esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.10 Madagascar AGB Maps  
[PERSON] et al. [95] provided an AGB map of Madagascar forests for 2010, with a spatial resolution of 250 m. [PERSON]'s map was derived by relating field biomass to MODIS EVI and VCF, topography, and climate data through the use of Random Forest regression technique. [PERSON] et al. [96] added forest inventory data collected from 1995-2013 and GLAS data acquired from 2003-2009 to this dataset and then estimated biomass at GLAS footprints. They then calculated correction factors and interpolated to produce a correction factor map using a kriging interpolation method, which was used to update [PERSON]'s map. Validation of the updated biomass map with field data showed the R\({}^{2}\) was 0.71, higher than the original aboveground biomass map (R\({}^{2}\) = 0.62), and RMSE was 74.1 t/ha, lower than the 2016 map with a RMSE of 81 t/ha.
# Potential Use of Paper Mill Sludge in Improving Soil Quality for Plant Growth
## 3 Results and Discussion
### Organic Matter of Soil Modifiers  
Figure 6 shows the changes in the organic matter content of the soil modifiers before and after the composting process. Peat moss had an organic matter content of approximately 80%, whereas PMS contained approximately 50% organic matter. Organic matter in PMS can improve soil fertility by enhancing soil structure, water-holding capacity, nutrient availability, and microbial activity, leading to improved plant growth [1; 5; 7].  
The mixtures of COP37/PMS and TOP55/PMS initially had similar organic matter contents, which decreased to comparable levels following composting. This reduction in organic matter content was likely due to the decomposition of organic matter by microorganisms during the composting process. The decline in organic matter content after composting could have implications for the agricultural use of soil modifiers. While soil modifiers with high organic matter content can enhance soil structure and fertility, their effectiveness may diminish after composting. Composting played a meaningful role in breaking down the soil modifiers into nutrient-rich soil amendments. This process might help to reduce their C/N ratio, making them more available to plants.  
Figure 5: Available phosphorus of soil modifiers before and after composting.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 3 Transformer Architectures
Utilizing various techniques to generate tokens, we investigated four distinct Transformer architectures for the AMR task, namely TransDirect, TransDirect-Overlapping, TransIQ, and TransIQ-Complex. These methods are explained further in the following subsections.
## 6 Conclusions  
A real-scale case study involving partial replacement of 4% of the limestone aggregate by two wastes from the pulp and paper industry (dregs and grits) in a bituminous mixture  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Linear Bituminous** & **Circular Bituminous** & **Circular Bituminous** \\  & **Mixture** & **Mixture (Dregs)** & **Mixture (Grits)** \\ \hline Consumers & 0.014 & 0.007 & 0.006 \\ Local communities & 63.702 & 62.394 & 59.276 \\ Society & 0.365 & \(-\)0.110 & \(-\)0.086 \\ Value chain actors & 38.032 & 19.294 & 19.176 \\ Workers & 59.954 & 32.933 & 32.031 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Social life cycle performance for linear and circular asphalt pavement.  
Figure 8: Social improvements achieved for circular model.  
(AC14 surface type) for the top layer road pavement was made and used to evaluate the different dimensions of life cycle sustainability in a circular economy model approach. The main conclusions are the following:  
* The incorporation of dregs and grits as alternative raw material in the production of a bituminous mixture does not affect its technical performance as a top layer pavement, keeping the expected values in the standards for an AC14 surface type road pavement.
* These solutions avoid landfilling these wastes with savings for the paper and pulp industry and, at the same time, promote the saving of natural resources in the construction sector.
* In terms of sustainability and its economic, social, and environmental dimensions, the circular case solution reduces impacts compared to the linear solution, even with a low level of replacement of limestone aggregate (<4%). For instance, more than 100% reduction in society-related impacts were assessed with this simple replacement.
* The development of the circular model in this way guarantees the local consumption of dregs and grits, which also prevents increasing the transport distance of wastes as alternative raw material that could negatively impact the environmental parameters. In this solution, it was possible to guarantee waste consumption below a 30 km radius of its generation facilities.  
Conceptualization, V.M.F.; methodology, F.S. and F.-J.R.-D.; validation, H.P., M.M. and V.M.F.; formal analysis, F.S., H.P. and F.-J.R.-D.; investigation, F.S., H.P. and F.-J.R.-D.; writing--original draft preparation, F.S. and F.-J.R.-D.; writing--review and editing, M.M. and V.M.F.; supervision and project administration, V.M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Union H2020 Program through the project \"PAPERCHAIN--New market niches in the paper & pulp industry\", grant number 730305, 2017-2021.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors wish to acknowledge the contributions to this work of their partners in the Paperchain project, namely, The Navigator Company, RAIZ, Megavia and Gaiker Institute.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization of Cold and Hot Brew Coffee
#### 3.3.5. Caffeine Content in Cold and Hot Brew Coffee  
Further analysis showed that caffeine was present in both samples (Table 9). The caffeine content was significantly higher in CBC than in its hot brew counterpart.  
The amount of caffeine is significantly higher in CBC compared with hot brew coffee. This is similar to the findings by [PERSON] and [PERSON] (2017), whereby cold brew samples had higher concentrations of caffeine, regardless of the roasting profile [4]. This is contradictory to the finding by [PERSON] et al. (2020), where the caffeine concentration was similar in both cold and hot brew coffee samples [33].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **Chemical Class** & **Cold Brew Coffee (\%)** & **Hot Brew Coffee (\%)** & \(p\)**-Value** \\ \hline
1-methyl-1H-pyrrole & Pyrrole & 0.26 \(\pm\) 0.36 & ND & \\ Pyridine & Pyridine & 2.47 \(\pm\) 3.49 & 1.24 \(\pm\) 1.00 & \\ Furan, 2-(methoxy)- & Furan & 0.28 \(\pm\) 0.40 & ND & \\ Pyrazine, methyl- & Pyrazine & 1.90 \(\pm\) 0.07 & 0.53 \(\pm\) 0.75 & \\ Pyrazine, 2,5-dimethyl- & Pyrazine & 0.80 \(\pm\) 0.01 & ND & * \\ Pyrazine, 2,6-dimethyl- & Pyrazine & 0.66 \(\pm\) 0.06 & ND & * \\ Pyrazine, ethyl- & Pyrazine & 1.20 \(\pm\) 0.08 & 0.58 \(\pm\) 0.82 & \\ Pyrazine, 2-ethyl-6-methyl- & Pyrazine & 1.06 \(\pm\) 0.03 & 0.38 \(\pm\) 0.54 & \\ Pyrazine, 2-ethyl-5-methyl- & Pyrazine & 0.65 \(\pm\) 0.00 & ND & \\ Pyrazine, 3-ethyl-2,5-dimethyl- & Pyrazine & 0.71 \(\pm\) 0.02 & ND & * \\ Furural & Aldehyde & 9.03 \(\pm\) 0.08 & 4.18 \(\pm\) 4.00 & \\ Furfuryl formate & Aldehyde & 1.67 \(\pm\) 0.04 & ND & * \\ Ethanone, 1-(2-furanyl)- & Furan & 1.93 \(\pm\) 0.06 & 0.72 \(\pm\) 1.01 & \\
2-furancarboxaldehyde, 5-methyl- & Furan & 7.64 \(\pm\) 0.08 & 3.28 \(\pm\) 3.21 & \\
2-furanmethanol, acetate & Furan & 7.96 \(\pm\) 0.11 & 2.34 \(\pm\) 2.21 & \\ Furan, 2,2\({}^{\prime}\)-methylenebis- & Furan & 1.05 \(\pm\) 0.03 & 0.52 \(\pm\) 0.73 & \\
1H-Pyrole-2-carboxaldehyde, 1-methyl & Pyrrole & 0.87 \(\pm\) 0.07 & 0.36 \(\pm\) 0.51 & \\
2-furanmethanol & Furan & 6.56 \(\pm\) 0.13 & 2.86 \(\pm\) 2.79 & \\ Oxime,-methoxy-phenyl- & Oxime & 7.08 \(\pm\) 0.11 & 8.30 \(\pm\) 8.55 & \\
1H-Pyrole,1-(2-furanymethyl)- & Pyrrole & 0.72 \(\pm\) 0.07 & 0.40 \(\pm\) 0.57 & \\ Furan, 2-(2-furanylmethyl)-5-methyl- & Furan & 0.26 \(\pm\) 0.36 & 0.25 \(\pm\) 0.35 & \\
2-methoxy-4-vinylphenol & Phenol & 0.28 \(\pm\) 0.39 & ND & \\
3(2H)-furanane, dihydro-2-methyl- & Ketone & ND & 0.25 \(\pm\) 0.35 & \\ \hline \end{tabular}
\end{table}
Table 8. Volatile compounds of cold and hot brew coffee.  
\begin{table}
\begin{tabular}{c c c} \hline
**Compound (\(\upg\)/g)** & **Cold Brew Coffee** & **Hot Brew Coffee** & \(p\)**-Value** \\ \hline Caffe & 40,807.61 \(\pm\) 2742.61 & 29,710.21 \(\pm\) 1228.60 & 0.00979 * \\ \hline \end{tabular}  
* Statistically significant \(p<\) 0.05; results are the mean \(\pm\) standard deviation from triplicate extraction.  
\end{table}
Table 9. Comparison of the caffeine content of cold and hot brew coffee with LC-ESI-MS/MS.
# Analysis of Biodiesel Influence on Unregulated Gaseous Emissions of Diesel Motor Vehicles
## 1 Introduction  
Constantly growing energy consumption and the degradation of environmental quality require the search for alternative fuels in order to replace the petroleum fuels that are used nowadays in motor vehicles. One of the most often applied alternative fuels is biodiesel. The main advantage of biodiesel is its renewability and reduction in greenhouse gases [1]. Biodiesel can be used either in its pure form or in the form of diesel mixtures. For that reason, there have been various studies focused on diesel engines, which combust diesel mixtures. In general, it is possible to say that the application of biodiesel causes an increase in fuel consumption, reduction in HC emissions, CO emissions and particulate matter emissions as well as a moderate higher amount of NOx emissions [2, 3, 4, 5, 6]. The influence of unregulated emissions was also investigated by [PERSON] et al. [7] using three diesel engines. They recorded the reduced emissions of hydrocarbon and acetaldehyde. The USEPA [8] also presents lower levels of acetaldehyde, formaldehyde and xylene. According to [PERSON] et al. [9] the interesting unregulated emissions, namely aldehyde and 1,3-butadiene, are almost independent on the kind of fuel. [PERSON] et al. [10] performed investigations with the following results: the reduced amount of formaldehyde, 1,3-butadiene, toluene and xylene, and the increased amount of acetaldehyde and benzene. [PERSON] et al. [11] stated a lack of published scientific research information concerning the impact of biodiesel on unregulated emissions. Taking into consideration all the above-mentioned facts, we can say that it is necessary to carry out another scientific research works oriented around unregulated gaseous emissions of automotive diesel engines. In order to supplement a gap of knowledge in this area, in our study, the unregulated gaseous emissions of diesel engines are investigated by means of a special 13-regime test cycle, which was directly developed for the diesel engine. The analyzed unregulated emissionsinclude formaldehyde, acetaldehyde, 1,3 butadiene, propene, benzene, toluene and xylene. All these matters are classified as substances harmful for the atmosphere.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 5 Static Cost Effectiveness Analysis of a TFBN
### Cost  
Even though the actual cost of an MPC system depends on its capital expenditure of nodes and their associated links to connect them, the product of the degree of node and network diameter is an acceptable static criterion to assess the cost of an MPC system. The degree of a node is directly proportionate to the cost of the router of a node, and the cost of a node is directly proportional to the cost of an MPC system. Connecting all the nodes by communication links forms the MPC system. The cost of an MPC system inculating by node degree is to connect the communication links between the nodes to form the MPC system. The diameter of a network is the highest hop distance from the source to the destination using the shortest path algorithm among all distinct source-destination pairs. Therefore, the product of these two parameters named degree and diameter is a wise criterion to compare different interconnection network topologies.  
The cost of different networks with two different sizes are estimated and charted in Table 2. It is presented that the cost of TFBN is lower than all Level-2 hierarchical networks and conventional mesh and torus networks with 256 nodes. However, the TFBN is a little bit costlier than Level-3 hierarchical networks, and much less than conventional mesh and torus networks with 4096 nodes. This slightly high cost incurred due to the high degree of node even though the diameter is quite lower than those hierarchical networks considered in this article.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 2 Materials and Research Methods
### Detection Method  
The existence of the wheel step made it inevitable that there would be an unprocessed area between the adjacent wheel paths, and the scallop height had an effect on the surface accuracy of the machined surface. Therefore, the scallop height profile and surface shape after the test were detected. Because of the larger amount of step in the rough grinding, the scallop height as larger. This was more convenient for the detection of scallop height.  
Contact profilometry (Talysurf CLI2000, Taylor Hobson Ltd., Leicester, UK) was used to detect the selected position of the surface after rough grinding (two center lines of the off-axis parabolic surface), as shown in Figure 3. The probe detects the wavy contour of the surface along the step direction, and measures the distance from the adjacent peak to the trough of the scallop height contour in the vertical direction, that is, the true scallop height value [32]. An optical interference profiler (NewView 9000, ZYGO Corporation, CT, Middlefield, USA) was used to detect the surface topography and measure the roughness of the off-axis parabolic surface after rough grinding and finish grinding. The surface accuracy of the machined surface was guaranteed via finish grinding, so the off-axis parabolic surface after finishing was detected. A three-coordinate measuring machine (PRISMO, ZEISS, Berlin, Germany) was used to realize the contact measurement of the surface accuracy [33; 34]. By controlling the probe to scan the entire machining surface, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Rough** & **Finish** \\ \hline Mesh number & \#200 & \#500 \\ Wheel rotation \(n\) (r/min) & 8000 & 8000 \\ Feed rate \(\
u\) (mm/min) & 500 & 300 \\ Wheel diameter \(D\) (mm) & 15 & 15 \\ Wheel inclination \(\alpha\) (\({}^{\circ}\)) & 20 & 20 \\ Step pitch \(l\) (mm) & 4 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grinding process parameters.  
Figure 2: Schematic of the raster path: **(a)** step along the meridian direction; **(b)** step along the arc vector direction.  
three-dimensional coordinate point cloud data of the actual machining surface was obtained.
## 4 Testing of the Aircraft Bay Humidity Model  
To verify the accuracy of the bay humidity model, the humidity dataset of a specific day was selected and used to produce two sets of bay humidity prediction values using model 1 based on Equation (7) and model 2 based on Equation (10). Predicted values were compared with measured values. The humidity dataset of a randomly selected day was processed. Two humidity variation curves were plotted for each of the selected bay groups using the model prediction data and measured data, as shown in Figure 4. The selected group of bays were: Kerosene tank (measurement point 3, closed, type I), tail bottom (measurement point 5, closed, type II), exhaust nozzle (measurement point 7, open, type III), where \(m_{3}=0.8\), \(s_{3}=\)1.40, \(p_{3}=0.594\); \(m_{5}=0.5\), \(s_{5}=1.48\), \(p_{5}=0.813\); \(m_{7}=2.4\), \(s_{7}=0.93\), \(p_{7}=0.994\); \(t_{00}=18.35\)\({}^{\circ}\)C, \(h_{00}=63.92\%\) RH, \(q_{0}=-2.9875\%\) RH/\({}^{\circ}\)C (\(s_{i}=1\) at night).  
Figure 4: Comparison of measured and predicted humidity values.  
A statistical analysis was performed on measured humidity values at three measurement points and the predicted humidity values produced by the two models. Results are listed in Table 3.  
The maximum deviation is  
\[max(h)=max\left|h_{ij}-\hat{h}_{ij}\right| \tag{11}\]  
The average deviation is  
\[\text{ave}(h)=\frac{1}{n}\sum_{j=1}^{n}\left|h_{ij}-\hat{h}_{ij}\right| \tag{12}\]  
The correlation coefficient is  
\[r=\sqrt{1-\frac{\sum_{j=1}^{n}\left(h_{ij}-\hat{h}_{ij}\right)^{2}}{\sum_{j=1} ^{n}\left(h_{ij}-\overline{h}_{i}\right)^{2}}} \tag{13}\]  
where \(h_{ij}\) is the \(j\)-th humidity value of the \(i\)-th bay obtained according to the humidity model; \(\hat{h}_{ij}\) is the measured \(j\)-th humidity value of the \(i\)-th bay; \(n\) is the number of data points measured at one measuring point, \(n\) = 24 h \(\times\) 60 min/h \(\times\) 1 min\({}^{-1}\) = 1440; \(\overline{h}_{i}\) is the average humidity of the \(i\)-th bay.  
From Table 3, it can be observed that the humidity values predicted by the model are in good agreement with the measured humidity values, with an average deviation less than or equal to 7.3% RH. Given the current practice of preparing an environmental spectrum in which normally only a humidity of 60% RH or higher is considered [14] and every 10% RH is converted into a standard humid air action time, the two humidity prediction models were established and may be able to serve as effective tools in the preparation of local environmental spectra of bays.
# Audio Time Stretching Using Fuzzy Classification of Spectral Bins
## 1 Introduction
Therefore, each spectral bin should be allowed to belong to all of the classes simultaneously, with a certain degree of membership for each class. This kind of approach is known as fuzzy classification [30; 31]. To this end, in [32], a continuous measure denoted as tonalness was proposed. Tonalness is defined as a continuous value between 0 and 1, which gives the estimated likelihood of each spectral bin belonging to a tonal component. However, the proposed measure alone does not assess the estimation of the noisiness or transientness of the spectral bins. Thus, a way to estimate the degree of membership to all of these classes for each spectral bin is needed. In this paper, a novel phase vocoder-based TSM technique is proposed in which the applied phase propagation is based on the characteristics of the input audio. The input audio characteristics are quantified by means of fuzzy classification of spectral bins into sinusoids, noise, and transients. The information about the nature of the spectral bins is used for preserving the intra-sinusoidal phase coherence of the tonal components, while simultaneously preserving the noise characteristics of the input audio. Furthermore, a novel method for transient detection and preservation based on the classified bins is proposed. To evaluate the quality of the proposed method, a listening test was conducted. The results of the listening test suggest that the proposed method is competitive against a state-of-the art academic TSM method and commercial TSM software. The remainder of this paper is structured as follows. In Section 2, the proposed method for fuzzy classification of spectral bins is presented. In Section 3, a novel TSM technique which uses the fuzzy membership values is detailed. In Section 4, the results of the conducted listening test are presented and discussed. Finally, Section 5 concludes the paper.
## 2 Materials and Methods
### Methods
#### 2.2.2 Spectral Preprocessing  
Due to the high pixel resolution of the original hyperspectral images, this study calculated and analyzed the average spectra of 204 hyperspectral images of grape leaves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{3}{c}{**Predicted Values**} \\ \cline{2-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Actual Values} & Positive & TP (True Positive) & FN (False Negative) \\  & Negative & FP (False Positive) & TN (True Negative) \\ \hline \hline \end{tabular} Note: Predicted values matching the true values are marked as true, while mismatches are marked as false.  
\end{table}
Table 1: Confusion matrix of the binary classification.  
Figure 3: Architecture of the VGG16-U-Net model.  
The spectral preprocessing methods employed in this research include SNV, MSC, fast Fourier transform (FFT) [41], first derivative (FD), and second derivative (SD). These methods were combined in various ways to better capture spectral features.  
SNV was applied to minimize spectral differences caused by variations in optical path, grain size distribution, and surface roughness among samples. Each sample was standardized by subtracting the mean and dividing by its own standard deviation [21]. SNV removes additive scattering without altering the original spectral shape, but it does not address multiplicative scattering [42]. MSC is commonly used to mitigate multiplicative scattering. The main objective of MSC is to reduce or eliminate the impact of physical properties such as particle size, shape, or surface roughness on spectral data [21]. FFT was employed for frequency domain analysis, transforming spectral data into the frequency domain. This facilitates the analysis and processing of periodic signals, noise filtering, and signal compression, helping to eliminate periodic noise components in the data. Derivatives have the capability to remove both additive and multiplicative effects from spectra. FD removes only the baseline, while SD removes both the baseline and linear trends [43]. To avoid excessively reducing the signal-to-noise ratio in the corrected spectra, SG smoothing was applied before FD and SD preprocessing [43].