esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 4 Fast Time Simulation Results
### LECMZMU Sector
#### 4.1.2 Environment and Airspace User Cost-Efficiency  
Figure 8 shows the values obtained for the LECMZMU sector. These results are categorized by lateral _LOS_, longitudinal _LOS_ and total _LOS_ (lateral and longitudinal). This classification is made according to the type of resolution maneuver that the ATCO of the RAMS software has cleared the aircraft to perform, i.e., a level change (FL) or a horizontal maneuver (vector).  
The greatest benefits are obtained in the resolution of lateral _LOS_ by applying a horizontal maneuver (vector). Resolution maneuvers in the horizontal plane are preferred by ATCOs compared to vertical ones (FL changes). This is due to several factors, such as less traffic disruption, the fact that aircraft may be flying at or near their optimum FL, and no FL modification is desired.  
These values should be placed in context regarding the sector's characteristics. According to NEST data [46], in LECMZMU, the average flown distance is 84 NM, and the average flight time in the sector is 11 min. Consequently, if lateral _LOS_ with horizontal resolution maneuvers are analyzed, the results obtained are as followed:  
* Distance: reduction, on average, of 5.69 NM per _LOS_ saved, which represents 6.78% of the total flown distance in the sector.
* Delay: reduction, on average, of 0.68 min per _LOS_ saved, which represents 6.18% of the total flight time in the sector.
* Fuel and _CO_2 emissions: reduction, on average, of 28.7 kg in fuel and 90.50 kg in _CO_2 emissions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Scenario** \\ **LECMZMU** \\ \end{tabular} & \begin{tabular}{c} **Capacity** \\ **(No. of** \\ **Aircraft/hour)** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Aircraft/hour** \\ \end{tabular} & \begin{tabular}{c} **Difference** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Percentage of** \\ **Capacity Increase** \\ **(\%)** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Baseline scenario** \\ \end{tabular} & 46.95 & 46 & - & - \\ \hline \
\begin{tabular}{c} **Scenario 1** \\ **ASMT (no CD&R)** \\ \end{tabular} & 47.78 & 47 & 0.83 & 1.8\% \\ \hline
\begin{tabular}{c} **Scenario 2** \\ **ASMT (CD&R)** \\ \end{tabular} & 48.06 & 48 & 1.1 & 2.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Capacity results in LECMZMU.  
Figure 7: LECMZMU capacity Ad Hoc separation—Scenario 2.  
For longitudinal \(LOS\), benefits are obtained, although they are smaller than for the lateral ones. The distribution of horizontal resolution maneuvers and level change maneuvers is equitable. Resolution maneuvers by level changes provide greater benefits in terms of fuel consumption and \(CO_{2}\) emissions reductions. In general, favorable average results are obtained for the analyzed indicators, with all of them yielding positive outcomes. In other words, for each \(LOS\) saved, there is a decrease in flown distance, fuel consumption, \(CO_{2}\) emissions, and delay.
## 2 Materials and Methods
### Methodology  
As commented on in Section 1, the methodology initially proposed by [PERSON] et al. [19] and followed by [PERSON] et al. [34], is applied as closely as possible in this investigation to assess whether a given APE value is _uniquely_ linked to a specific DNI spectrum shape. However, as discussed in Section 1, such a methodology is improved here by adding a detailed analysis of variance, as argued by [PERSON] et al. [32]. In that study, the coefficient of variation (CV)--defined as the percentage ratio of the standard deviation (SD) to the mean--was shown to provide a better insight into the scatter around the mean than SD alone.  
Based on previous experience, the design of the APE analysis procedure used in this work was articulated with the steps described below:  
1. The trapezoidal rule was used to calculate the broadband DNI between 350 and 1050 nm for each experimental direct spectrum. (That calculated DNI value was obviously smaller than the reading from the pyrheliometer, for which the spectral range was \(\approx\)290-4000 nm.)
2. Equation (1) was used to calculate the APE for each DNI spectrum.
3. The wavelength range was divided into fourteen 50-nm bands--from 350 to 1050 nm--for each spectral instance. The fractional contribution of each 50-nm spectral band to the broadband irradiance (\(R_{c}\)) calculated in step 1 was obtained.
4. All the DNI spectral measurements were grouped into APE intervals of 0.02-eV width. More specifically, eight intervals ranging from 1.74-1.76 eV to 1.88-1.90 eV were considered. Spectral measurements having an APE within \(\pm\)0.01 eV of the central value of each of these intervals were binned inside them.
5. For every APE interval and within each 50-nm band, spectral measurements with \(R_{c}\) values below the 10 th percentile or above the 90 th percentile were eliminated to minimize the influence of outliers.
6. For every APE interval defined in step 4, mean values of \(R_{c}\) (denoted \(<\)\(R_{c}\)\(>\)) were calculated in each 50-nm band. Values of SD and CV were also obtained for each of these bands to determine the dispersion of the values of \(R_{c}\) around its mean value for every APE interval.  
As mentioned in Section 1, it should be kept in mind that the analysis carried out by [PERSON] et al. [34] considered seventy-one 10-nm spectral bands into which the 350-1050 nm waveband was divided instead of the fourteen 50-nm bands considered here (step 3, above).
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 2 Study Area and Database  
### Study Area  
The study was conducted over an area of 50 km by 50 km (altitude between 200 and 900 m), located in Catalonia, Spain (centered on 1.195\({}^{\circ}\)E and 41.665\({}^{\circ}\)N, as shown in Figure 1). The climate is typically Mediterranean: The mean daily air temperature varies between 4 \({}^{\circ}\)C and 28 \({}^{\circ}\)C and the average annual precipitation is around 376 mm (347 mm in 2015, 385 mm in 2016, and 397 mm in 2017) (Figure 2) [20]. The zone of this study contains several crops types, for a total of 87439 plots, with plot slopes lower than 10%. Plot delineations and crop types were provided by the Geographical Information System for Agricultural Parcels ([[http://agricultura.gencat.cat/ca/serveis/cartografia-sig/aplicatius-tematics-geoinformacio/sigpac/descarregues/](http://agricultura.gencat.cat/ca/serveis/cartografia-sig/aplicatius-tematics-geoinformacio/sigpac/descarregues/)]([http://agricultura.gencat.cat/ca/serveis/cartografia-sig/aplicatius-tematics-geoinformacio/sigpac/descarregues/](http://agricultura.gencat.cat/ca/serveis/cartografia-sig/aplicatius-tematics-geoinformacio/sigpac/descarregues/))). The dominant crops are barley (37817 plots), wheat (21562 plots), fallow (5781 plots), corn (5156 plots), bean (3604), alfalfa (3244), oat (1730), and rapeseed (1626). These crops represent 96% of cultivated fields in the study area. The slopes of plots are lower than 10%. A total of 235 Sentinel-1 (S1) images in ascending and descending modes, as well as 24 Sentinel-2 (S2) images acquired between September 2017 and September 2019, were used in this study.  
Figure 1: Study site in yellow.
# Deformation Behaviour of Cold-Rolled Ni/CNT Nanocomposites
## 1 Introduction
Some works investigate how these conditions can influence the texture evolution of some alloys [7; 8; 9; 10; 11; 12]. Included in a wide range of processing techniques, rolling is a very recurrent plastic deformation process in industrial environments. It is also commonly used to study the formation of crystallographic textures in metals. Thus, this deformation technique is used in the present work. The texture of a rolled sheet metal is often represented by its components, i.e., characteristic grain orientations. Each texture component is defined by a crystallographic plane [hkl], parallel to the rolling plane; and a crystallographic direction <uvw>, parallel to the rolling direction [6]. [PERSON] et al. [13] addressed the evolution of the texture and microstructure of cold-rolled nickel. The authors used backscatter electron diffraction (EBSD) to characterize recrystallized and deformed samples (with thickness reductions between 20 and 90%). This study concluded that during cold-rolling, the grains became more elongated, the average misorientation of the grains decreased as shown by the boundaries' character distribution, and the main texture components observed varied with the applied deformation. For thickness reduction greater than 50%, the main components observed were of the shear type. The deformation structures and mechanical properties of 2519 aluminium when cold-rolled with a thickness reduction of up to 80% were also studied by [PERSON] and [PERSON] [14]. Among other techniques, the authors relied on EBSD analysis, which allowed the analysis of the influence of cold-rolling on the microstructure, dislocation density, and crystallographic texture of the samples. The dislocation density increased with the applied strain, which increased in the tensile strength of the samples. The authors also observed dislocations organized in cellular structures (up to 40% thickness reduction) and shear bands for greater deformations. The present texture components also changed and evolved during the deformation, with the texture after 80% thickness reduction mainly composed of the copper component. Other studies were carried out on the deformation of metals and their influence on the dislocation structures and textures formed, and they concluded that the dislocations organize themselves into cell walls, whose interior contains a small dislocation density (see, for example, [PERSON] and [PERSON] [15]). In this context, studying how reinforcement can influence texture evolution with plastic deformation is crucial. Few studies investigate the deformation behaviour during the tensile test in nanocomposites, but these works do not consider texture evolution [16; 17; 18; 19; 20]. The main objective of this work is to contribute to a detailed understanding of the deformation behaviour of nanocomposites and how the presence of CNTs reinforcement can affect texture evolution. The nanocomposites and the matrix were produced by powder metallurgy and deformed by cold-rolling, and the texture evolution was studied by an EBSD analysis.
## 3 Results  
### Batch Extraction  
#### 3.1.1 Di-(2-ethylhexyl)phosphoric Acid (HDEHP)  
(a) Phase Stability  
During solvent extraction, it is necessary that only two liquid phases remain in the system. It is known that in the case of a cationic exchanger class of extractants, there is a possibility of the third phase formation [37; 38]. Table 1 presents data on the stability of heterogeneous systems containing 1 mol\(\cdot\)L\({}^{-1}\) solutions of HDEHP in dodecane and aqueous phases containing different amounts of nitric acid and neodymium and praseodymium nitrates. In general, due to the nature of HDEHP as a cationic exchanger and a weak acid, the phase stability of such systems increases with a decrease in the REE content in the solution and with an increase in acidity that results in lower concentration of REE-HDEHP complexes. The instability of such systems is associated either with the formation of the third phase (see Figure S1), or with the formation of a homogeneous gel. Gel formation usually occurred at high REE levels in the solution. The third phase is a precipitate of REE organic salts insoluble in the organic phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**C(Pr + Nd), g-L\({}^{-1}\)** & **10** & **25** & **50** & **75** \\ \hline
**C(HNO\({}_{3}\)), mol\(\cdot\)L\({}^{-1}\)** & & & & \\ \hline
0.005 & stable & Third phase & Gel & Gel \\ \hline
0.03 & stable & stable & Third phase & Gel \\ \hline
0.07 & stable & stable & stable & Gel \\ \hline
0.1 & stable & stable & stable & Gel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Third phase formation in systems containing nitric acid and didymium. Organic phase—1 mol\(\cdot\)L\({}^{-1}\) of di-(2-ethylhexyl)phosphoric acid (HDEHP) in dodecane.  
Figure 1: A schematic diagram of simulating a 10-stage countercurrent cascade with 5 separatory funnels: F: feed aqueous solution of the REEs, S: fresh organic phase, E: extract and R: raffinate.  
Based on the data on phase stability, we selected the extraction system with the maximum content of REE--50 g\(\cdot\)L\({}^{-1}\) and 0.1 mol\(\cdot\)L\({}^{-1}\) nitric acid--for further research.  
(b) Influence of Nitric Acid Concentration  
In the following experiments, initial total content of Nd and Pr was 50 g\(\cdot\)L\({}^{-1}\), and nitric acid concentration was varied from 0.1 to 1 mol\(\cdot\)L\({}^{-1}\). As expected (due to cationic nature of extractant), with an increase in the content of nitric acid in the aqueous phase, both the extraction efficiency and the selectivity of this extractant towards Nd(III) in the presence of Pr(III) decreases (Figure 2). The maximal _SF_-value in a concentration range of 0.1-1 mol\(\cdot\)L\({}^{-1}\) of nitric acid is 1.3. The organic phase capacity was 20 g\(\cdot\)L\({}^{-1}\) (0.1 mol\(\cdot\)L\({}^{-1}\) of nitric acid) and 5 g\(\cdot\)L\({}^{-1}\) for 1 mol\(\cdot\)L\({}^{-1}\) of nitric acid.  
Thus, this extraction system is not appropriate for the separation of Nd(III) and Pr(III) due to the low selectivity and high sensitivity to the acidity of the aqueous phase.
## 2 Materials and Equipment
### Equipment  
In this work, different experiments were conducted including coreflooding, contact angle, interfacial tension, and dynamic adsorption measurements. The coreflooding experiments were conducted using 6-inch-length and 1.5-inch-diameter core samples. All experiments were conducted at 100 \({}^{\circ}\)C and a 500 psi backpressure and at an injection rate of 0.5 cm\({}^{3}\)/min. The coreflooding setup (Figure 1) consists of an oven, a core holder, an injection pump, a pressure controller, and transfer cylinders. The core holder and the injection pump are positioned horizontally to minimize the gravity effect. The coreflooding experiments were conducted by first saturating the rocks with the formation brine and then the core samples were flooded with oil. The flooding setup was used to inject oil into the rock samples displacing the brine out of the cores, and the produced effluents were monitored. The maximum oil saturation was achieved, once no more brine is coming out of the rocks, indicating the rock samples were at the irreducible water saturation and maximum oil saturation. Thereafter, the samples were aged at high-pressure and high-temperature conditions to ensure reasonable reservoir conditions. The samples were aged at 100 \({}^{\circ}\)C and 2500 psi for two weeks to ensure that the rock samples are saturated with the oil. The flooding experiments were started by injecting SW as a secondary recovery process, until no more oil was produced. Thereafter, a GLDA solution was injected in a tertiary mode, until no more oil was recovered.  
In addition, contact angle measurements were also conducted to evaluate the wettability condition for the rock/fluid system. First, the baseline was defined by measuring the contact angle for the rock/brine/oil system, and then, the impact of introducing different types of GLDA solutions on the wettability status was examined. All contact angle measurements were performed at 100 \({}^{\circ}\)C and 1000 psi for 24 h. In addition, IFT experiments were conducted to assess the impact of adding different types of chelating agents to the brine-oil interface. The effects of using Ca\({}_{2}\)GLDA, Na\({}_{4}\)GLDA, and K\({}_{4}\)GLDA on the IFT reduction were determined. Finally, dynamic adsorption measurements were conducted to understand the adsorption behavior of GLDA types on the rock surfaces. All experiments were conducted at a temperature of 100 \({}^{\circ}\)C.  
Figure 1: Coreflooding setup.
## 2 Beidou-Binocular Cooperative Navigation Model
### Beidou Navigation Positioning Module
#### 2.1.1 Principle of Beidou Navigation Positioning  
The Beidou Navigation System (BDS) is a high-precision positioning system based on global satellite positioning technology. By receiving signals from multiple satellites and combining ground reference stations and real-time dynamic differential positioning (RTK) technology, it can achieve positioning accuracy at the centimeter or even millimeter level. The basic measurement model for Beidou navigation positioning is as follows:  
\[P=\rho+c\cdot(\delta t_{r}-\delta t_{s})+I+T+\epsilon \tag{1}\]  
where \(P\) is the pseudorange measurement value, i.e., the measurement distance between the receiver and the satellite; \(\rho\) is the true geometric distance; \(c\) is the speed of light; \(\delta t_{r},\delta t_{s}\) are the clock errors of the receiver and the satellite, respectively; \(I\) is the ionospheric delay; \(T\) is the tropospheric delay; and \(\epsilon\) is the error term that includes multipath effects and measurement noise.  
Through differential positioning technology, most of the errors (such as clock errors and some ionospheric delays) can be effectively eliminated, resulting in a significant increase in positioning accuracy. Figure 1 shows a schematic diagram of the application of RTK technology in the BBCN model.  
RTK technology further utilizes real-time correction information provided by reference stations to achieve high-precision positioning via carrier-phase measurements.
## Materials and Methods
### Experimental Data Collection
#### 2.2.2 Design of Experiments  
The experimental data-set used as physical input is based on 45 tests, resulting from all the possible different combinations of the main process variables. Namely, process variables considered are: temperature, current density, anode gas composition and flow rate, like Table 3 summarizes. In detail, temperature is varied according to three levels: 700, 750 and 800 \({}^{\circ}\)C. The anode feeding is changed simulating five syngas mixtures resulting from different processes (\"synA\" from wood air-gasification, \"synB\" from sewage sludge air-gasification, \"synC\" from wood oxy-gasification, \"synD\" from steam methane reforming and \"synE\" from the anaerobic digestion of organic waste). Since syngas are heterogeneous gas mixtures made of H\({}_{2}\), CO\({}_{2}\), CH\({}_{4}\), CO and N\({}_{2}\), the total flow rate is expressed with an auxiliary variable, defined as \"equivalent hydrogen\" (definition given at Equation (3). Equivalent hydrogen flowrate is set to three levels and expressed per unit cell active area (10.8, 14.7, 23.2 mL\(\cdot\)min\({}^{-1}\cdot\)cm\({}^{-2}\)) to vary the fuel utilization (see the relation between the fuel utilization coefficient \(U_{f}\) and the flowrate of equivalent hydrogen at Equation (4).  
\[\dot{n}_{H_{2,\ \sigma}}=\dot{n}_{H_{2}}+\dot{n}_{CO}+4\dot{n}_{CH_{4}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Symbol** & **Value** \\ \hline Minimum performance Gradient & - & 0.00001 \\ Maximum validation failures & - & 600 \\ Performance Goal & - & 0 \\ Learning rate & \(LR\) & 0.01 \\ Learning rate increasing ratio & \(LR_{inc}\) & 1.05 \\ Learning rate decreasing ratio & \(LR_{dec}\) & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training settings.  
\[U_{f}=\frac{i}{2F\;\dot{u}_{H_{2\;eq}}} \tag{4}\]  
As unique feature for each single test, the fuel total indicator \(\varphi\) is assumed and it will be shown while presenting experimental results. \(\varphi\) is calculated according to Equation (5), as the ratio between \(U_{f}\) and the fuel mixture dilution fraction (DF, see Equation (6).  
\[\varphi=\frac{U_{f}}{DF} \tag{5}\]  
\[DF=x_{CO_{2}}+x_{N_{2}} \tag{6}\]
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### Data Collection and Audio Preprocessing  
The diversity of voiceprint data is essential for training a model with strong general capabilities. This includes characteristics such as language, dialect, gender, age, and intonation. A diverse dataset helps the model maintain a high level of recognizability across various real-world application scenarios. By learning from different datasets, the model can extract detailed features and improve identification accuracy. In this study, data were collected from the Common Voice database [30], which provides a wide variety of voices in different languages and genders for research use. Users can also contribute by uploading their own voice samples. During the modeling process, 10 cases were collected, with each case containing 60 sound clips ranging from 3 to 4 s.  
The dataset used in this study consists of voice samples collected from 10 unique cases, with 60 original samples per case. To address the challenge of limited data diversity, GANs were employed to augment the dataset. For each case, GANs generated an additional 40 synthetic samples, resulting in a total of 100 samples per case after augmentation. The final dataset size across all cases is therefore 1000 samples, comprising 600 original and 400 GAN-generated samples. This augmentation ensured a balanced representation of diverse voice characteristics, enhancing the robustness of the model training process.  
The pre-processing phase involves manually tagging the voice data into distinct categories, as the downloaded audio is untagged. Once tagged, the data are stored in separate folders for later use. Before building the model, the collected audio must be categorized and stored in specific folders where the code will be executed. The next step involves converting all audio files into WAV format to ensure consistent data input for the model. The modeling was carried out using Google Colab, which provided a cloud-based runtime environment. Zero-padding was applied to standardize the length of all audio  
Figure 1: Algorithm flowchart.  
samples. This ensures uniform input dimensions for the CNN while preserving the original data integrity, avoiding truncation or distortion.