esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.  
train location; SINS; OD; map matching; Kalman filter
## 2 Multi-Fidelity Machine Learning
### Algorithm  
The multi-fidelity machine learning framework is flexible, and the algorithms for both the low- and high-fidelity models are exchangeable. Such an advantage ensures that the algorithms can be determined and optimised based on the specific tasks and data experiments. The machine learning algorithms investigated in this study include Multi-Layer Perceptron (MLP) [41], Random Forest (RF) [42], and eXtreme Gradient Boosting (XGBoost) [43]. These three algorithms are popular and representative in the field of machine learning [44].  
MLP is a classical feedforward neural network model that also falls within the field of deep learning. MLP operates by transforming input data through a series of layers, with each layer composed of multiple neurons. Each neuron computes a weighted sum of the inputs, applies a nonlinear activation function (such as ReLU or Sigmoid), and passes the result to the next layer. The network learns by adjusting the weights through a process called backpropagation, which minimises the error between the predicted output and the actual target. By stacking multiple hidden layers, MLPs are capable of learning complex, non-linear relationships in the data, making them suitable for tasks such as classification, regression, and pattern recognition.  
RF is an ensemble learning method that combines multiple decision trees to improve predictive performance and reduce overfitting. During training, RF builds multiple trees using different subsets of the data and randomly selects features for each split to ensure diversity among the trees. The final prediction is obtained through majority voting (for classification) or averaging (for regression) across all trees. RF is robust to noise, less prone to overfitting than individual decision trees, and can effectively handle high-dimensional data.  
XGBoost is a highly efficient and scalable implementation of gradient boosting, designed to optimise both accuracy and computational speed. It builds an ensemble of decision trees in a sequential manner, where each tree corrects the errors of the previous ones. XGBoost uses second-order derivatives to enhance optimisation, and it supports regularisation (L1 and L2) to prevent overfitting. It also includes techniques like tree pruning and parallel processing, making it highly effective for large-scale datasets in machine learning. XGBoost is particularly well-suited for tasks requiring high predictive power.  
An exemplar of multi-fidelity machine learning using MLP is shown in Figure 4. Note that the input of the high-fidelity models always has one more dimension than that of the low-fidelity models, which serves as the preliminary low-fidelity prediction.  
Figure 3: Multi-fidelity machine learning framework.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 2 Materials and Methods
### Spectral Processing  
Preprocessing of the FTIR spectra was performed to enhance signal clarity and ensure comparability across samples. Using the OMNIC software's Macros/Basic tools, preprocessing steps included baseline correction, automatic smoothing, scale normalization, and adjustment of display limits. Processed spectra were then exported as CSV files containing wavenumber (cm\({}^{-1}\)) and absorbance values. For visualization, both individual and mean absorbance spectra per group (Feta, non-Feta white primed cheese) were plotted against the corresponding wavenumbers. Spectral regions below 600 cm\({}^{-1}\) were excluded from all analyses. Samples flagged as adulterated by ELISA results were removed from the dataset. For each sample, absorbance values were ordered by increasing wavenumber and three signal representations (raw, first derivative, second derivative) were computed, as shown in Table 1.
## 5 Results and Discussions
### Trailing Vortices  
It is critical to study the characteristics of trailing vortices. Figure 9 presents the three-dimensional structure of the trailing vortices behind the different impeller blades. The trailing vortices were produced behind each blade. A large part of the impeller energy was consumed in the trailing vortices, while only a tiny part of the energy could be transferred to the far end of the flow field for the suspension process of solid particles. Comparing with Figure 9a-d, the trailing vortices near the PBT were characterized by large size, narrow shape and small influence range, which are not beneficial features for energy transfer.  
Figure 8: Liquid velocity vectors profile around the different impeller blades. (**a**) PBT, (**b**) PBPT, (**c**) BCBT, (**d**) BCPBT.  
Figure 7: Comparison of radial profiles of solids velocity with experimental results (\(z=0.2H\)). (**a**) Axial velocity component \(u_{x}\), (**b**) radial velocity component \(u_{r}\), (**c**) tangential velocity component \(u_{\theta}\).  
Nevertheless, the novel impellers were able to significantly change the trailing vortex structures. The large trailing vortices observed in the wake of the PBT blades were replaced with multiple, weaker vortices in the wake of the novel impellers. It can be proved that the novel impellers could effectively reduce the size of the trailing vortex through the apertures and gaps around the impeller blades, which are conducive to improving energy utilization and enhancing mixing efficiency. Many small-scale vortices were formed at the apertures and gaps of the novel impellers. The turbulent kinetic energy around the vortices was more prominent, which was in favor of improving stirring efficiency. In addition, the pressure at the center of these vortices was relatively low, giving the impeller some extra driving force and reducing energy consumption to a certain extent.
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 5 Experimental Results
### Experiments and Results
Subfigure (**a1**) shows the confusion matrix of _User1_’s private model when _User1_’s copy was queried by samples signed by _User1_. Subfigure (**a2**) shows the confusion matrix of _User2_’s private model when _User1_’s copy was queried by samples signed by _User2_. Subfigure (**b**) shows the same results for _User2_’s model. parameters of the private model. On the other hand, our framework allows embedding the WM from scratch or by fine-tuning; the latter contributes to efficiency. Regarding **generality**, even though in our work we use image classification tasks that output softmax layer probabilities (confidence) for the input image with each class, _KeyNet_ can be extended to cover a variety of ML tasks that take images as input and output multiple values such as multi-labeling tasks, semantic segmentation tasks, image transformation tasks, etc.
## 4 Discussion  
### Analysis of the GPP Trends and Reasons for Variation  
Recent MODIS satellite data showed that despite the fact that China covers only 6.6% of the global vegetated area, it contributes as much as a quarter to the global greening. China's afforestation and forest conservation efforts are crucial factors in achieving this outcome [47], the conclusion supported by our study. Since the 1970s, China has launched several significant ecological construction projects, with at least six directly related to land greening and carbon sequestration: the Three-North Shelterbelt Project, Natural Forest Resource Protection Project, Grain for Green Project, Yangtze River/Pearl River Basin Shelterbelt Project, Beijing-Tianjin Sand Source Control Project, and Grazing Ban and Grassland Conversion Project [48; 49; 50]. These projects cover the vast majority of China's territory.  
The large-scale ecological projects related to vegetation restoration can increase vegetation cover. Higher vegetation cover means more leaves to absorb the solar energy, which can increase the total photosynthetic rate of the vegetation, thereby enhancing the land ecosystem's carbon absorption capacity [51]. Higher vegetation cover can also reduce the temperature of the soil surface and lower the temperature of the vegetation through transpiration [52]. This helps maintain the relative humidity within the vegetation, improving water absorption and utilization efficiency. More efficient water usage allows vegetation to maintain a higher photosynthetic rate under limited water conditions [53]. Previous studies have also suggested that increased vegetation cover contributes to terrestrial carbon sinks. [PERSON] et al., using a process-based diagnostic system model, found that the global increase in Leaf Area Index (LAI) led to a 12.4% accumulation of terrestrial carbon sinks [54]. [PERSON] et al. discovered that large-scale ecological projects improved the environment in Northern China, leading to a significant increase in GPP in the Loess Plateau and Northeast Plain regions, with increased LAI being the main driver behind the GPP increase in these areas [55].
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 2 Materials and Methods
### SAR Backscatter
Ranging between 0 and 1, RVI is used for measuring the randomness of scattering in microwave signal [38]. It is close to 0 for a smooth bare surface and as vegetation grows the value increases till the crop reaches to the end of growth cycle and it is affected by vegetation water content and sensitive to the biomass [39]. RVI calculation needs quad-polarized data, thus for full polarization, RVI is retrieved by the Equation (5). \[\text{RVI}\,=\frac{8\sigma^{0}\,_{HV}}{\sigma^{0}\,_{HH}+\sigma^{0}\,_{VV}+2 \sigma^{0}\,_{HV}}, \tag{5}\]  
where \(\sigma^{0}_{\text{HH}}\) and \(\sigma^{0}_{\text{VV}}\) are co-polarized backscattering coefficients and \(\sigma^{0}_{\text{HV}}\) is cross-polarized backscattering coefficient in power units. According to the [PERSON] et al. [40] the assumption that supposes \(\sigma^{0}_{\text{HH}}\approx\sigma^{0}_{\text{VV}}\) then Equation (5) can be reduced to the form as Equation (6). \[\text{RVI}_{\text{HH}}=\frac{4\sigma^{0}\,_{HV}}{\sigma^{0}\,_{HH}+\sigma^{0} \,_{HV}}, \tag{6}\]  
[PERSON] et al. [41] studied the RVI and concluded that RVI\({}_{\text{HH}}\) is useful when just two polarizations are available and can be an appropriate approximation of the surface scattering if the interaction between the surface plane and vegetation is insignificant. Since Sentinel-1 is dual polarization and has VH and VV polarizations, following [PERSON] et al. [40] assumption of possibility to modification of RVI in case of availability of two polarizations we assume an alternative to RVI for dual polarization as shown in Equation (7). \[\text{RVI}\,=\frac{4\sigma^{0}\,_{VH}}{\sigma^{0}\,_{VV}+\sigma^{0}\,_{VH}}, \tag{7}\]  
The index is about the contribution of volume scattering which is indicated by cross-polarized response. Pre-processing steps of satellite images were carried out using open source tools of Sentinel Application Platform (SNAP) software [42]. Mean backscatter values and temporal variation of backscatter for each field for three different crop types relying on backscatter statistic results are extracted using _rasterstats_ zonal statistics and interpolated point queries function with a Python module [43]. The module is used to summarize geospatial raster datasets to extract information based on vector geometries [44]. Quantum GIS [45], an open source GIS software is applied to draw the region of interest (ROI) polygons as vector geometries used in _rasterstats_ zonal statistics function. To decrease the effects of mix of the classes, the polygons are set at proper interval from the edges of the field boundary and homogeneous pixels are selected for the evaluation. Figure 7 shows the flowchart of Sentinel-1 dual polarimetric SAR data processing.
# China's Water Intensity Factor Decomposition and Water Usage Decoupling Analysis
## 3 Results  
### Water Intensity and Factor Decomposition Analysis  
#### 3.1.1 Analysis of Decomposition Effect in Each Year  
According to Equations (3)-(6), three effects and their respective contribution rates from 2000 to 2020 are shown in Figure 3. The total effect of each year is negative, indicating that the water intensity is decreasing year by year, signaling water saving considerations. The total effect from 2002 to 2003 was the smallest, with value of \(-64.50\).  
Technical effects in the last 20 years are negative and the technical effect contribution rate is the largest among three effects, indicating it has an inhibitory impact on the water  
Figure 3: Effects and contribution rates in China from 2000 to 2020.  
Figure 2: China’s added value of various industries from 2000 to 2020.  
intensity, while technological innovation is the most effective measure for water saving. The technical effect fluctuated greatly, with its largest value from 2002 to 2003 of \(-\)59.91 and highest contribution rate in 2001 of 2481.29%. For the industrial structure effect, it was negative except for 1.62 in 2003 and 0.38 in 2020, meaning that it restricted the water intensity in most years. As for for the regional scale effect, it fluctuated greatly from 2000 to 2001, reaching 39.49, and it was stable with values between \(-\)1 and 2 from 2003 to 2020.  
The three effects in each industry were also explored in China through the LMDI in Figure 4. The technical effects are all negative for the three industries, which means that the water intensity of the three industries all declined with technological innovation. It also fluctuated greatly before 2011, with the largest absolute value of \(-\)36.24, \(-\)12.52 and \(-\)11.15, respectively, during 2002 to 2003, then it tended to be flat. Besides, it fluctuated most within primary industry, due to the largest proportion of China's primary industry in current water usage structure.  
The industrial structure effect on the three major industries has different characteristics. In primary industry, it has increased from \(-\)18.21 to 2.63, shifting from a restriction effect to a promoting effect from 2018 to 2020. In secondary industry, it changed from strong promotion to weak promotion, and finally into a restriction effect, which is mainly attributed to the intensive management of industrial development with the increasing industry output. In tertiary industry, it remains essentially unchanged. Therefore, the industrial structure effect has restricted the water usage in China, indicating the industrial transformation in China has impacted on water usage reduction in the last 20 years.
## 2 Displacement Reconstruction Using Measured Acceleration Data
### Elimination of Trend Items and De-Noising  
The least square method of polynomial fitting is most commonly used to eliminate the interference of trend terms [19]. In this work, a moving average method of least squares was used in combination with a filter to eliminate the interference of random fluctuations in measured data; thus, the influence of random errors was reduced, and real displacement results of the structure were obtained.  
First, based on the moving average method of least squares, a polynomial function of degree \(m\) was obtained [15]:  
\[Y(t)=a_{0}+a_{1}t+a_{2}t^{2}+\cdots+a_{m}t^{m} \tag{4}\]  
The original data with the minimum mean square error were obtained by the data smoothing approach:  
\[A(a_{0},a_{1},\cdots,a_{m})=\sum_{i=-u}^{u}{[\sum_{j=0}^{m}a_{j}t_{i}^{j}-Y_{ i}]}^{2} \tag{5}\]  
where \(u\) is the sliding order and \(m\) is the smoothing number. In order to determine undetermined coefficients and minimize the error sum of squares between the function\(Y\)(\(t\)) and discrete data, the partial derivative of \(a_{k}(k=0,1,\ldots,m)\) in Equation (5) was considered to be zero. Hence, the linear equations of element \(m+1\) were obtained as:  
\[\sum_{i=-u}^{u}Y_{i}i_{i}^{k}=\sum_{j=0}^{m}a_{j}\sum_{i=-u}^{u}t_{i}^{k+j} \tag{6}\]  
In Figure 1a, a standard sinusoidal superposition signal with 0.8 octaves of random noises and the drift term is displayed, and Figure 1b presents data processed by the moving average method. It is noticeable that random noises and trend terms could be simultaneously removed. Therefore, the moving average method was applied to remove the interference of noise in the following analysis.
## 5 Overview of Odour Analytical Measurement Methods
### Emission Measurements  
The emission of malodorous gases can be determined, similar to other gaseous pollutants, if the nature of the emission source is known [113]. Emission sources include the following:  
* organised (e.g., covered activated sludge chambers in wastewater treatment plants) and unorganised (e.g., stables or landfills);
* active (with forced gas flow, e.g., aerated chambers in wastewater treatment plants or biofilitlers) and passive (e.g., solid waste landfills, sludge sites);
* point (e.g., chimneys that emit gases), surface area (e.g., sludge areas) and volume (e.g., stables).  
The most important aspect in the determination of gas emissions is that the sample taken for analysis is representative. Therefore, samples are taken differently depending on the type of source. We can determine the point emission if we know the concentration of the odorous substances and have data on the volume flow of the emitted gas (Equations (5) and (6)) [114; 115].  
\[E=S_{\text{OC}(\text{ou/m}^{3})}.V \tag{5}\]  
\[E=S_{\text{O}(\text{ug/m}^{3})}.V \tag{6}\]  
where \(E\)-emission from a point source [ou/s] or [ug/m\({}^{3}\)] and \(V\)-volume flow rate of the emitted gas [m\({}^{3}\)/s].  
Gas samples for the determination of emissions from such sources are taken directly from the emitted stream, e.g., using the so-called \"lung method\" by averaging the samples collected in a certain time interval. Static or dynamic dilutions can be used for sampling. The measurement of surface emissions is somewhat more difficult. It requires the use of a special chamber for sampling surface emitters. For active source measurements, the air flow rate in the sampling chamber is closely related to the air flow through the active source, while for passive sources the air flow rate is forced by the sampler (typically 0.2-0.3 m/s) [116]. The calculations are based on the knowledge of the operating parameters of the sampler [116], the surface area of the emitter and the odour concentration values (Equations (7) and (8)) [54; 114; 115].  
\[E=\frac{S_{OC(\omega_{\text{Hz}}/\text{m}^{3})}\cdot V_{pr}\cdot P}{P_{pr}} \tag{7}\]  
\[E=\frac{S_{O(\omega_{\text{Hz}}/\text{m}^{3})}\cdot V_{pr}\cdot P}{P_{pr}} \tag{8}\]  
where \(E\)-emission of the source \(P\) [ou/s] lub [ug/s]; \(V_{pr}\)-air flow rate in the sampling chamber [m\({}^{3}\)/s]; \(P\)-emitter area [m\({}^{2}\)] and \(P_{pr}\)-emitter area under the sampling chamber (in the sampler) [m\({}^{2}\)].  
Due to the difficulties in determining the air flow through volumetric sources, the measurement of their emissions is the most complicated. In the case of stable buildings, the flow can be estimated by determining the amount of air exchanged in the building per unit of time [117].
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Hyperparameters of the Learning Model  
Hyperparameters for the ConvLSTM include the epoch number, batch size, and loss function type. Eight parameters of ocean weather were trained through a model combining the denoising AutoEncoder and the ConvLSTM. In the case of six parameters of ocean weather obtained from ECMWF, since it is a total of 20 years of data, the initial 16 years of data were used as a training set, and the remaining 4 years of data were used as a validation set (2 years) and a test set (2 years). In addition, in the case of two parameters of ocean weather obtained from HYCOM, since it is a total of 4 years and 4 months of data, the initial 4 years of data were used as a training set and the remaining 4 months of data were used as a validation set (2 months) and a test set (2 months). The batch size and number of epochs were tracked for the training set, validation set, and test set. The epoch number was determined using the convergence step from the line where no overfitting occurred. As a result of training progress, most models typically converge between 40 and 50 epochs. In this study, MAE was used in all cases for accuracy comparison. Since RMSE tends to overestimate outliers in the overall data, MAE is more appropriate for this study using time-series data for the long term. In the case of average error, the whole data are firstly calculated as MAE by comparing them with the original data and averaged. In addition, the land included in each area was also excluded from the error calculation. Due to the nature of the ConvLSTM, an image was used as an input, and the value of the land was calculated as 0. If the error of the land is included in the error, the total error will be smaller. In this study, we grasped this, calculated MAE for all areas and time series, and averaged it.