esa-sceva/satcom-chunk-collection huggingface dataset:
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### Inter-Satellite Cross-Validation Method
#### 2.5.2 Jason-3 Satellite Radar Altimeter Products  
Launched on 17 January 2016, the Jason-3 satellite is an international cooperative altimetry mission jointly developed by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the Centre National d'Etudes Spatiales (CNES). As a successor to the TOPEX/Poseidon and Jason-1 missions, Jason-3 continues a nearly 30-year legacy of high-accuracy sea level measurements. It is a non-Sun-synchronous satellite with a mean altitude of 1336 km, an inclination of 66\({}^{\circ}\), and a repeat cycle of 9.9156 days.  
Since the Jason-3 Geophysical Data Records (GDRs) have undergone rigorous calibration and validation using in situ measurements and cross-satellite comparisons, we utilized Jason-3 Level 2 GDR 1 Hz data (from 1 July to 31 December 2023) to evaluate the accuracy of the NSSC iGNSS-R altimeter data products.  
Figure 9: Flowchart of sea surface height inversion.
## 4 Discussion
Although the measurements were performed exclusively by trained and experienced personnel, it cannot be completely ruled out that inter-individual differences in the application of pressure may have caused a certain variability in the results. Future studies could attempt to further standardize the influence of pressure, for example by using mechanically calibrated pressure sensors to objectively control tissue stress during imaging. These approaches could help to further improve the validity and reproducibility of the results. Therefore, minimizing the pressure applied by the measurement probe is crucial. Proper alleviation of pressure ensures that the tissue remains in its natural state, allowing for more accurate and reliable visualization and quantification of the vascular structures. This is essential for obtaining high-quality 3D images and for ensuring that the spatial characteristics of the vessels are represented accurately.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the Cubature Kalman Filter Algorithm
#### 3.3.1. Predict  
\[X_{(i,k|k-1)}=chol(P_{(k-1|k-1)})\xi_{i}+\hat{x}_{(k-1|k-1)} \tag{23}\]  
where \(P_{(k-1|k-1)}\) represents the state error covariance matrix, and \(chol\{\}\) represents the Cholesky decomposition of the matrix.  
Determine the cubature points following the propagation of the nonlinear state equation.  
\[X^{*}_{(i,k|k-1)}=f\Big{(}X_{(i,k|k-1)},u_{k-1}\Big{)} \tag{24}\]  
The forecast state is calculated using Equation (25).  
\[\hat{x}_{(k-1|k-1)}=\sum_{i=1}^{N}\omega_{i}X^{*}_{(i,k|k-1)} \tag{25}\]  
Determine the predicted error covariance matrix using Equation (26).  
\[P_{(k-1|k-1)}=\sum_{i=1}^{N}\omega_{i}X^{*}_{(k|k-1)}\Big{(}X^{*}_{(k|k-1)} \Big{)}^{T}-\hat{x}_{(k|k-1)}\hat{x}^{T}_{(k|k-1)}+Q_{(k-1)} \tag{26}\]
## 3 Results and Discussion  
### Validation of Analysis  
Results of the conducted validation study are presented in Table 1. Linearity for all the elements was high (\(R^{2}>0.998\)), with the exception of calcium (\(R^{2}=0.9969\)), potassium (\(R^{2}=0.9970\)), and magnesium (\(R^{2}=0.9977\)). Precision and accuracy satisfy the required values given by the established method, as well as IDL and MDL, which proved to be quite low indicated the possibility of detection and quantitation of certain elements at trace levels. Data showed that relative standard deviation (RSD) was below 6% (maximal allowed value is 10%), while accuracy was in the range of 87%-114%, which fits in the allowed value's range of 100% \(\pm\) 30%. Overall, the obtained results fulfill the requirements of the established method from the determination of metal and trace elements in water samples [20].  
Intra-day and inter-day precisions are also presented in Table 2 for both spiking levels (Table A1, Appendix A). Results showed that values were lower than 10% for all elements. This indicated that the establish method is repeatable, with the satisfactory level of the inter-day precision.
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 4 Numerical Results
### Preparation for Real-Time ANN-Based Scheduling Approach  
The network inside the ANN-based approach is constructed in PyTorch, and its main parameters are listed in Table 4.  
Before starting to train the ANN model, we need to sample the data that is consistent with the style of Input (29) and Output (31). The quality of an ANN model can be judged by its cross-entropy loss function in Equation (38) during training, as depicted in Figure 9.  
\[\theta_{loss}\triangleq-\sum_{n=0}^{2}\hat{y}_{n}\log y_{n} \tag{38}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Name** & **Value** & **Description** \\ \hline learning\_rate & 0.001 & Learning rate of ANN \\ batch\_size & 64 & Batch size of dataset \\ hidden\_layer & (300,100,80,40) & Size of hidden layers \\ num\_exp\_iter & 20 & Number of iterations in the training process \\ num\_epochs & 1000 & Number of epochs for gradient descent for each training iteration \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for the training of the Artificial Neural Network (ANN).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Name** & **Value** & **Description** \\ \hline dist\_max & 10 & Maximum distance \\ dist\_min & 0 & Minimum distance \\ num\_type & 4 & Number of capability type of MS \\ n\_max & 5 & Maximum number of MRs in each MS \\ n\_min & 1 & Minimum number of MRs in each MS \\ c\_max & 50 & Maximum unit cost of MR \\ c\_min & 1 & Minimum unit cost of MR \\ v\_max & 40 & Maximum capacity value of MR \\ v\_min & 10 & Minimum capacity value of MR \\ p\_max & 200 & Maximum service quality value of MR \\ p\_min & 5 & Minimum service quality value of MR \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for MP and MS in the experimental environment.  
Figure 9: Cross-entropy loss of train/test dataset over 1000 training epochs.  
As shown in Figure 9, after 1000 epochs, the training loss and testing loss both reached a small range and gradually converged. As the number of training epoch increases, the difference between training loss and testing loss becomes smaller and smaller, which indicates that the proposed ANN-based approach has the ability for generalization. Figure 10 plots the prediction accuracy as Equation (39) to evaluate the performance of the ANN model.  
\[\theta_{accu}\triangleq\frac{\sum\limits_{j,c,j,k\in\mathcal{J},\mathcal{C}, \mathcal{I}_{k},K}\mathbbm{1}_{y(j,c,i,k)=\mathbf{y}(j,c,i,k)}}{\sum\limits_{c \in\mathcal{C},\mathcal{I}\in\mathcal{K}}\mathbbm{1}_{x_{j}^{(c)}=k}\cdot| \mathcal{J}_{c}|\cdot|\mathcal{I}_{k}|} \tag{39}\]  
After 1000 iterations, the prediction accuracy on the test set gradually converges to a high region about 94.7%, and other evaluation metrics are listed in Table 5. These metric values demonstrate the effectiveness of the ANN-based completion status prediction model.
## 3 Measurement Procedure
### Permeability  
Permeability (\(\kappa\)) is defined as the rate of fluid circulation through a porous medium. The complexity of the internal configuration of the porous medium means that there is no general relationship that determines the value of \(\kappa\) as a function of the effective porosity \(\tau\) (fortuosity), defined in terms of the path, from the entrance to the outlet, that the fluid has to travel through the medium and pore size, etc. [31]. Despite this complexity, some approaches aim to simplify this internal structure [32] so as to obtain an expression for permeability by solving the Navier-Stokes (N-S) equations at pore level [33; 34].  
Hence, the use of permeability measurement devices [35; 36; 37; 38; 39] permits indirect determination of wick permeability by measuring the pressure loss (\(\Delta\)P) and flow rate through the medium. The flow at pore scale must be in the Darcy's regime, which means that the Reynolds number (Re) must be Re \(<\) 10 [40].  
In this section, we intend to determine the effect of pore size (D) on the permeability of the medium on an experimental basis. A JEOL (JSM5900 LV, ELIONIX, Tokyo, Japan) scanning electron microscope (SEM) was firstly used to determine the pore size, while the porosity level was determined by directly measuring sample volumes and weights on our permeability test bench. The main features of the SEM are shown in Table 2.  
On the permeability test bench, methanol flow tests were performed at low speeds (0-0.0012 m/s), as shown in Figure 4, in order to characterize the Darcy's curve from which the permeability (slope) could be obtained.  
Permeability quantifies the degree of fluid movement within a solid material. A specific fluid flows through a closed circuit with the following main elements: a pump, a flow-meter, a reservoir, a sample holder, a by-pass, and a differential pressure gauge, as depicted in detail in Figure 4 (left). A simplified scheme is shown on the right side of this figure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Resolution** & 3 nm @ 30 kV (SEI: Secondary electron imaging) \\ \hline
**Sample** & 8” max., 125 mm X, 100 mm Y \\ \hline
**Beam Current** & 0.5–1 nA \\ \hline
**Accelerating Voltage** & 20 kV \\ \hline
**Working Distance** & 10–12 mm \\ \hline
**Imaging** & 12–20 arrows \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical features of JEOL (JSM5900 LV) scanning electron microscope (SEM).  
The pump circulates the fluid inside the closed circuit, discharging it into the reservoir. The reservoir presents a liquid-free zone preventing vapor flow inside the circuit, which would otherwise distort the results. The sample holder, where the sample is placed for measurement, has two pressure measurement points situated immediately before and after the sample. These points, to which the manometer is connected, determine the pressure loss in the sample. A flow-meter measures the amount of circulating fluid inside the circuit. Finally, a by-pass around the sample holder is inserted for fine-tuning of flow control throughout the whole sample.
# System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS
## 2 Materials and Methods
### Acquisition of Study Data  
This study sought to model and simulate road icing, and appropriate data were required for this purpose. First, data types for the model construction were selected, and actual data to perform simulation were acquired. Spatial data included the Digital Elevation Model (DEM), river system, roads, and bridges. DEM has a resolution of 5 m, and it was used through interpolation into a resolution of 1 m. Meteorological data were acquired from the Automated Synoptic Observing System (ASOS, Suncheon), and included air temperature, cloudiness, vapor pressure, wind speed, and precipitation [22; 38; 39].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Average Summer** & **Average Summer** & **Average Winter** & **Average Winter** & **Annual** \\  & **Temperature** & **Temperature** & **Temperature** & **Temperature** & **Temperature Range** \\  & **(Suncheon, \({}^{\circ}\)C)** & **(Jeollanam-do, \({}^{\circ}\)C)** & **(Suncheon, \({}^{\circ}\)C)** & **(Jeollanam-do, \({}^{\circ}\)C)** & **(Suncheon, \({}^{\circ}\)C)** \\ \hline
2020 & 23.67 & 24.00 & 1.67 & 3.39 & 22.0 \\
2019 & 23.07 & 23.63 & 3.05 & 4.87 & 20.0 \\
2018 & 24.53 & 25.32 & 2.07 & 3.12 & 22.9 \\
2017 & 24.23 & 24.60 & \(-\)0.50 & 1.17 & 24.7 \\
2016 & 24.15 & 24.60 & 1.80 & 3.39 & 22.4 \\
2015 & 23.17 & 23.25 & 2.20 & 3.40 & 21.0 \\
2014 & 22.77 & 23.18 & 0.90 & 2.65 & 22.6 \\
2013 & 24.73 & 25.16 & 1.83 & 3.22 & 23.4 \\
2012 & 24.27 & 24.51 & \(-\)0.03 & 1.50 & 24.3 \\
2011 & 24.03 & 24.00 & 0.13 & 1.34 & 23.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Annual temperature in Suncheon (in summer and winter over the last ten years).  
Figure 2: Honam highway in Suncheon.  
Tables 3 and 4 show the contents of the spatial and meteorological data. The spatial data were acquired from the 18,583 points obtained by dividing the 27 km section of the Honam Highway in Suncheon. Meteorological data were assigned to these road points by multiplying the ASOS data by a random number generated in the \(\mp\)2% range.
## 3 Performance Parameters  
The main parameter used to assess DSL-based system performance is the achievable bit rate on the (generic) copper pair connecting a generic NT at distance \(d\) from the Cabinet:  
\[R_{b,q}(d)=R_{s}\sum_{k\in I_{c}}B[\rho_{k,q}(d)], \tag{1}\]  
where \(I_{c}\) is the set of sub-carriers indices assigned to DS or US transmission, \(R_{s}\) is the symbol rate, \(B[\cdot]\) is the number of bits that can be allocated on the \(k\)-th sub-carrier in accordance with the following criterion:  
\[B[x]=\left\{\begin{array}{ll}b_{max}&\text{if }x\geq b_{max}\\ x&\text{if }b_{min}\leq x<b_{max}\\ 0&\text{otherwise}\end{array}\right. \tag{2}\]  
where \(b_{min}\) and \(b_{max}\) are the minimum and maximum number of bits, respectively, that can be allocated per sub-carrier. The \(\rho_{k,q}(d)\) in (1) is:  
\[\rho_{k,q}(d)=\log_{2}\left(1+\frac{SINR_{k,q}(d)}{\Gamma}\right) \tag{3}\]  
and \(\Gamma\) is the \"performance gap\", defined in [20]. In the following derivations we consider the approximated Zero Forcing (aZF) vectoring pre-coding and cancellation algorithms for DS and US transmissions, respectively [21]. For this algorithm the order of the pre-coding/cancellation matrix is identified by an integer \(q\) as indicated in the following of the present Section.  
Let \(SINR^{(i)}_{k,q}(d_{i})\) be the Signal to Interference plus Noise Ratio (SINR) on the \(k\)-th sub-carrier at frequency \(f_{k}\) for the (generic) \(i\)-th reference NT at distance \(d_{i}\) from the Cabinet. It can be written as:  
\[SINR^{(i)}_{k,q}(d_{i})=\frac{|H_{D,k}(d_{i})|^{2}P_{k}}{\eta_{k}+I^{(q)}_{F, j,k}(d_{i})}. \tag{4}\]  
where \(H_{D,k}(d)\) is the direct channel transfer function at frequency \(f_{k}\); \(\eta_{k}\) is the background noise power; \(P_{k}\) is the power transmitted on the \(k\)-th sub-carrier.  
In the DS case we assume \(P_{k}=P\) for each \(k\) and for each NT connected to the same cable from the Cabinet. This avoids harmful FEXT of high power users on low power ones in the non-vectoring case. When vectoring is present, the power of transmitted symbols can be increased/decreased with respect to \(P\) in accordance with the resulting pre-coding matrix. For US transmissions upstream power back off (UPBO) is considered.  
The \(I_{F,j,k}^{(q)}(d_{i})\) is the residual FEXT power obtained after the application of vectoring pre-coding/cancellation of order \(q\), whose expression is provided in the following of this Section for DS and US after a short review of the aZF pre-coding/cancellation technique.  
The sum bit rate of the \(K_{m}\) copper pairs assigned to the \(m\)-th node at the \(m\)-th distributor is:  
\[R_{b}(d_{m})=\sum_{l=1}^{K_{m}}R_{b,q}^{(l)}(d_{m}), \tag{5}\]  
where \(R_{b,q}^{(l)}(d_{m})\) is given in (1) and the apex \((l)\) in (5) refers to the bit rate on the \(l\)-th copper pair connected to the \(l\)-th NT of the \(m\)-radio node at the \(m\)-th distributor at distance \(d_{m}\) from the Cabinet.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs)  
From the late 1970s to the early 2010s, the most common methods for ASR were _Hidden Markov models_ (HMMs) [33]. Using a _maximum likelihood_ approach, HMMs sought max \(P(T\mid S)\), i.e., the best candidate text \(T\) for input speech utterance \(S\), where \(T\) is a sequence of words and \(S\) a temporal sequence of spectral vectors at the frame rate. For efficiency (as \(P(T\mid S)\) is highly complex), with [PERSON] theorem, one instead maximizes \(P(S\mid T)\)\(P(T)\), with \(P(T)\) being a language model and \(P(S\mid T)\) an acoustic model. An HMM can model a phoneme, a word, or a longer speech unit. It consists of a sequence of states, each representing a spectral pattern of speech (corresponding to a position of the VT), with timing transitions between states for successive frames. Using an off-line model trained with much speech, HMM transitions are assigned probabilities (to model durations), and a probability density function (pdf, e.g., mixture of Gaussians) is estimated for each state's spectrum. While ASR requires frame-based decisions, SV only needs one binary speaker decision for an entire utterance. As a result, SV usually reduces the HMM approach to use only a (single-state) _Gaussian mixture model_ (GMM). Gaussians are commonly used as they need only a mean and variance to specify (their relevance for the central limit theorem is not the reason for their choice).  
As SD needs an identification for each speech portion, HMMs are sometimes used in a post-processing stage, to estimate the most likely sequence of speaker activities, e.g., utilizing subspace models or clustering via variational Bayes VB-HMM [34]. Other forms of post-processing include _system fusion_, where multiple SD models combine results [5].
# MGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
## 3 Results
In the table, we can see that the null hypothesis is not rejected only in the case when we compare MIGHTv1, MIGHTv2 and regular averages for USA; in all other cases, the null hypothesis is rejected. Further, to see the specific differences among the approaches using absolute error, we perform the Nemenyi test as a post-hoc procedure on the data from the UK. The results are presented in Tables 9 and 10, where we can see that the null hypothesis is rejected only in the cases when MIGHTv1/v2 is compared to regular averages/medians. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Use Case/Measure**} & \multicolumn{2}{c}{**MIGHTv1**} & \multicolumn{2}{c}{**MIGHTv2**} \\  & **Average** & **Median** & **Average** & **Median** \\ \hline Potassium (K) in Fruits & 84.0 \% & 84.7 \% & 66.9\% & 76.0\% \\ Sodium (Na) in Fruits & 79.9\% & 88.9\% & 69.4\% & 82.6\% \\ Sodium (Na) in Vegetables & 82.0\% & 88.0\% & 74.0\% & 78.0\% \\ Protein (PROT) in Meats & 87.0\% & 88.0\% & 80.9\% & 82.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage of cases when MIGHT gives smaller error than regular averages or medians. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Country/Measure** & **Average** & **Median** \\ \hline Italy & \(5.82\times 10^{-5}\) & \(1.14\times 10^{-4}\) \\ UK & \(1.36\times 10^{-4}\) & \(5.02\times 10^{-4}\) \\ USA & \(9.90\times 10^{-2}\) & \(9.90\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical comparison between absolute error obtained from MIGHTv1, MIGHTv2, and regular averages/medians for three countries by using the Friedman test. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Average** & **MIGHTv1** & **MIGHTv2** & **Regular** \\ \hline MIGHTv1 & / & \(5.38\times 10^{-3}\) & \(2.05\times 10^{-4}\) \\ MIGHTv2 & \(5.38\times 10^{-3}\) & / & \(9.89\times 10^{-3}\) \\ Regular & \(2.05\times 10^{-4}\) & \(9.89\times 10^{-3}\) & / \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results from Nemenyi post-hoc test for UK obtained by using MIGHTv1 with average, MIGHTv2 with average, and regular average.
## 3 Results
### Attachment Limits GP-Driven Entry into SH-SYSY Cells
We could confirm expression and correct subcellular localization of these attachment proteins (Figure S6A,B) in the respective SH-SYSY derived cell lines. Next, we transduced the novel overexpressing SH-SYSY cell lines, SH-SYSY/Empty Vector, SH-SYSY wild type (WT), or HEK293T with mock, EBOVpp, MARVpp, VSV-Gpp, or NoEnvpp lentiviral particles coding for firefly luciferase. Strikingly, ectopic expression of any of the four attachment protein tested was sufficient for robust transduction of SH-SYSY cells by both, EBOVpp or MARVpp (Figure 8B), but not by NoEnvpp. As expected, NPC1 overexpression did not increase filovirus susceptibility, supporting the notion that filovirus attachment proteins had been missing. Among the ectopically expressed proteins, DC-SIGN increased EBOV GP-driven transduction by 364-fold as compared to SH-SYSY WT. TIM-1, Axl, and Mer had a less potent yet significant effect on EBOV GP-mediated entry with fold change values of 66.7, 9, or 3.5, respectively. The transduction by MARVpp was stronger than by EBOVpp for both Axl (14.1) and Mer (10.2) tyrosine kinases, but not for TIM-1 (54.4). Moreover, DC-SIGN expression augmented MARVpp transduction by 15-fold, but this was not statistically significant. Finally, PtdSer receptors Mer and TIM-1 enhanced VSV-Gpp transduction by approx. 3-fold, as reported before [23; 69]. However, Axl failed to promote transduction. These results indicate that attachment was the limiting step of filovirus GP-driven entry into SH-SYSY cells, and that filoviruses can utilize multiple alternative surface proteins for successful attachment to cells.
## 4 Current Status and Identified Needs
### Level 2 Land Surface Reflectances  
The first satellite-based Level 2 products for land applications are the Bottom-Of-Atmosphere (BOA) products, often referred as Surface Reflectance (SR). The BOA products are the output of the Atmospheric Correction (AC) process, consisting in the estimation and removal of the atmospheric contribution to the TOA at-sensor radiometric signal. The uncertainty budget associated to the atmospheric correction includes all approximations used to simulate the inherent radiative transfer process in the coupled surface-atmosphere system, the uncertainties in the estimation of the atmospheric trace gases and particle concentrations and their scattering and absorption properties. Typically, in the solar spectrum domain and for the commonly used VIS, NIR and SWIR spectral bands, the uncertainty budget is dominated by the uncertainty in the estimation of the aerosol and water vapour optical properties. The rigorous estimates of uncertainties in SR products are a key input to be propagated into the subsequent bio-geophysical retrieval algorithms. Nevertheless, as of today, no operational ESA SR products are provided with an uncertainty estimate at pixel level, and this is considered a major drawback for a correct assimilation of this dataset into geophysical models.  
Furthermore, BRDF correction of SR products and normalisation to a standard illumination and viewing geometry is a prerequisite for blending data from multiple sensors and for minimising high frequency \"noise\" in temporal series [117]. This additional correction is not included yet as part of the currently available ESA SR products (see Table 1), which are provided as atmospherically corrected reflectances at the sun-viewing conditions of the satellite measurement. The provision of BRDF-corrected SR products, normalised to a standard sun-viewing geometry, is being addressed in the frame of the Sen2 Like project for harmonising Sentinel-2 and Landsat-8 time-series [12], and it is under development for both the S2 and S3 SR products as part of a recent ESA/Copernicus project, started in early 2021 and named COPA (Copernicus: 4 Core Products Algorithm Studies).  
Within this section, we present the current status and highlight the needs of infrastructure and methodologies for the validation of SR products. The needs identified in the text are listed in the Table 3 at the end of the section with derived recommendations.