conclusion that some of them may also result from misclassifications as the classes of high variability no longer show an equal distribution.
## 4 Discussion
### Uncertainty of the Developed Methods
Therefore, the conductance method is more sensitive to the partition of energy; in other words, it suffers more uncertainty from Beer's law. Data from only four sites were used to evaluate the performance of the developed methods. It is inadequate to show the real potential of the two methods. With more and more in situ observations from different ecosystems, understanding of the underlying mechanism between SIF and T will be deepened. Recently, SIF products with higher temporal-spatial resolution from different satellites, different bands [69; 70], and derivative products based on machine learning [71; 72; 73; 74] have become available. T can be estimated at larger scales with the WUE method and conductance method by combining remote-sensing SIF data with remote-sensing-based ET/T models like TSEB [75; 76] and PML [77] or assimilating SIF into land surface models in future studies.