esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Sampling and Sedimentological Analysis  
Duplicate sediment cores were collected in January 2016 at the central and the deepest part of LGSP (13.5 m) and LCSP (18 m). The cores were obtained using a UWITEC gravity core equipped with a 6 cm diameter plexiglass tube, covered with aluminium foil to protect from sunlight and stored at 4 \({}^{\circ}\)C until the analyses. The cores were upward sectioned every 1 cm, freeze-dried and homogenised using a mortar and pestle to analyse biogenic silica and fossil pigments. For determination of the stable isotopes, the samples were oven-dried and homogenised using a mortar and pestle before the analysis. The biogenic silica (BSi) was extracted with the alkaline method and determined by molybdate-blue spectrophotometry [59].  
The magnetic susceptibility (MS) was measured at 1 cm intervals using the Bartington MS2E sensor and the Multisus v2.44 software. The grain-size analysis was performed using a Malvern Mastersizer 3000 laser particle size analyser. A quantity of 5 mg of each sediment layer was treated with H\({}_{2}\)O\({}_{2}\) (30%) in a thermal bath to remove the organic matter and then combined with distilled water to suspend sediments uniformly prior to analysis. The percentage by volume of each size fraction was analysed using the Gradistat program to determine conventional particle size statistics [60; 61]. Grain-size and magnetic susceptibility were used as an indicator of input of terrigenous materials, allochthonous to the lake. These analyses were conducted in the laboratory of Paleolimnology and the laboratory of Environmental Sciences Center EULA-Chile at the University of Concepcion, Chile.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Laguna Chica** & **Laguna Grande** \\ \hline Urban area & 4.9\% & 4.1\% \\ Water transparency (m) & 5.2 (3.8–6.8) & 3.7 (3.0–6.1) \\ Water temperature \({}^{\circ}\)C & 17.4 (12.5–24.0) & 18.1 (12.2–24.0) \\ Annual mean precipitation & 1.235 mm & 1.235 mm \\ (mm) & 7.0 (6.5–7.73) & 7.0 (6.5–7.6) \\ Conductivity (\(\mu\)S/cm) & 71 (50–90) & 84 (35–107) \\ Dissolved oxygen (mg/L) & 9.2 (5.9–10.9) & 8.8 (4.9–10.7) \\ Alkalinity (meq/L CaCO\({}_{3}\)) & 0.4 (0.4–0.5) & 0.5 (0.5–0.6) \\ TDS (mg/L) & 42.3 & 52.6 \\ Total phosphorus TP (mg/L) & 20 & 50 \\ Total nitrogen TN (mg/L) & 170 (60–320) & 230 (80–340) \\ Chlorophyll \(a\) (\(\mu\)g/L) & 1.20 & 6.42 \\ Primary productivity & 2.44 & 22.68 \\ (mgC/m\({}^{3}\)/hr) & & 22.68 \\ Trophic state & Mesotrophic-Eutrophic & Eutrophic-Hypereutrophic \\ Macrophytes & Abundant & Major abundance \\ Summer stratification & Yes & No \\ Phytoplankton richness & 39 & 57 \\  & Bacillariophyceae & Chlorophyceaeae \\ Main phytoplankton groups & Desmideaceae & Bacillariophyceaeae \\  & Cyanobacteria & Desmideaceae Cyanobacteria \\  & _Microcytsis_ sp. (blooms) & _Microcytsis_ sp. (blooms) \\  & _Dolicospermum_ sp. & _Dolicospermum_ sp. \\ Reports of Cyanobacteria & _Merismopedia_ sp. & _Pseudomanabena_ sp. \\  & _Oscillatoria_ sp. & _Gomplospheria_ sp. \\  & _Aphanocapsa_ sp. & _Chroococcus_ sp. \\  & _Snovelella_ sp. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 4 Identity-Based Full Homomorphic Encryption Scheme
### Construction
* **IBFHE.Dec**\((MPK,\mathbf{s}_{id},\mathbf{C})\) : Input the master public key \(MPK\), user's secret key \(\mathbf{s}_{id}\) and ciphertext \(\mathbf{C}\) to decrypt. Set the vector \(\omega=\big{[}\big{\lfloor}\frac{q}{2}\big{\rfloor},0,\ldots,0\big{\rfloor}\in \mathbb{Z}_{q}^{m^{\prime}}\).Compute \(\mu^{\prime}=\mathbf{s}_{id}^{\mathsf{T}}\cdot\mathbf{C}\cdot\mathbf{M}^{-1}(\omega)\) and output \(\Big{\lfloor}\frac{2\mu^{\prime}}{q}\Big{\rfloor}\).
###### Abstract  
A major challenge for massive multiple-input multiple-output (MIMO) technology is designing an efficient signal detector. The conventional linear minimum mean square error (MMSE) detector is capable of achieving good performance in large antenna systems but requires computing the matrix inverse, which has very high complexity. To address this problem, several iterative signal detection methods have recently been introduced. Existing iterative detectors perform poorly, especially as the system dimensions increase. This paper proposes two detection schemes aimed at reducing computational complexity in massive MIMO systems. The first method leverages the symmetric accelerated over-relaxation (SAOR) technique, which enhances convergence speed by judiciously selecting the relaxation and acceleration parameters. The SAOR technique offers a significant advantage over conventional accelerated over-relaxation methods due to its symmetric iteration. This symmetry enables the use of the conjugate gradient (CG) acceleration approach. Based on this foundation, we propose a novel accelerated SAOR method named CGA-SAOR, where CG acceleration is applied to further enhance the convergence rate. This combined approach significantly enhances performance compared to the SAOR method. In addition, a detailed analysis of the complexity and numerical results is provided to demonstrate the effectiveness of the proposed algorithms. The results illustrate that our algorithms achieve near-MMSE detection performance while reducing computations by an order of magnitude and significantly outperform recently introduced iterative detectors.  
amss: MIMO; iterative methods; low complexity; signal detection; linear MMSE 1
## 5 Numerical Examples
### Example 8
Equation (80) becomes  
\[\mathbf{x}=\mathbf{u}_{0}+\lambda_{0}\mathbf{V}\mathbf{b}-\mathbf{V}\mathbf{A} \mathbf{u}_{0}.\]  
As shown in Table 10, the DOA is more accurate than the DOA with \(\alpha_{0}=1\). \begin{table}
\begin{tabular}{c c c c c c} \hline \(m\) & \(\mathbf{5}\) & \(\mathbf{8}\) & \(\mathbf{10}\) & \(\mathbf{12}\) & \(\mathbf{15}\) \\ \hline ME & \(7.24\times 10^{-5}\) & \(1.73\times 10^{-4}\) & \(2.95\times 10^{-4}\) & \(1.02\times 10^{-3}\) & \(4.99\times 10^{-3}\) \\ \hline IN & 174 & 42 & 23 & 16 & 10 \\ \hline \end{tabular}
\end{table} 
Table 7: Example 8 with \(n=2000\) and \(q=100\): ME obtained by the DOA and INs for different \(m\). \begin{table}
\begin{tabular}{c c c c c c} \hline \(m\) & \(\mathbf{10}\) & \(\mathbf{12}\) & \(\mathbf{15}\) & \(\mathbf{18}\) & \(\mathbf{20}\) \\ \hline ME & \(1.39\times 10^{-4}\) & \(6.82\times 10^{-5}\) & \(3.20\times 10^{-5}\) & \(1.70\times 10^{-5}\) & \(1.26\times 10^{-5}\) \\ \hline IN & 123 & 72 & 41 & 25 & 19 \\ \hline \end{tabular}
\end{table} 
Table 8: Example 8 with \(n=500\) and \(q=2000\): ME obtained by the DOA and INs for different \(m\). \begin{table}
\begin{tabular}{c c c c c c} \hline \((q,n)\) & **(1000,500)** & **(1500,500)** & **(1500,1000)** & **(2000,500)** & **(2500,1000)** \\ \hline \(\|\mathbf{Q}^{\mathrm{T}}\mathbf{Q}-\mathbf{I}_{n}\|\) & \(1.81\times 10^{-12}\) & \(2.55\times 10^{-12}\) & \(8.99\times 10^{-12}\) & \(2.20\times 10^{-12}\) & \(1.14\times 10^{-11}\) \\ \hline QR & \(3.18\times 10^{-10}\) & \(6.91\times 10^{-10}\) & \(1.73\times 10^{-9}\) & \(1.61\times 10^{-9}\) & \(4.56\times 10^{-9}\) \\ \hline DOA & \(2.49\times 10^{-13}\) & \(2.66\times 10^{-13}\) & \(2.46\times 10^{-13}\) & \(1.77\times 10^{-13}\) & \(1.24\times 10^{-13}\) \\ \hline \end{tabular}
\end{table} 
Table 9: Example 8 with different \((q,n),\ q>n\): ME obtained by QR and the DOA.
## 5 Conclusions  
By employing 2D-PECA for building a 2D spatiotemporal chaotic system, this paper proposes a novel 2D-DPRCML system. There are three innovations in the proposed system: a dynamically pseudo-random coupling scheme based on the iterative results of 2D-PECA, the introduction of perturbations generated by the 2D-PECA and the different and variable sign of perturbation for each lattice. The simulation and comparison analysis results demonstrate that the 2D-DPRCML system possesses stronger chaotic properties, including higher KED and KEB, and better aperiodicity in the bifurcation diagram. The good chaotic behavior of 2D-DPRCML enlarges the optional parameter pair space, which indicates that the key space can be extended when the proposed system is implemented in cryptography. Moreover, the return map in phase space is uniform, implying that 2D-DPRCML is resistant to the attack of phase-space reconstruction. Statistics and simulation results illustrate the excellent uniformity, randomness and unpredictability of 2D-DPRCML's output sequence. Additionally, the correlation intensity among any two lattices in this system is weak, thus strengthening the system's security. To sum up, all these good properties of 2D-DPRCML  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Total Number of Lattices** & **Pass Rate/\%** & **Pass Rate of All Sub-Tests/\%** \\ \hline
2D-CML & 100 & 0 & — \\
2D-NLCML & 100 & 0 & — \\
2D-MCPML & 100 & 0 & — \\
2D-DPRCML & 100 & 98 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of frequency test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Sub-Tests** & _p-Value_ & **Result** \\ \hline  & \(x=-5\) & 0.846715 & Pass \\  & \(x=-4\) & 0.47397 & Pass \\  & \(x=-3\) & 0.494628 & Pass \\  & \(x=-2\) & 0.823367 & Pass \\  & \(x=-1\) & 0.907663 & Pass \\  & \(x=1\) & 1 & Pass \\  & \(x=2\) & 0.623372 & Pass \\  & \(x=3\) & 0.183073 & Pass \\  & \(x=4\) & 0.111202 & Pass \\  & \(x=5\) & 0.118907 & Pass \\  & \(x=6\) & 0.140314 & Pass \\  & \(x=7\) & 0.146234 & Pass \\  & \(x=8\) & 0.157794 & Pass \\  & \(x=9\) & 0.15543 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._enable it to be well applied in the field of cryptography, such as the S-box generation, pseudo-random number generators and image encryption.  
Conceptualization, H.N. and Y.D.; methodology, H.N.; software, H.N. and Y.D.; validation, H.N., G.Z. and Y.D.; formal analysis, H.N. and Y.D.; investigation, H.N.; resources, G.Z. and Y.M.; data curation, H.N.; writing--original draft preparation, H.N.; writing--review and editing, H.N., G.Z., Y.M. and Y.D.; visualization, H.N. and Y.D.; supervision, G.Z.; project administration, G.Z.; and funding acquisition, G.Z. and Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the First-class Discipline Construction Project of Beijing Electronic Science and Technology Institute (No: 3201017) and the National Natural Science Foundation of China (No: 61772047).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 5 Analysing General XAI
#### XAI in Terms of Design Space
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \multirow{2}{*}{**Methods for Explainability**} & \multirow{2}{*}{**References**} & \multicolumn{4}{c}{**Explanations**} & \multicolumn{4}{c}{**Stage**} & \multicolumn{4}{c}{**Scope**} & \multicolumn{4}{c}{**Design Space**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-13}  & & **N** & **R** & **T** & **V** & **A**h** & **Ph** & **L** & **G** & **P** & **O/A** & **A** & **M/S** & **Count** \\ \hline ANFIS & [273, 278, 279, 280] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & 9 \\ Anchors & [281] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & 8 \\ Feature Importance & [282, 283, 284, 285] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & & 7 \\ LIME & [270, 272, 275, 276] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & 7 \\ RetainVis & [286] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & 7 \\ SHAP & [21, 271, 272, 277] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & & 7 \\ SRM & [274] & & & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & 7 \\ SurvLIME-KS & [287] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & 7 \\ TreeExplainer & [271] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ & & & ✓ & 7 \\ BB-BC IT2 FLS & [288] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & & ✓ & 6 \\ CIE & [289] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & 6 \\ ExNN & [290] & & & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & 6 \\ \hline \end{tabular}
\end{table} 
Table 6: XAI methods with associated types of explanations (_N_: Numeric, _R_: Rules, _T_: Textual, _V_: Visual), stage (_A_lr_: Ante-hoc, _Ph_: Post-hoc), scope (_L_: Local, _G_: Global) or explainability, and the design spaces (_P_: Prediction, _O_/_A_: Optimisation/Automation, _A_: Analysis, _M_/S: Modelling/Simulation).
## 2 Data and Preprocessing
### Data Preprocessing Using Unscramber 9.7  
The visible and near-infrared spectra of lunar samples contain noise and various external disturbance factors in the data measurement. Before using PLS method to retrieve mineral content of lunar regolith, the spectra data should be pre-processed first. By pre-processing the spectra data of lunar samples, the noise information and irrelevant variables can be effectively eliminated. The influence of various non-target factors on the spectra can be eliminated so as to highlight the spectral features and improve the accuracy of the model [13].  
In this paper, the original spectral data were pre-processed with the multivariate statistical software the Unscrambler 9.7 of Camp Company, Sweden. Common preprocessing  
Figure 1: (**a**) is the reflectance spectra of 10,084 sampling point of lunar mare, and (**b**) is the reflectance of 14,141 sampling point of lunar highland.  
methods include continuum-removal, natural logarithmic variation, first-order derivative, second-order derivative, multiple scattering correction (MSC) and so on.  
In this paper, the PLS regression relationship between the spectral and mineral content of lunar regolith of samples of lunar highland and lunar mare for LSCC data was constructed. The two sets of samples in LSCC data were pre-processed by continuum-removal, first derivative, second derivative and multiple scattering correction (MSC) respectively. Finally, MSC was chosen as the best pretreatment method to measure the error index and the number of principal components. MSC can effectively eliminate scattering caused by uneven particle distribution and particle size difference [14]. After MSC pretreatment of LSCC data, the effects of sample size, density, surface roughness and other surface factors on the spectra were eliminated, spectral drift was effectively eliminated, and spectral features were enhanced [15]. MSC can maximize the expression of dependent variable information on the premise of fewer principal components, and at the same time ensure small error, which plays a positive optimization role for the model (Figure 2). Figure 2a,b compare the spectral changes of a typical lunar sea sampling point (10,084) and a lunar highland sampling point (14,141) before and after MSC preprocessing.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 3 Results
### Dry Density Prediction by Thresholding of Soil Images
Figure 6 shows the relationship of \(\rho_{d}\) and \(n^{\prime}\) calculated with the images of Figure 1.  
Figure 5: \(n^{\prime}\) of the threshold soil images. The soil images are the same as in Figure 4b–f. \(n^{\prime}\) of the simple thresholding is calculated at one interval of the threshold values from 0 to 255. Figure 6: The relationship of \(\rho_{d}\) and \(n^{\prime}\) according to thresholding methods. In the Otsu method, as the \(\rho_{d}\) increases from 1.1 t/m\({}^{3}\) to 1.2, 1.3, and 1.4 t/m\({}^{3}\), the threshold values of 78, 80, 84, and 85 were selected, respectively. The coefficient of determination (\(R^{2}\)) of simple thresholding was greater than that of the Otsu method. As a result of this example image set, using a single threshold value gave better results than automatically determined by the Otsu method. However, results may vary in other soil conditions. The correlation coefficients of \(\rho_{d}\) and \(n^{\prime}\) under various soil conditions are summarized in Table 2.  
Twelve digital images were used to calculate one correlation coefficient. Each set of 12 images was produced by triplicate soil samples with dry densities of 1.1, 1.2, 1.3, and 1.4 t/m\({}^{3}\) under one water content condition. For the Otsu method and Triangle method, \(n^{\prime}\) was calculated according to the threshold value automatically selected according to the algorithm. According to Equation (5), \(n\) and \(\rho_{d}\) have a proportional relationship with \(-1\) as the proportionality constant. Therefore, the closer the correlation coefficient is to \(-1\), the better the \(\rho_{d}\) prediction using \(n^{\prime}\). Table 2 shows that \(\rho_{d}\) and \(n^{\prime}\) show a strong negative correlation in all thresholding methods at a water content of 20% or greater, indicating that \(\rho_{d}\) can be predicted using \(n^{\prime}\). However, when the water content is less than 15%, there is no correlation or a positive correlation, which means that these threshold methods cannot be used robustly for soil density prediction. Additionally, the determination of most proper threshold values for simple thresholding is still a problem. Therefore, it is necessary to derive new predictors of \(\rho_{d}\).
# The Concept of Using 2D Self-Assembly of Magnetic Nanoparticles for Bioassays
## 2 Materials and Methods
### Measurement Characterization  
The prepared samples were investigated using various measurement techniques:  
* Atomic-force microscopy (AFM) measurements provided by Flex Axiom-Nanosurf to visualize deposits from drying magnetic colloids;
* Optical microscopy provided by Keyence VHX-970F digital microscope;
* Zeta potential for determining stability of magnetic colloids, and dynamic light scattering (DLS) method, where Zetasizer Ultra Malvern was used;
* Raman spectroscopy with the use of the Renishaw InVia Quantor spectrometer to determine the kind of magnetic iron oxides in the magnetic colloids and confirm the presence of fluorescein deposited on a 2D array of ordered chains of magnetic nanoparticles;
* Transmission electron microscopy (TEM) with the energy-dispersive X-ray spectrometer (EDX), to confirm the size of the coated magnetic nanoparticles and their chemical composition.  
For the Raman spectroscopy method, lasers with two different powers corresponding to wavelengths \(\lambda=785\) nm and \(\lambda=532\) nm were used. Due to luminescence effects in the case of fluorescein, the laser of wavelength \(\lambda=785\) nm was selected, which generated Raman spectra with distinct peaks. In the case of LDPE, the laser wavelength \(\lambda=532\) nm was used.
# Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
## 9 Security Analysis and Practical Consideration
In the case of perfect secrecy, based on (106), we obtain the following dependence  
\[E_{BC}=\frac{\left|\widetilde{K}_{s}\right|_{max}}{\widetilde{n}_{p}}=\frac{1} {\bar{n}_{p}}\left(C_{2}-C_{1}-\frac{t}{n_{IR0}}\cdot\frac{C_{2}+C_{1}}{2\left| K_{G}\right|+t}\right)\,, \tag{112}\]  
which is shown in Figure 16.  
Figure 15: Dependence of the public channel load measure \(E_{BC}\) on \(\left|K_{G}\right|\) for different values of \(r\) in the ideal security operating mode. Based on these dependencies, we can conclude that in the perfect secrecy operating mode, for \(|K_{G}|=512\), this ratio is not less than \(1/8\). In other words, for the online operation of an autonomous, perfectly secret CR KSG system, the public channel must support transmission speeds approximately eight times greater than the rate of the information source being protected. From Figure 15, we observe that for the same values of \(K_{G}\), and a secret key \(K_{S}\) equivocation of approximately 128 bits (\(r=0.2\)), this requirement is significantly lower, just around 5 times. In the era of modern telecommunications and the internet, this demand does not pose a serious challenge. In offline operation mode, the limitation of the transmission speed over the public channel only affects the time needed to distill the required amount of secret keys. The practical application of autonomous CR KSG systems encompasses a wide range of scenarios, from commercial to strategic uses for the security of critical infrastructure, diplomatic correspondence, and cover communications during military operations. Since specialized and complex cryptographic key generation and distribution systems are typically developed at strategic levels, autonomous CR KSG systems are ideal as backup systems that can assume their function in the event of failure, compromise, or destruction of the primary systems. In military applications, these systems become especially attractive due to the widespread availability of mobile internet, such as Starlink. In the event of disruption of standard secure communication links, which are critical for the successful conduct of military or special operations, autonomous CR KSG systems can take over the role of generating and distributing cryptographic keys. For example, the war in Ukraine has demonstrated that the ubiquity of the Starlink system potentially enables perfectly secure communication with central or regional commands even in conditions where military units are completely isolated or surrounded, despite not having been previously supplied with cryptographic keys or having exhausted them entirely.
## 3 Results and Discussions
### Microwave Absorption Properties
Finally, GO and FeSiAl have a synergistic effect as, at a high frequency, GO can lower FeSiAl's permeability and prevent a skin effect, therefore forming a better impedance matching. It can be concluded that the GO/FeSiAl epoxy compounds have strong absorption and a broad effective bandwidth covering the operating frequency of 5G antennas with a thin coating so that it could provide a potential means for efficient microwave absorption for a 5G base station. Figure 5:0 Attenuation constant (\(\alpha\)) of epoxy compounds.
# Nutrition in Pediatric Intensive Care: A Narrative Review
## 5 Macronutrients  
### Proteins  
Low protein delivery in combination with proteolysis and a negative protein balance remains a common problem in PICU settings. The minimal protein dose of 1.5 mg/kg/day is recommended by several guidelines [1; 4; 18]; however, a much higher dose may be needed to maintain a neutral or even positive protein balance. The protein/amino acid dose should, however, be limited to 3 g/kg/d in neonates and infants and 2 g/kg/d in stable adolescents [33]. Proteins are polymers of amino acids. The amino acids can be divided into three groups: essential, semi-essential and nonessential. The essential amino acids cannot be synthesized in vivo; therefore, they must be supplemented by oral/enteral or parenteral form. Semi-essential (conditionally essential) amino acids can become essential in several clinical settings: infancy, critical illness, trauma, burns and others. The amino acid classification is listed in Table 2. The so-called pharmaconutrition (e.g., glutamine, arginine supplementation) is not currently recommended for PICU settings [1]. However, in preterm neonates, arginine (necrotizing enterocolitis prevention), tyrosine and cysteine supplementation can be associated with improved outcomes [33]. The energy content of 1 g of protein is around 4 kcal (17 kJ) and is equal to 1 g of carbohydrates. E delivery in the form of proteins/amino acids in the mixed enteral or parenteral formula should be between 15 and 30%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Essential** & **Semi-Essential (Conditionally Essential)** & **Nonessential** \\ \hline Argin & Cysteine & Alanine \\ Histidine & Glutamine & Asparagine \\ Isoleucine & Hydroxyproline & Aspartate \\ Leucine & Proline & Glutamate \\ Lysine & Taurine & Glycine \\ Methionine & & Serine \\ Phenylalanine & & Tyrosine \\ Threonine & & \\ Typtorphan & & \\ Valine & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amino acid classification by the possibility of synthesis in vivo.