esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Method
### Materials
#### 3.1.2 Data Preprocessing  
**For the Time-series Dataset.** In this study, preprocessing the stock price dataset is essential, especially during data cleaning and handling missing values. It is common to have missing data in stock price records due to various issues, such as collection errors or delays from market fluctuations. To address these gaps, we use methods like forward fill and backward fill. Forward fill replaces a missing value with the most recent known data point, allowing continuity in the sequence. Conversely, backward fill uses data from the following day if the previous data point is also missing. These methods help maintain temporal coherence in short periods of missing data. For longer gaps or irregular patterns, linear interpolation provides a simple way to estimate missing values by averaging adjacent data points. This approach can be extended to more complex interpolation methods, like polynomial or spline interpolation, which are better suited to nonlinear trends commonly found in stock markets. Besides filling in missing data, detecting and handling outliers is another crucial aspect of data cleaning. Outliers, which significantly deviate from the rest of the data, can be detected with box plots or Z-scores. If an outlier is identified, we may choose to either remove it or replace it with the average of the neighboring values to ensure smoother input data. Finally, we apply Z-score normalization to standardize the dataset, giving it a mean of 0 and a standard deviation of 1. This sequence of steps helps ensure high-quality, reliable input data for training our deep learning models.  
**For the Image Dataset.** In this study, we applied three data augmentation techniques, including CutOut, CutMix, and Mosaic, to improve the model's robustness and adaptability, as shown in Figure 3. CutOut involves masking random sections of an image, encouraging the model to recognize objects even when parts are missing. CutMix combines two images by overlaying parts of one onto another, creating blended samples that help the model learn from mixed-feature data, which is particularly useful for addressing class imbalances. Mosaic stitches together four images into a single frame, introducing diverse contexts and patterns that enhance the model's ability to generalize across varied backgrounds and categories.
## 2 Linear and Nonlinear Methods
### Artificial Neural Network Approach
#### 2.2.3. GRU  
Similar to the LSTM neural network, the gated recurrent unit (GRU) neural network is also a variant of recurrent neural networks [38], and it has fewer parameters because there is no output gate. The cell structure of GRU is shown in Figure 3, and the procedure on updating the cell state and calculating the output of GRU can be expressed as  
\[\mathbf{z_{t}} =\mathbf{\sigma}_{g}(\mathbf{W_{z}}\mathbf{x_{t}}+\mathbf{U_{z}}\mathbf{h_{t-1}}+\mathbf{ b_{z}}), \tag{11}\] \[\mathbf{r_{t}} =\mathbf{\sigma}_{g}(\mathbf{W_{r}}\mathbf{x_{t}}+\mathbf{U_{t}}\mathbf{h_{t-1}}+\mathbf{ b_{r}}),\] (12) \[\mathbf{\hat{h}_{t}} =\mathbf{\phi_{h}}[\mathbf{W_{h}}\mathbf{x_{t}}+\mathbf{U_{h}}(\mathbf{r_{\tau}}\odot \mathbf{h_{t-1}})+\mathbf{b_{h}}],\] (13) \[\mathbf{h_{t}} =(1-\mathbf{z_{t}})\odot\mathbf{h_{t-1}}+\mathbf{z_{t}}\odot\mathbf{h_{t}}, \tag{14}\]  
where \(\mathbf{z_{t}}\) and \(\mathbf{r_{t}}\) are activation vectors of the update gate and reset gate, respectively; and \(\mathbf{\hat{h}_{t}}\) and \(\mathbf{h_{t}}\) represent the candidate activation vector and output vector, respectively.
## 3 Governing Equations and Numerical Implementations
### Viscous Flux Treatments
#### 3.4.3 Thin-Layer Approximation  
As a compromise model between the accuracy and efficiency of numerical methods, thin-layer approximation is considered. When the flow separation is not significant, or the normal gradient of the stress terms is significantly greater than the stream-wise and circumferential gradients, the thin-layer approximation can be used [22; 30; 31]. This approach neglects stream-wise (\(\xi\)) and circumferential (\(\zeta\)) viscous terms, and thus only considers normal (\(\eta\)) viscous fluxes. The governing equation for thin-layer approximation is then reduced as  
\[\begin{split}&\left[\left(\frac{3\Gamma_{r}}{2\Delta t}+\frac{ \Gamma}{\Delta\tau}+\frac{\partial\hat{A}}{\partial\xi}+\frac{\partial\bar{B }}{\partial\eta}+\frac{\partial\mathbb{C}}{\partial\xi}-\frac{\partial^{2}B _{v\eta}}{\partial\eta^{2}}\right)\Delta\hat{Q}^{n+1,k}\right]\\ &\quad=-\left[\Gamma_{e}\left(\frac{3\hat{Q}^{n+1,k}-4\hat{Q}^{n }+\hat{Q}^{n-1}}{2\Delta t}\right)-\hat{S}^{n}+\frac{\partial\hat{E}^{n+1,k}}{ \partial\xi}+\frac{\partial\left(\bar{r}^{n+1,k}-\bar{r}_{v}^{n+1,k}\right)} {\partial\eta}+\frac{\hat{Q}^{n+1,k}}{\partial\xi}\right]\end{split} \tag{17}\]  
[PERSON] et al. [9] applied a thin-layer approximation to the prediction of axisymmetric and three-dimensional cavitation flows for several configurations. Although some of the results were compared to measured data, the accuracy and efficiency were not analyzed in detail, specifically with regard to the full viscous approach, one of main goals in this study.
## 3 Results and Discussion
### Implications for Further Research  
The workplaces' close examination of the use of their chemicals has led to questions that have implications for further research. The first is on the adequacy of WHMIS training in most workplaces. The norm is now to have relatively short, on-line training with an online quiz. Feedback from the WHSCs showed that this training is inadequate, a finding which has been reported by other studies [47]. Further research on how WHMIS training can be improved is therefore needed.  
Secondly, the study has led to questions on how to improve the quality of SDSs. The examination of SDSs by the WHSCs while filling in the information for the CHAP-Risk tool revealed that information is presented in different ways in different SDSs. This is despite the requirement that SDSs have a uniform structure since the federal and provincial WHMIS 2015 legislation was passed to align with the Globally Harmonized System of Classification and Labelling of Chemicals. In addition, sometimes the chemical's hazard information was not provided in Section 2, or information that was confusing to workers was provided. Our study confirms the ongoing need to improve SDSs in terms of their comprehensibility, accuracy, and usefulness [36; 37].  
Thirdly, an unexpected finding of the study has also opened the door to further study on the communication issues between members of the WHSC and the rest of the workforce. Some research has been conducted on the functioning and effectiveness of WHSCs in Ontario [48], but more exploration of this important issue is needed.  
Finally, since the CHAP-Risk tool is based on existing research on chemical assessment approaches, there are some inherent limitations that come from both the positive and negative aspects of this research. Much of the existing research on hazard assessment has focused on health hazards and has only dealt with solids and liquids. This means that the CHAP-Risk tool, and other current tools, are missing hazard information on environmental and physical hazards and are also missing the ability to make risk assessment for gases. These aspects open the possibility for further enhancements of the CHAP-Risk tool. In comparison to other similar tools which are used in Canada and abroad, such as COSHH Essentials [21], Stoffenmanager [22], and the NIOSH Occupational Exposure Banding e-Tool [25], the CHAP-Risk tool has responded to local industry feedback [8] by providing a tool that is relatively simple to use, is free, uses software that is readily accessible and well-known, and is able to be stored on in-house IT systems instead of using an online portal. As such, CHAP-Risk has particular advantages for SMWs and responds to the recently adopted Global Framework on Chemicals which identifies the need for best-practice tools for hazard and risk assessment for chemical management (i.e., Target B4) [49].
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Research Methods for Antimicrobial Activities of ZnO Nanostructures
#### 4.1.3 Antimicrobial Measurements in Liquid Culture Media  
During the incubation period, the turbidity of the growth solution increases with bacterial growth. The liquid turbidity and cell proliferation can be measured by periodically measuring the optical density. The technique does not require reagents and special handling [143; 144; 145; 146; 147]. The basic principle of this method is to judge the quality of antibacterial performance by observing the absorbance at a specific wavelength in a spectrophotometer and regularly monitoring the corresponding bacterial growth. [PERSON] et al. used ZnO nanomaterials as antibacterial agents to conduct growth inhibition experiments on _E. coli_ and achieved excellent experimental results as shown in Figure 8 [101].  
Figure 8: Bacterial growth curve of _E. coli_ under different ZnO-NFs concentrations.
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 4 Results and Discussion  
According to reference data, the landfast ice thickness was 20-40 cm, and its extent was stable during the 12 days between the overflights. Landfast ice extended from Raahe on the eastern shores of the Bay of Bothnia to Kemi and Tornio in the north and then it continued on the Swedish side. The fast ice zone completely encircled Hailuoto Island of Oulu. Coherence magnitude is calculated based on Equation (1), interpretation of the interferogram over our area of interest is done using Equation (6), and they are analyzed with respect to the weather conditions during the image acquisitions.
## 2 Measurement Principles and Device
### The Structure of a Cryogenic Vacuum Apparatus  
As shown in Figure 1, the vacuum chamber is primarily equipped with a blackbody, a sample heating stage, and a liquid nitrogen blackbody. The external computer controls the horizontal motor to move horizontally, enabling the measurement of signals from these three components, ultimately achieving the purpose of measuring the sample's emissivity. The external temperature controller can independently regulate the temperatures of the blackbody and the sample via the blackbody heating stage and the sample heating stage, respectively, and during measurements, the temperatures of both are set to be the same. A rotating platform is installed between the sample heating stage and the lifting platform, allowing the computer to manipulate the rotation of the sample during emissivity measurements. Additionally, a high-power light bulb is mounted at the top of the vacuum chamber. Its purpose is to be turned on at the end of the measurement to raise quickly the internal temperature using its radiation, allowing the gas valve to be opened earlier and saving time during the heating process. The cylindrical tank has a diameter of 1.5 m and a length of 1.5 m. Currently, the movement range of the internal movable platform is 600 mm.  
The Fourier transform infrared spectrometer was bought from the Bruker company, model V80V. It can work under vacuum condition and has a wavelength range covering 250 nm to 100 mm.  
The optical diagram of the normal emissivity measurement device measured by the low-temperature vacuum method is shown in Figure 2A. The aperture of the diaphragm matches the exit aperture of the Fourier spectrometer, resulting in parallel light. However, most emission measurement devices designed in the literature follow the optical path used by PTB, as shown in Figure 2B. There is an off-axis ellipsoidal mirror in the light path, so it is divergent light.  
The main advantage of using the parallel light scheme is that the sampling area is significantly larger, resulting in a better signal-to-noise ratio. This is particularly important for low-temperature objects.  
Figure 1: Device photo and device schematic diagram.  
Figure 2: The difference diagram of the optical path. (**A**) The parallel optical path. (**B**) Convergent optical path used by other devices.
## 4 Results
### Data Comparison with M3 TRIOS System--Matchup Analysis
The MARD of 7.1% was dominated by the contribution for data at 870 nm, where the water reflectance was very low and absolute differences, e.g., arising from skyglint correction, translated into very large relative differences. The spectral variations of RMSD and MARD between PANTHYR and M3 TRIOS for \(E_{d}(\lambda)\) and \(\rho_{w}(\lambda)\) over these 29 matchups are shown in Figure 8. The short wavelength scale variability for the RMSD of \(E_{d}(\lambda)\) in Figure 8a was related to the similar spectral variability of \(E_{d}(\lambda)\) itself, e.g., the atmospheric oxygen absorption feature at 762 nm or the Frauenhofer lines related to solar photosphere absorption at 516-518 nm, and the way the slightly different central wavelengths of each \(E_{d}\) instrument  
Figure 7: Scatterplot for selected wavelengths comparing PANTHYR data with M3 TRIOS system data for (**a**) \(\overline{E_{d}}\), (**b**) \(\overline{L_{d}}\), (**c**) \(\overline{L_{u}}\), and (**d**) \(\overline{\rho_{w}}\) for 29 matchup stations. Linear regression statistics are given, including the root-mean-square difference (RMSD) and the mean average relative difference (MARD). under-resolved these features. The MARD of \(E_{d}(\lambda)\) between PANTHYR and M3 TRIOS (Figure 8b) was \(<\)5% for the full spectral range 400-900 nm. The common radiometric calibration of the two \(E_{d}\) instruments just prior to this experiment helped limit MARD; however, a more detailed characterization of these two instruments, particularly including an analysis of their \"cosine\" angular response and perhaps straylight and non-linear responses, may improve results in the future. The spectral variation of RMSD between PANTHYR and M3 TRIOS for \(\rho_{w}(\lambda)\) shown in Figure 8c suggests an overall spectral shape typical of \(L_{u}(\lambda)\) spectra, i.e., upwelling radiance from green water and reflected \"blue\" skyglint. Possible causes of this difference are multiple and certainly include imperfect correction of skyglint, but may also include different BRDF at the different azimuth angles, propagation of \(E_{d}(\lambda)\) differences, \(L\) radiometer calibration and characterization (straylight, non-linearity, polarization, thermal sensitivity, etc. ), etc. A detailed analysis is beyond the scope of this technology-proving paper, which concludes merely that there is no major cause for concern at present. The MARD between PANTHYR and M3 TRIOS shown in Figure 8d for \(\rho_{w}(\lambda)\) was \(<\)5% for the spectral range 410-580 nm, with higher MARD around 600 nm. The much higher MARD for wavelengths higher than 700 nm are clearly related to the very low \(\rho_{w}(\lambda)\) for these waters, showing some similarity with the pure water absorption coefficient spectrum for 700-900 nm [(26)] and with the phytoplankton absorption coefficient at 660-680 nm. In such conditions, the MARD has no practical relevance because a satellite validation analysis would generally consider absolute differences between PANTHYR and satellite or simply not use such data.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Data Pre-Processing  
(1) Pre-Processing of Original Geochemical Data  
First, based on Python, a mean removal operation was conducted on the original X-Y-Z geochemical data of the ore-forming elements to remove the background noise, that is, subtracting the mean values from each value in the X-Y-Z dataset. And then, normalized them into the range [0, 1]. After that, based on Surfer v11.0 and using the Kriging interpolation algorithm, the normalized data of each element were expanded into a matrix of size 405 \(\times\) 500, and then it was saved as a single-band image in \"Info ASCII Grid  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Element** & **Ag** & **As** & **Au** & **B** & **Be** & **Bi** & **Cu** & **F** & **Hg** \\ \hline Maximum & 4500 & 248.7 & 93.5 & 660 & 12 & 272.34 & 287.8 & 24,600 & 5511 \\ Minimum & 10 & 1.24 & 0.2 & 2.9 & 0.7 & 0.036 & 0.8 & 80 & 4.5 \\ Median & 60 & 8.60 & 0.8 & 38 & 2.1 & 0.24 & 13.3 & 340 & 16 \\ Average & 71.22 & 9.83 & 1.24 & 42.05 & 2.25 & 0.53 & 14.01 & 380.05 & 21.44 \\ CV & 1.87 & 0.97 & 2.43 & 0.  
(*.grd)\" format, so that it is readable for the eCognition Developer and Python platforms. Finally, these *.grd files were concatenated along the dimension of channels and saved as a multi-band *.geotif image, termed Image I.  
(2) Multiresolution Segmentation  
Using the business image processing software eCognition, the FNEA (Fractal Net Evolution Approach) multiresolution segmentation was conducted on Image I. The scale parameter is empirically set as 3.0, the shape parameter is set as 0.1, and the compactness as 0.5. Finally, we got 663 segmented objects as shown in Figure 2. Note that the Fe\({}_{2}\)O\({}_{3}\)-band in Image I was not involved in the segmentation process. This is because: as the only major element (or say rock-forming element), the incorporation of Fe\({}_{2}\)O\({}_{3}\) may misguide FNEA to pay more attention to regional diagenesis, rather than metallogenesis.  
(3) Find the Centroid of Each Object  
First, draw the minimum enclosing rectangle (MER) for each image object, and the centroids of the MERs will act as the centroids of the corresponding objects, see Figure 2. However, if the centroid falls on or outside the boundary of an image object, it will be recalculated in the following way: (i) generating the skeleton lines for each object; (ii) removing the suspension lines along the main skeleton line; and (iii) finding the midpoint of the main skeleton line, and taking it as the centroid of the corresponding object. Thanks to the skimage.morphology, numpy, and other Python packages, these processing steps can be easily implemented on a computer.  
Finally, we note that image segmentation is the process by which an original image is partitioned into some homogeneous regions/objects [6], so the spectral features of the centroids can be used to represent the spectral features of the corresponding image objects. Our proposed algorithm is developed based on this assumption.  
Figure 2: The multiresolution segmentation result and the centroids within each image object.
## 5 Results
### Experimental Configuration  
We used the configurations shown in Table 3 and a correlation between the distance and time to obtain the speed estimation accuracy of the models considered (five statistical methods and three machine learning methods), allowing us to present a comparison among them, see Tables 4 and 5.  
Figure 12: Flowchart for defining a predictive velocity model.  
We use the Scikit-Learn library [54] to carry out the model experimentations of Linear Regression (LR), Ridge Regression (CR), Lasso (Absolute Minimum, Selection, and Contraction Operator - Lasso), Bayesian Ridge Regression (BCR), Elastic Net Regression (Elastic Net), Random Forest Regression (RRF) and Support Vector Machine Regression (SVMR). The Scikit-Learn library determines how we can interact with the algorithm configuration, as shown in Table 3. Even when each variable has the same name, its functionality depends on how the algorithm uses this data to interpret and carry out its task; we can see further details in [54].  
We use the Tensor Flow library [55] to implement the Multilayer Perceptron (MLP) neural network. The MLP network used in this work has two input neurons, five neurons in the hidden layer, and one neuron in the output layer.  
We published the dataset and source code of the experimentation at [[https://github.com/garay54/Regression_Models](https://github.com/garay54/Regression_Models)]([https://github.com/garay54/Regression_Models](https://github.com/garay54/Regression_Models)), accessed on 30 January 2022.  
obtained by each of the proposed methods. It also shows the standard deviation of the errors obtained. As mentioned above, the metric used for the precision was the mean absolute error, and the scale presented is kilometers over an hour (km/h).  
It is worth mentioning that no experiments were performed in the first lane due to the lack of samples corresponding to that lane. Tables 4 and 5 show a similar performance behavior in the statistical methods. They have a better performance than the random forest, support vector machines, and neural networks. It can be said that the method with the best performance was linear regression with a minimal difference. Equation (8) shows the equation of the linear regression with the resulting coefficients.  
\[Speed=(0.00568)*Distance-(7.2141)*Time+(23.725) \tag{8}\]  
where _Distance_ and _Time_ are the inputs of the estimation process. _Distance_ is expressed in pixels and takes values from 701.38149 to 1206.1857 pixels; _Time_ is expressed in seconds and takes values from 0.4164 to 2.0150 seconds.
## 2 Methodologies
### Geographical Locations Case Studies  
The search for geographic locations to use for the case study is conducted by considering: areas where hydropower plants are present; areas afferent to the most important rivers in a given region; areas whose crop type is grassland, with sparsely forested areas; and geographic areas where snowfall is rare, since snow and its effects on the water balance of a catchment are here ignored. The methods described in the subsections above are applied to two geographical locations in the Italian regions of Sicily and Sardinia, respectively, shown in Figure 1.  
The two sites selected are near Paterno (Sicily) and near Busachi (Sardinia), whose geographical position is 37\({}^{\circ}\)53\({}^{\prime}\)47\({}^{\prime\prime}\) N longitude 14\({}^{\circ}\)86\({}^{\prime}\)14\({}^{\prime\prime}\) E and 40\({}^{\circ}\)01\({}^{\prime}\)97\({}^{\prime\prime}\) N and 8\({}^{\circ}\)84\({}^{\prime}\)85\({}^{\prime\prime}\) E, respectively. Both sites are located in catchments of the main rivers of the two islands (in terms of catchment size), namely the Simeto (Sicily) and the Tirso (Sardinia). In addition, the sites are located near hydroelectric power plants (dams), which are among the largest hydroelectric power plants on the two islands in terms of installed capacity. The soil type in both localities is loam, and the vegetation cover type is pasture, with rare and small woodland clusters. This implies that the interception of precipitation by foliage does not decisively impact the relationship between precipitation and runoff. Satellite data reveal that the geographic site chosen for Sicily has slightly higher soil moisture saturation rates than the site in Sardinia. This is probably due to the amounts of annual precipitation, which are greater for the Sicilian site. The sites are affected by a Mediterranean climate where snowfall is very rare. This complies with the modelling of water availability, as snow and its impact on the hydrological cycle are neglected in this work.