esa-sceva/satcom-chunk-collection huggingface dataset:
# Raman Spectra of ZrS\({}_{2}\) and ZrSe\({}_{2}\) from Bulk to Atomically Thin Layers
## Appendix A Crystal Growth of ZrX\({}_{2}\)  
The synthesis of ZrX\({}_{2}\) was performed following a typical solid state reaction. Powders of 99.5% Zr from abcr GmbH (Karlsruhe, Germany), 99.98% S from Sigma-Aldrich (Saint Louis, MO, USA), and 99.999% Se from Alfa-Aesar (Lancashire, UK) were mixed in a stoichiometric ratio, sealed in an evacuated quartz ampoule (P \(\sim\)5 \(\times\) 10\({}^{-5}\) mbar, length = 25 cm, internal diameter = 1.5 cm) and heated from room temperature to 900 \({}^{\circ}\)C at 1.5 \({}^{\circ}\)C/min. Then, the temperature was kept constant for eight days and slowly cooled down (0.07 \({}^{\circ}\)C/min).  
For obtaining large crystals, 4 mmol of the previous material was mixed with I\({}_{2}\) as a transport agent ([I\({}_{2}\)] \(\sim\) 5 mg/cm\({}^{3}\)) in an evacuated quartz ampoule (P \(\sim\) 5 \(\times\) 10\({}^{-5}\) mbar, length = 50 cm, internal diameter = 1.5 cm). The quartz tube was placed inside a three-zone furnace with the material in the leftmost zone. The other two zones were heated up in 3 h from room temperature to 700 \({}^{\circ}\)C and kept for two days. After this, the leftmost side was heated up to 750 \({}^{\circ}\)C in 3 h and it was established in the three-zone furnace a gradient of 750 \({}^{\circ}\)C/700 \({}^{\circ}\)C/725 \({}^{\circ}\)C. Then the temperature was kept constant for 20 days and cooled down naturally. As shown in the insets of Figure 11, with this process we could obtain crystals with a length up to some centimeters.  
The obtained crystals were analyzed by EDX and powder X-ray diffraction. The amount of elements obtained in weight was Zr: 57.7% \(\pm\) 1.4% and S: 42.4% \(\pm\) 1.0% for ZrS\({}_{2}\) and Zr: 35.7% \(\pm\) 1.2% and Se: 64.3% \(\pm\) 1.7% for ZrSe\({}_{2}\). These values are in good agreement with the expected ones (for ZrS\({}_{2}\), Zr: 58.4% and S: 41.6%; for ZrSe\({}_{2}\), Zr: 36% and Se: 64%). The refinement of the X-ray pattern revealed a trigonal crystal system with a P-3m1 space group and a unit cell determined by \(\alpha\) = \(\beta\) = 90\({}^{\circ}\) and \(\gamma\) = 120\({}^{\circ}\) and a = b = 3.6628(5) A, c = 5.8270(10) A for ZrS\({}_{2}\) and a = b = 3.7711(4) A, c = 6.1282(9) A for ZrSe\({}_{2}\). The obtained results are in accordance with the ones reported in the literature [30; 41].
# Food Classification and Meal Intake Amount Estimation through Deep Learning
## 3 Food Classification and Meal Intake Amount Estimation through Deep Learning
### Meal Intake Amount Estimation
The slope angle \(\theta_{slope}\) is 30 degree; thus,  
\[\sqrt{\left(\frac{R}{a}\right)^{2}-1}=\tan\theta_{slope}=1/\sqrt{3}, \tag{2}\]  
From (2), \(a\) and \(h\) are  
\[a=\sqrt{3}R/2, \tag{3}\]  
\[h=R-a=\left(2-\sqrt{3}\right)R/2. \tag{4}\]  
The base area \(A_{food}\) is equal to \(\pi r^{2}\); then, \(r\) is  
\[r=\sqrt{A_{food}/\pi}. \tag{5}\]  
Since the angle between \(R\) and \(h\) is also equal to \(\theta_{slope}\), \(R\) is calculated as follows:  
\[R=r\times\csc\theta_{slope}=2r=2\sqrt{A_{food}/\pi}. \tag{6}\]  
The result of substituting \(R\) into (4) is  
\[h=2\sqrt{A_{food}/\pi}\times\left(2-\sqrt{3}\right)/2=\left(2-\sqrt{3}\right) \sqrt{A_{food}/\pi}. \tag{7}\]  
The volume equation of the spherical cap is  
\[V_{sph}=\frac{1}{3}\pi h^{2}(3R-h). \tag{8}\]  
By substituting (6) and (7) into (8), the food volume \(V_{sph}\) for the spherical cap is calculated as follows:  
Figure 8:0Though the food in the soup category has the spherical cap shape, it is liquid contained in a bowl. The slope angle of the food is not fixed at 30 degree but depends on the curvature of the bowl. If \(R\) is given, \(h\) is calculated through the Pythagorean theorem as follows:  
Figure 8:1  
For the food with the cone shape, the radius of the base surface \(r\) is also estimated through (5). The food height \(h\) is calculated to be \(r\tan\theta_{slope}=\sqrt{A_{food}/3\pi}\) as shown in Figure 9b. The food volume of the cone-shaped food \(V_{cone}\) is estimated as follows:  
Figure 8:2  
Though the height of the cuboid is hard to estimate, it can be empirically predicted that the food volume decreases in proportion to the base area of the food. Therefore, the food volume for the cuboid shape is  
Figure 8:3  
where \(H_{food}\) is the predefined height depending on the food type. For each food, the meal intake amount is estimated by comparing the food volumes between the pre- and post-meal images.
## 3 Results
### Adequacy  
Each resolution estimates a similar annual mean relative ET across the scheme for all years and all levels; it follows the similar inter-annual trends in both the Wonji and the Metehara (Figure 2). In the Wonji, the annual relative ET, averaged across all years (2009-2018), ranged from 0.61-0.78 for L3, 0.63-0.76 for L2, and 0.67-0.75 for L1. The Metehara had higher mean relative ET and a higher variation in relative ET as compared to the Wonji for all levels. In the Metehara, the mean annual relative ET across all years ranged from 0.88-0.94 for L3, 0.81-0.90 for L2, and 0.80-0.88 for L1.  
Across levels, the L3 mean relative ET is frequently higher than the L2 and the L1 ETo in both the Wonji and the Metehara. The L3 relative ET variation is consistently higher in the Wonji, which is reflected in the larger quartile range shown in Figure 2. The variation between plots was the highest in 2018 for both the Wonji and the Metehara.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Product** & **Dataset** & **Wonji** & **Metehara** & **ODN** & **Koga** & **Zankalon** \\ \hline ETla (mm/year) & L3 (30 m) & 1498 (0.17) & 1648 (0.34) & 1832 (0.14) & 793 (0.19) & 1394 (0.12) \\ ETla (mm/year) & L2 (100 m) & 1433 (0.12) & 1557 (0.34) & 1664 (0.12) & 884 (0.15) & 1368 (0.09) \\  & L1 (250 m) & 1480 (0.11) & 1591 (0.33) & 1736 (0.13) & 926 (0.16) & 1406 (0.08) \\ \hline AGBP (ton/ha) & L3 (30 m) & 37.2 (0.21) & 46.2 (0.16) & 16.4 (0.27) & 2.3 (0.19) & 2.3 (0.16) \\  & L2 (100 m) & 40.1 (0.14) & 49.1 (0.16) & 16.1 (0.19) & 2.9 (0.15) & 2.4 (0.11) \\  & L1 (250 m) & 40.0 (0.14) & 48.7 (0.15) & 16.1 (0.20) & 2.9 (0.16) & 2.4 (0.10) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean annual ETla and AGBP for irrigated schemes for 2009–2018 (scheme coefficient of variation (CV) reported in brackets).  
Figure 3 shows the relative ET in 2018 for sugarcane plots in the Wonji and in the Metehara derived from WaPOR. All three levels can pick up spatial trends in relative ET. All levels show lower relative ET in the north-eastern part of the Wonji Scheme and the higher relative ET in the eastern and the north-western parts of the Wonji. In the Metehara, all levels showed that the centre of the scheme has higher relative ET, and the southern part of the scheme has lower relative ET. The higher resolutions can capture plot-to-plot variability better. A clear example is in the western part of the Wonji scheme. L1 shows all plots in this area ranging from 0.55-0.80, whereas in the same region, L3 is identifying plots to range from 0.35-0.85.
## 3 Desalination Performance Tests  
The CDI system experiments were evaluated using a CDI unit cell that has been described in Figure 3 and previous papers [6; 18; 26], and the electrodes were soaked in 10 mM NaCl for 10 min. under a vacuum condition before the desalination tests. The uncoated and LDH coated activated carbon electrodes (d = 5 mm) was used as the electrodes, and a nylon spacer (thickness = 0.2 mm) was placed between the electrodes. A hole with a diameter of 4 mm was provided in the middle of the lower compartment electrode and the current collector so that the influent would flow between the two electrodes.  
The 10 mM NaCl aqueous solution was pumped at a flow rate of 10 mL/min. using a peristaltic pump (MINIPLUS 3, Gilson Inc., Middleton, USA). The ion adsorption/desorption tests were carried out by applying a potential (charging: 1.2 V; discharging: 0 V) for 10 min at each step using an automatic battery cycler (WBC S3000, WonA Tech Corp., Seoul, Korea). The conductivity of the effluent was measured by a flow-type conductivity meter (F-54 BW, HORRIBA, Kyoto, Japan). The tests were held in a thermostatic chamber (KCL-2000, EYELA, Tokyo, Japan) to maintain the room temperature (298 K) during the operation. The accumulated salt adsorption capacity was calculated from the conductivity changes during the charging step. The representative desalination performance results from triplicate experiments are presented in this study.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### Lessons Learned and Trends in Development
This aspect represents a design requirement for not only the hardware components of the BSs, APs, and the user terminals, but also the network  
Figure 9: Graph-based model of the design process for resource management algorithms (adapted with permission from Ref. [108]. 2020 IEEE). self-organization schemes which they operate on. These algorithms need to be designed with the consideration of minimizing the power consumption, which will otherwise become intolerably large as the networks in 5G and beyond encompass an ever-increasing number of nodes [112]. EE balances the computational complexity of the RA and power consumption, and it is relevant to all other aspects except Coexistence Capacity. * Delay and Age of Information (AoI). Traditionally, wireless networks have had different latency and throughput requirements depending on whether the transferred information is to be perceived by the recipient in real time or not (and also how \"real time\" is defined for a particular scenario). With the increasing connectivity between machines and cyber-physical systems, the delay requirements have become more diverse. Recently, the AoI as a more general metric has been proposed [113], denoting the cumulative timing delays that consider both the physical and communication mechanisms that comprise the operation of the network's nodes. The AoI is dependent on the number of nodes and the maximum number of iterations for the communication process' completion. This aspect is related to all the rest. * Coexistence capacity. This aspect describes the extent to which the application permits/enables coexistence of the network with others of different RATs, and is associated with the nodes' communication capabilities and the operator's policies. It is the most important determining factor for the complexity of the resource management scheme. This aspect is related to RA, SSh, Delay, and Security. * Data caching. If the deployment scenario (such as V2V and cellular offloading) requires caching at the UEs/CPs, its realization (by determining which nodes are to cache what information, and how they are clustered) may have greater importance than the EE. That is due to the power conservation gained from avoiding the unnecessary transmissions from the BS/AP to each user. This aspect is related to the RA, Delay, EE, and Security. * Security. As the services provided by the wireless networks, as well as the RATs that facilitate them, become more complex and demanding, security protocols need to be additionally enhanced to provide user data protection and prevention of unauthorized spectrum access. Achieving this will require supplementary procedures implemented in the various nodes via FL-based distributed methods [107; 114], and a non-negligible overhead that is used for encryption of user data, authorization, and jamming of the malicious users for protection on the physical layer and of the authorized users' access to the spectrum. Consequently, it is relevant to all the other aspects.
# Nutritional Factors Associated with Dental Caries across the Lifespan: A Review
## 4 Effects of Poor Nutrition on Oral Health and Dental Caries
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Micrountient** & **Dental Effect** & **Nutrition Source** & **Deficiency** & **Supplements** \\ \hline  & Facilitates the stimulation of saliva production, thereby promoting the removal of acids and food particles from teeth, concurrently neutralizing acid and providing protection against dental decay. & Leafy green vegetables (halo, and brocol), orange and yellow vegetables (carro, sweet potatoes, pump, and others), tomatoes, red pellet, pentyl pepper, cantalo, mango, hover, liver fish, oil, milk, and eggs. & Elevated susceptibility to caries was observed in instances of vitamin A deficiency. A proposed mechanism for this vulnerability postulates that it may be attributed to an increase in enamel solubility. & Deficiency in vitamin A is common in children and pregnant women. Supplements may be suggested in this case. \\ \hline Vitamin B6 [53, 55] & Has emerged as a promising content for safeguarding against dental caries. & Beef liver, tuna, salmon, fortified cereals, chickens, poultry, and some vegetables and fruits & The deficiency of vitamin B6 can have implications for dental integrity, disrupting essential processes like enamel formation and increasing the risk of caries development. \\ \hline Vitamin K [53, 55] & Has surfaced as a noteworthy prospect for protecting against dental caries. & Spinach, brocol, iceberg, lettuce, and fats and oils & Insufficient levels of vitamin K may compromise dental health by impeding optimal mineralization processes, potentially contributing to enamel hypothesis and increasing the susceptibility to caries formation. \\ \hline Vitamin C [47, 53] & Plays a crucial role in promoting the health of the gingiva and facilitating the healing process. & Citrus (orange, kiewi, lemon, and grapefruit), bell peppers, tomatoes, cruciferous vegetables (brocol, brassspots, cabbage, and cauliflow), and white potatoes & Depletion in humans leads to gingival bleeding, regardless of oral hygiene. & Only recommended if there is deficiency of vitamin C. \\ \hline  & Activates a pivotal role in the formation of enamel and dentin during the tooth development process due to its interaction with receptors in ameloblasts and odontoblasts. & Dark-green lealed vegetables, cheese, milk, cool-diver oil, oyster mushrooms, eggs, and certain species of wild salmon & The insufficiency of vitamin D can lead to enamel hypothesis, a noteworthy factor contributing to early childhood caries. & For strict vegans (those avoiding milk) and elderly, vitamin D supplements could be taken in combination with minerals as either vitamin Dalone or minerals alone may not provide systemic benefits. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Micromutrients and their role, nutrition source, deficiency effect, and supplements.
# Recent Advances in the Heterologous Biosynthesis of Natural Products from _Streptomyces_
## 4 Host Engineering Approaches
### Regulators  
Regulators positively or negatively affect the expression of target BGCs by directly or indirectly regulating the transcription of target genes. _Streptomyces_ contains many transcriptional regulator families, such as TetR, LuxR, MarR, and ArffR. Each family has different functions. The TetR family regulator is the most popular in _Streptomyces_ and controls the expression of multiple genes in secondary metabolite BGCs. For example, the expression of GdmRIII led to increasing the production of gelatinamycinin while decreasing the production of elaiophylin in _Streptomyces autolyticus_ CGMCC0516 [133]. AccR bound to a 16-nucleotide palindromic binding motif in the promoter of target genes resulted in the decreasing biosynthesis of malonyl-CoA and methylmalonyl-CoA in _S. avernitilis_. The production of avernectin was enhanced by 14.5% when _accR_ gene was deleted [134]. The LuxR family regulator is a pathway-specific transcriptional regulator of BGCs, including AniF-activated anisomycin production [135]. The MarR family regulators contain winged helix-turn-helix DNA binding domains, which can work like transcriptional repressors or activators. [PERSON] et al. indicated that SAV4189, a MarR-family regulator, acts as an activator of avernectin production in _S. avernitilis_. They also produced the heterologous expression of _sav_ 4189 in _S. coelicolor_, resulting in enhancing the antibiotic production of this strain [136]. [PERSON] et al. engineered _S. lividans_ STB5 by integrating global regulator genes, such as _mdfA\({}_{\alpha\
u}\), lmrA\({}_{\alpha\
u}\), nusG\({}_{\kappa}\)_, and _afRS\({}_{\delta}\)_, and deleting a transcription regulator gene, namely, _wblA\({}_{sl}\)_, to achieve _S. lividans_ LJ1018. The production of murayaquinone, hybrubins, actinorhodin, dehydroarbelomycin, and actinomycin D achieved via heterologous expression in _S. lividans_ LJ1088 were increased 96-, 29-, 23.3-, 12.7-, and 3.5-fold compared with the production achieved via heterologous expression in _S. lividans_ STB5, respectively [137]. Therefore, for improving the heterologous expression level of BGCs, host strains should be engineered by deleting repressor genes and integrating activator genes.
## 5 Conclusions  
Based on the study findings, it was concluded that the exercise protocol of this study utilized moderate-intensity exercise of 2.4-3.9 METs. The energy cost of each training exercise was 179-291 kcal. The physiological parameters, such as the heart rate (\(p\) = 0.001 *) for the lunge and the rate of perceived exertion (\(p\) = 0.005 *) for the bent-over row, differed significantly but did not reach significant levels for the respiratory exchange ratio or systolic and diastolic blood pressure.
## 2 Materials and Methods
### Model Structure and Training Process  
Research is based on open-source software and libraries. Python [31] is the programming language of choice. In addition, NumPy [32], Pandas [33], statsmodels [34], Matplotlib [35] and Seaborn [36] libraries are imported for processing, management and visualization data. The model based on an ANN was developed in Pytorch [37], an open source library available online at: [[https://pytorch.org](https://pytorch.org)]([https://pytorch.org](https://pytorch.org)) (accessed on 17 June 2021).  
The ANN model used in this study consists of two parts: the first part handles the sequential (time series) meteorological data and is based on two LSTM layers and two fully connected layer. The goal of this part of the model is to provide the \"state\" of the clay according to the meteorological data. The output of the first part serves as an input to the second part of the model, consisting of two fully connected layers with ReLU activation, handling clay blasting data input data (8 features).  
The output of the whole model is a 2D vector (two predicted features), for the purpose of this research the model is trained to predict the Stemming Length [m] and Explosive Charge Mass [kg]. However, the approach is flexible and enables on-the-go changing of input and/or output feature number, meaning, the number of input and/or output features can be changed by desire. For the purpose of this research in order to evaluate the influence of meteorological data on blasting in clay soil, we ran two versions of the model.  
The \"model_1\" had only 2 meteorological input features (precipitation and mean daily air temperature from meteorological station Varazdin), while the \"model_2\" had 8 input features (precipitation, minimum and maximum air temperature recorded at meteorological stations Varazdin and Novi Marof, and additionally relative humidity recorded at meteorological station Varazdin). Both models were run in Jupyter Notebooks on an PC with Intel i5 8400 processor, 16 GB of RAM and RTX 2060 Super discrete GPU with 8 GB of video memory. Both models were trained on the GPU, with same hyperparameters, 4 batches of data per iteration, \(5\times 10^{-5}\) learning rate, 120 hidden nodes in the LSTM layers with 0.3 dropout and 5 nodes in the fully connected hidden layers. Due to a regression problem, Mean Squared Error (MSE) loss function was used, together with the AdaMax [38] optimizer handling the gradient descent during backpropagation process when the network was trained.  
To acquire best possible model results, several trial and error runs were undertaken, with the goal of finding best possible combination of hyperparameters. Training was considered successful right before the model started to overfit to training data, so model_1 was trained for 50 epochs while model_2 was trained for 60 epochs; due to more input features, the network needed more iterations to adapt to training data. To enable results reproducibility, torch.manual_seed has been used and set to 14.
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 4 Methods
### Machine Learning Approach  
Gradient Boosting is a type of ensemble-supervised machine learning algorithm that improves the accuracy of predictive models by combining multiple simpler base models (\"weak learners\", for example, decision trees) into a stronger ensemble [19]. The process is iterative, and the subsequently added learners are specifically trained to correct the errorsmade by the previous models. These errors are identified by large residuals computed in the previous iterations. Instead of predicting the actual target value, each new learner focuses on predicting the residuals. Therefore, the direction of improvement is determined by the negative gradients of the loss function, which indicates how the ensemble should be adjusted to minimize errors [20,21].  
In practical applications, Gradient Boosting can be adapted to various types of data and to regression and classification problems. An arbitrary selection of the base model and the loss function make Gradient Boosting a very flexible and versatile method that can be adjusted to a specific type of problem. For example, popular Gradient Boosting frameworks such as CatBoost, XGBoost, and LightGBM have been successfully applied in various industries, e.g., finance [22], healthcare [23], energy storage systems [24].
## 2 Numerical Calculation Model
### Mesh Independence Check  
A mesh independence check must be conducted to assess the suitable mesh density for achieving highly precise results while minimizing computational resources. The numerical model (Figure 3) was rebuilt with three structured mesh levels, and the simulation results for the mass flow rate and indicated power are presented in Table 1. Clearly, the difference in the indicated power and mass flow between tests 2 and 3 was less than 0.1%. The mesh of item 2 was then utilized.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **1** & **2(Ref)** & **3** \\ \hline No of mesh \(/10^{6}\) & 1.27 & 1.73 & 2.05 \\ \(POW/POW_{ref}\) & 0.9844 & 1 & 1.00045 \\ \(m/m_{ref}\) & 0.9817 & 1 & 1.0034 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mesh convergency study of the mesh level.  
Figure 4: Program flowchart of the Helmholtz energy.