esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Basic Principle  
### Analysis of Imaging Characteristics for Small Targets  
Photonic imaging is a complex process subject to interference from multiple factors. The degradation and deterioration of images are inevitable at various stages of the entire imaging system chain [10]. Specifically, this section simplifies the analysis of the full-chain photonic imaging system, describing the process as follows: target signals are transmitted through atmospheric radiative transfer and modulated by the optical system before reaching the focal plane of the remote sensing detector. The detector then integrates and samples these signals to obtain digital image signals [11].  
In addition, the interaction between atmospheric radiation and the optical system inevitably leads to significant degradation of the target signal. Due to the diffusion of light energy, the optical system acts as a spatial low-pass filter during imaging, filtering out high-frequency components of the signal while retaining the lower frequencies. The imaging produced by the optical system is often not ideal [12; 13].  
The Point Spread Function (PSF) of an optical system describes how a point source's energy spreads on the image plane. This is often represented using functions like the Bessel or Gaussian function, noted as PSF\({}_{opt}\).  
The principle of a PSF imaging simulation involves convolving the original two-dimensional light-distribution image with the optical system's PSF to obtain the corresponding 2D light distribution of the optical simulation image. The mathematical expression for this operation is  
\[I(x,y)=O(x,y)\times\text{PSF}_{opt}(x,y) \tag{1}\]  
In real-world cases, fast-moving weak and small targets show noticeable motion relative to the detector. This motion causes energy changes, leading to variations in the target image's diffusion through the imaging chain [14]. Figure 1 illustrates an example of the image blur for a weak and small moving target.  
Assume the radius of the blur spot of the target on the focal plane is \(r\) pixels and the number of pixels occupied by the blur spot is \(N_{0}\). After \(t\) seconds of exposure, due to relative motion, the weak and small moving target will experience motion blur, causing the number of pixels occupied in the image to change:  
\[dx=\frac{v\cdot t}{\xi}(\text{pixel }) \tag{2}\]  
\[N=N_{0}+2r\cdot dx=N_{0}+\frac{2r\cdot v\cdot t}{\xi} \tag{3}\]  
Here, \(t\) represents the relative displacement of the target during the exposure time, measured in units of pixel count; \(N\) represents the number of pixels occupied by the target under motion blur; \(v\) is the speed of motion; and \(\xi\) is the pixel size.
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 2 Materials and Methods
### Use Cases  
The preparation of the data acquisition started by choosing the designated areas for two different use cases, selecting locations for samples, and requesting official authorization to fly over the designated areas. The designated areas were chosen near the \"Pedrogao Grande\" region (the location of the deadly wildfires of 2017). This area has a high density of forest, with many challenging urban-forest interfaces that are ideal for validating the tools and the CHAMELEON bundles.  
Two different types of designated areas were envisaged: a road heavily surrounded by forest and a site with an isolated building in the forest, with a problematic urban-forest interface. The first designated area is a segment of road N236, near the village of \"Derreada Cimeira\" (Castanheira), with GPS coordinates: \(39^{\circ}58^{\prime}53^{\prime\prime}\) N \(8^{\circ}09^{\prime}24^{\prime\prime}\) W, which is illustrated in Figure 5 on the left side. This segment has been chosen because of the high density of trees next to the road. It is a good example of the high density of vegetation that surrounds many of the region's roads.  
The second use case addresses a site with a single isolated building, near coordinates \(39^{\circ}55^{\prime}09.0^{\prime\prime}\) N \(7^{\circ}55^{\prime}28.3^{\prime\prime}\) W, which is shown on the right side of Figure 5. This is an example that represents many other buildings in the area that have been authorised near forests with a high density of trees and vegetation.
## 4 Empirical Propagation Models
### COST 235 Model  
This model distinguishes between wooded environments with the presence of leaves and those without. In this study, the equation with the presence of leaves is considered [44]. The equations for calculating excess loss added to the free space loss are given by:  
\[Att_{COST}=\left\{\begin{matrix}26.6\times f^{-0.2}\times d^{0.5},se&sem follows\\ 15.6\times f^{-0.009}\times d^{0.26},se&comflhas\end{matrix}\right. \tag{10}\]  
* \(Att_{COST}\) is the excess attenuation according to the COST235 model (dB);
* \(d\) is the distance between Tx and Rx (m);
* \(f\) is the system's operating frequency (MHz).
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 5 Evaluation
### Impact of Malicious Data Owners on Accuracy  
In general, the number of malicious data owners in the system has a great impact on the global model accuracy. Figure 4 depicts the accuracy that the QuoTa algorithm achieves in 50 rounds of communication for the global model when the percentage of malicious data owners in the system is 20%, 40%, and 60%. It can be seen that the QuoTa algorithm has good robustness to different percentages of malicious data owners. The convergence speed and convergence accuracy of the global model decrease when the malicious data owners account for a high percentage. Fortunately, the decrease is within an acceptable range for the proposed QuoTa. Overall, our algorithm can effectively resist the influence of malicious data owners.
## 6 Description of the Developed Design
### Payload Area  
The catamaran arrangement creates a large deck area, which is to be used to store all liquid hydrogen containers, electrolyser containers, and any other required payload. The payload area is estimated at 3744 m\({}^{2}\) (because of the inclined arrangement of the struts and the required walkable areas, the length of the payload area is assumed to be 60.2 m and the width 62.2 m). Considering two levels of stacking on the deck and a utilisation rate of 70% (the rest of the space is used, for example, for accessing containers or the container interface, etc.), a total of 44 40-ft equivalent containers can be stored on the deck. Since 14 40-ft containers are needed for the liquid hydrogen storage and five 20-ft containers for the electrolysers, 27 40-ft equivalent containers remain available for other equipment.
## 3 Results
### Frost Heaving
#### 3.1.2 Summary of Frost Heaving Tests  
The frost heaving tests showed various observations in terms of time and heaving amount with respect to the mixing ratios. The time to reach an \(h_{\text{ult}}\) was defined as \(t_{\text{ult}}\). The heaving ratio (\(\eta\)) is defined as \(h_{\text{ult}}\) over the initial specimen height (\(h_{0}\)) in a percentage, and the total heaving ratio (\(\Delta\eta\)) is defined as the absolute heaving amount (\(h_{\text{ult}}-h_{\text{min}}\)) over \(h_{0}\) in a percentage, as shown in Equations (1) and (2), respectively:  
\[\eta=\frac{h_{\text{ult}}}{h_{0}}\times 100\;[\%] \tag{1}\]  
\[\Delta\eta=\frac{h_{\text{ult}}-h_{\text{min}}}{h_{0}}\times 100\;[\%] \tag{2}\]  
where \(h_{\text{min}}\) indicates the maximum frost shrinkage. If there is no frost shrinkage (\(h_{\text{min}}=0\)), \(\Delta\eta\) is the same as \(\eta\).  
The results are shown in Figure 6. When the mixtures had a low \(M\) and high \(S\) (e.g., h-M0-S90 and h-M5-S90), \(h_{\text{ult}}\) was observed at a relatively earlier freezing duration (e.g., \(-2\) h) (Figure 6a). Except for these the two cases, \(t_{\text{ult}}\) was generally measured within the range of 8-10 h. These observations suggest that at a high \(S\), the pure gravel ballast exhibits quicker frost heaving. As \(S\) is lowered, the stabilization of frost heaving becomes slower regardless of \(M\). Therefore, it is concluded that a proper drainage system at the gravel ballast-subgrade interface can prevent the occurrence of frost heaving.  
The symbol \(\eta\) represents the general trends of frost heaving on gravel ballast-subgrade soil mixtures (Figure 6b). Except for a few cases, the general trends of \(\eta\) exhibited an increase as \(S\) and \(M\) increased. When \(S=90\%\), \(\eta\) was approximately 300-900% and 140-230% higher than those for \(S=70\%\) and 80%, respectively. The trends of \(\Delta\eta\) were generally similar to those of \(\eta\); however, the level of \(\Delta\eta\) for \(S=70\%\) increased because of frost shrinkage (Figure 6c). Therefore, it is concluded that the frost heaving for \(S=70\%\) is lower than that for \(S=80\%\) and 90%; however, the total fluctuation may have to be considered during freezing.
# The Use of Volcanic Powder as a Cement Replacement for the Development of Sustainable Mortars
## 2 Materials and Methods
### Sample Preparation and Environmental Conditions  
Three kinds of cement mortars have been analyzed in this study, a control mortar prepared with ordinary Portland cement CEM I 42.5 R (CEM I) and accomplishing the Spanish and European standard UNE-EN 197-1 [30] requirements, and two mortars with a 10% and 20% replacement of the abovementioned commercial ordinary Portland cement CEM I by volcanic powder (named VP10 and VP20, respectively). In all the mortars, a water cement ratio of 0.5 and an aggregate cement ratio of 3:1 were used. Fine aggregates are siliceous with a particle size that complied with the stipulations of the UNE-EN 196-1 [31] standard.  
For the mechanical strength tests, \(40\times 40\times 160\) mm samples were prepared. For the durability and microstructural tests, cylindrical samples 10 cm in diameter and 15 cm high were used. The cylinders were kept in a chamber at 20 \({}^{\circ}\)C and 95% relative humidity for 24 h. After this, the specimens were turned out and sawed into 1 cm and 5 cm high cylinders. Finally, all the samples (prisms and cylinders) were maintained in a room at 20 \({}^{\circ}\)C and 100% relative humidity until the testing ages (28, 200, and 400 hardening days).
# Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems
## 2 Materials and Methods
### Data Preprocessing
#### 2.4.2 Model Construction  
Univariate LSTM and MV-LSTM are two models used for predicting the temporal characteristics of \(x_{i}\) column data. MV\(-\)LSTM expands one-dimensional \(x_{i}\) data into multiple dimensions. In MV-LSTM, secondary indicators that have a significant impact on the quality characteristics are included, as they play a crucial role in determining the objective function.  
The MV\(-\)LSTM neural network was constructed to design the LSTM layer and dense layer, with the number of neuron cells serving as parameters to regulate the prediction performance. The relationship between the target and acquisition variables can be expressed as follows:  
\[Z=\begin{bmatrix}X_{1}&Y_{1}\\ X_{2}&Y_{2}\\ \vdots&\vdots\\ X_{n}&Y_{n}\end{bmatrix}=\begin{bmatrix}x_{11}&x_{12}&x_{13}&\cdots&Y_{1}\\ x_{21}&x_{22}&x_{23}&\cdots&Y_{2}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ x_{n1}&x_{n2}&x_{n3}&\cdots&Y_{n}\end{bmatrix} \tag{13}\]  
The ADAM algorithm is utilized for optimizing prediction performance by adjusting hyperparameters. The corresponding analysis involves specifying hyperparameters and training all specified parameters to select the solution with the most parameters. The input variables are the number of LSTM hidden layers, the number of dense hidden layers, the number of neurons, the memory step, three parameters for training different models, and the RMSE loss function. Finally, the optimal parameters are determined. The main process of the model is depicted in Figure 5.  
A gateway was deployed to collect and analyze the manufacturing data of drinking fountains produced continuously on a single production line. The data were supplemented with new data to compensate for missing values and aligned with respect to product sequence number. A total of 400 sequential product data were used as the training set, while 100 product data were used as the test set. The data were normalized and formatted to facilitate their input into the model. The multivariable LSTM parameters were trained using the 400 product data, and an optimal prediction was obtained by adjusting these parameters. After adjusting the prediction results, the parameter file was exported. Finally, through comparative analysis, the best method for quality prediction of the water dispenser production line was determined.
# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
###### Abstract  
Morphological analysis is the fundamental and preliminary task for Natural Language Processing (NLP) applications, which involve speech and language. Kannada is a low-resource language belonging to the Dravidian language family, which is highly agglutinative and morphologically rich in nature, where dataset development is happening rapidly due to the increasing demands of NLP tools. This study presents a hybrid approach that integrates rule-based and Transformer-based techniques, aiming to maximize their strengths while minimizing the respective limitations. In the Kannada language, the analysis of inflections has been challenging due to morphological richness, and to address this issue, 85 paradigms are created using Littoolbox of Apertium. Further, a Transformer model is trained with the generated nominal data to generate the morphological analysis for the out-of-vocabulary inflections. The hybrid approach can be easily extended to new words as they are added to the dictionary. The obtained results are on a test set for inflections in Kannada precision: 0.924; recall: 0.925; and F1 score: 0.925. The main contributions include rule extraction for paradigm design at the word level, morphological analysis for nouns, verbs, adjectives, pronouns, and indeclinables on a benchmark dataset and morphological analysis generation using the Transformer architecture.  
Keywords:computational linguistics; morphological analysis; Kannada language; transformer; Natural Language Processing (NLP) +
Footnote †: journal: Applied
## 4 Experiments and Results
### Time-Based Hash Storage
The total fee was determined by multiplying the gas price at the time of block creation by the gas units consumed. In our experiments, we set a gas limit to 44,500 units and a maximum priority fee of 0.001 Gwei per transaction. To convert fees from Gwei to USD, we used CoinGecko, a publicly accessible cryptocurrency data aggregator that provides real-time exchange rates, including ETH-USD. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time Intervals (Seconds)** & **Mean** & **Median** & **Count** & **(\%)** \\ \hline
00.00–12.82 & 9.18 & 9.16 & 967 & 47.17\% \\
12.82–20.50 & 16.43 & 16.39 & 739 & 36.05\% \\
20.50–27.51 & 23.69 & 23.63 & 213 & 10.39\% \\
27.51–35.19 & 30.85 & 30.78 & 99 & 4.83\% \\
35.19–41.97 & 38.09 & 38.08 & 22 & 1.07\% \\
41.97–50.00 & 45.45 & 45.49 & 10 & 0.49\% \\ \hline Overall & 14.83 & 15.88 & 2050 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of cluster analysis metrics for block confirmation times. Figure 3: Histogram illustrating the distribution of block confirmation times, highlighting variability in transaction processing speeds within the blockchain network. To evaluate the effect of the time of day on block confirmation times, we grouped transactions into three equally sized timespans, each spanning eight hours. Table 5 reports the metrics for each transaction group. Figure 4 shows the results of this analysis as a plot of the distribution of transaction fees in Gwei and USD. Figure 5 presents boxplots of transaction execution times, while Figure 6 displays boxplots of transaction fees.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 1 Introduction and Motivation
### LEO-PNT versus GNSS
This setup can improve the clock stability in LEO satellites by using GNSS clock as a reference. It can also provide a reliable system for tracking LEO's position, time, and velocity. In addition, configuring LEO satellites to rebroadcast GNSS-like signals on other frequency bands can help these signals mitigate most atmospheric effects. Furthermore, LEO's higher signal strength can increase the availability of these signals in hard-to-reach places [4; 10; 11; 12; 13]. Studies have also shown a significant improvement in positioning accuracy for users that receive their signals from LEO-augmented GNSS satellites [14; 15]. LEO-PNT as an alternative solution to GNSS have also been studied, e.g., in [4; 16]. The main research questions in such an approach are how to build an optimal LEO constellation for PNT purposes, as well as what receiver architectures are the most suitable to process LEO signals for accurate and robust PNT solutions. While the research on GNSS is a mature field, the research on LEO-PNT is a relatively new field that has gained more attention during the past couple of years. Therefore, there are still many open challenges related to LEO-PNT, and some of them are related to finding appropriate models for the various sources of errors, including atmospheric errors, for LEO-PNT signals. There are currently no available LEO-PNT measurements in open access that can be used to validate our model. Therefore, our approach has been to first validate the model with existing GPS data, then infer how such a model would be suitable in LEOcontext and identify the open challenges in adapting such a model to LEO. The next subsection discusses the various sources of errors in satellite-based positioning and the importance of having suitable ionospheric delay models.
## 3 Results and Discussion
### OPLS-DA Analysis of Soluble Compounds
Galactinol had the highest VIP (11.92), while 3-ketosphinganine had the highest fold change value (FBW/IBW = 163.26). Most of the differentially expressed alcohols in FBW were present in higher concentrations compared to IBW, and it is believed that they were produced through yeast fermentation. Sphinganine and 3-ketosphinganine, found in high concentrations in FBW, are metabolites involved in the synthesis and metabolic pathway of sphingomyelin, a major structural lipid in eukaryotic membranes, and could have been produced and released by yeast during fermentation. All differentially expressed acids in FBW showed several times- to several hundred times-higher concentrations than those in IBW, with no exceptions. 2-Isopropylmalic acid had the highest fold change (FBW/IBW = 351.38), followed by (S)-2-hydroxyglutarate (FBW/IBW = 60.91) and citraconic acid (FBW/IBW = 48.55). As acids in fermented wine are mainly transformed from sugars during fermentation, which barely occurs in IBW, the higher concentration of acids in FBW is an important sensory characteristic, but less so in IBW. In general, appropriate concentrations of volatile acids are ideal for producing high-quality wines, and a deacidification treatment could help to improve the flavor quality of FBW [21]. In addition, more esters would be formed through the esterification of alcohols with organic acids during the fermentation, post-fermentation, and aging processes, which is confirmed by the higher levels of esters in FBW. In addition, the differential expression of amino acids was also analyzed. L-Saccharopine had the highest fold change (FBW/IBW = 43.07), followed by Ile-Val (FBW/IBW = 18.45) and 3-aminobutanoic acid (FBW/IBW = 12.10). L-Saccharopine (an intermediate compound in the metabolic pathway of L-lysine), Ile-Val (a dipeptide consisting of two essential amino acids isoleucine and valine), and 3-aminobutanoic acid had higher concentrations in FBW than in IBW. On the other hand, L-glutamate and L-aspartate, which contribute to a savory or umami taste in foods, had higher concentrations in IBW than in FBW. Although these compounds do not have a strong or distinctive taste or flavor, they provide specific contributions to the final wine flavor. The concentration of purines/pyrimidines, esters, and other compounds in FBW were mostly higher than in IBW, with only a few exceptions. For instance, the level of anthocyanin pigment cyanidin 3-glucose, which generates red color, was higher in IBW. 1,3,5-Benzenetriol, an odorless and tasteless phenolic compound commonly found in wine, had a concentration in IBW that was 11.93 times higher than that in FBW. However, the exact role of 1,3,5-benzenetriol in contributing to bayberry taste remains unclear and further research is required to determine its effect on flavor and aroma. Additionally, the anthocyanin pigment cyanidin 3-glucose cation, which gives bayberry fruits their red or purple color, had a concentration in IBW that was 2.92 times higher than that in FBW.
## 2 Materials and Methods  
### Investigated Compressor  
The compressor used in this paper is TTL-1, an in-house designed high-pressure ratio centrifugal compressor. The compressor can achieve a peak isentropic efficiency of 79.7% and peak pressure ratio of 8.12. A precise explanation of evaluating the TTL-1 compressor performance has been previously described by [PERSON] et al. [26]. Some of the compressor geometry and the operating condition specifications are shown in Table 1.  
To improve the performance of high-pressure ratio centrifugal compressors, investigation of the radial gap ratio, splitter blades, casing treatment, tandem blades, 3D features and some of the designs features of TTL compressors can be helpful. The radial gap between the impeller trailing edge and diffuser leading edge is studied in this paper as a design feature for developing the high pressure ratio centrifugal compressor.  
The schematic of radial gap ratio is represented by the radius ratio r\({}_{3}\)/r\({}_{2}\) in Figure 1. As the impeller trailing edge radius (r\({}_{2}\)) is constant, varying the diffuser leading edge radius (r\({}_{3}\)) contributes to the various radial gap ratios. In this study, in order to investigate the effects of different radial gap sizes on the performance of the compressor, we varied the radial gap size from 1.04 to 1.14 by adding 0.02 to the size of the previous radial gap. Other geometry characteristics of the compressor are kept constant during this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Datum Compressor TTL-1**} \\ \hline \multicolumn{3}{c}{Impeller} \\ \hline Number of blades & \(Z_{1}\) & 24 \\ Maximum rotational speed & N\({}_{\text{max}}\) (rpm) & 52,440 \\ Normalized leading edge hub radius & r\({}_{\text{1h}}\)/r\({}_{2}\) & 0.26 \\ Normalized leading edge tip radius & r\({}_{\text{1h}}\)/r\({}_{2}\) & 0.65 \\ \hline \multicolumn{3}{c}{Diffuser} \\ \hline Number of Vanes & \(Z_{\text{D}}\) & 19 \\ Area ratio & A\({}_{\text{4}}\)/A\({}_{\text{Treat}}\) & 2.0 \\ Blade turning & \(\beta_{\text{4}}\)\(-\)\(\beta_{\text{3}}\) & 15\({}^{\circ}\) \\ Normalized leading edge radius & r\({}_{\text{3}}\)/r\({}_{2}\) & 1.10 \\ Normalized trailing edge radius & r\({}_{\text{4}}\)/r\({}_{2}\) & 1.48 \\ \hline \multicolumn{3}{c}{Stage performance} \\ \hline Peak total pressure ratio & max \(\pi_{\text{1-4}}\) & 8.12 \\ Maximum corrected mass flow & \(\dot{\text{m}}\) & 2.6982 \\ Peak isentropic efficiency & \(\eta_{\text{1-4}}\) & 79.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compressor Specifications.  
Figure 1: Vaned diffuser geometry.