esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fundamentals of IRT
Pseudocolors can be used to display data taken in areas of the electromagnetic spectrum that are invisible to the naked eye (for example, infrared radiation) or to highlight values (for example, by converting greyscale to different colors). Since commonly used sensors do not distinguish the wavelength of the radiation, the image produced is monochromatic. The resulting thermogram is displayed in pseudocolors. Based on the color palette, different areas can be assigned shades based on temperature [46]. A radiometric thermogram includes a palette--the temperature scale and its associated temperature in \({}^{\circ}\)C (\({}^{\circ}\)F). Analyzing monochromatic colors is appropriate for some applications and a specific palette of colors is appropriate for others. The choice of color palettes allows flexibility in analysis. The standard color palettes for thermograms are iron, grayscale, and rainbow [47]. Depending on the manufacturer, color palettes can be named differently, or additional types can be added [48]. The temperature scale can be fixed or vary with the highest/lowest temperature point. The scale setting can be performed in the analysis software or in the corresponding menu of the infrared camera. A radiometric thermogram is the result of a thermal camera scan, and each point in it represents a measured surface temperature [49]. Thermal cameras are capable of detecting temperature changes below 0.05 \({}^{\circ}\)C. When using the iron palette, the hottest areas are shown in white or red on the thermograph and the coolest in blue and black. Increased temperature (hot spots) usually indicates inflammation or increased blood flow, while lower temperature spots (cold spots) indicate decreased blood flow [50; 51]. The amount of infrared radiation emitted is different for each part of the animal [52]. According to the studies conducted, the recommended range of emissivity values is 0.95-0.97 for furred skin in mammals, e.g., see [53; 54; 55], but according to [56], sufficient determination of fur emissivity is essential to obtain accurate temperature data, which is influenced by various factors and thus the emissivity range used cannot be applied to all individuals or species.
# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 3 Methodology  
The morphological analysis of new words plays a significant role in developing NLP tools. A rule-based method with paradigms is the most suitable for analyzing a fixed number of inflections from the words present in morphologically rich languages. But the limitation on the fixed number of words in a dictionary makes it impossible to add every new word into a paradigm, as there could be the addition of words to a language based on cultural and socio-logical influences. Hence, a transformer-based morphological analyzer has been proposed in this paper. The proposed method can be applied to any low-resource morphologically rich Indic language; as a case study, we have considered the Kannada language. The main objectives of building the Transformer based on a morphological analyzer are as follows:  
1. Designing paradigms based on morphological rules of inflection in Kannada, considering gender, number, tense, person, and case for specific word categories.
2. Generating morphological analysis for words in the Kannada dictionary using the designed paradigms, thereby creating a unique dataset that can be utilized for further NLP tasks in Kannada.
3. Training a Transformer model to perform morphological analysis of nominal inflections using the generated dataset and hence developing a hybrid model. The proposed model can also generate analysis for words not present in the dictionary.  
The high-level architecture for low-resource language in developing the morphological analyzer is shown in Figure 1:  
The input to the system is a dictionary that has two parts, the root word and the meaning. The text must be in Unicode format. The root word and the part-of-speech (POS) tag have to be extracted, discarding the meaning for morphological analysis. The detailed process of creating a morphological analyzer is explained in the further subsections.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Architecture
#### 2.1.3 Decoder Network Design  
The decoder (shown in Figure 5) aims at extracting messages and accepts the stego video frame, which is the output, of the encoder as the input. The function realized by the decoder and the encoder is mutually inverse, so the network structure is basically similar, made up of one conv-module, one multi-scale feature extraction module, and one final convolutional layer.  
Figure 4: The encoder component of PyraGAN.  
1. \(F\)-dimensional feature maps of stego video frames can be obtained through a conv-module.
2. Feature extraction is carried out in three ways, where the feature size is down sampled to the original, the half, and the quarter, respectively. Through conv-module, \(F\)-dimension, \(2F\)-dimension, and \(4F\)-dimension features can be achieved, respectively, which help to integrate cover video frames from different neighborhood spaces and realize comprehensive feature learning.
3. The features of the three methods are all reshaped to \(H\times W\) by up sampling.
4. They, together with the first \(F\)-dimension feature maps, are concatenated to form a tensor of size \(8F\times H\times W\).
5. They are sent to the last convolution layer, which implements accurate prediction and outputs the extracted secret information of message. Note that Leaky ReLU and BN are used in all but the final conv-module.  
Figure 5: The decoder component of PyraGAN.  
When training the network, the encoder and decoder are jointly trained, that is, the network forward operation, gradient descent, parameter update, and other operations are implemented synchronously. This process of embedding secret messages into cover video frames to obtain stego video frames and extracting messages from stego video frames through one network operation is the core of end-to-end steganography. When the encoder is PyraGAN_F16_3, the decoder also sets the intermediate features \(F\) to 16 and adopts three channels. When the encoder is PyraGAN_F32_2, the decoder also sets the intermediate features \(F\) to 32 and adopts two channels, that is, the channel in the blue area of Figure 5 is disabled.
# Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series
## 3 Advanced Deep Learning Techniques
### Bi-Directional Recurrent Neural Networks  
Similar with the LSTM networks, one of the most efficient and widely utilized RNNs architectures are the Bi-directional Recurrent Neural Networks (BRNNs) [24]. In contrast with the LSTM, these networks are composed by two hidden layers, connected to input and output. The principle idea of BRNNs is that each training sequence is presented forwards and backwards into two separate recurrent networks [20]. More specifically, the first hidden layer possesses recurrent connections from the past time steps, while in the second one, the recurrent connections are reversed, transferring activation backwards along the sequence. Given the input and target sequences, the BRNN can be unfolded across time in order to be efficiently trained utilizing a classical backpropagation algorithm.  
In fact, BRNN and LSTM are based on compatible techniques in which the former proposes the wiring of two hidden layers, which compose the network, while the latter proposes a new fundamental unit for composing the hidden layer.  
Along this line, Bi-directional LSTM (BiLSTM) networks [25] were proposed in the literature, which incorporate two LSTM networks in the BRNN framework. More specifically, BiLSTM incorporates a forward LSTM layer and a backward LSTM layer in order to learn information from preceding and following tokens. In this way, both past and future contexts for a given time \(t\) are accessed, hence better prediction can be achieved by taking advantage of more sentence-level information.  
In a bi-directional stacked LSTM network, the output of each feed-forward LSTM layer is the same as in the classical stacked LSTM layer and these layers are iterated from \(t=1\) to \(T\). In contrast, the output of each backward LSTM layer is iterated reversely, i.e., from \(t=T\) to \(1\). Hence, at time \(t\), the output of value \(\overset{\leftarrow}{h}_{t}\) in backward LSTM layer \(L\) can be calculated as follows  
\[\overset{\leftarrow}{f}_{t} = \sigma\left(W_{\tilde{f}}^{L}[h_{t-1}^{L},h_{t}^{L-1}]+b_{\tilde{f }}^{L}\right),\] \[\overset{\leftarrow}{i}_{t}^{L} = \sigma\left(W_{\tilde{i}}^{L}[h_{t-1}^{L},h_{t}^{L-1}]+b_{\tilde{ i}}^{L}\right),\] \[\overset{\leftarrow}{o}_{t}^{L} = \sigma\left(W_{o}^{L}[h_{t-1}^{L},h_{t}^{L-1}]+b_{o}^{L}\right),\] \[\overset{\leftarrow}{c}_{t}^{L} = \overset{\leftarrow}{f}_{t}^{L}\cdot\overset{\leftarrow}{c}_{t- 1}^{L}+\overset{\leftarrow}{i}_{t}^{L}\cdot\overset{\leftarrow}{c}_{t-1}^{L},\] \[\overset{\leftarrow}{c}_{t}^{L} = \tanh\left(W_{\tilde{c}}^{L}[h_{t-1}^{L},h_{t}^{L-1}]+b_{\tilde{c }}^{L}\right),\] \[\overset{\leftarrow}{h}_{t} = \overset{\leftarrow}{o}_{t}^{L}\cdot\tanh\left(\overset{ \leftarrow}{c}_{t}^{L}\right).\]  
Finally, the output of this BiLSTM architecture is given by  
\[y_{t}=W\left[\overset{\rightarrow}{h}_{t},\overset{\leftarrow}{h}_{t}\right] +b.\]
## 5 Discussion
### Static Coulomb Stress Changes  
Seismic triggering is a process by which earthquake-related stress changes can induce or delay seismic activity in the surrounding area or trigger other earthquakes from a long distance. The calculation of static Coulomb failure stress change (\(\Delta\)CFS) related to earthquake slip has been applied to explain the seismic observation, including the distribution of aftershocks, the earthquake sequence, and the static state of areas that are usually active after major earthquakes [44]. The \(\Delta\)CFS is an important indicator of earthquake stress triggering research [45]; according to previous studies, when the \(\Delta\)CFS change is up to 0.01 MPa, it is enough to trigger an earthquake [46]. After the Yangbi earthquake, part of the accumulated stress was released due to the rupture of the seismogenic fault, and the remainder of the stress was transmitted throughout the surrounding areas, resulting in an aggregation of stress in the study area. Increasing Coulomb stress may promote fault rupture activity, while a decrease in the Coulomb stress may promote fault rupture [47]. From the perspective of regional structure, the eastward slip of the Qiangtang block in the central Qinghai-Tibet Plateau was blocked by the South China block, causing strong deformation [48]. In the area, there are the Weixi-Qiaohou fault and the northern section of the Red River fault. As a large right-lateral strike-slip fault in the southwest of the Sichuan-Yunnan diamond block, the Red River fault has an extremely complex tectonic background and frequent seismic activities in this area [49].  
In order to further understand the impact of the Yangbi Ms 6.4 earthquake on the surrounding geological structures, in this article, we analyzed the coseismic rupture on the stress changes in the study area. Based on the results of the fault slip distribution, in this paper, the Matlab-based software Coulomb 3.3 was used to calculate the coseismic static Coulomb stress change triggered by the Yangbi event. The effective coefficient of [50] the friction and the shear modulus were set to 0.4 and 3.32 \(\times\) 10\({}^{10}\) N/m\({}^{2}\)[51]. The \(\Delta\)CFS at the depths of 3 km, 6 km, 9 km, and 12 km for the receiver fault with a strike of 132.4\({}^{\circ}\), a dip of 76.2\({}^{\circ}\), and a rake of \(-\)176.3\({}^{\circ}\) were calculated, respectively. The Coulomb stress results are shown in Figure 6. In addition, we collected the aftershocks with magnitudes greater than 3.0 after 21 May 2021, combined with the slip distribution, the results indicated that: as the depth increases, the coulomb stress variation of the NW trend Weixi-Qiaohou fault is large, the \(\Delta\)CFS in the north of the middle section of the Eryuan-Midu fault are positive, the Coulomb stress is positive indicating that the risk of subsequent earthquakes in this area will increase, and we should continue to pay more attention to this area.
###### Abstract
An example of a new and dynamically developing technology is the method based on aerobic granular sludge (AGS). In this case, the sludge formed is a compact structure, in which an important role is played by filamentous bacteria and extracellular polymeric substances (EPS) [19]. On the one hand, it has been proven that the taxonomic and chemical composition of AGS, and also the structure, allows for the improvement of wastewater treatment efficiency, and, on the other hand, it may pose difficulties in the typical AD process performed in the existing infrastructure [20]. So far, only a few studies have focused on the verification of AGS parameters that affect the final effects of methane fermentation [19; 21]. The complex morphology, intricate structure, size, and chemical composition of AGS have been proven to influence the course of AD. In the case of AGS, the hydrophobicity, density, granule compactness, EPS composition, and a high proportion of filamentous bacteria in the structure significantly reduce degradation and AD conversion rates [22]. For this reason, pre-treatment methods are recommended that would allow for the disintegration of granules, an increase in the solubility of molecules and, consequently, the efficient course of anaerobic stabilization of sewage sludge [20; 21]. A promising and prospective pre-treatment technology consists of low-temperature conditioning with the use of solidified carbon dioxide (SCO\({}_{2}\)) [21]. This is an innovative technology in which SCO\({}_{2}\) is expected to be recovered during cryogenic enrichment and the upgrading of biogas to bio-methane. Cryogenic technologies are based on the fact that different raw biogas components have different liquefaction temperatures. Since SCO\({}_{2}\) is stable in atmospheric conditions and has many different applications, its production becomes the preferred method of capturing and recovering CO\({}_{2}\) from biogas [23]. Bio-methane can also be cooled to a liquid state, and in this form, it can be distributed and consumed via existing LNG systems [24; 25]. Biogas purification is associated with the improvement of the technical functionality and energy efficiency of the process. This feature not only increases the technology's potential to improve the efficiency and cost-effectiveness of fermentation processes but can also help reduce CO\({}_{2}\) emissions into the atmosphere, which is a significant advantage in terms of environmental protection and is in line with the assumptions of the current climate policy [26]. This manuscript is the first ever to present research on the thermophilic methane fermentation of AGS pre-treated with SCO\({}_{2}\). The present study focused on an evaluation of the AGS pre-treatment with SCO\({}_{2}\) on the thermophilic AD technological effects. It analyzed the effect of this disintegration technology on the changes in the concentrations of dissolved organic and biogenic compounds and the yield and kinetics of biogas and methane production in periodical reactors, as well as enabled the development of an empirical organizational model of biogas production.
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 2 Materials and Methods  
In this study, the soil hyperspectral technique was utilized to evaluate CLQ. The flowchart of the method is shown in Figure 1. First, we selected the optimal spectral variables of dry soil samples using the GBDT-VIF method. Secondly, comparing PLSR to BPNN algorithms, the optimal algorithm was determined using the 10-fold cross-validation method. Thirdly, HJ-1A data were transformed into spectral values of dry soil using the linkage model. Then, the relationship model between CLQ and the spectral variables of dry soil samples was constructed. Finally, regional-scale CLQ using HJ-1A data was estimated based on the relationship model.
## 3 Methodology
### Statistical Methods  
In this subsection, we explain in detail the characteristics of each forecasting method we apply, as well as the reasons for considering each one of them. We describe first the statistical methods which  
Figure 6: This figure shows the STL decomposition of the time series of one cell in the grid. Each time-series of a cell can be expressed as the sum of its components: trend, seasonal and residual.  
Figure 7: An example of the data after converting the uni-variate time series of a single cell \(S\) into a supervised dataset made up of pairs \((x,y)\), by using lags of the observed variable \(X\), and a rolling window through the whole time series.  
have historically dominated the time series forecasting research landscape. The main reason we have chosen to experiment with statistical methods is their ability to capture the linear relationships that exist in the temporal structure of our time series data as shown in Figure 4. Particularly good in this task are auto-regression models and among the many ARIMA forecasting method.
# R-PreNet: Deraining Network Based on Image Background Prior
## 4 Experiments
### Ablation Study
#### 4.2.2 Effectiveness on IFM Module  
To investigate the effectiveness of the feature fusion module, two different network architectures were compared: (a) with the RCP module, but the RCP high-dimensional features were directly connected with the rainy image features into the network, and (b) with the RCP module and the IFM module, which used interactive fusion to combine the RCP high-dimensional features and the rainy image features together into the network. Networks with and without the FIM module, as well as baseline algorithms JORDER [13], RESCAN [28], and PReNet [7], were trained and tested on the datasets Rain100L, Rain100H, and Rain14000, respectively. Table 2 shows the quantitative results of the above algorithms in PSNR and SSIM. Both quantitative and visual results showed that the recurrent network with IFM module outperforms the network without IFM module and the baseline network.  
The data in the tables are, respectively, PSNR and SSIM, where PSNR is expressed as the peak signal-to-noise ratio between the image after rain removal and the original rain image. The larger the PSNR value between the two images, the more similar it is. The general benchmark of PSNR is 30 dB, and the degradation of images below 30 dB is more obvious. The SSIM is represented here as the structural similarity between the image after the rain and the ground-truth, and the value is a floating-point number between zero and one. According to the experimental data, R-PReNet, by protecting background  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**MethodsPSNR/SSIM** & **PReNet** & **R-PReNet** & **JORDER [12]** & **RESCAN [28]** & **DDN [14]** & **GMM [23]** \\ \hline Rain100H & 29.46/0.899 & 30.76/0.916 & 26.54/0.835 & 28.88/0.866 & 26.05/0.8056 & 14.50/0.4164 \\ \hline Rain100L & 37.48/0.979 & 38.87/0.984 & 36.61/0.974 & - & 34.68/0.9671 & 28.66/0.8652 \\ \hline Rain14000 & 32.60/0.946 & 33.03/0.963 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of synthetic datasets on network structure with and without RCP module.  
information, has improved image background and details in visual effects and PSNR and SSIM quality evaluation data compared with PReNet. However, since this algorithm protects the background of the image and optimizes the rain removal effect from the details, the improvement of PSNR/SSIM will not be very large.
## 2 Data  
### Advanced Scatterometer  
MetOp is a series of three polar orbiting meteorological satellites which form the space segment component of the overall European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System. The ASCAT instrument is on-board the MetOp-A and -B that were launched in 2006 and 2012, respectively. ASCAT is a real-aperture C-band (5.255 GHz) vertically polarized radar with three fan beam antennas that measure the backscattered signal from the ocean surface, pointing to the left-hand side of the sub-satellite track and three fan-beam antennas pointing to the right-hand side [20]. The system has two 550-km swaths, separated from the satellite ground track by about 360 km. The physical basis for the wind retrievals is Bragg scattering (cm-wavelength). Changes in ocean surface roughness induced by the wind stress alter the backscattered power depending on the incidence angle (30\({}^{\circ}\)-60\({}^{\circ}\) for scatterometers). Level 2 (L2) wind product was acquired from the EUMETSAT User Services for ASCAT-A and -B on 28 May 2016 at 02:51 UTC (49844 orbit) and 03:42 UTC (19158 orbit), respectively, at 50-km resolution and 25-km resolution ([34; 35]; coastal product).  
The coastal product has a 12.5-km cell spacing, which is adequate to resolve mesoscale phenomena, and it also has a smaller error with respect to buoys, in comparison with the 50-km resolution product [36]. It has an accuracy that is characterized by a root mean square error smaller than 2 m s\({}^{-1}\) and a bias of less than 0.5 m s\({}^{-1}\) in wind speed and also includes quality flags that account for cells that are subject to rain contamination. Moreover, this product reports the wind inversion residual values, known as the maximum likelihood estimation (MLE; [37]), to screen the quality of the wind vector, although this metric is not always effective in identifying rain-contaminated cells [27]. The MLE flag is set when the measured triplet has an anomalously large distance to the GMF cone, while the 2D-Var quality control flag (VarQC) is set during the 2-D variational (2 DVAR) ambiguity removal when a wind vector is spatially inconsistent with the background wind [38]. Along with the wind retrievals, the L2 product also reports numerical weather prediction model (background) winds used in the ambiguity removal process, i.e., stress-equivalent winds [39] by the European Centre for Medium-Range Weather Forecasts (ECMWF) model.  
ASCAT level 1b (L1B) full resolution product data were also acquired from the EUMET-SAT User Services. This product type contains 192 geolocated full resolution backscatter values along with incidence angles and azimuth angles and this number is common for all beams. Also reported are flags that may affect the quality of the measurements, such as the noise, gain, height of the satellite, etc. Last but not least, a flag for the presence of land is being reported that takes into account the latitude and longitude of the backscatter measurement. L1B data were obtained for same date and times as the previous products.