esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Albedo Recovery  
#### 3.2.1 Network Structure  
We assume that the face is Lambertian and illuminated from the distant. Under the Lambertian assumption, we represent the lighting and reflectance model as second-order Spherical Harmonics (SH) [49,50], which is a natural extension of the Fourier representation to spherical function. In SH, the irradiance at a surface point with normal \((n_{x},n_{y},n_{z})\) is given by  
\[B(n_{x},n_{y},n_{z}|\Theta^{sh})=\sum_{k=1}^{b^{2}}\Theta_{k}^{sh}H_{k}(n_{x}, n_{y},n_{z}), \tag{1}\]  
where \(H_{k}\) are the \(b^{2}=3^{2}=9\) SH basis functions, and \(\Theta_{k}^{sh}\) is the corresponding \(k\)th illumination coefficient. Since we consider colored illumination, there are totally \(3\times 9=27\) illumination coefficients with nine coefficients for each of the R, G, and B channels. The texture of a surface point can be calculated by multiplying the irradiance and albedo of the point. To sum up, the texture under certain illumination is a function of normal, albedo, and illumination, and can be expressed as  
\[\begin{array}{rl}T\left(p\right)&=f_{sh}(A\left(p\right),N\left(p\right),SHL ),\\ UV_{T}\left(p\right)&=f_{sh}(UV_{A}\left(p\right),UV_{N}\left(p\right),SHL), \end{array} \tag{2}\]  
where \(p\) represents a pixel (2D) or point (3D), and \(SHL\) denotes the SH illumination coefficients.  
Inspired by [PERSON] et al. [45], we propose an end-to-end network that can recover the missing part in the incomplete \(UV_{T}\) and generate its complete version \(UV_{A}\). As can be seen in Figure 2, we concatenate the incomplete \(UV_{T}\) with its horizontally flipped image as input of the network. The proposed network follows an encoder-decoder structure, in which the encoder module extracts a common feature from input image, and the albedo decoder and the normal decoder decode the complete albedo \(UV_{A}\) and the complete normal \(UV_{N}\) from the common feature, respectively, and the light decoder computes the spherical harmonics illumination coefficients \(SHL\) from the concatenation  
Figure 1: Pipeline of proposed method.  
of common feature, albedo feature, and normal feature. At last, following Equation (2), a rendering layer is used to recover the texture based on the above decoded attributes.
## 5 Computational Cost Reduction Methods
### Clustering Techniques
#### 5.2.2 Fuzzy C-Means Clustering  
Unlike the K-means clustering technique, the membership of each RP is not crisp in the fuzzy C-means clustering approach [45]. Fuzzy C-means clustering can be divided using AOI into \(K\) subsets as shown in Figure 7 with clustering centers \(C=(C_{1}\),..., \(C_{k})\)\(\epsilon\)\(R^{M}\) that verify the minimum cost function, which is a sum of the squared error between the RP's fingerprint and the clustering center. Its formula is given in Equation (10) as follows:  
\[J_{m}=\sum_{i=1}^{M}\sum_{j=1}^{K}u_{ij}^{m}d_{ij}^{2}\quad,\ 1\leq m<\infty \tag{10}\]  
where, \(m\) is greater than 1, and \(u_{ij}\) is the degree of membership of RP\({}_{\text{i}}\) in the cluster \(j\), and \(\sum_{i=1}^{K}u_{ij}=1\). The \(d_{ij}=\|x_{i}-c_{j}\|^{2}\) represents the Euclidean distance between the PR\({}_{\text{i}}\) fingerprint and the vector of cluster center C\({}_{\text{j}}\). The value of \(u_{ij}\) is randomly chosen between (0-1). Fuzzy partition is achieved through an iterative optimization shown in Equation (10). In order to find \(u_{ij}\) and \(C_{j}\), the Equation (11) and Equation (12) should be applied:  
\[u_{ij}=\frac{1}{\sum_{k=1}^{K}\left(\frac{\|x_{i}-c_{j}\|}{\|x_{i}-x_{k}\|} \right)^{\frac{2}{n-1}}} \tag{11}\]  
\[c_{j}=\frac{\sum_{i=1}^{M}u_{ij}^{m}x_{i}}{\sum_{i=1}^{M}u_{ij}^{m}} \tag{12}\]  
The iteration will be stopped when \(max_{ij}=\left(\|u_{ij}^{(k+1)}-u_{ij}^{(k)}\|\right)<\varepsilon\), where \(\varepsilon\) is a criterion value which falls in between (0-1), and k is the iteration steps.
## 3 Results and Discussion  
### Chemical Structure of Cured Matrix  
The SSNMR spectra of cured samples were shown in Figure 2. The peak positions were consistent for the two samples, indicating the same curing mechanisms and no changes in chemical structure were induced by the varied concentration of initiators. The signals at 126.0 ppm and 136.5 ppm coming from -C=C- of SA9000 [52] were not detected, showing that all the -C=C- of SA9000 had participated in the reaction.  
Figure 2: The SSNMR spectra of cured samples.  
The FTIR spectra of cured P/T-0.25 and P/T-1.50 together with that of SA9000 and TAIC were shown in Figure 3. The FTIR spectra of P/T-0.25 and P/T-1.50 were identical in terms of the number and position of the peaks, which agreed with SSNMR results that there are no differences in chemical structure caused by initiator concentrations.  
The existence of the absorption peaks at 1645 and 3086 cm\({}^{-1}\) suggested that some-C = C- groups from TAIC did not participate in the crosslinking reaction [52]. To quantify the amount of unreacted -C = C- groups from TAIC, the peak at 3086 cm\({}^{-1}\) was employed for the calculation using Lambert-Beer law [53] with the following steps:  
The powder from P/T-0.00-L was weighed out in five different amounts and thoroughly mixed with the appropriate amount of KBr powder to keep the total mass of the mixture at -200 \(\pm\) 0.5 mg. The mixture was pressed into a disc for the FTIR scanning. The obtained spectra were shown in Figure 4 and the calculated mole numbers of -C = C- (n\({}_{\text{-C-C-}}\)) in TAIC in the samples were listed in Table 3. The peak areas were plotted against the n\({}_{\text{-C-C-}}\) in Figure 5 and a linear regression equation y = 74.79x was obtained with the R\({}^{2}\) = 0.975. By calculating the peak areas obtained from the FTIR spectra of cured P/T-0.25 and P/T-1.50 (Figure 3b), the residual ratio of the -C=C- of TAIC in these two samples was 23.33% and 17.5%, respectively, revealing that more -C=C- in TAIC participated in the reaction as the mass fraction of the initiator increased.  
Figure 4: FTIR spectra of P/T-0.00-L samples of different amounts.  
Figure 3: (a) The FTIR spectra of SA9000, TAIC, cured P/T-0.25, and P/T-1.50; (b) locally enlarged FTIR spectra of cured P/T-0.25 and P/T-1.50.
# Navigation Scenario Permutation Model for Training of
## 3 Results
### Distribution Analysis of Selected Navigation Elements  
Here, we selected the \"course altering angle at waypoint\" element and the \"distance between waypoints\" element. The values of each element were extracted from a determined range. The \"course altering angle at waypoint\" element was extracted from negative 90 degrees to positive 90 degrees, and the \"distance between waypoints\" element was extracted from 1 nautical mile to 20 nautical miles. The extracted frequency distributions of the selected elements were then fitted to the probability density distributions. Figures 6 and 7 show each distribution of each selected navigation element.  
Figures 6 and 7 show the different shapes of distribution for each navigation element. As can be seen, the distribution of the \"course altering at the waypoint\" element was fitted in the normal distribution in a symmetrical shape, because the course altering has two directions of portside and starboard side. The distribution of the \"distance between waypoint\" element was fitted in the inverse Gaussian distribution of right-skewed shape, because the distance has positive values only.  
Figure 6: Distribution of “course altering angle at waypoints” element.
## 4 Experimental Results
### Experimental Design  
In the experimental data, we propose that the splitting ratio was 7:1:2 for the training, validation, and test sets. The training set was used to train the model. Hyperparameter tuning was conducted in the validation set to avoid overfitting, and the superior model was selected. The test set was used for comparing the performance of the proposed model and other models. According to the cyberbullying data statistics for the training set, the maximum number of sentences for content and comments was 64 and 146, respectively. The maximum number of words for title, content, and comment was 10. The models' parameter combinations were used to identify the best model according to the classification performance for the validation set. Table 5 presents the detailed parameter settings of the model.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline HSAN, HAN, GRU, GRU(Soft), GRU(Squashing), TextCNN, TextRCNN, DropCNN & Learning rate Epoch Hidden and embedding size Dropout rate & \(1\times 10^{-3},1\times 10^{-4},1\times 10^{-5}\) \\ SVM & Learning Epoch Epoch Hidden and embedding size Dropout rate & \(500\) \\ SVM & Kernel Gamma Cost & RBF, linear, poly, sigmoid \\ \hline Random Forest & Number of estimator & \(10\), \(20\), \(30\), \(40\), \(50\), \(60\), \(70\), \(80\), \(90\), \(100\) \\ Text CNN & filter sizes & \((2\), \(3\), \(4)\) \\  & number of channels & \(300\) \\ \hline DPCNN & filter size & \(3\), \(4\), \(5\) \\  & number of channels & \(300\) \\ \hline CapsuleNet & routing & \(2\), \(5\), \(10\) \\ \hline BERT & Learning rate & \(1\times 10^{-4},1\times 10^{-5},1\times 10^{-6}\) \\  & \(\begin{array}{l}\text{Learning rate}\\ \text{Pretrained model}\end{array}\) & \(\begin{array}{l}\text{bert-base-chinese, distilbert-base-}\\ \text{multilingual-cased}\text{and}\\ \text{allenai/longformer-base-4096}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings of the compared models.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 1 Introduction
In recent years, a novel testing technique, named nanoindentation, to identify the material parameters for thin polymer layers was developed, a comprehensive review and the realization of the method for hyperelastic and viscoelastic models are presented by [PERSON] et al. [24; 25; 26]  
In this paper, the Ogden model is taken to characterize the hyperelastic properties of rubber-like materials. A professional method based on the pattern search algorithm (PSA) [27; 28] and the Levenberg-Marquardt algorithm (LMA) [29] was developed to fit the unixial tension, biaxial tension, planar tension, and the simple shear experimental data of hyperelastic materials. Then, combining the generalized Maxwell model and the Ogden model as the constitutive model, the method that can identify the parameters in the hyper-viscoelastic models by using experimental data with different loading histories was also developed [24; 25] and three groups of virtual relaxation tests and four groups of simple shear tests were employed to verify the accuracy and reliability of the proposed method.
## 3 Methodology
### Dataset Specification  
In this research, the TRAC-2 dataset [22] is selected based on specific criteria to ensure its relevance and suitability for evaluating our proposed model performance in multilingual contexts. This dataset includes labeled comments in English, Hindi, and Bangla, which provides a valuable resource for analyzing cyberbullying detection across languages with limited data availability. The inclusion of aggressive, misogynistic (i.e., gender-discriminatory), and non-aggressive labels further aligns with our model focus on identifying harmful online content in diverse linguistic settings.  
The dataset used to describe our complex approach comes from TRAC-2 [22]. It comprises 25,000 comments from three social media platforms, namely Facebook, YouTube, and Twitter, in three languages, namely English, Bengali, and Hindi [45]. More specially, according to the guidelines provided by [22], since comments are, of course, of different sizes, in order to have equal-sized comments, string padding has been used, so that the final comments' lengths were 30, 38, and 33 words for English, Hindi, and Bangla, respectively. On the other hand, the longest comments' lengths were 58, 72, and 62 words for English, Hindi, and Bangla, respectively. The shared task includes two groups: Sub-task A (aggressive comments) and Sub-task B (misogynistic comments). Sub-task A consists of three classes: (_i_) no comments are annotated as covertly aggressive (CAG), (_ii_) the direct aggressive comments are annotated as overtly aggressive (DAG), and (_iii_) non-aggressive comments are annotated as non-aggressive (NAG). Similarly, Sub-task B contains two classes: GEN and NGENA. A comment that indicates a man, woman, or transgender individual is annotated as GEN, and a comment that does not indicate gender is annotated as NGEN. In particular, for the sake of simplicity, we used all the comments for our analysis, considering both the emotionally problematic parts and the safe parts of comments. Clearly, a finer analysis could be devised, such as extracting only the emotionally problematic parts from comments. This research aspect, while very interesting, is outside the scope of our paper, and is left for future work. All three datasets contain both training and test sets. In our project, we conducted experiments on Sub-task A, as the features of Sub-task A align perfectly with our goal of prediction, which is cyberbullying detection. The statistics of the dataset provided by the organizations are displayed in Table 1.  
To provide more details, some examples of the text data stored in the target dataset are shown in Figure 1.
## 3 Results and Discussion
### Characteristics and Economic Analysis of the Biosystems  
In designing a feasible biosystem, the main goal is to meet a performance level and minimize capital and operating costs. Table 4 offers a comparison of economic and operational characteristics of the biological system and the enzyme system. The basis of the evaluation was to scale up the infrastructure of the reactor system by 10 for field applications. The treatment cost was estimated per one-year cost, and related infrastructures, including the reactor itself, piping, filter device, and aeration device, were calculated based on 10-year life amortizations. The electricity cost was calculated at $25 per month. The results indicate the total costs of the immobilized _Ochrobactrum_ sp. CUST210-1 biosystem ($6750 y\({}^{-1}\)) were higher than those of the immobilized ChrR biosystem ($6050 y\({}^{-1}\)). This is due in part to the CUST210-1 biosystem's requirement of a filter device to remove suspended solids from tannery wastewater. The biological treatment system can be applied to treat wastewater with high concentrations of Cr(VI) (\(\sim\)425 mg L\({}^{-1}\)), and the effluent can comply with industrial wastewater discharge standards (\(<\)0.5 mg L\({}^{-1}\)). The biological treatment system required a long LRT, and thus, the allowable inflow rate was low (Table 5). In addition, a filter device and DO monitoring were required to improve the removal performance during the operating period. By contrast, the immobilized enzyme system was suitable for treating diluted Cr(VI)-containing wastewater (\(\sim\)150 mg L\({}^{-1}\)), and the effluent could comply with strict water quality standards and meet the standards for fisheries and drinking water (\(<\)0.05 mg L\({}^{-1}\)). The ChrR biosystem also operated at a short LRT, and thus, the allowable inflow rate was high. However, DO monitoring and regular replacement of ChrR beads were required to improve performance during the operating period. To date, this is the first report to compare the economic and operational characteristics of biosystems for Cr(VI) removal.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 6 Conclusions  
In this paper, the mechanisms of microbial degradation of heavy metals in soil are explored, and the comparison of the different abilities of degradation and the reasons for the investigation, as well as the factors that may affect adsorption capacity, are discussed. We conclude the following:  
1. The mechanisms of microbial degradation of heavy metals are mainly biosorption, biomineralization, and co-metabolism, and biosorption is the main degradation mechanism. Both biosorption and biomineralization can be divided into a variety of physiological processes.
2. Microbes have different abilities to degrade heavy metal, and the degradation ability mainly depends on degradative plasmids and spores. Usually, _Escherichia coli_ K-12 adsorb the majority of heavy metal ions, and the adsorption capacity of _Pseudomonas_ and _Bacillus_ are strong.
3. The optimum pH ranges of microorganisms are various. Most microorganisms have suitable pH values in 5.5-6.5, except for _Bacillus jeotgali_. Ambient temperature affects the ability of microorganisms to adsorb heavy metals. Although the optimum temperature is related with heavy metal and microbial species, the optimum temperature for most microorganisms is generally between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C.
4. The difference in concentrations of six heavy metal ions, and the presence or absence of competitive ions will affect the adsorption capacity of heavy metals for organisms.
5. Composite repair systems, such as microbial plant joint repair systems and chemical microbial joint repair systems, can often improve repair efficiency.  
[PERSON] wrote the manuscript in consultation with [PERSON]. All authors discussed the results and commented on the manuscript.  
This study was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2017 ZY03).  
The authors declare no conflict of interest.
## 4 Results
### Validation of Results  
The non-justified hot spots were evaluated in the R-JPEG formatted thermal images with the DJI Thermal Analysis Tool 3 software (version 3.2.0), which enables image analysis and temperature measurements of the photographed surface. The temperature measurement of the two areas of the current landfill (B and C) shows that the hot spots are at 33.3 or 27.3 \({}^{\circ}\)C compared to the 23.7 or 19.5 \({}^{\circ}\)C temperatures of the surroundings, thus verifying the existence of a hot spot which has to be further evaluated in order to take preventive measures.  
In addition, the areas of both hot spots are close to some of the 79 collection wells located along the entire length of the landfill, which are shown in green in Figure 14.  
Figure 14: The overlapping of the identified hotspots and the LFG recovery well locations enables evaluation of the performance of the emissions control system.  
Figure 13: Detail of the thermal and RGB photointerpretation of non-justified hot spot F in the oldest area of the landfill.  
The collection wells capture the LFG for energy recovery, avoiding emissions and other associated safety issues, while eliminating the LFG generated and temperature increases. Despite being close to some of the wells, the temperature in these two areas is high, which may indicate possible problems in the recovery of nearby wells or the need for new wells.  
On the other hand, in the two hot spots on the right of the image (E and F), which are around the oldest landfill, at a lower elevation, the absence of LFG collection wells can be observed. This condition may imply the emission of biogas to the atmosphere and a consequent increase in temperature as the gas migrates through the deposited MSW layers. In the identified area related to a possible material stockpile, the temperature is 26.4 \({}^{\circ}\)C, which is higher than the 24.9 \({}^{\circ}\)C of the nearby road and the 18.2 \({}^{\circ}\)C of the surrounding vegetation. This anomaly may therefore be due to the decomposition of organic matter, ashes from energy recovery deposited in that area of the landfill, etc. Finally, the hot spot of the homogeneous zone without vegetation (G) shows temperatures of 23.8 \({}^{\circ}\)C compared to 19.7 \({}^{\circ}\)C in the surrounding cold zones. In this case, in the absence of any other reason for or interpretation of the RGB image, a possible LFG emission can be intuited due to the absence of collection wells in this location.  
In short, it can be assured that the 3D thermal model enables the monitoring of the landfill from a thermal point of view, providing managers with a tool that enables the detection of possible hot spots in MSW for evaluation and the application of the most appropriate corrective measures according to the incidence shown.
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## Materials and Methods
### Sentinel-1 Pre-Processing  
Images acquired by spacecraft sensors usually can be distorted in brightness and geometry as a result of a number of environmental circumstances and system factors [35]. Before using the products in any analyses, the radiometric and geometric distortions should be removed, or minimized. In this study, the preprocessing of the Sentinel-1 SAR data with the Sentinel-1 Toolbox intergraded in SNAP contains few steps: (i). Data preparation; (ii). Radiometric Calibration; (iii). Multilooking; (iv). Spackle Reduction; (v). Terrain Correction; (vi). DN to dB Conversion.  
The data preparation consists of selecting the study area, selecting the data type needed for the study, selecting the date, and downloading the SAR product. In this study, twelve Ground Range Detected (GRD) Interferometric Wide (IW) swath Sentinel-1 images taken over the Balikdami wetland in Turkey, were downloaded from the Copernicus Open Access Hub.  
After the download, radiometric and terrain calibration, as well as speckle reduction on the images has been performed. Radiometric calibration corrects the SAR image so that the pixel values represent the radar backscatter of the reflected surface. Compared to the optical image data, the biggest difference in the appearance of radar imagery is its poor radiometric quality [35], thus it is difficult to make a visual interpretation of a SAR image. Speckle can be caused by random constructive and destructive interference resulting in salt and pepper noise over the SAR image [36]. As speckle is one of the biggest noise in SAR data, it should be reduced before performing any analyses. The products have been filtered with Lee Sigma filer \(5\times 5\) window size. Terrain correction geocodes the image by correcting SAR geometric distortions with the help of a digital elevation model (DEM) and it produces a map projected product. With Geocoding the image is being converted from Slant Range or Ground Range Geometry into a Map Coordinate System. Terrain correction corrects SAR geometry effects such as foreshortening, layover, and shadows. For the terrain correction, a Range Doppler Terrain Correction with a digital elevation model of 30 m has been used. All the pre-processing steps have been performed in the SNAP software by ESA using the Sentinel-1 toolbox. The digital number values have been converted into backscattering values in decibel (dB) scale following Equation (9).  
\[\beta^{\circ}_{db}=10*\log_{10}(\beta^{\circ}) \tag{9}\]  
where \(\beta^{\circ}\) is the digital number value of the image, and \(\beta^{\circ}_{db}\) is the backscattered value in dB.