esa-sceva/satcom-chunk-collection huggingface dataset:
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 3 System Architecture
### Pilot-Based Channel Estimation
#### 3.1.2 Least Squares Channel Estimation  
In this work, we used the pilot-based channel estimation approach proposed by [PERSON] et al. [27]. Originally, the approach was used for a single input multiple output (SIMO) OFDM system. We applied the same approach for a single input single output (SISO) OFDM for a shallow UWA channel-based model on a Rician distribution, explained in Section 4. In this approach, the channel estimates were obtained via pilots and then maximum ratio combining (MRC) was used to estimate the transmitted data.  
Consider the OFDM system of Figure 1. Let:  
* \(I_{p}\) be the set of those subcarriers that carry the pilots;
* \(y[n]\) be the signal on \(n\)th subcarrier after the extraction of pilots;
* \(s_{n}\) be the pilot symbol with \(n\in I_{p}\);
* \(H_{k,n}\) be the channel transfer function for \(n\)th subcarrier of the \(k\)th OFDM symbol;
* \(\omega_{n}\) be the AWGN on the \(n\)th subcarrier;
* \(\mathcal{E}_{p}\) be the transmit power of the pilot symbol.  
The signal \(y[n]\) is then given by:  
\[y[n]=\sqrt{\mathcal{E}_{p}}H_{k,n}s_{n}+\omega_{n},\ n\in I_{p}. \tag{5}\]  
Let \(\boldsymbol{h}=\left[h_{0},\ldots,h_{L-1}\right]^{T}\) represent a vector of the impulse response of a Rician channel where each element of the vector is a complex Gaussian variable with a zero mean and \(L\) represents the number of taps. Consider a matrix \(\boldsymbol{F}\) having \(L\times N\) dimensions where \(N\) represents the number of subcarriers in the OFDM system. Each element of matrix \(\boldsymbol{F}\) is defined as:  
\[\left[\boldsymbol{F}\right]_{l,n}:=e^{(\frac{\left(\pi l-1\right)\left(n-1 \right)}{N})}. \tag{6}\]  
Let \(\boldsymbol{F}_{p}:=\left[\mathbf{f}_{n_{1}},\ldots,\mathbf{f}_{n_{p}}\right]\) where \(\mathbf{f}_{n_{i}}\) is the \(n\)th column of \(\boldsymbol{F}\). Define G as:  
\[\text{G}:=\Big{(}\mathcal{E}_{p}\mathbf{F}_{p}\mathcal{D}^{\mathcal{H}}\big{(} s_{p}\big{)}\mathcal{D}\big{(}s_{p}\big{)}\mathbf{F}_{p}^{\mathcal{H}}\Big{)}^{ -1}\Big{(}\sqrt{\mathcal{E}_{p}}\mathcal{D}\big{(}s_{p}\big{)}\mathbf{F}_{p}^{ \mathcal{H}}\Big{)}^{\mathcal{H}}. \tag{7}\]In this equation, \(\mathcal{D}\big{(}s_{p}\big{)}\) is a diagonal matrix, \(s_{p}\) are the pilots in a diagonal, and \(\mathcal{H}\) represents the Hermitian. If the estimated channel impulse response is \(\overset{\boldsymbol{\wedge}}{\boldsymbol{h}}\), obtained by using LSE, then:  
\[\overset{\boldsymbol{\wedge}}{\boldsymbol{h}}=\text{Gy}=\boldsymbol{h}+ \boldsymbol{\eta} \tag{8}\]  
with \(\boldsymbol{\eta}=\text{G}\omega\)[28]. Finally, the estimate of the channel transfer function for the \(k\)th OFDM symbol and the \(n\)th subcarrier--denoted by \(\hat{H}_{k,n}\)--is given by:  
\[\hat{H}_{k,n}=\hat{t}_{n}^{n}\overset{\boldsymbol{\wedge}}{\boldsymbol{h}}. \tag{9}\]
## 2 System Model
### Modeling the Line of Sight Probability
Similarly, \(P_{n}^{UL2U}\) is labeled as the transmitter power of the \(n\)th U2U link that reuses the \(m\)th uplink. The interference received by the BS from the U2U links is calculated as  
\[I_{m,BS}(t)=\sum_{n=1}^{N}\rho_{n,m}P_{n,BS}^{m}(t)=\sum_{n=1}^{N}\rho_{n,m} \frac{P_{n}^{UL2U}}{10^{P_{max,n}(t)/10}}. \tag{11}\]  
Therefore, the signal-to-interference-plus-noise-ratio (SINR) of the \(m\)th U2B link is calculated as  
\[\gamma_{m,BS}(t)=\frac{p_{k,BS}^{m}(t)}{\sigma^{2}+I_{m,UL2U}(t)}, \tag{12}\]  
where \(\sigma^{2}\) is the variance of the AWGN with a mean equal to zero. The data transfer rate of the \(m\)th U2B link is expressed as  
\[R_{m}^{UL2B}(t)=log_{2}(1+\gamma_{m,BS}(t)). \tag{13}\]
## 5 An Example of Using the Proposed Method
### Simulation for Identifying the Desired Level of Costs
#### 5.2.1 The Basic Variant  
Let us assume that the decision maker is interested in reducing the unit production cost to 24 \(\upepsilon\). To test whether there exist solutions, the problem under consideration is reformulated into an inverse problem. A solution to the inverse problem is sought using constraint programming, which requires that decision variables, their domains, and constraints, including relationships between variables (e.g., their mutual impact on one another), are specified. The domains for the considered variables are as follows: \(D_{9}\) = [45], \(D_{10}\) = [830], \(D_{11}\) = [54; 55; 56], \(D_{12}\) = [24; 25], \(D_{13}\) = [14; 15], and \(D_{14}\) = [7]. The domains include only integer numbers and the simulation step is related to the increase of these numbers by one for each variable separately. As a result, all available solutions, referring to all numbers in the domains, are sought.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & **Model** & \(V_{1}\) & \(V_{2}\) & \(V_{3}\) \\ \hline \multirow{4}{*}{Learning} & ANN LM & 0.005 & 0.001 & 0.003 \\  & ANN GDX & 0.521 & 0.051 & 0.118 \\  & LR & 0.637 & 0.072 & 0.142 \\  & Average & 1.312 & 0.134 & 0.326 \\  & ANN LM & 0.496 & 0.067 & 0.129 \\ Testing & ANN GDX & 0.602 & 0.049 & 0.085 \\  & LR & 0.913 & 0.106 & 0.138 \\  & Average & 1.826 & 0.180 & 0.548 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of root mean square errors (RMSEs) for estimation models.  
The inverse problem is implemented in Mozart/Oz software, which is a multiparadigm programming language. Mozart/Oz contains most of the major programming paradigms, including logic, functional, imperative, object-oriented, concurrent, constraint, and distributed programming. The major strengths of Mozart/Oz are related to its constraint and distributed programming components, which are able to effectively solve many practical problems, for example, timetabling and scheduling problems [40].  
Table 2 presents eight possible solutions (variants of the production process) that take into account the specified constraints and variables. The minimal unit production cost appears for the values of variables in the fifth variant.  
The changes presented in Table 2 regard three variables \(V_{11}\), \(V_{12}\), and \(V_{13}\). The set of possible solutions informs the decision maker what changes may be incorporated into the production process to reduce the unit production cost.  
Let us assume that the decision maker's expectations regarding the unit production cost decrease from 24 to 22 \(\epsilon\). There is no solution by the indicated domains that satisfies this new value of the preferred cost. This triggers the next step in the problem solving procedure, i.e., testing whether it is possible to extend the domains related to the selected variables.
## 3 Results
### The Control of Charge Increases Drastically the Self-Production Ratio  
Figure 4 represents the results obtained on 19 October 2021. During this cloudy day, 17 charges occurred, for a total energy consumption of 141 kWh and for a total PV production of 221 kWh.  
Figure 4a shows that the charges, without any control, would have been carried out mainly between 7:30 and 10:30 a.m. Figure 4a shows that the cumulative load curve is located rather above the red curve, which means that the self-production rate is low (in that case, it is equal to 34%). On the contrary, Figure 4b shows that the loads are happening mainly below the red curve, which suggests that the self-production rate is high (here 90%).  
Figure 5 represents the results obtained on 6 January 2022. On this particular sunny day, 15 charges occurred, for a total energy consumption of 227 kWh and a total of forecast PV production of 551 kWh. This particular day, the self-production rate increase from 31% to 93% thanks to the control algorithm.  
These results emphasize that the charging control may increase drastically the rate of self-production during both sunny days and cloudy days.
## 3 Results
### Temperature Monitoring and Compensation  
As it was previously mentioned, MOF-FP optical sensor is affected by temperature variations, leading to crosstalk between temperature and relative humidity measurements. Figure 5 shows the response of the MOF-FP sensor (calibration) towards temperature showing a linear response with sensitivity of 0.00377\(\pi\) rad/\({}^{\circ}\)C.  
Figure 4: Experimental set up: (**a**) schematic set up and (**b**) real picture of the set up.  
To avoid this undesirable crosstalk a commercial FBG (sensitivity of 10 p.m./\({}^{\circ}\)C) was also buried in contact to the MOF-FP sensor and simultaneously monitored. This temperature reference showed the behavior illustrated in Figure 6 Measurements shown were taken during a week. As can be noticed in Figure 6, the FBG optical interrogator has a wavelength resolution of 5 p.m., which means a temperature resolution of 0.5 \({}^{\circ}\)C.  
Results shown in Figure 6 show a good agreement between the dynamics of humidity and temperature. Temperature shows oscillations of 2 \({}^{\circ}\)C depending on the time of the day. During day hours temperature rises and when sun sets (soil sample was at direct exposition of sun light) it decreases -2 \({}^{\circ}\)C. Humidity results follows the same trend showing great variations (decreasing humidity) during the day and slower ones at night. These variations agree temporarily with temperature, showing bigger slopes during sunshine hours.  
With a temperature reference and being the sensitivity of the optical sensor to temperature variations known, its influence can be cancelled. Thus, the FFT phase shift induced by temperature variations (deduced through the FBG sensor) can be directly subtracted to the MOF-FP sensor's results. This compensation can be made either in real time or by post processing. In this study, it was directly implemented in the acquisition software. Results that are shown in Figures 7-9 are already temperature compensated ones.  
Figure 5: Optical sensor’s response towards temperature variations.  
Figure 6: (**a**) Temperature and humidity measured during a week in Soil1 and (**b**) a detailed region.
## 3 Results  
### NBR Time Series of Vegetation Health across the Landscape  
Statistical averages of NBR (Figure 3) calculated from the Sentinel-2 time series showed a positive increase in the NBR average between 2015 and 2022. For the whole study area, this mean value--which is an ecological indicator--was estimated as 0.034 \(\pm\) 0.023 in 2015, and 0.104 \(\pm\) 0.019 in 2022. These results probably imply improved local ecological conditions related to human intervention.  
From a geospatial perspective, four components were distinguished to explain the vegetation health status of the studied landscape. Generally, highly stressed vegetation, represented by the orange color in Figure 4, occupies the southwestern part of the study area in a northeastern direction. This area borders the southeast of the Togodo National Park and was estimated to comprise 47% (9952.21 \(\pm\) 0.82 ha) of the total study area in 2015 (Figure 5), increasing slightly to 50% (10,397.43 \(\pm\) 0.87 ha) in 2017. However, in 2022, the highly stressed vegetation component underwent a significant fragmentation. It was transformed into an area of less-stressed vegetation (Figure 4). In 2022, the spatial fraction of unstressed vegetation was wholly transformed into less-stressed vegetation. This was especially the case in the southern periphery of Togodo South National Park. Thus, the less-stressed fraction increased from 16% (3335.81 \(\pm\) 0.23 ha) in 2015 to 39% (8177.96 \(\pm\) 0.34 ha) in 2022.  
Figure 2: Workflow of the whole approach.  
Figure 4: Spatial patterns of vegetation ecophysiological conditions in 2022.  
Figure 3: Distribution of normalized burn ratio (NBR) means across the time series.  
The vegetation that was unstressed before 2022 was essentially located in the buffer zone of the southern Togodo National Park, and mainly in the transboundary Mono River biosphere (Figure 4). Estimated at 22% (4594.44 \(\pm\) 0.23 ha) in 2015, this area decreased drastically over the time series.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 2 Li Exploration Using Remote Sensing
### Main Challenges
Although Sentinel-2 represents the free multispectral product with better spatial resolution and, therefore, the one with the best capability to overcome the pegmatite exposition problem, several misclassification errors resulted from the absence of bands in the TIR region. These misclassifications include the spectral mixing between Li pegmatites and the metasolimentary host rocks [69]. Additionally, the relatively high number of classes, identified in the image for the classification process, contributed to a higher data complexity that may have hindered the obtained results. The reduction of the number of classes, through masking of classes with no interest in Li exploration, will certainly help to optimize the algorithm performance, as recognized by the authors [69]. Another critical challenge to Li exploration based on satellite data is that in spite of the many diagnostic spectral features of Li minerals, to the best of our knowledge, it was not possible to identify so far, a unique spectral feature solely caused by the presence of Li in the structure of minerals. Taking all this into account, it is our opinion that there are yet several developments to be made in the field of remote sensing applied to Li-mineralizations, and new algorithms and methodologies to be developed in space and Earth sciences. More insights on other remote sensing data products are given in Chapter 3.
# Attention Score Enhancement Model Through Pairwise Image Comparison
###### Abstract
The PAE model enhances attention maps by comparing pairs of training and reference images, allowing it to highlight small differences that the ViT might overlook. This approach aligns with previous work on attention-based feature enhancement for distinguishing subtle variations [23; 25]. The key innovation of the PAE model lies in combining attention scores in regions of high similarity between images, enabling it to better distinguish subtle visual variations, particularly in borderline images within CDT data [1; 4]. This enhanced attention mechanism improves classification accuracy for borderline cases in CDT datasets, an essential factor in cognitive impairment screening, as shown in prior studies on the digital Clock Drawing Test [5; 26; 27]. Additionally, by emphasizing fine-grained local features that previous models may have overlooked, the PAE model delivers more accurate and reliable results, outperforming both API-Net and ViT, especially in handling visually similar images [2; 16]. In this study, we evaluated the performance of the proposed Pairwise Attention Enhancement (PAE) model in comparison with CNN models (including API-Net, which is based on CNN) [8; 10] and ViT models [28; 29]. The CNN models achieved accuracies between 76.68% and 80.33%, demonstrating strong feature extraction capabilities but showing limitations in capturing complex patterns within the dataset [2; 9; 15]. Specifically, API-Net, with its enhanced pairwise interaction mechanism, outperformed other CNN models, achieving an accuracy of 80.33% [2]. Among the ViT models, the best-performing configuration reached an accuracy of 91.89%, excelling in modeling global relationships within images [18]. However, the PAE model demonstrated superior performance across key metrics, with its best configuration achieving an accuracy of 92.69%. This improvement is attributed to the PAE model's ability to fine-tune attention scores and effectively capture subtle visual differences between similar images, a notable advancement where traditional feature extraction methods face challenges [21; 24]. By overcoming the limitations of CNN and ViT models, the PAE model is expected to serve as a valuable tool for enhancing accuracy in the diagnostic assessment of cognitive impairments through improved local and global feature learning [6; 17; 30]. The remainder of this paper is organized as follows:  
Section 2 provides a detailed description of the dataset used in this study, including the preprocessing and augmentation techniques applied to the CDT data. Section 3 introduces the proposed PAE model, explaining its architecture and core mechanisms, particularly how it enhances attention maps through pairwise image comparisons. Section 4 presents the experimental setup and results, showcasing the performance improvements of the PAE model compared to existing methods such as API-Net and ViT. Finally, Section 5 concludes the paper with a discussion of the findings, potential limitations, and directions for future research.
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 2 Materials and Methods
### Review Protocol and Publication Quality  
In order to ensure transparency and reproducibility, the review was conducted in accordance with a structured protocol inspired by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The literature selection process consisted of four key steps: identification, pre-selection, eligibility assessment, and final inclusion. A diagram showing this process is shown in Figure 3.  
Figure 3: PRISMA-type flow diagram illustrating the literature selection process, including the steps of identification, pre-screening, eligibility assessment, and final inclusion of publications in the review.  
During the identification phase, a total of 165 documents were retrieved from the Scopus database using the query TITLE (Combustion AND Machine Learning OR Genetic Algorithms). The search was limited to publications from the years 2015-2024 written in English and at the final stage of publication. In the initial screening, duplicates were removed, and titles and abstracts were analyzed for thematic relevance. Studies that did not directly involve the application of machine learning or genetic algorithms in the context of combustion, fuel design, or energy systems were excluded. During the eligibility assessment phase, a full-text analysis was conducted. Publications were included in the next stage if they met the following criteria:  
* Addressed practical or conceptual applications of ML or GAs in the analysis of combustion, gasification, or fuel design processes,
* Provided an adequate description of the methodology,
* Were peer-reviewed journal articles or conference proceedings,
* Originated from one of the 15 selected countries active in this research area (including China, India, Germany, Iran, Japan, Malaysia, Poland, Saudi Arabia, the USA, and the UK).  
The final selection resulted in 97 publications being included in the detailed analysis. Publications from non-technical fields (e.g., psychology, social sciences, arts) and papers without specific applications or combustion process modeling were excluded. However, conceptual or review papers were included if they offered relevant methodological contributions or identified research gaps in the studied area.  
The quality assessment of the publications was based on citation count, source type (journal or conference), and journal ranking (e.g., Q1-Q2). Priority was given to studies with a solid experimental foundation and clearly described methodology, even if the citation count was still low due to recent publication. Each publication was independently evaluated by two authors in terms of thematic relevance, completeness, and clarity of methods. In cases of disagreement, consensus was reached through discussion.
# Data Interpolation by Near-Optimal Splines with Free Knots Using Linear Programming
## 3 Problem Formulation  
### General Problem Formulation  
For a given set of data points \(\{x_{i},y_{i}\}_{i=1}^{n}\) such that \(A\ =\ x_{1}<\ldots<x_{n}=B\), a spline function \(S(x)\) of degree \(r\) with the knots at \(x_{knot_{1}},\ldots,x_{knot_{K}}\) is a real valued function defined on the interval \([A,B]\) such that  
1. \(S(x)\) passes through all the data points, i.e., \(S(x_{i})=y_{i}\);
2. In each interval \(\left(A,x_{knot_{1}},\ldots,x_{knot_{2}}\right),\ldots,\ (x_{knot_{K}},B)\), where \(K\) is the number of knots, \(S(x)\) is given by a polynomial of degree \(r\) or less;
3. \(S(x)\) and its derivatives of orders \(1,2,\ldots,r\)-\(1\) are continuous in the interval \((A,B)\) and
4. The \(r\)th derivative of \(S(x)\) can be discontinuous at a finite number of points.  
Thus, a spline function of degree \(r\) is a piecewise polynomial function with \((r-1)\) continuous derivatives. We could also say that it is a member of \(C^{r-1}\) for which the \(r\)th derivative is a step function. In other words, it is an \(r\)th order indefinite integral of a step function. Polynomial pieces of a spline are \"spliced\" appropriately at the knots where the \(r\)th derivative jumps from one value to another (often with opposite signs), enabling it to turn or change its shape, while all derivatives up to \((r-1)^{\text{\tiny{ff}}}\) order remain continuous, imparting smoothness to the spline. It should be noted that \(x\)-coordinates of given data points \(\{x_{i},y_{i}\}_{i=1}^{n}\) are generally not the knot points.  
The problem of finding the optimal spline of \(r\)th degree is  
\[\min_{f\in C^{r-1}}\left\|f^{(r)}\right\|_{\infty} =\min_{f\in C^{r-1}}\max\left|f^{(r)}\right| \tag{1}\] \[s.t.\,f(x_{i})=y_{i}, \tag{2}\]  
where \(\{x_{i},y_{i}\}_{i=1}^{n}\) are given data points.  
The formulation (1) and (2), though simple and elegant, is unfortunately not easy to solve. This formulation amounts to the optimization over a Banach space [51] of continuous functions on a compact set \([A,B]\), which is well-known to be notoriously difficult ([31], p. 3). Within (1) and (2), the decision variable is a function \(f\) that minimizes \(\left\|f^{(r)}\right\|_{\infty}\). While the problem of data interpolation by quadratic splines has been accomplished [4], interpolation by splines of higher orders is far more challenging. Our key contributions to resolve this difficulty are to consider obtaining a near-optimal solution in a computationally efficient manner, and to prove that the near-optimal splines approach optimal splines as discretization mesh is refined. This will be achieved by discretizing the entire space into segments over which the optimization is performed by replacing continuous function with their discrete counterparts and by converting the resulting problem into a linear programming problem that can be solved in polynomial time by using Simplex method [52; 53; 54], as will be explained next.