esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic and Active THz Graphene Metamaterial Devices
## 1 Introduction
Through careful design, graphene-based non-linear transmission line structures and mode-selective transmission line structures can be used in various integrated devices, such as modulators, phase shifters, and antennas [39; 40; 41]. However, limited by the traditional complicated and time-consuming graphene manufacturing process, the manufacturing of multilayer and large-area patterning graphene metamaterials remains a challenge. This review aims to discuss the development of terahertz metamaterial devices utilizing graphene. Section 2 introduces the linear and the nonlinear properties of graphene in the THz region. Section 3 focuses on various applications of graphene metamaterials. Finally, Section 4 provides conclusions and a future outlook on this field.
# Properties and Recognition of Atom Graphs
## 3 Atom Graphs Are Atom Graphs of Chordal Graphs  
A graph is an atom graph if it is isomorphic to the atom graph of some graph. Similarly, for a given graph class \(\mathcal{C}\), a graph is an atom graph of a graph of \(\mathcal{C}\) if it is isomorphic to the atom graph of some graph of \(\mathcal{C}\). The atom graph recognition problem consists of determining whether a given graph is an atom graph. According to Definition 3, the connected components of the atom graph of a graph are the atom graphs of the connected components of this graph. It follows that a graph is an atom graph if and only if each one of its connected components is, so we may, without loss of generality, assume that the given graph is connected. We obtain similar definitions by replacing 'atom graph' with 'clique graph' for which we may also assume that the given graph is connected.  
We immediately deduce from Property 3 the following characterization.  
**Characterization 8**.: _A graph is an atom graph if and only if it is an atom graph of a chordal graph._  
Thus, the atom graph recognition problem can be reduced to an atom graph of a chordal graph recognition problem. It is not the case for clique graph recognition, which is NP-complete [26], whereas clique graph of a chordal graph recognition is polynomial [17]. As far as we know, the complexity of recognizing an atom graph (or equivalently an atom graph of a chordal graph) is an open question.
# Facile Method by Bentonite Treated with Heat and Acid to Enhance Pesticide Adsorption
## 3 Results and Discussion
### Characterisation of Bentonite and Modified Bentonite
#### 3.1.8 Particle Size Analysis  
The particle size distributions are shown in the supplementary information in Figure S2. The mean particle sizes of B, BC\({}_{500}\), BC\({}_{500}\)A\({}_{0.5}\), BA\({}_{0.5}\) and BA\({}_{0.5}\)C\({}_{500}\) are 7.15, 11.50, 8.71, 27.36, and 97.08 \(\upmu\)m, respectively (as mentioned in the supplementary information in Table S1). The samples were treated first with acid, BA\({}_{0.5}\)C\({}_{500}\) and BA\({}_{0.5}\) showing bimodal particle-size distribution. The BA\({}_{0.5}\) sample has the maxima at approximately 11 \(\upmu\)m (74.73%) and 25 \(\upmu\)m (25.27%) (Figure S2b). The large particle size seen for BA\({}_{0.5}\) may be due to the formation of a stable aggregate via hydrogen bonding between silanol groups, while BA\({}_{0.5}\)C\({}_{500}\) exhibits the maxima at approximately 13 \(\upmu\)m (58.39%) and 229 \(\upmu\)m (41.61%) (Figure S2c). The larger particle size may result from the occurrence of dehydroxylation between the aggregated particles due to calcination. On the other hand, the particle size distributions for BC\({}_{500}\)A\({}_{0.5}\) and BC\({}_{500}\) show only monomodal distributions, with maxima at about 9 and 11 \(\upmu\)m, respectively (Figure S2d,e). The BC\({}_{500}\)A\({}_{0.5}\) particle size is smaller than that for BC\({}_{500}\) due to acid erosion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Main Characteristics** & **B** & **BC\({}_{500}\)** & **BC\({}_{500}\)A\({}_{0.5}\)** & **BA\({}_{0.5}\)** & **BA\({}_{0.5}\)** & **C\({}_{500}\)** \\ \hline Specific surface area (m\({}^{2}\)/g) & 31.76 & 32.19 & 67.86 & 77.12 & 80.09 \\ Pore surface area (m\({}^{2}\)g\({}^{-1}\)) & 30.901 & 31.301 & 55.354 & 64.704 & 69.154 \\ Pore volume (cm\({}^{3}\) g\({}^{-1}\)) & 0.1246 & 0.1247 & 0.1750 & 0.2281 & 0.2456 \\ Zeta potential (mV)-pH 7 & \(-\)35.5 & \(-\)22.5 & \(-\)5.55 & \(-\)11.4 & 4.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Surface area, mean pore diameter, and zeta potential of the bentonite and modified bentonite samples.  
Figure 5: \({}^{27}\)Al MAS NMR spectra for bentonite and modified bentonite (Spinning side-bands are marked with asterisks).
# Advanced Air Mobility: Opportunities and Challenges Deploying eVTOLs for Air Ambulance Service
## 4 Analysis and Findings
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Min** & **Max** \\ \hline Cruise Altitude (ft) & 500 & 5000 \\ Medical Equipment Weight (lb) & 200 & 400 \\ Pilot Training (USD per year) & 10,000 & 30,000 \\ Paramete and EMT Training (USD per year) & 10,000 & 20,000 \\ Indirect Operating Cost (\% of DOC) & 5\% & 50\% \\ Bad Debt (\% of Operating Cost) & 10\% & 20\% \\ Electricity Price (USD/kwh) & 0.1 & 0.3 \\ Profit Margin (\% of Cost) & 10\% & 30\% \\ Disembarkation Time (in mins) & 3 & 5 \\ Climb Descend Distance (miles) & 1 & 2 \\ Energy Conversion Efficiency (\%) & 90\% & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Monte Carlo simulation assumptions. medical transport. Fixed costs can be reduced if they are spread over a greater number of medical transports (e.g., increasing aircraft utilization). However, for an eVTOL aircraft, the number of transports will also be impacted by battery recharging times that increase total call time and reduce availability of eVTOL compared to hybrid and gas-powered aircraft and rotorcraft. Both increased aircraft utilization and increased aircraft unavailability time due to charging are particularly unfavorable for the air ambulance use case because both can notably reduce dispatch reliability.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 5 Implementation
### VLC Subsystem
Figure 11: VLC receiver circuit diagram. VLC is a short-range technology capable of providing substantially high data rates. However, this technology is also interesting for IoT because of the ubiquity of LED lamps in both indoor and outdoor environments. Figure 13 depicts two estimated SNR maps at two different distances, taking into account the implementation parameters presented in Table 2. The left graph corresponds to a typical indoor environment, such as an office or a house, while the right graph could be considered representative of industrial environments. In both cases, the SNR within the coverage area (limited by the receiver's FOV) leads to a BER metric below \(10^{-6}\) (the worst case at the boundaries of the coverage area). Figure 12: Energy-normalized optical spectrum of the used VLC lamp. This measure was obtained using an integrating sphere with a 30 cm diameter and an optical spectrum analyzer (BTS256 LED tester). Figure 13: Signal-to-noise ratio (SNR) map at a 3 m height (**left**). SNR map at a 6 m height (**right**). In both cases, the receiver has been considered to point upwards. As mentioned above, VLC has been demonstrated to reach Gbps data rates [34]. Nonetheless, these results have generally been obtained under strict alignment [35] or laboratory conditions. Moreover, the used electronics is not cost-efficient, and the power consumption is not low-power compliant. In this work, since this architecture is IoT-oriented and is based on a LoRa backbone, the VLC data rate was selected to be close to the backbone's speed in order to alleviate bottlenecking. In addition, this allows for a good SNR performance and robustness against misalignment.
## 4 Discussion
### Developing and Detailing a Protocol for Isolation and Characterization of Gingipains  
#### 4.1.1 Gingipains Preparation  
1. Prepare 500 mL of _P. gingivalis_ HG66 cell-free culture supernatant by centrifuging it for 30 min at 12,000 rpm, 4 \({}^{\circ}\)C. Discard the pellet and preserve the supernatant on ice.
2. Add 750 mL of ice-cold acetone into the brownish supernatant slowly with vigorous agitation.  
Figure 5: Detail protocol to isolate and characterize the gingipains.  
Obtain the protein pellet by centrifugation for 30 min at 12,000 rpm, 4 \({}^{\circ}\)C.
* Resuspend the protein pellet with 10 mL of ice-cold 4_4\({}^{\prime}\)_-dithiopyridine disulfide buffer (DTDS buffer) and incubate on ice for 1 h with agitation.
* Transfer the protein into a dialysis bag (MWCO 12,000 to 14,000) and dialyze overnight at 4 \({}^{\circ}\)C with 2 changes.
* Concentrate the protein with ultrafiltration using Amicon PM-30 membrane for 30 min at 5000 rpm, 4 \({}^{\circ}\)C.
* Keep the protein sample at \(-20\)\({}^{\circ}\)C for future use.  
4.1.2 Isolation of Gingipains Using Passive-Diffusion Mediated Gel Elution Method Sample preparation  
1. Transfer 100 \(\mu\)L of gingipains sample into the sterile 1.5 mL microcentrifuge tube.  
[Note: Sample volume can be increased to obtain more purified protein]
2. Mix the gingipain sample with 20 \(\mu\)L of 5\(\times\) non-reducing solubilizing sample buffer (without SDS and \(\beta\)-mercaptoethanolhanol)
3. Load samples onto Native-polyacrylamideamide gel.
## 3 Methodology
### Definition of Snowpack Wet-Snow Dynamics
#### 3.1.3 Melting Cycles Definition  
A snow-melting cycle generally has three phases: (i) moistening, (ii) ripening, and (iii) melting runoff [11]. First, in the moistening phase, in which average snowpack temperature increases until the snowpack is isothermal, the snowpack is accumulating energy in the form of latent heat in order to be able to start melting. Second, in the ripening phase, the actual melting begins but the meltwater is retained in the snowpack. That is, the liquid water content of the snowpack increases, but water does not go out of the snowpack. Third, in the melting-runoff phase, further energy inputs produce a water output from the snowpack. From that time onwards, water follows its path until reaching the streamflow.  
Wet-snow conditions can be detected by using SAR imagery and therefore relate these states with melting phases in the snowpack. For that, over the \(\Delta\sigma\) signal, we defined a pixel as a wet snow pixel if it has a value lower than \(-\)3.00 dB (see Section 3.1.1). In each melting cycle, we defined the Local Minima (LMs) of the \(\Delta\sigma\) time series as possible melting-runoff onsets [43]. More than one LMs can be identified in the same melting cycle depending on the aging process of the snowpack (i.e., snowpack refreezing, rain on snow events).  
At a catchment scale, individual pixels' LMs were aggregated. That is, for each date, the total number of pixels with LMs in the catchment was calculated (\(\Sigma\)LM). The time series of this \(\Sigma\)LM defines the potential area of the catchment contributing to the melting runoff in each date. A larger number of \(\Sigma\)LM implies larger snow contributing areas to the melting runoff. The melting cycles were defined combining the \(\Sigma\)LM time series with the FSC time series (see Section 3.1.2). A melting cycle was defined if there was an increase in the number of pixels with \(\Sigma\)LM and FSC was maintained or decreased (Figure 3). That is, the existence of large amounts of minima during a date with significant loss of snow area confirm the possible contributions of water to the system. Then, for each melting cycle the absolute LM within the melting cycle was identified. This LM will provide the actual melting runoff onset for each pixel in this cycle, which is the Local Minima onset (LMo).  
Figure 3: Scheme of melting cycle definition.
## 2 Framework for Coordinated Operation
### Coordinated Operation Method Based on ADMM Algorithm  
For the synergy between power and gas networks and coordination operators, the synergy among the three decision makers is achieved through consensus based ADMM method. Consensus variables are introduced to reflect the interaction between upper and lower operators. In each iteration, the upper coordination operator checks the convergence and updates the consensus variables, while the lower electric operator and natural gas operator solve the local optimization problem at the same time. The cooperative operation mode of the third party is: Based on the synchronous ADMM decoupling algorithm, a higher-level third-party cooperative operator (CO) is introduced to solve the electricity network subproblem and the natural gas network subproblem simultaneously, and the relevant information is transferred to the third-party scheduling department to realize the cooperative operation.  
The structure of ADMM with coordination is shown in Figure 1 composed of five steps:  
1. Parameter initialization: Initialize the coupling variable values \(G_{i}^{g,1}\) and \(H_{e}^{g,1}\) of the power grid sub-problem and the natural gas network sub-problem respectively. Initialize the coupling variable value \(j_{e}^{g,1}\) of the third-party dispatching department, set the ADMM algorithm step size \(\rho\)land \(\rho\)2. Initialize the Lagrange multiplier \(\lambda_{ie,1}^{k}\) and \(\lambda_{ie,2}^{k}\) of the grid sub-problem and the gas grid sub-problem respectively.
2. Simultaneously solve the electricity network subproblem and the gas network subproblem.
3. Update ADMM multiplier and third-party coupling variable.
4. Convergence criterion: The original residual is the coupling unbalance, and the dual residual is the difference before and after the iteration of the coupling variable.
## 4 Results and Discussion
### Apriori Analysis Results
In that work, in 40% of inventory locations identified as highly susceptible, drain distances were from 48 to 100 m. Very high NDVI values were evident in unlabeled and landslide areas, but not in vulnerable slopes and no-landslide areas, confirming that reliance on aerial photographs was not appropriate because of thick vegetation cover over the study area. In other studies, NDVI was not used (for this reason); however, forest age, density, and type were used [4]. Low elevation values (15-43 m msl) were associated with unlabeled and landslide classes, with less support for the latter. Elevation values were very low (0-15 m) in vulnerable slopes (with high support), but were insignificant in no-landslide class. The elevation layer was found to be insignificant in the identification of the occurrence of a landslide in another study [4] using chi-squared automatic interaction detection. High values of the soil permeability index (from high to somewhat excessive) were found with all classes in the inventory. However, in association with another antecedent (soil depth), it shows high support for most classes, except vulnerable slopes. Moderate soil depths (20-50 cm) occur over all the classes and are not considered an effective indicator to be used in association with the unlabeled data class. For soil thickness, which generally ranges from 0 to 100 cm, thicknesses between 50 and 100 cm have the highest susceptibility occurring in almost 88% of the training dataset. A thicker soil is likely to carry more water [30]. TWI was not significantly observed, by analysis, as antecedent factor to initiate or motivate the landslides. Tree age, with very low intensity (short aged to non-forest), had little significance in the vulnerable slopes class but was insignificant in other classes, making this antecedent factor ineffective in the current study. This noneffectiveness accords with previous landslide susceptibility analysis [4,28].
## 3 Results and Discussion
### Characterization of Ligands
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entry** & **Catalyst (mg)** & **Solvent** & **Time (min)** & **Yield (\%)** \\ \hline
1 & MNPs-\(N\)-propyl-benzoguanamine-SO3H (3) & H\({}_{2}\)O & 300 & 45 \\
2 & MNPs-\(N\)-propyl-benzoguanamine-SO3H (3) & EtOH:H\({}_{2}\)O & 210 & 53 \\
3 & MNPs-\(N\)-propyl-benzoguanamine-SO3H (3) & EtOH & 180 & 68 \\
4 & MNPs-\(N\)-propyl-benzoguanamine-SO3H (6) & EtOH & 120 & 84 \\
5 & MNPs-\(N\)-propyl-benzoguanamine-SO3H (9) & EtOH & 210 & 75 \\
6 & MNPs-\(N\)-propyl-benzoguanamine-SO3H (11) & EtOH & 300 & 63 \\
7 & PTSA (6) & EtOH & 480 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimization of reaction condition for synthesis of (**L\({}_{1}\)–L\({}_{6}\)**) ligands. MNPs: magnetic nanoparticles; PTSA: p-Toluenesulfonic acid. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compounds** & **HCN (ppm)** & **CHO (ppm)** & **Ar-H (ppm)** & **CH\({}_{3}\) (ppm)** & **OH (ppm)** \\ \hline
**L\({}_{1}\)** & 10.04 & - & 8.02–7.50 & - & - \\
**L\({}_{2}\)** & 10.08 & - & 8.66–6.95 & 3.80 & - \\
**L\({}_{3}\)** & 9.72 & - & 8.34–6.82 & - & 1.16 \\
**L\({}_{4}\)** & 10.06 & 10.99 & 8.16–7.69 & - & - \\
**L\({}_{5}\)** & 8.64 & 10.07 & 8.03–6.95 & 3.79 & - \\
**L\({}_{6}\)** & 9.73 & 10.18 & 8.41–7.45 & - & 1.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \({}^{1}\)H NMR spectral data of the ligands. observed in 310 and 340 nm is due to the \(\mathrm{\SIUnitSymbolMicro n}\)\(\rightarrow\)\(\mathrm{n}^{\star}\) transition. However, \(\mathrm{\SIUnitSymbolMicro n}\)\(\rightarrow\)\(\mathrm{n}^{\star}\) and \(\mathrm{\SIUnitSymbolMicro n}\)\(\rightarrow\)\(\mathrm{n}^{\star}\) absorption peaks exhibited different behaviors due to the nature of substituents. In the UV-Vis spectra of the ligands with withdrawing group (NO\({}_{2}\)) the \(\mathrm{\SIUnitSymbolMicro n}\)\(\rightarrow\)\(\mathrm{n}^{\star}\) peak shifted to a lower wavelengths (blue-shift) as compared to the ligands, whereas with electron donating group (OH) the \(\mathrm{\SIUnitSymbolMicro n}\)\(\rightarrow\)\(\mathrm{n}^{\star}\) peak observed in higher wavelengths (red-shift).
## 2 Antenna Array Design  
To build the antenna array, we start by analyzing the performance of a COTS antenna. The ceramic antenna is a low-cost, low-form-factor passive patch antenna commercialized for GNSS applications. Owing to these attributes, along with its performance, the adequate datasheet, and 3D models that the manufacturer provides for this antenna, it was selected as the commercial antenna to consider for the design of the array. Simulations are performed with full-wave solver Computer Simulation Technology (CST) studio suite 2023 by Simulia.
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 3 MADDPG-Based Resource Allocation
### The MADDPG Algorithm
```
1: Initialize the actor network \(\mu_{n}(\mathbf{o_{n}^{k}};\theta_{n}^{u})\) and the critic network \(Q_{n}(\mathbf{s_{k}^{k}};\theta_{n}^{Q})\) for each agent \(n\) at time step \(k\);
2: Initialize the target network \(\mu_{n}^{\prime}(\mathbf{o_{n}^{k}};\theta_{n}^{u^{\prime}})\) and the critic network \(Q_{n}^{\prime}(\mathbf{s_{k}^{k}};\theta_{n}^{Q^{\prime}})\) for each agent \(n\) at time step \(k\);
3: Initialize the experience replay buffer \(\mathcal{B}_{n}\) for each agent \(n\);
4:for episode = 1 to E do
5: Initialize state \(\mathbf{s}^{k}=\{\mathbf{o_{1}^{k}},\mathbf{o_{2}^{k}},\dots,\mathbf{o_{N}^{k }}\}\) and \(k=1\)
6:while step \(k<K\)do
7:for each agent \(n\)do
8: Get \(\phi_{n}^{k}\), select action \(\mathbf{a_{n}^{k}}=\mu_{n}(\mathbf{o_{n}^{k}})\)
9: Execute actions \(\mathbf{a_{k}}\) and obtain the reward \(r_{n}^{k}\) and the next state \(\mathbf{o_{n}^{k+1}}\)
10: Store \((\mathbf{s_{k}},\mathbf{a_{k}},r_{n}^{k},\mathbf{s_{k+1}})\) in \(\mathcal{B}_{n}\)
11:endfor
12:endwhile
13:for each agent \(n\)do
14: Sample a random mini-batch of \(\mathcal{S}\) samples from the experience replay buffer \(\mathcal{B}_{n}\)
15: Update the critic network by minimizing the loss function \(\mathcal{L}(\phi_{n}^{Q})\) in (24)
16:if\(k\mod\mathcal{C}=0\)then
17: Update the parameters \(\phi_{n}^{Q}\) using the deterministic policy gradient in (23)
18: Update two target networks: (25) and (26)
19:endif
20:endfor
21:endfor
```  
**Algorithm 2** RIS-MADDPG Algorithm. To ensure the reproducibility of our results, all of the hyperparameters, training configurations, and simulation environment details have been clearly specified in Table 1, and the algorithmic process has been fully described in Algorithm 2.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 3 The ECG Circuit Design  
The magnitude of an ECG signal varies from microvolt to millivolt range [9,10]. Therefore, the signal should be amplified in order to be distinguished from noise, and high and low frequency noises need to be filtered out.  
The main task of the ECG circuit is to make the ECG signal more measurable by amplifying the small ECG signal collected from the heart, as well as to filter out the external and internal noise, in addition to reducing the common-mode interference using a right leg drive (RLD).
## 3 Results
### Statistical AnalysisFresh and End Product of Silage
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multirow{2}{*}{**pH**} & \multicolumn{2}{c}{**WSC (g/kg DM)**} & \multicolumn{2}{c}{**LAB (log10 cfu/g)**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Aerobic Bacteria** \\ **(log10 cfu/g)** \\ **(log10 cfu/g)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Yes Log10 cfu/g/** \\ **and Mould** \\ \end{tabular} } & \multicolumn{2}{c}{**NDF (g/kg DM)**} & \multicolumn{2}{c}{**ADF (g/kg DM)**} \\ \hline \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{_p_**-values**} & \multicolumn{1}{c}{**SEM**} \\ \hline SS & \(<\)0.0001 & 0.38 & 0.0013 & 4.18 & 0.0453 & 1.3 & 0.0013 & 0.66 & 0.0013 & 0.08 & 0.0013 & 0.08 & 0.0013 & 0.08 \\ SS-MTD & 0.0004 & 0.52 & 0.0001 & 4.75 & 0.0001 & 1.44 & 0.0001 & 0.98 & 0.0001 & 0.61 & 0.0001 & 0.61 & 0.0001 & 0.61 \\ SS-HY1 & 0.0004 & 0.52 & 0.0002 & 5.47 & \(<\)0.0001 & 1.44 & 0.0002 & 0.14 & 0.0002 & 0.61 & 0.0002 & 0.61 & 0.0002 & 0.61 \\ SS-HY2 & 0.0001 & 0.46 & 0.0003 & 5.03 & 0.05 & 1.31 & 0.0003 & 0.66 & 0.0003 & 0.57 & 0.0003 & 0.57 & 0.0003 & 0.57 \\ SS-HY3 & 0.0001 & 0.48 & 0.0005 & 4.94 & 0.117 & 1.36 & 0.0005 & 0.98 & 0.0005 & 0.24 & 0.0005 & 0.24 & 0.0005 & 0.24 \\ NS & 0.0005 & 0.13 & 0.0005 & 4.44 & 0.0463 & 0.21 & 0.0005 & 0.14 & 0.0005 & 0.68 & 0.0005 & 0.68 & 0.0005 & 0.68 \\ NSMTD & 0.1/0.0001 & 0.23 & 0.0005 & 4.05 & 0.900 & 0.15 & 0.0005 & 0.14 & 0.0005 & 0.70 & 0.0005 & 0.70 & 0.0005 & 0.70 \\ NS-HY1 & \(<\)0.0001 & 0.23 & 0.0004 & 4.50 & 0.900 & 0.15 & 0.0004 & 0.14 & 0.0004 & 0.70 & 0.0004 & 0.70 & 0.0004 & 0.70 \\ NS-HY2 & 0.0006 & 0.15 & 0.0003 & 4.80 & 0.016 & 0.27 & 0.0003 & 0.23 & 0.0003 & 0.22 & 0.0003 & 0.22 & 0.0003 & 0.22 \\ NS-HY3 & 0.0001 & 0.26 & 0.0004 & 4.55 & 0.010 & 0.25 & 0.0004 & 0.25 & 0.0004 & 0.20 & 0.0004 & 0.2 & 0.0004 & 0.2 \\ \hline \hline \end{tabular}  
1 \(\lx@sectionsign\) Sweet sorghum; NS Napier grass; no additive control; MTD/1, _L plantarum, Kosyl; HY1 Lactobacillus plantarum; HY2 Pololecross acillate; HY3 Pololecross acillate; HY5 Pololecross acillate; WSC, water-soluble carbohydrates; NDF, neutral detergent fibre; ADE, add detergent fibre; 3 SEM, standard error of means.  
\end{table} 
Table 6: Statistics analysis of pH, WSC and microbial population of sweet sorghums and Napiergrass on day 60 of incubation compared to fresh grasses.