esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 2 Problem Formulation
### Time-Frequency Sparsity of Speech  
In the research of speech signal processing, the TF sparsity of speech is a widely accepted assumption. More specifically, when there is more than one speaker in the same spatial space, the speech TF sparsity implies the following [5]. (1) It is likely that only one speaker is active during certain time slots. (2) For the same time slot, if more than one speaker is active, it is probable that the different TF points are dominated by different speakers. Hence, the TF sparsity of speech can be modeled as:  
\[S_{m}(\tau,\omega)S_{n}(\tau,\omega)=0,m\
eq n \tag{1}\]  
where \(S_{m}(\tau,\omega)\) and \(S_{n}(\tau,\omega)\) are the speech spectral at (\(\tau\),\(\omega\)) for the \(m\)th speaker and \(n\)th speaker, respectively. (3) In practice, at a specific TF point (\(\tau\),\(\omega\)), it is most probably true that only one speech source with the highest energy dominates, and the contributions from the other sources can be negligible.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 1 Introduction  
The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight small satellites was launched on 15 December 2016 into a common circular Earth orbit at \(\sim\)520 km altitude and 35 deg inclination. Each satellite carries the same Global Navigation Satellite System Reflectometry (GNSS-R) radar receiver, which measures GPS navigation signals reflected by the Earth's surface back into space [1]. The original CYGNSS science objectives were focused on the measurement of near-surface wind speed over the ocean, and early mission validation work reported the uncertainty in those wind speed measurements [2; 3; 4]. The scope of the mission has expanded over time to also include GPS reflections over land, from which soil moisture [5], inland water extent [6], and freeze/thaw state [7] can be estimated. The results presented here revisit and expand upon the earlier assessments of ocean wind speed performance. The retrieval algorithm used to produce the standard mission wind speed data product has recently been revised and improved upon, and the latest version is considered here. A new mission wind speed product, optimized for retrieval in major storms (defined as Category 3 or higher, i.e., with a maximum sustained wind speed \(>\)49 m/s), is evaluated. The new product also retrieves lower wind speeds as well. The performance of an alternate retrieval algorithm, developed at the NOAA Centerfor Satellite Applications and Research, is also assessed. Additional performance characterization tests not previously considered are also included. One is triple colocation validation, which allows for the decomposition of statistical differences between test and reference wind speed estimates into their individual component errors. Another characterization test is included which assesses the performance of wind speed measurements with respect to the persistence of the wind, defined as the change in wind speed in the recent past, prior to the satellite's measurement. This is a new type of performance assessment which has particular relevance to GNSS-R measurements, as discussed in detail below.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 3 Results and Discussion
### Evaluation of the Applicability of the Integrated Drought Map
In this study, we used multivariate statistical modeling using BPCA to develop a OneMap drought index that unifies existing measures of drought conditions, including meteorological, agricultural, and hydrological drought indices. This study also recommends implementing step-by-step drought management action plans using the OneMap drought index to generate drought forecasts and warnings, thus promoting concerted and effective responses from local governments and authorities. The action plan for integrated drought management should be configured with connectivity by drought degree (attention, caution, alert, or severity), by field (local government, agriculture, or living), and by the ministry involved. The action plan for integrated drought management should also be configured to coordinate drought response in three areas (local government, agriculture, and living), and it should be a link between the drought countermeasures of the Ministry of the Interior and Safety and those of local governments, such that the drought countermeasures of local governments can be linked step-by-step to drought management policies issued by central ministries and water supply agencies.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 3 Results
### Validation of Estimated GLASS GPP
#### 3.2.2 Validation against FLUXNET GPP  
Scatter plots between estimated GLASS GPP and FLUXNET GPP, and scatter plots between MODIS GPP (MOD17 C05 GPP and MOD17 C55 GPP) and FLUXNET GPP over different vegetative land cover types were analyzed, as shown in Figures 9-11.  
R\({}^{2}\) between estimated GLASS GPP and FLUXNET GPP in DBF could be as high as 0.86 in 2004, 0.81 in 2008 and 0.79 in 2012, and RMSE was 1.74 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2004, 2.22 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2008 and 2.12 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2012. As for the MOD17 C55 GPP, RMSE was 2.80 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2004, 2.23 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2008 and 3.19 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2012. For the MOD15 C05 GPP, RMSE was 2.40 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2004, 3.03 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2008 and 3.28 g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) in 2012. The comparison demonstrated that estimated GLASS GPP was more accurate, compared to MODIS GPP (MOD17 C05 GPP and MOD17 C55 GPP) in DBF. In EBF, RMSE between our estimated GLASS GPP and FLUXNET GPP in 2004, 2008 and 2012 were much lower than RMSE between MODIS GPP and FLUXNET GPP. Scatter plots of estimated GLASS GPP were distributed more closely around the 1:1 line, but many MODIS GPP scatter plots are decentralized and are distributed below the 1:1 line. R\({}^{2}\) between estimated GLASS GPP and FLUXNET GPP could be as high as 0.63, 0.65 and 0.64 in 2004, 2008 and 2012, respectively. But R\({}^{2}\) between MODIS GPP (MOD17 C05 GPP and MOD17 C55 GPP) and FLUXNET GPP were relative smaller. In ENF, we found that RMSEs between our estimated GLASS GPP and FLUXNET GPP in 2008 and in 2012 are marginally higher than RMSE between MODIS GPP and FLUXNET GPP in 2008 and 2012. In CRO, estimated GLASS GPP and MODIS GPP were much lower than FLUXNET GPP within growing seasons. Therefore, most of the scatter plots were below the 1:1 line, which indicates that both our estimated GLASS GPP and MODIS GPP were underestimated. In GRA, we found that the RMSE was lower between estimated GLASS GPP and FLUXNET GPP than that between MODIS GPP and FLUXNET GPP in 2004. Most scatter plots of MODIS GPP were located under the 1:1 line, which demonstrates that MODIS GPP is underestimated most of the time. In MF, RMSE was lower between estimated GLASS GPP and FLUXNET GPP in 2004 and in 2008. MODIS GPP was overestimated in 2008 at BE-Bra and US-PFa.  
In conclusion, we found that RMSE between estimated GLASS GPP and FLUXNET GPP was the smallest in DBF, EBF, CRO, GRA and MF. But in ENF, RMSE between MOD17 C55 GPP and FLUXNET GPP was the smallest. Therefore, estimated GPP achieved the highest precision in DBF, EBF, CRO, GRA and MF.  
Figure 9: Validation of estimated GLASS GPP against FLUXNET GPP.  
Figure 10: MODIS C05 GPP validation against FLUXNET GPP.  
Figure 11: MODIS C55 GPP validation against FLUXNET GPP.
# Biopurification of Oligosaccharides by Immobilized _Kluyveromyces Lactis_
## 3 Results
### Lactose Removal of Human Milk Oligosaccharides (HMOs) and Galactooligosaccharides (GOSs)  
Lastly, we evaluated the selective consumption of immobilized _K. lactis_ from the semi-purified HMOs and GOSs. HMOs and GOSs were the oligosaccharides that exhibited biological functions such as prebiotic or bifidogenic activity. However, those beneficial effects can be maximized in the absence of lactose, which can be utilized by most bacteria. Therefore, it is crucial to eliminate the residual lactose from the mixture of oligosaccharidescharides to guarantee their functionalities.  
Semi-purified HMOs were prepared from human milk by the removal of lipids and proteins. Semi-purified HMOs and GOSs were dissolved in water to a final concentration of 20 g/L, and freshly-prepared immobilized _K. lactis_ was added directly without the addition of any media components. Lactose concentrations in the semi-purified HMOs and GOSs were 12 g/L and 2.5 g/L, respectively.  
LC/MS analysis was performed to observe the quantitative changes of the oligosaccharides. By considering retention time and accurate mass, biologically possible compositions of hexose (Hex), _N_-acetylhexosamine (HexNAc), fucose (Fuc), and _N_-acetylheuraminic acid (NeuAc) were determined. A total of 11 major HMOs and GOSs with a degree of polymerization (DP) of 3 to 6 including lactose were monitored during the biopurification (Figure 3).  
Within 8 h, lactose in the solution of semi-purified HMOs or GOSs was rapidly consumed to a negligible amount. The overall distribution of HMOs was not changed during the lactose removal process. In particular, fucosyllactose (2\(0\)1_0, Hex\({}_{2}\)Fuc\({}_{1}\)) and sialyllactose (2\(0\)0_1, Hex\({}_{2}\)NeuAc\({}_{1}\)), which are the simplest structures of HMOs, were preserved during the process. In the biopurification of GOSs, the relative concentrations of DP 4 and DP 5 decreased slightly while DP 3 increased.  
Figure 3: The change of the peak area of lactose and the relative abundances of oligosaccharides during the biopurification of (**A**) human milk oligosaccharides (HMOs) and (**B**) galactooligosaccharides (GOSs) by immobilized _K. lactis_. Moreover, the representative total ion chromatograms of (**C**) HMOs and (**D**) GOSs are shown. Ions in the shaded area were confirmed to be lactose. Due to the characteristics of the porous graphitized carbon cartridge (PGC) column, oligosaccharidesides with aldehyde form were separated into two peaks which indicated the \(\alpha\)– and \(\beta\)– forms.  
After the elimination of lactose, however, the overall distribution of oligosaccharidesadries was not changed, which indicated that the GOSs, except for lactose, were not consumed by immobilized _K. lactis_.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
###### Abstract  
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST \(k\)-\(\omega\) model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain.  
wind flow prediction; complex terrain; flow separation; turbulence model; RSM turbulence model +
Footnote †: journal: applied sciences
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 3 3D Reconstruction as an Optimization Problem  
The 3D reconstruction of a material by using statistical methods, such as the well-known Quiblier's method [5] and its variants [7; 8; 35], has the following weaknesses:  
1. The computation of the coefficients needed to define the linear and the non-linear filters is a laborious task.  
2. Only one correlation function can be incorporated into the algorithm at a time to code the morphological properties of the material.
3. There is no proof of optimality for the resultant porous media distribution.
4. The need to binarize the computed 3D model to derive the final porous media distribution.  
On the other hand, the nature-inspired, optimization-based 3D reconstruction algorithms exhibit the following properties, which alleviate the aforementioned limitations of the statistical methods:  
1. There is no need to compute coefficients of any filter (they do not exist).
2. Several correlation functions can be used simultaneously in the same objective function and therefore the morphological properties of the material can be described more accurately.
3. Although there is no analytical proof, regarding the optimality of the solutions derived by the nature-inspired optimization algorithms, experience has shown that the solutions are near optimal.
4. There is no need to apply a binarization method in order to derive the final 3D model since the optimization algorithms can directly provide the solutions in a binary form.  
To apply a nature-inspired optimization algorithm for reconstructing a 3D model of porous media, two important issues need to be addressed appropriately. First, the task of 3D reconstruction should be defined in terms of optimization, where the appropriate cost (objective) function needs to be defined to describe the fitness of each solution of the real system. Second, the solutions should be coded appropriately through an efficient data representation scheme such that the algorithm operates in the real search space with the lowest possible complexity. Both issues are hereafter discussed in detail.
## 3 Classification of Organic Matter in Shale and Its Morphological Characteristics
### Depositional Organic Matter
#### 3.1.1 Bioclasts
(**a**) Graptolite periderm showing a banded or vein-like structure, Y101H3-8#, 3788.24 m; (**b**) Graptolite periderm with microcracks and segmented structures, Y101H3-8#, 3784.36 m; (**c**) Graptolite periderm exhibiting microcracks and segmented structures, Y101H3-8#, 3787.78 m; (**d**) Graptolite periderm cavity displaying a “cortical bandage” structure, Y101H3-8#, 3792.11 m; (**e**) Chitinozoan vesicles showing a vase-like shape, Y206#, 3869.84 m; (**f**) Chitinozoan vesicles with a symmetrical structure, Y206#, 3875.5 m.  
* Typical Vase-Shaped Morphology. * Symmetrical Structure. * Length Ranging from several tens to hundreds of \(\upmu\)m.
* Acritarchs: Acritarchs are microfossils with organic walls, whose phylogenetic relationships remain uncertain. These fossils may represent a polyphyletic assemblage, consisting of organisms with different evolutionary lineages [60; 61]. Under scanning electron microscopy (SEM), acritarchs typically appear as circular or elliptical grains, often closely associated with surrounding minerals, and exhibit distinct boundaries. Apatite within the organic matter (Figure 3) suggests that acritarchs likely originated from plankton. 4. Encapsulated organic matter: Encapsulated organic matter refers to organic material trapped within the siliceous shells of organisms during sedimentation. Under scanning electron microscopy (SEM), distinct boundaries are visible, typically showing a two-layer structure: the outer layer consists of a siliceous shell, which may be circular, elliptical, or semicircular in shape, while the inner layer is composed of pure organic matter. The morphology of the organic matter is shaped by the shell, preserving its overall form in accordance with the shape of the siliceous shell. Encapsulated organic matter generally exhibits little to no porosity. Microfr  
Figure 3: SEM image of acritarchs. (**a**) Acritarchs, Y101H3–8#, 3754.94 m; (**b**) Acritarchs, Y101H3–8#, 3780.05 m; (**c**) Acritarchs, Y101H3–8#, 3783.94 m; (**d**) Acritarchs, Y206#, 3861 m; (**e**) Acritarchs, Y206#, 3876.75 m; (**f**) Acritarchs, Y206#, 3877.75 m.  
between the siliceous shell and the organic matter (Figure 4). This type of organic matter primarily originates from cross-sections of sponge spicules and radiolarians, although identifying specific biological species under SEM is challenging. Studies, however, suggest that the organic matter encapsulated within these siliceous shells is in situ depositional organic matter, rather than migrated organic matter [62].
## 2 Description of the Algorithm
### Improved ResNet-NAM  
Indoor positioning scenarios are characterized by a small amount of sensor data and high real-time requirements. Therefore, we choose ResNet18, which is a smaller network with the advantages of fast training speed and low risk of overfitting. ResNet18-1D version includes 8 Basicblock structures, and each Basicblock structure includes two 3\(\times\)1 convolutional modules with the same number of channels. To distinguish the feature importance, this algorithm adds the NAM module after the 2 nd convolution module of all the Basicblock structures. The Basicblock-NAM structure is shown in Figure 2.  
Figure 1: Overview of the method.  
The NAM calculates the variances between channels of the input feature vector \(f\) using the scale factor \(\gamma\) in the normalized BN. The variances are multiplied with the weight \(\omega_{i}\) corresponding to each scale factor \(\gamma_{i}\), and then the weights factor \(M_{NAM}\) is obtained by the sigmoid activation function. The number of \(M_{NAM}\) is the same as the number of feature channels, and the numerical magnitude reflects the importance of different feature channels. Finally, the output features are obtained by multiplying \(M_{NAM}\) with the input feature vector element by element, and the input and output feature sizes remain constant. The weights are calculated as follows:  
\[M_{NAM}(f)=sigmoid(\omega_{\gamma}(BN(f))) \tag{1}\]  
In Equation (1), \(\omega\) is the proportion of the scaling factor \(\gamma_{i}\) among all scaling factors, \(f\) is the input feature vector of the NAM, and BN( ) is the inter-channel variance calculated as follows:  
\[BN(f)=\gamma\frac{f-\mu_{f}}{\sqrt{\sigma_{f}^{2}+\epsilon}}+\beta \tag{2}\]  
In Equation (2), \(\mu_{f}\) and \(\sigma_{f}\) are the mean and standard deviation of the small batch \(f\), \(\epsilon\) and \(\beta\) are trainable affine transformation parameters (scale and displacement). The improved ResNet18-NAM input and output features are related as:  
\[y=F(x,\{W_{i}\})+x \tag{3}\]  
In Equation (3), \(x\) and \(y\) are the input and output features of ResNet18-NAM, and \(F(x,\{W_{i}\})\) is the residual mapping function. If the dimensions of the input features \(x\) and \(F(x,\{W_{i}\})\) are the same, the values of the corresponding channels are added directly; otherwise, linear mapping is required to match them.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 3 Systematic Errors
### Propagation Path (Atmosphere)
#### 3.2.2 Tropospheric Delay
\[phase\ cycle=spatiotemporal\ variation\ \left(\frac{\text{mm}}{\text{km}}\right)\times \frac{2}{\lambda(\text{mm})} \tag{9}\]  
Delay induced by the hydrostatic term is parameterized by total atmospheric pressure, and the resulting hydrostatic delay in zenith direction is defined by surface height (\(z_{0}\)), latitude (\(\Phi\)), and total surface pressure (\(P_{s}\)), given in Equation (10) [28,102,103]. The hydrostatic fringe pattern is smooth and the impact is minimal because the delay induced by the hydrostatic component is just a few millimeters throughout the entire interferogram. Since the spatial fluctuation of total surface pressure is negligible in flat terrain, the impact may be ignored if the frame is less than 50 km [28,43,77]. However, in regions with significant topography, the hydrostatic component is correlated with surface elevation, so the delay should not be ignored [25,104]. \[\delta_{hydro}^{z}=k_{1}\times 10^{-6}\frac{287.053}{9.784(1-0.0026\text{cos} \,2\Phi-0.00028z_{0})}P_{s} \tag{10}\]  
The wet term is much more spatially variable compared to the hydrostatic term and is the dominant contributor for tropospheric delays. Therefore, precipitable water vapor (PWV) in millimeters is defined as integrated precipitable water vapor from the surface to the tropopause [102]. This is given by Equation (11), where \(\rho_{1}\) is density of liquid water (\(10^{6}\) g/m\({}^{3}\)) and \(\rho_{v}\) is density of water vapor (kg/m\({}^{2}\)) [28]. To acquire the density of liquid water and water vapor, one needs temperature, pressure of water vapor, and saturation vapor pressure information. The zenith delay produced by the wet component is proportional to PWV [105,106], given by Equation (12) [28]. \[PWV =\frac{1}{\rho_{1}}\int_{z_{1}}^{z}\rho_{v}dz \tag{11}\] \[\delta_{\text{act}}^{z} =6.5 PWV \tag{12}\]  
Figure 4: The impact of the spatiotemporal variation in the troposphere at L-band, S-band, C-band, and X-band wavelengths. The tropospheric effect has a significantly larger influence on shorter-wavelength sensors. Tropspheric delays are made up of a large range of phase variation between two acquisitions due to the fact that weather conditions can change rapidly and that both the wet and hydrostatic components are topographically dependent. In general, a typical range for tropospheric delay variation is between 8 and 64 mm, equivalent to a 0.3-2.3 phase shift for C-band sensors, but values can rise to more than 120 mm during extreme weather events such as storms, resulting in a large 4.2 phase shift on interferograms [28]. Obviously, tropospheric delay appears to have a significant influence on the observed phase changes. Therefore, it has been one of the most prominent InSAR subjects studied, and numerous correction strategies have been developed. To name a few, common correction methods include ground meteorological observations [103; 107], GPS observations [108; 109; 110; 111; 112; 113], weather models [114; 115; 116; 117], and optical sensors [118; 116; 119].