esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Satellite positioning is one of the main navigation technologies in unmanned aerial vehicles (UAVs), the accuracy of which has an important impact on the safety, stability, and flexibility of UAVs. The parameters of integer ambiguity are important factors affecting the accuracy of satellite positioning. However, the accuracy of the integer ambiguity cannot be guaranteed when only a few epoch data can be obtained in the fast positioning such that the identification matrix of the integer ambiguity parameters is seriously ill-conditioned and the information of position deviation is enlarged. In this paper, an error checking and correcting strategy is proposed, where a Least-square Ambiguity Decorrelation Adjustment-Grey Wolf Optimization (LAMBDA-GWO) Method combined with the Tikhonov regularization method is developed to improve the accuracy of integer ambiguity for fast satellite positioning. More specifically, the LAMBDA-GWO is first used to search the integer ambiguity parameters. To reduce the ill-condition of the integer ambiguity parameter identification matrix, the Tikhonov regularization method is introduced to regularize the identification matrix such that a reliable integer ambiguity floating-point solution can be obtained. Furthermore, the correctness of the integer ambiguity is checked according to the prior accuracy information of the initial coordinates and the Total Electron Content (TEC), and the part that fails the test is corrected by the Grey Wolf Optimization (GWO) Method. Finally, experimental studies based on a 522 m baseline and a 975 m baseline show that the identification success rates of the proposed method are both above 99%, which is 12% and 23% higher than that of traditional LAMBDA, respectively.  
unmanned aerial vehicles; satellite positioning; integer ambiguity; ill-condition; parameter identification +
Footnote †: journal: Applied Sciences
## 4 The Optimization Model
### Design Constraints
#### 4.2.1 Blade Design Constraints  
In order to verify the structural stability of the blade, the structural design needs to withstand the extreme wind condition. Some representative sections are selected to calculate the strain and verify the strength. These sections are at the blade root and the airfoils' locations along the blade span on both the upper and lower surfaces. A maximum strain condition is used as [17]:  
\[\varepsilon_{50}\leq\frac{1}{\gamma_{f}\gamma_{m}}\varepsilon_{ult} \tag{4}\]  
where the \(\varepsilon_{50}\) is the strain at 50-year extreme wind condition, the \(\varepsilon_{ult}\) is ultimate strain, the partial safety for loads (\(\gamma_{f}\)) is set as 1.35 and the partial safety factor for materials (\(\gamma_{m}\)) is set as 1.1 according to IEC 61400-1 requirement. The representative numbers of ultimate strain is set as 0.03. The maximum tip deflection of the blade under extreme loading is added to ensure adequate stiffness. In this work, considering the blade length is set as a variable, the maximum tip deflections with various blade lengths are set as:  
\[\delta_{opt}\leq 1.05\cdot\delta_{orig}\cdot\frac{R_{opt}}{R_{orig}} \tag{5}\]  
where \(\delta_{opt}\) is the maximum tip deflection of the optimal blade, the \(\delta_{orig}\) is the maximum tip deflection of the original blade, \(R_{opt}\) is the length of the optimal blade, and \(R_{orig}\) is the length of the original blade.  
Figure 8: Schematic of tower structure.
## 3 Bioactive Compounds of _Citrus_ Seeds
### Phenolic Compounds
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subclass** & **Seeds** & **Compound** & **Content** & **Ref.** \\ \hline \multirow{8}{*}{_Citrus_} & & Narirutin & 4704.7 \(\pm\) 309.34 & \multirow{8}{*}{[47]} \\  & & Hesperidin & 40,399.7 \(\pm\) 1876.28 & \\  & & Eriocitrin & 747.0 \(\pm\) 35.07 & \\  & & Didimin & 1584.3 \(\pm\) 86.96 & \\  & & Apigenin7-(malonyliosyl)-glucose & 36.0 \(\pm\) 2.46 & \\ \cline{2-5}  & & Hesperidin & 884.50 \(\pm\) 17.50 & \\  & & Neohesperidin & 452.20 \(\pm\) 63.60 & \\  & & Kaemberol & 18.55 \(\pm\) 2.14 & \\  & _Grapefruit_ & Eriocitrin & 94.70 \(\pm\) 18.40 & \\  & & Rutin & 443.0 \(\pm\) 95.30 & \\  & & Naringin & 1324.60 \(\pm\) 76.60 & \\  & & Naringenin & 57.45 \(\pm\) 9.90 & \\ \hline \multirow{8}{*}{_Lemon_} & & Hesperidin & 907.39 \(\pm\) 8.64 & \multirow{8}{*}{[43]} \\  & & Eriocitrin & 1051.60 \(\pm\) 44.60 & \\ \cline{1-1}  & & Naringin & 389.79 \(\pm\) 0.98 & \\ \cline{1-1}  & & Neohesperidin & 111.06 \(\pm\) 3.45 & \\ \cline{1-1}  & & Naringenin & 22.82 \(\pm\) 0.98 & \\ \cline{1-1}  & & Rutin & 76.80 \(\pm\) 26.20 & \\ \cline{1-1}  & & Catechin & 15.79 \(\pm\) 1.15 & \\ \cline{1-1}  & & Kaemberol & 15.12 \(\pm\) 0.47 & \\ \hline \multirow{8}{*}{_Phenolic acids_} & _Omage_ & Caffeic acid & 84.7 \(\pm\) 3.38 & \\  & & _p_-Hydroxybenzoic acid & 153.6 \(\pm\) 9.96 & \\ \cline{1-1} \cline{2-5}  & & Gallic & 78.40 \(\pm\) 12.20 & \\ \cline{1-1}  & & Syringic & 7.23 \(\pm\) 0.12 & \\ \cline{1-1}  & & Rosmarinic & 16.00 \(\pm\) 0.17 & \\ \cline{1-1}  & & tr-Ferulic & 206.10 \(\pm\) 34.00 & \\ \cline{1-1}  & & tr-2-Hydrocinnamic & 11.32 \(\pm\) 0.55 & \\ \hline \multirow{8}{*}{_Lemon_} & & Gallic acid & 93.42 \(\pm\) 8.07 & \\ \cline{1-1}  & & tr-Ferulic acid & 85.13 \(\pm\) 2.68 & \\ \cline{1-1}  & & Syringic acid & 8.46 \(\pm\) 0.09 & \\ \cline{1-1}  & & tr-2-Hydrocinnamic acid & 10.99 \(\pm\) 1.12 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Concentration values (mg/kg) of phenolic acids and flavonoids content in three main groups of _Citrus_ seeds.
## 5 Experimental Results
### Experimental Settings  
#### 5.2.1 Step 1: Training of the Semantic Segmentation Model  
The algorithm begins by training the semantic segmentation model on the source dataset. Concerning the selection of the source dataset, we demonstrated in Section 5.2.6 (Discussion part) that our algorithm shows higher efficiency if we choose Potsdam domain as source and Vaihingen domain as target. In fact, Potsdam is far larger than Vaihingen (3800 images in Potsdam compared to 459 images in Vaihingen). Hence, to maximize the efficiency of our algorithm, we should select a large dataset as the source to learn the global patterns of the data. Then, our domain adaptation algorithm is used to treat domain shift that exists between the source and the target datasets. Consequently, we selected in our experiments Potsdam dataset as the source dataset and Vaihingen as the target dataset. Once the dataset was ready, we applied a state-of-the-art semantic segmentation model adapted to our requirements in aerial imagery. We chose the Bilateral Segmentation Network (BiseNet) [50], which is the fastest segmentation model tested on the Cityscapes dataset [13]. It achieves a speed of 65.5 frames per second with a mean IoU of 74.7 % on this dataset [51]. In the processing of aerial images, the speed factor is very important to have the ability to process video streams captured in real time from a drone. The architecture of BiSeNet is represented in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain Shift Factor** & **Resolution** & **Sensor** & **Class Representation** \\ \hline Trees & low & high & medium \\ Cars & low & low & low \\ Clutter & low & high & high \\ Impervious Surfaces & low & low & low \\ Buildings & low & high & low \\ Low vegetation & low & high & high \\ \hline \hline \end{tabular}
\end{table}
Table 2: Domain shift analysis when passing from Potsdam to Vaihingen.  
A GPU machine containing the following features was used to carry out experiments associated with this study:  
* Processor: Intel Core i9 (Coffee Lake architecture, 6 cores)
* GPU: Nvidia GTX 1080, 8 GB dedicated
* Memory: 32 GB RAM
* Operating system: Windows 10 and Linux (Ubuntu 16.04)  
To train BiSeNet on the Potsdam dataset, we used semantic segmentation Suite [52], which is an open-source framework where several segmentation models are implemented in Tensorflow [53]. ResNet101 [54] was used as the front end for the BiSeNet network. The training is run for 80 epochs, we set 1 image as the batch size. Image augmentation techniques are not used. ADAM [55] is used as an optimizer during the training with a learning rate 0.0001. As shown in Figure 8, the average segmentation accuracy surpasses 85% on the validation dataset in less than 15 epochs.  
The Figure 9 shows the convergence of the loss of BiSeNet over the epochs.  
Figure 8: Progress of segmentation accuracy of BiseNet trained on Potsdam.  
Figure 7: The Architecture of BiSeNet (Bilateral Segmentation Network).  
After the training, model weights are saved to be used later in Step 5.
## 5 Methods for Identification and Quantification of Coumarins
### Analysis  
Several methodologies have been developed for coumarins' analysis. The selection of the appropriate technique is contingent upon several factors, including the particular coumarins of interest, the intricacy of the sample matrix, and the availability of suitable equipment. Often, a variety of methods are used together to accurately identify and quantify the amount of coumarins in a sample.  
The first phase for the analysis is the process of separation, which was initially carried out using paper chromatography, thin layer chromatography and colorimetric tests. Currently, high-performance liquid chromatography (HPLC) is the most widely used method [168]. The chromatographic techniques mostly used to analyse coumarins are described below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spirit Beverage** & **Sample Preparation** & **References** \\ \hline \multirow{8}{*}{Wine Spirit} & Coumarins should be extracted, since this matrix is complex and rich in phenolic & [36] \\  & compounds. An ether extraction was therefore carried out & [38] \\ \cline{2-3}  & Samples directly analysed (without preparation) & [38] \\ \cline{2-3}  & Adding an internal standard to the samples, filtering them through a 0.45 μm & [39,40,152,164,165] \\  & membrane and analysing them by direct injection & \\ \cline{2-3}  & De-alcoholised samples (to 8\% alcohol) and filtered through a 0.45 μm membrane & [22] \\ \hline \multirow{4}{*}{Brandy} & Samples directly analysed (without preparation) & [38] \\ \cline{2-3}  & Samples filtered at 0.20 μm membrane and directly injected & [123] \\ \cline{2-3}  & De-alcoholised samples (to 8\% alcohol) and filtered through a 0.45 μm membrane & [22] \\ \hline \multirow{4}{*}{Whiskey} & Samples extracted with ethyl acetate & [35] \\ \cline{2-3}  & Samples directly analysed (without preparation) & [38] \\ \cline{2-3}  & De-alcoholised samples (to 8\% alcohol), filtered through a 0.45 μm membrane & [22] \\ \hline \multirow{4}{*}{Sugar cane spirit} & Samples extracted with ethyl acetate (Rum) & [35] \\ \cline{2-3}  & SPE extraction (Cachaca) & [43] \\ \cline{2-3}  & Samples filtered at 0.45 μm polyethylene membrane and directly injected & [166] \\ \cline{2-3}  & For fluorescence detection, the samples were not prepared; for UV-Vis detection, the & \\ \cline{2-3}  & samples were diluted 30 times with an ethanol/water solution (40:60\% \(\
u/\
u\)) (Cachaca) & [167] \\ \cline{2-3}  & De-alcoholised samples (to 8\% alcohol), filtered through a 0.45 μm membrane (Rum) & [22] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample preparations by spirit beverage.
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 5 Results
### Analysis of the Prediction Effect of Hyperband-CNN-BiLSTM-AT Model
#### 5.1.2 Comparative Analysis of CNN-BiLSTM-AT Model Structure
(3) Feature Analysis of the Best CNN-BiLSTM-AT Model  
1 Key Features Highlighted by the Attention Mechanism  
To interpret how the 1 CNN-1 BiLSTM-AT model identifies financial risks, we extracted and visualized the attention weights learned by the model. Specifically, the attention scores assigned to each financial indicator were averaged across all samples and time steps to obtain a global importance score. These scores were visualized in a horizontal bar chart (see Figure 7), where the top 10 features are ranked by their average attention weights. The results show that the model places the highest attention on the current asset turnover, working capital ratio, current assets ratio, inventory turnover, operating profit margin, current liabilities ratio, net profit margin on fixed assets, earnings before interest and taxes  
The analysis combines the results of the attention mechanism and the ablation study to understand better how the model identifies key financial indicators. Several indicators, such as the working capital ratio, current liabilities ratio, net profit margin on total assets, and earnings before interest and taxes per share, are important in both methods, suggesting they play a consistent and critical role in financial distress prediction.
## 5 Results
### Analysis of Simulation Results
#### 5.3.2 Results from Simulation Experiments Involving Dust  
Figure 16 illustrates the results from the simulation experiment involving dust. To have a more intuitive impression of this weather effect, visibility was used as a measure. These results are similar to fog, shown in Figure 13, which is reasonable, although the number of points decreases already at higher visibilities during dust storms compared with fog. As dust storms contain mainly SiO\({}_{2}\), the scattering anisotropy of dust particles is lower than that of water droplets (see Figure 3), and therefore the atmospheric loss is higher, leading to the observed behavior.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 4 Optimal Path Discovery Phase  
In this phase, we try to find an optimal path to disseminate the warning message. In this regard, we present two algorithms.
## 4 Discussion
### Study Strengths and Limitations  
We consider this study as a first step in the investigation of recommendable training and rehabilitation exercises for the neck/shoulder muscle based on elastic bands. The strengths of the study is the direct measures using EMG to assess the activation of representative neck muscle groups and instead of subjective assessment. Additionally, the design of the study using randomization of the exercises is a strength, since this ensures that the fatigue development within each type of exercise is not biased by its timewise performance.  
The limitations of present study relate to the population only including a small group of males similar to majority of employees in the job groups specifically challenged with high exposures of the neck muscles due to helmet wearing or high accelerations (fighter pilots, helicopter pilots, construction workers, etc.). Additionally, the population consist of healthy subjects without serious neck/shoulder disorders. Therefore, extrapolation of our results to patients with neck/shoulder injury should be undertaken with caution. We cannot conclude that patients suffering from neck/shoulder pain will show similar muscle activation patterns when performing the exercises that are studied here.  
Furthermore, EMG activity during the dynamic exercises was normalized to measurements of EMG activity during static MVCs to quantify the level of muscle activation. Given the inherent methodological limitations that are associated with surface EMG, only a rough estimate of the absolute level of muscle activation can be inferred while using this method. Fine wire electrodes for some of the deeper layers of neck extensor muscles could have further detailed the contribution of activity from each of the neck extensors. Finally, more muscles and contraction intensities could be studied to give an even more complete activation pattern for optimal recommendations of training exercises.
## 2 Results and Discussion
### Development of the Microextraction Methodologies
#### 2.2.1 Optimization of the HS-SPME Methodology
An increase in the temperature directly affects the distribution of the analytes between the HS sampling mode and the fiber coating, as it increases the partition coefficient, which improves the capacity for extracting higher amounts. However, higher temperatures could also decrease the partition coefficient between the sample matrix and the fiber phase, leading to losses in efficiency and sensitivity [10]. In this sense, a temperature of 30 \({}^{\circ}\)C was selected, as it showed a lower variability in between replicates for all five monoterpenoids (RSDs \(\leq\) 8.5%).
## 4 Discussion
### Effects of Uncertainties in MODIS Products on Their Relationship with Annual GPP  
In Section 2 we discussed the uncertainty introduced in the MODIS products by possible cloud contamination of the pixels and how to minimize it by temporally compositing the datasets.  
In the case of MOD15, the propagation of algorithm uncertainties presents additional issues in the estimation of GPP. As described in Section 2.1, the algorithm uncertainty of LAI and FPAR was estimated through ground validation studies. Here, we assess the influence of MOD15 uncertainty on the GPP estimate through comparison with CYCLOPES [80], the other existing global LAI dataset.  
CYCLOPES LAI has a 1/112 degree spatial resolution and 10-day temporal sampling. The algorithm utilizes a neural network approach to retrieve LAI from SPOT VEGETATION reflectance data. We processed the CYCLOPES dataset to 30-day composites similarly to the MOD15 LAI 30-composites and calculated the correlation with annual GPP. In contrast to the high correlation between MODIS annual mean LAI and GPP, CYCLOPES annual mean LAI had an \(R^{2}\) of only 0.02 when compared to the annual flux tower GPP. Thus, annual GPP estimates appear to be sensitive to the uncertainty of the LAI products. The differences between MOD15 and CYCLOPES were mostly pertaining to: (a) Definition of LAI, with CYCLOPES measuring effective LAI while MODIS algorithm retrieves true LAI accounting for leaf clumping; (b) Nadir view TOA reflectances are input to the CYCLOPES LAI algorithm, while spectral surface reflectances with varying view angles are input into the MODIS algorithm; (c) Early saturation of the CYCLOPES LAI product, with maximum values limited to LAI = 4 while MODIS retrieves LAI of values up to 7 (includes both main algorithm and backup algorithm retrievals); (d) Classification errors due to differences in land cover mapping. These algorithm differences suggest caution when applying the proposed GPP estimation method to algorithm dataset different than MOD15.  
Another potential source of uncertainty could be caused by the spatial resolution of the datasets. We used 250 m resolution data of MOD13, 1 km resolution data of MOD15 and MOD17, and flux tower point GPP data. We avoided the uncertainty caused by within-pixel spatial heterogeneity by applying a strict selective process to use only data for towers located over homogenous land covers, as described in Section 2. Also, [PERSON] _et al._[23] showed that comparisons between flux tower GPP and EVI were largely insensitive to the selection of a 3 km by 3 km grid versus a single 1 km pixel. Thus, we believe that our analysis was hardly affected by spatial resolution effects.  
Unfortunately, the number of tropical forest flux sites was very small compared to boreal or temperate forest sites. In this study, we used 4 sites in South East Asia and 1 site in South America. More data for tropical forest are needed to improve the GPP estimation over these regions, likely altering the regression for the tropics.
## 2 Biologically Active Polysaccharides--Structure and Function
### Algal and Lichen Polysaccharides
The structure of this polysaccharide is complex and includes sulfo-groups, rhamnose, xylose, glucuronic acid, and iduronic acid. The ulvan chain is comprised of rhamnose sulfate molecules linked by an \(\alpha\)-1,4 bond of glucuronic or iduronic acid. In turn, the algae, _Codium fragile_ and _Codium cylindrium_, contain even more complex and branched galactans [76]. The anti-cancer activity of marine microalgae has not yet been thoroughly investigated; however, it was reported that microalgae extracts (e.g., _Attheya longicornis_, _Chaetocros socialis_, _Chaetocros fucellatus_, _Skeletonema marinoi_, and _Porosira glacial_) may be also a source of bioactive anti-cancer polysaccharides [77]. The polysaccharides isolated from lichens are mainly \(\alpha\)-glucans, \(\beta\)-glucans, galactomannans, and complex heteroglycans. The first isolated lichen polysaccharides were lichen and isolichenan and isolichenan from _Cetraria islandica_. Lichenan and pustolan, isolated _from Evernia prunstri_, and several polysaccharides obtained from _Cora pavonia_ and _Colleman leptosporum_, are classified as \(\beta\)-glucans, while isolichenan is a representative of \(\alpha\)-glucans. The biological activities of lichens' polysaccharides are antiviral, antitumor, and immunostaining [78]. It was also reported that lichenan and isolichenan-rich polysaccharide-fractionshave anti-tumor activity. Similar properties were found for polysaccharides isolated from _Usnea runescens_ and _Lasallia pensylvania_; moreover, polysaccharides derived from _Evernia prunastri_ show activity against sarcoma 180 cells [79]. Lichenan and other lichen-derived \(\beta\)-glucanas have an immunostimulating effect by inducing the release of cytokines, reactive oxygen species, and nitric oxide. These polysaccharides also stimulate the release of arachidonic acid metabolites involved in inflammatory reactions [80]. Moreover, it was confirmed that \(\alpha\)-glucan from _Ramalina elastri_ induces apoptosis and leads to cell death [79].
###### Abstract
Biochar is considered as a high potential engineered adsorbent for many types of environmental remediation applications [34]. Biochar derived from different waste streams like agricultural industries presents numerous advantages, such as cost-effectiveness, providing of continued feedstock, the possibility of circulating resources in the economy, and sustainable vaporization options for waste [29]. Many literature works have reported that biochar derived from agricultural by-products can be effectively utilized as an adsorbent in different environmental applications, such as for water treatment, as a soil amendment in agriculture, for abating greenhouse gas in soil, and as a stabilizer in the anaerobic digestion process [35; 36; 37]. Rice is one of the major consumable foods in more than half of the world. Rice is an annual plant crop and is mostly cultivated in areas with high rainfall. Many Asian countries have an agrarian economy and produce a significant amount of agricultural waste related to the rice industry. In general, agricultural wastes are considered as biomass residues that can be divided into two categories, namely crop residues and agro-industrial residues. Currently, many farmers and other stakeholders who engage with the rice industry are practicing open field burning or open dumping processes to clean their land for the next cultivation instead of extracting their full value [38]. This leads to huge environmental pollution and adverse health effects on humans as well as on animals. Thus, it is possible to minimize these negative effects by considering the valorization process for this potential feedstock by using it as a valuable material or source to the national economy. Considering the waste quantities and other physical and chemical properties, rice industry by-products have a high potential of generating energy, extracting nutrition elements, minerals, and biochemicals through different valorization processes. However, their potential is still underexplored due to issues related to their supply chain, appropriate technologies for pre-treatment and cost-effective methods, and feasible business models to facilitate valorization of the rice industry by-products in many developed and developing countries. Therefore, the aim of this review paper is twofold:  
1. Reviewing the literature concerning the potential use of rice industry by-products (rice straws, rice husk, and rice husk ash) for the removal of fluoride and arsenic in drinking water. 2. Assessing main strengths and limitations in a large-scale implementation of rice industry by-products as an adsorption material in the water treatment sector.