esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Calculation of the Costs  
The method suggested for calculating the costs of the SATH(r) platform (\(LCS_{SATH}\)) is based on the costs of the life-cycle of the project considering all of its phases [28]: conception and definition (\(C_{1}\)), design and development (\(C_{2}\)), manufacturing (\(C_{3}\)), installation (\(C_{4}\)), exploitation (\(C_{5}\)) and dismantling (\(C_{6}\)) of the concrete floating wind farm, such as Equation (1) is shown [28].  
\[LCS_{SATH}=C_{1}+C_{2}+C_{3}+C_{4}+C_{5}+C_{6} \tag{1}\]  
The investment of the farm is based on \(C_{1}\), \(C_{2}\), \(C_{3}\) and \(C_{4}\) costs in the first years of the construction of the platforms and the farms. \(C_{5}\) is developing during all the years of the exploitation of the farm, when it has initiated its operational life. \(C_{6}\) is considering only in the last year of the life of the farm (20 years in this study).  
First, conception and definition cost depend on the cost of legal aspects (\(C_{11}\)) and the cost of the previous studies (wind resource, the sea states and the seabed) (\(C_{12}\)), as Equation (2) is shown [28].  
\[C_{1}=C_{11}+C_{12} \tag{2}\]  
Second, the design and development cost is based on the cost of the special purpose vehicle (SPV) cost (\(C_{21}\)), as Equation (3) is shown [28].  
\[C_{2}=C_{21} \tag{3}\]  
Third, the cost of manufacturing all the components of the farm depend on the wind turbine manufacturing cost (\(C_{31}\)), the SATH(r) platform manufacturing cost (\(C_{32}\)), the mooring manufacturing cost (\(C_{33}\)), the anchoring manufacturing cost (\(C_{34}\)) and the electric system manufacturing cost (\(C_{35}\)), as Equation (4) is shown [28].  
\[C_{3}=C_{31}+C_{32}+C_{33}+C_{34}+C_{35} \tag{4}\]  
On the other hand, the cost of installing the farm is composed of the costs of installing [29]: the wind turbine (\(C_{41}\)), the SATH(r) platform (\(C_{42}\)), the mooring and anchoring system (\(C_{43}\)) and the electric system (\(C_{44}\)), as Equation (5) is shown [28].  
\[C_{4}=C_{41}+C_{42}+C_{43}+C_{44} \tag{5}\]  
The exploitation cost is function of the assurance cost (\(C_{51}\)), the administration and operations cost (\(C_{52}\)), the maintenance cost (\(C_{53}\)), the onshore logistics costs (\(C_{54}\)) and the offshore logistics cost (\(C_{55}\)), as Equation (6) is shown [28].  
\[C_{5}=C_{51}+C_{52}+C_{53}+C_{54}+C_{55} \tag{6}\]Finally, the dismantling cost is dependent on the dismantling of each of the components of the farm; mainly, the wind turbine dismantling (\(C_{61}\)), the SATH(r) platform dismantling (\(C_{62}\)), the mooring and anchoring dismantling (\(C_{63}\)) and the electric system dismantling (\(C_{64}\)), as Equation (7) is shown [28].  
\[C_{6}=C_{61}+C_{62}+C_{63}+C_{64} \tag{7}\]
## 1 Introduction
Usually, the stalked type trichome is dominant along the leaf veins, and the sessile type is dominant in the non-vein parts [23]. The stalk type has more cannabinoids and terpen than the sessile type [24]. Cannabinoid biosynthesis in trichomes follows the same initial pathway (Figure 1) [14]. Hexanoic acid is produced through the oxidative cleavage of fatty acids in the cytoplasm, and geranyl diphosphate (GPP), which converts olivetolic acid to cannabigerolic acid (CBGA), is derived from the MEP pathway in chloroplasts [12]. The produced CBGA produces acidic precursor cannabinoids by the respective synthases (cannabidiolic acid synthase, tetrahydronnabinolic acid synthase, and cannabichromenic acid synthase) [25]. The biosynthesis of these cannabinoids involves movement between the cytosol, plasmid, and apoplast, but the migration method of the precursors has not yet been identified [26]. The 'Cherry Wine' variety used in this experiment is a hybrid of The Wife \(\times\) Charlotte's Cherries and is a CBD-only variety with a total THC content of less than 0.3% [27]. Initially, wild cannabis provided beneficial seeds, fibers, and drugs for humans. Cannabis breeders combine several genes to develop economically valuable fiber, seed, and medicinal hemp [28]. In vitro studies have shown that CBDA synthase produces CBDA and THCA at a ratio of approximately 20:1, implying that the higher the CBD content, the greater the likelihood that the THC content will exceed legally acceptable standards [30]. In general, if the THC content of an inflorescence is less than 0.2-1.0%, it is classified as hemp, and if it exceeds that, it is classified as marijuana [31; 32]. Cannabis with these characteristics is listed as a schedule-1 illicit drug by the UN [33]. It can be grown under highly restricted conditions in Korea and Singapore, the only Asia-Pacific region licensed for growing cannabis for medical use in Australia, New Zealand, and Thailand [34]. Similarly, in the United States, many states still classify cannabis as an illegal drug [35]. Owing to the characteristics of cannabis, smart farms are considered suitable for the production of medicinal hemp. This is because security is periodically upgraded [36], space can be used efficiently, environmental factors can be adjusted [37], and yield predictions are more accurate [38]. [PERSON] et al. (2014) described the smart farm as an automated system that extended to the farm level, with automatic lighting, climate control, fire and smoke detection, auto-lock and release doors, humidity and moisture control, pre-set wash times, feeder control, and remote mobile connectivity. Smart farms can be controlled remotely using the Internet of Things (IoT) and smartphone technology [39; 40]. This study focused on analyzing the characteristics and selection of traits involved in cannabidiol production of medicinal hemp lines that were successfully grown under artificial light and nutrient solutions.
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 3 Methodology
### Base Model Stage  
In order to reduce the learning time of DL techniques, specially in CNN-based classifications tasks, TL is employed to rapidly train a CNN model without initializing its weights  
Figure 1: A block diagram of the structure of the proposed classification model.  
from scratch [34]. Additionally, TL is especially useful for tasks where adequate training samples are not available to train a model from scratch, such as medical image detection for rare or emerging diseases. Practically, TL imports the model parameters from a pre-trained model from other tasks. The transferred parameters have good initial values and only require a few minor adjustments to be better curated for the task at hand [35]. The benefit is that it takes less training time and is possible to achieve higher accuracy with small data samples. In our proposed model, we selected the Xception pre-trained model to be the base model, which was trained on the ImageNet dataset.  
The Xception model is used for feature extraction with the CNN architecture. In 2017, Xception was developed by [PERSON], author of _Keras_. It is another enhancement of the Inception V3 proposed by Google [36]. It is a newer form than Inception and is called Xception because it was the \"extreme version of Inception\" [37]. It is a CNN architecture that consists of a linear stack of depth-wise separable convolution layers with residual connections. The feature extraction base is formed by 36 convolutional layers that are formulated into 14 modules with outlined linear residual connections [38]. Moreover, the Xception model works on one-by-one convolution first, followed by channel-wise spatial convolution. The separable convolution layer has fewer parameters and a lower computational cost than a traditional convolutional layer. There is no intermediate activation in Xception. As a result, we selected the Xception model in our design. Figure 2 demonstrates the structure of the Xception model, as described in [39].
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
###### Abstract  
In this study, we prepared a novel calcium alginate-disodium ethylenediamineetraacetate dihydrate hybrid aerogel (Alg-EDTA) by chemical grafting and vacuum-freeze-drying to remove heavy metal ions from wastewater. Experimental results show that the as-prepared Alg-EDTA adsorbent has a high affinity for heavy metal ions, such as Cd\({}^{2+}\), Pb\({}^{2+}\), Cu\({}^{2+}\), Cr\({}^{3+}\), and Co\({}^{2+}\), and can adsorb \(>\)85% of metal ions from the corresponding solution. Alg-EDTA also exhibits high selectivity toward Cd\({}^{2+}\), and the maximum adsorption capacity for Cd\({}^{2+}\) reached 177.3 mg/g, which exceeds the adsorption capacity of most reported Cd\({}^{2+}\)-adsorbents. Adsorbent regeneration can be achieved by a simple acid-washing process, and adsorption performance of Alg-EDTA remains stable after repeated use. All these findings indicate that Alg-EDTA has a promising prospect in the treatment of heavy metal ions wastewater.  
heavy metal ions; sorbent; aerogel; sodium alginate +
Footnote †: journal: Applied Chemistry
## 2 Materials and Methods
### Extraction and Purification of Polysaccharides and Preparation of Oligomers Extracts  
The alginates and fucoidans were extracted from (SM) and (CM) according to the [PERSON] et al., method with slight modifications (25). Briefly, 200 g of algal powder were soaked in 2% of formaldehyde for 24 h to 48 h at room temperature under stirring to discard all phenolic compounds and pigment. The dried residues were treated with HCl (pH: 2-3.5) for 3 h at 60 \({}^{\circ}\)C to convert alginate salts into alginic acid. The precipitates were suspended in 2-3% of sodium carbonate solution for 3 h at 80 \({}^{\circ}\)C to transform the alginic acid into sodium alginates, and the filtrates were used to extract fucoidans (FUCCM) from (CM). Alginates (ALCM and ALSM) and FUCCM fractions were purified by rinsing with ethanol several times at 96\({}^{\circ}\) followed by ultrafiltration using the Vivaflow 50R-200 system (Sartorius Lab Instruments GmbH & Co., KG, Goettingen, Germany). The final samples were then lyophilized for 72 h (Figure 1).  
The LMW alginates OACM and OASM were obtained from ALCM and ALSM, respectively. The radical hydrolysis was performed using hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) (26). Briefly, alginates were dissolved in distilled water at a concentration of 10 g/L and shaken for 1-2 h at 70 \({}^{\circ}\)C. H\({}_{2}\)O\({}_{2}\) was added to the solution in a 1:1 (\(w/w\)) ratio, and the mixture was incubated for 6 h (70 \({}^{\circ}\)C, 300 rpm). The reaction was stopped by adding 96\({}^{\circ}\) (1/10, \(v/v\)) ethanol and stirring for 1 h at room temperature to remove the residual H\({}_{2}\)O\({}_{2}\). The samples were lyophilized, dissolved in distilled water, and washed several times with ethanol (96\({}^{\circ}\)). At the end of the experiment, the H\({}_{2}\)O\({}_{2}\) was quantified to ensure its elimination. The oxidative fractions were finally re-dissolved in distilled water and freeze-dried for 72 h.  
The sulfated arabinagalactan-rich-protein, crude, and purified fractions (AGB and AGP) were extracted from the green seaweed (CD). As described in the previous study (27), the milled seaweed was pretreated with ethanol and the crude fraction (AGB) was extracted with 20 volumes of distilled water and precipitated with 96\({}^{\circ}\) ethanol. The AGP was obtained under the same conditions described above, followed by a step of purification by ultrafiltration using a membrane of 100 kDa MWCO. The resulting AGB and AGP fractions were dissolved in distilled water and lyophilized.  
Figure 1: Extraction and purification of sodium alginates (ALCM and ALSM) and fucoidans (FUCCM) from the brown algae _S. muticum_ (SM) and _C. myriethylloides_ (CM).
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 4 Experiment Results and Analysis
### Ablation Study
#### 4.5.2 Effectiveness of Dilation Rate  
The performance of the proposed scheme was assessed through four experiments using various dilation rate configurations to assess the effectiveness of the convolution kernels of various scales (see Table 4). The four experiments had the following setups: the dilation rates used were 1, 2, and 3 in the first, second, and third columns, respectively. In the fourth columns, the dilation rates used were 1, 2, and 3 in ascending order. The data presented in Table 4 indicate that when all three modules had a dilation rate of 1, the SSIM values were higher than those for dilation rates of 2 and 3. This was because SSIM focused on the pixel-to-pixel relationship in the calculation. When all three modules had a dilation rate of 1, the pixels of the whole image were not ignored, which resulted in a higher SSIM. When all three modules had a dilation rate of 2, the PSNR was higher than when the modules had a dilation rate of 1. This was because PSNR calculates the value of a single pixel; therefore, with a dilation rate of 2, the current pixel can be supplemented with a wider range of features in the image, increasing the module's robustness for use across different environmental images. Both the SSIM and PSNR were lowest when a dilation rate of 3 was applied. This was because the insufficient size of the training image generated dilation factor gridding effects [16], resulting in module underperformance in this experiment. MARCAFNet combines the modules of three scales to simultaneously obtain the features of each pixel on small-scale learning images and obtain the features of a wider range of pixels on large-scale learning images, thereby generating favorable results.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 4 Discussion
### Uncertainties  
Shandong Province has a very wide area of cropland, but is affected by the topography, and some of the cropland is highly fragmented and spatially distributed, facing serious mixed image meta-problems even on the basis of a 10 m resolution. Furthermore, it is an agricultural area dominated by smallholder farming, which is a strong flexibility in the family-based farming practices and an orientation towards economic return [67; 68]. Especially in hilly areas, intercropping is very common. The complicated cropping pattern makes crop identification more difficult [69]. This research has better completed the winter wheat extraction based on the GEE platform, but considering the special characteristics of the cropland itself, there are still parts of the study that need to be improved:  
Firstly, the complex topography of Shandong leads to the fragmentation of cropland, and the scattered distribution of cropland plots creates more mixed image pixels. This, together with the combination of cropland and various vegetation cover, makes cropland parcels more difficult to be identified in the imagery, and prone to misclassification or being neglected. This research used Sentinel-2 image data with a 10 m resolution, which is higher than many other remote sensing imageries, but it is still deficient for an accurate identification of wheat.  
Secondly, it is well known that high-quality training data are necessary for the supervised classification method. However, it is difficult to realize due to the high cost and time-consuming field surveys, especially for the vast area of Shandong. Therefore, the sample points in this study are mainly obtained by visual interpretation according to sample characteristics and expert knowledge based on a small amount of survey data, and the samples are not absolutely accurate.  
Finally, Shandong Province covers a vast area and spans east-west and north-south. Also, there is a certain time difference between the coastal and inland agricultural climates. Generally, wheat in Shandong matures gradually from south to north and from west to east. This research has not yet taken into account the influence of the geographical span and local climate on crop phenology. In future research, a more detailed identification of winter wheat can be considered by dividing crop climatic zones.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise  
It is known that the set \(\mathcal{M}_{r}\) of matrices of rank at most \(r\) in the neighborhood of a matrix of rank \(r\) is a smooth manifold of order \(r(L+K)-r^{2}\) (ref. [21], [Ex:13, p.27 ] in [35]). This allows one to consider the linearization of the projector \(\Pi_{\mathcal{M}_{r}}\) and to approximate the projection on the set \(\mathcal{M}_{r}\) as a linear projection onto a tangent subspace at the desired point.  
Our approach is to estimate the variance \(\sigma^{2}\) used in information criteria (4) for a given rank \(r\) by using the matrix  
\[\mathbf{D}_{r}=\mathcal{T}_{L}\mathsf{X}-\Pi_{\mathcal{M}_{r}}\circ\mathcal{ T}_{L}(\mathsf{X}), \tag{7}\]  
rather than by constructing the maximum likelihood estimate of the signal. Since the computation of \(\Pi_{\mathcal{M}_{r}}\) is reduced to the summation of the first \(r\) summands of the singular value decomposition of the matrix \(\mathcal{T}_{L}(\mathsf{X})\), this allows for fast recalculation for different \(r\) and thus provides a fast method for estimating the rank of a signal.
## 5 Analysis of the Dynamic Response Law of Non-Homogeneous Slopes
### Dynamic Response Analysis of Non-Homogeneous Slopes
#### 5.2.2 Characterization of Vibration Wave Propagation in Blasting with Different Shape Parameters  
Figure 18a,b depict the scenarios of layered slopes where rock mass mechanical parameters decrease from the bottom to the top, indicating a decrease in rock hardness from the slope's foot to its crest. The elastic modulus, cohesion, and friction angle are assigned stochastic distribution shape parameters \(\beta\) of \(1,3,5,7\), and \(9\), representing different degrees of dispersion for these three parameters. These figures illustrate the variation of PGA with elevation for the slope surface and interior monitoring points under the influence of a blasting-induced vibration.  
As shown in Figure 18a, the PGA exhibits overall fluctuating changes with an increasing height, showing a trend of initially decreasing, then increasing, and decreasing again. Although there are occasional intersections of PGA values for different random distribution shape parameters at the same height, overall, the PGA decreases with increasing shape parameters. A comparison between conditions \(\beta=1\) and \(\beta=9\) reveals that the PGA values for the former are consistently higher at the same height. The maximum PGA values for all conditions follow the pattern of \(\beta=1>\beta=3>\beta=5>\beta=7>\beta=9\), indicating that the maximum PGA values also decrease with increasing shape parameters. The maximum PGA values for all conditions mostly occur in the relative height range of 0.1 to 0.2, near the blasting center. Although there is an increase in PGA values in the relative height range of 0.5 to 0.7, it remains lower than that near the slope crest, reflecting the slope's surface amplification effect. The maximum PGA value for condition \(\beta=1\) is 0.55 cm/s\({}^{2}\), while for condition \(\beta=9\), it is 0.42 cm/s\({}^{2}\), suggesting that the greater the dispersion of the rock mass physical mechanical parameters, the higher the overall peak acceleration of the blasting vibration waves.
## 5 Experiments
### Timing  
Finally, we measured the times required for predicting staff lines or the symbols of a staff line including data loading and pre- and postprocessing steps (see Table 10). The time for loading the model which is only required once is included. All times were measured on a Nvidia Titan X GPU and an Intel Core i7-5820K processor, while the FCN was implemented in Tensorflow. Moreover, we obtained the results only when using the CPU or a GPU if available.  
To process a full page the staff line detection requires 3.7 (CPU) and 3.5 (GPU) seconds, therefore about 3 min for the full dataset. The pre-processing step, which normalises the input image, takes about half of the time. The application of the FCN follows with about 40%, however the difference between CPU and GPU is minor. This can be explained by the shorter model loading time when using the CPU and the faster computation time but a required data transfer when using the GPU. The remaining time is consumed by the postprocessing which generates the final output.  
The symbol detection requires about 0.12 (CPU) and 0.15 (GPU) seconds for the prediction of a single staff, thus the complete dataset takes one minute to predict. Because no pre-processing besides cutting the image is required and the postprocessing step only consists of a connected component analysis, it is clearly faster than the pre- and postprocessing of the staff line detection, yet it is the most time consuming step. Compared to the staff detection, the CPU is even a bit faster due to the smaller input line image. In total about 4 minutes are required to predict both the staves and symbols on all 49 pages.  
Especially for training, GPU usage is almost indispensable to enable a fast back-propagation which computes the weight differences required for training the network. Compared to the CPU, the staff line training on the GPU that acts on the full page yields a speed-up of 30, which is justified with the convolution and pooling operations of the FCN that are highly optimised and perfectly suited for the GPU. Note that training a single instance is faster than prediction because pre- and postprocessing steps are not required.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Staff Line**} & \multicolumn{2}{c}{**Symbols**} \\  & **GPU** & **CPU** & **GPU** & **CPU** \\ \hline Predict & 3.5 & 3.7 & 0.15 & 0.12 \\ Train & 0.12 & 3.6 & 0.04 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prediction and training times in seconds. For the staff line detection the numbers denote the time to process a whole page, while for the symbol detection the reference is a single staff. Prediction includes pre- and postprocessing; however, training only refers to a single iteration.