esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.2 Remote Sensing Monitoring and Detection of Cropland Productivity
The formula for the difference in the improvement level of cropland production stability (\(S_{p}\)) is as follows:  
\[S_{p}=R_{n}-R_{h} \tag{9}\]  
\[R_{n}=\left(NP_{normal}-NP_{ drought}\right)*100/NP_{normal} \tag{10}\]  
\[R_{h}=\left(HP_{normal}-HP_{ drought}\right)*100/HP_{normal} \tag{11}\]  
where \(R_{n}\) is the change in productivity rate of drought-affected cropland in the non-project region, \(R_{h}\) is the change in productivity rate of drought-affected cropland in the project region, \(NP_{normal}\) is the productivity of the non-project region in normal years, \(NP_{ drought}\) is the productivity of the non-project region in disaster years, \(HP_{normal}\) is the productivity of the project region in normal years and \(HP_{ drought}\) is the productivity of the project region in disaster years. Improving the stability of cropland productivity is an index for evaluating the ability of the cropland to resist disaster and achieve stable and high yields after HSF construction. The higher the value of \(S_{p}\), the more stable cropland productivity due to HSF construction in the project region in dry years. Improvement of homogenous yield degree (HYD)  
Homogenous yield degree refers to the degree of uniformity and dispersion of high-yield fields after HSF construction, which can be used to measure the balanced yield increase capacity of HSF projects. If the cropland area of medium-low productivity decreases in the project region and the cropland area of high productivity increases, the effectiveness of the HSF project construction will be reflected in improved productivity. Based on this, the proportion index of cropland of medium-low productivity before and after the construction is selected to measure the balanced yield increasing the capacity of HSF construction projects. The formula for the proportion index change of medium-low productivity cropland is as follows:  
\[LP=\left(\frac{L_{a}}{S_{a}}-\frac{L_{b}}{S_{b}}\right)*100 \tag{12}\]  
where \(LP\) is the change in the percentage of medium-low productivity cropland before and after the project construction, \(S_{a}\) is the total cropland area in the project region before the project construction, \(L_{a}\) is the total area of medium-low productivity cropland in the project region before the project construction, \(S_{b}\) is the total cropland area in the project region after the project and \(L_{b}\) is the total area of medium-low productivity cropland in the project region after the project construction. The greater of \(LP\), the higher the homogenous yield degree of HSF construction.
## 4 Workload and Data Center Modeling
### Model Implementation and Evaluation  
The proposed models are implemented using Python 3.7.8, Numpy library version 1.19.5 and Google tensorflow 2.4.1. The analytical model is built based on the Google Log traces and has been used to test the machine learning model. Google trace describesthe demands of jobs and tasks as in the workload modeling. The resource demands do not follow one type of characteristic. Two workload types are used to test the proposed modeling methods. Figures 10 and 11 show a comparison between the three machine learning models' prediction accuracy compared to the analytical method. It is clear that all methods show good prediction values with minor deviations. The analytical approach shows closer predicted values to the real trace, especially in a trace that changes quickly, such as in Figure 11. However, Figure 10 shows that ML methods are closer to a real trace. Overall, all methods show good accuracy for all types of workloads, with some variation in the prediction accuracy. This is according to logs and modeling characteristics. In non-repeated logs, it is noted that LSTM strangles rapidly predicted non-periodic changes in traces, but, on the other hand, it is a very accurate prediction scheme for similar rhythmic logs. CNN is excellent for most cases but shows a shortage in small changes predictions. NN can be more generic for all types of logs and behavior schemes without any preferable accuracy for any type of workload.  
For data center modeling, Figures 12 and 13 show good prediction accuracy for all kinds of models. In data centers, the values are smoother in the provisioning of resources, and log changes are lower. The ML models are very close to each other; LSTM and NN have lower accuracies in general, with small changes in log values. CNN has higher accuracy in all cases, despite this not being shown with workload traces. This is because CNN works by correlating between logs attributes, where workload attributes have a high relation argument value. Since correlation argument values are very high, the significance of the small argument changes is neglected. The small changes in log values in data centers have significant impacts since they show log characteristics. Any small changes are correlated in convolution operations and show better prediction.  
For Alibaba, analytical modeling of the workload and data center traces show lower accuracy prediction. The analytical model does not match the log trace type characteristics that show intermediate prediction capabilities. On the other hand, machine learning methods provide 89% to 96% prediction accuracy. Figures 14 and 15 show the comparison between the analytical model and machine learning methods for Alibaba log prediction. In general, a summary of the differences between the prediction methods is presented in Table 4.  
Figure 11: Google Workload Type 2.  
Figure 10: Google Workload Type 1.  
Figure 14: Alibaba Workload Type 1.  
Figure 12: Google Data Center Provision Resources for WL1.  
Figure 13: Google Data Center Provision Resources for WL2.  
Figure 15: Alibaba Data Center Provision Resources for WL1.
## 4 Joint Power Allocation for Co3P JDT  
In this section, we extend our study to the Co3P JDT network with a non-unity TCPP constraint. In Rayleigh fading channels, the ECC achieved by the Co3P JDT network is given by Equation (21) [15]. \[\bar{C}_{3,jdt}=\left\{ \begin{array}{ll}\log_{2}\left(e\right)\sum\limits_ {i=1}^{3}\left\{\prod\limits_{j=1,j\
eq i}^{3}\left(\frac{P_{i}\bar{\gamma}_{ 1}}{P_{i}\bar{\gamma}_{1}-P_{j}\bar{\gamma}_{j}}\right)e^{\frac{1}{P_{i}\bar{ \gamma}_{1}}}E_{1}\left(\frac{1}{P_{i}\bar{\gamma}_{1}}\right)\right\},&\text{ if }P_{1}\bar{\gamma}_{1}\
eq P_{2}\bar{\gamma}_{2}\
eq P_{3}\bar{ \gamma}_{3},\\ \frac{P_{1}\bar{\gamma}_{1}\log_{2}\left(e\right)}{\left(P_{3}\bar{\gamma}_{ 3}-P_{1}\bar{\gamma}_{1}\right)^{2}}\left\{P_{1}\bar{\gamma}_{1}e^{\frac{1}{ P_{1}\bar{\gamma}_{1}}}E_{1}\left(\frac{1}{P_{1}\bar{\gamma}_{1}}\right)-P_{3}\bar{ \gamma}_{3}e^{\frac{1}{P_{3}\bar{\gamma}_{3}}}E_{1}\left(\frac{1}{P_{3}\bar{ \gamma}_{3}}\right)\right\}\\ +\frac{P_{3}\bar{\gamma}_{3}\log_{2}\left(e\right)}{P_{3}\bar{\gamma}_{3}-P_{1 }\bar{\gamma}_{1}}\left\{1-\frac{1}{P_{3}\bar{\gamma}_{3}}e^{\frac{1}{P_{3} \bar{\gamma}_{3}}}E_{1}\left(\frac{1}{P_{3}\bar{\gamma}_{3}}\right)+e^{\frac{1 }{P_{3}\bar{\gamma}_{3}}}E_{1}\left(\frac{1}{P_{3}\bar{\gamma}_{3}}\right) \right\},&\text{ if }P_{1}\bar{\gamma}_{1}\
eq P_{2}\bar{\gamma}_{2}=P_{3}\bar{ \gamma}_{3},\\ \frac{\log_{2}\left(e\right)}{2}\left\{3-\frac{1}{P_{1}\bar{\gamma}_{1}}+ \left(\frac{1}{P_{1}^{2}\bar{\gamma}_{1}^{2}}-\frac{2}{P_{1}\bar{\gamma}_{1}}+ 2\right)e^{\frac{1}{P_{1}\bar{\gamma}_{1}}}E_{1}\left(\frac{1}{P_{1}\bar{ \gamma}_{1}}\right)\right\},&\text{ if }P_{1}\bar{\gamma}_{1}=P_{2}\bar{\gamma}_{2}=P_{3}\bar{ \gamma}_{3}.\end{array} \right. \tag{21}\]  
We can rewrite the top of Equation (21) as  
\[\bar{C}_{3,jdt} = \frac{\log_{2}\left(e\right)P_{1}^{2}\bar{\gamma}_{1}^{2}}{\left( P_{1}\bar{\gamma}_{1}-P_{2}\bar{\gamma}_{2}\right)\left(P_{1}\bar{\gamma}_{1}-P_{3} \bar{\gamma}_{3}\right)}e^{\frac{1}{P_{1}\bar{\gamma}_{1}}}E_{1}\left(\frac{1} {P_{1}\bar{\gamma}_{1}}\right)\] \[+\frac{\log_{2}\left(e\right)P_{2}^{2}\bar{\gamma}_{2}^{2}}{\left( P_{2}\bar{\gamma}_{2}-P_{1}\bar{\gamma}_{1}\right)\left(P_{2}\bar{\gamma}_{2}-P_{3} \bar{\gamma}_{3}\right)}e^{\frac{1}{P_{2}\bar{\gamma}_{2}}}E_{1}\left(\frac{1} {P_{2}\bar{\gamma}_{2}}\right)\] \[+\frac{\log_{2}\left(e\right)P_{3}^{2}\bar{\gamma}_{3}^{2}}{\left( P_{3}\bar{\gamma}_{3}-P_{1}\bar{\gamma}_{1}\right)\left(P_{3}\bar{\gamma}_{3}-P_{2} \bar{\gamma}_{2}\right)}e^{\frac{1}{P_{3}\bar{\gamma}_{3}}}E_{1}\left(\frac{1} {P_{3}\bar{\gamma}_{3}}\right).\]  
To calculate the optimum CTP powers that maximize the ECC, differentiating Equation (22) with respect to \(P_{3}^{*}\) and setting the derivative to zero, we obtain Equation (23).
## 3 Results and Discussion
### Load-Deflection Diagrams and Energy Absorption  
The two stages of tests described in Section 3.1, related to type I and II cracks, respectively, can be associated to the two stages of the total reaction-midspan deflection diagrams of Figure 6 (hereafter, referred to as load-deflection diagrams). The main characteristics of load-deflection diagrams are listed in Table 2. In addition, the maximum and residual deflections, and the energies absorbed in the two stages of tests are plotted in Figure 8a,b. The first stage of load-deflection diagrams was similar for all tested series, which is explained by the local response upon impact and the formation of type I cracks in all tests. This agrees with the similar average peak load (\(R_{\text{max}}\)) and energy absorbed in the first stage (\(E_{1}\), calculated as the area below the load-deflection diagram in the first stage) shown in all series. The second stage of load-deflection diagrams is associated to the global response of the beams, the formation of type II cracks, and the activation of the stress ribbon mechanism in strengthened beams. From Figure 8b, the energy \(E_{2}\) (calculated as the area below the load-deflection diagram in the second stage) absorbed by strengthened specimens was significantly larger than unstrengthened beams. On average, the increase of absorbed energy \(E_{2}\) of RC-U35 and RC-U55 was 2.1 and 2.7 times the value of RC-U0, respectively. The capacity of the HPFRC layer to retain the damage of the specimens can also be demonstrated by the maximum midspan deflection (Figure 8a), which on average was 36% (RC-U35) and 39% (RC-U55) smaller than that of RC-U0 series, respectively.  
Figure 8: \(Cont.\)  
The development of interface debonding in strengthened specimens can be also analyzed with the shape of the last descending part of load-deflection diagrams (Figure 6). The integrity of the HPFRC layer in RC-U35 series corresponds with a descending part where the midspan deflection is partially recovered (i.e., the last descending branch can be seen as an unloading stage rather than a full collapse). In contrast, the last descending part of the load-deflection curves of RC-U55 series shows no deflection recovery, but sudden decrease of the load carried, which explains the spalling of the HPFRC layer. The existence of interface debonding can also be studied from the residual midspan deflection (\(v_{\text{res}}\) in Table 2, also represented in Figure 8a); it is clear that the specimens of series RC-U35 are only where the residual deflection after testing was significantly smaller than the maximum deflection.
## 1 Introduction
### Objectives and Contribution  
The main objectives of the work were to study the possibility of a multi-vector cyberattack detection in the IoT infrastructure based on a flow analysis and a deeper traffic analysis that takes into account IoT protocol features. This research aims to improve detection efficiency via various machine learning algorithm usages. The proposed approach deals with the set of network traffic features that may indicate the presence of cyberattacks in the IoT infrastructure and compromised IoT devices.  
Thus, the novelty of this work involves the approach used for IoT multi-vector cyberattack detection, which involves a flow-based features analysis. It enables decreased detection time and is scalable. On the other hand, if the flow-based feature analysis was unable to conclude the attack presence, a deep analysis of network traffic with the use of MQTT-based, DNS-based, and HTTP-based features analysis was employed.  
This paper is organized as follows. Section 2 presents the state-of-the-art. Section 3 describes the machine learning algorithms for cyberattack detection. Section 4 discusses the stages of the proposed IoT multi-vector cyberattack detection technique based on machine learning algorithms with the traffic features analysis. Section 5 proposes the experiments and the efficiency of the proposed approach. Finally, we present our conclusions and future research.
###### Abstract  
An anterior open bite is one of the most difficult malocclusions in orthodontic treatment. For such malocclusion, orthodontic miniscrew insertion into both buccal and palatal alveolar regions has been indicated for molar intrusion, but it involves a risk of tooth root injury. To solve the problem, a midpalatal miniscrew-attached extension arm (MMEA) is adopted. However, this method causes palatal tipping of the molar because intrusive loads were applied only from the palatal side. Currently, a transpalatal arch is added to avoid tipping movement, but it induces the patient's discomfort. Hence, the objective of this study was to evaluate the loading conditions for maxillary molar intrusion without tipping movement, only by MMEA through finite element (FE) analysis. FE models of maxillary right first molar and surrounding tissues were created. Three hook positions of MMEA were set at 6.0 mm perpendicular intervals in the occluso-apical direction along the mucosal contour. An intrusive unit load was applied from the palatal side of the molar, and various counter loads were applied from the buccal side. An optimal counter load for molar intrusion without palatal tipping was observed in each hook position. In conclusion, an ideal maxillary molar intrusion can be achieved only by MMEA with an optimal counter load.  
orthodontics; biomechanics; finite element analysis; anterior open bite; maxillary molar intrusion; midpalatal miniscrew; hook position; counter load; tipping movement Article
## 3 Plant Growth-Promoting Rhizobacteria, A Potential Approach for Bioremediation
Bacteria such as _Acinetobacter_, _Alcaligenes_, _Bacillus_, _Burkholderia_, _Enterobacter_, _Pantea_, _Pseudomonas and Staphylococcus_ have exhibited high antagonistic activities against _Alternaria alternata_, _Botrytis cinerea_, _Fusarium culmorum_, _F. oxysporum_, _F. solani_, _Gaeumanomyces graminis var. tritici_, _Phytophthora cryptogea_, _Pythium_ and promoted wheat growth and defenses against these same phytopathogens [87; 116; 117; 118]. Also, Rice inoculation with a combination of three PGP-_Pseudomonas fluorescens_ (Pf1, TDK1 and PY15) increased chitinase accumulation and enhanced disease resistance in rice plants against sheath rot disease provoked by _Sarocladium oryzae_[119]. Elsewhere, PGPR such as _Bacillus_, _Pseudomonas_, _Rhizobium_ and _Serratia_ were used as biocontrol agents against rice pathogens (_Burkholderia glumae_, _Cnaphalocrocis medialis_, _Cochliobolus myiabeanus_, _Hirschmanniella oryzae_, _Magnaporthe oryzae_, _Meloidogyne graminicola_, _Meloidogyne javanica_, _Pyricularia grisea_, _Rhizoctonia solani_ and _Xanthomonas oryzae pv. oryzae_) [80; 116; 120; 121].
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Spatial Coverage  
We consider the spatial coverage as two types, namely, 2D-networks and 3D-networks, which are the same as the network architecture of UWSNs. This taxonomy mainly focuses on which architecture of the localization algorithm is used or to what extent spatial coverage can be achieved. 2D-networks or 3D-networks are classified into two subcategories as anchor-free and anchor-based schemes. In UWSNs, a lot of localization schemes may employ the anchor node to help realize localization. However, some researchers also propose self-localization algorithms that do not need the anchor node.
## 4 Simulation and Experiment  
### The Simulation to Verify Accuracy  
The simulation waveform is shown in Figure 3, which consists of a strong peak and a weak peak far away from it. The total address number is 90. The peak data were generated by Gaussian linear function, the peak addresses were 51 and 71, and the FWHM were 6 and 4, respectively. The two methods were used to search peaks successively, which verified that ISZAC was superior to SZAC in accuracy. The introduction of the comparison method was able to correct the detection errors of SZAC in specific scenarios.  
Figure 2: Schematic diagram of data conversion and fusion processing.  
Figure 3a shows the results of SZAC with \(m=2\), \(H_{G}=4\), and \(T_{0}=1.5\). SZAC correctly identified peak addresses 51 and 71, but also misidentified some non-peak addresses as peak. Observe the local magnification map of address 71, note that addresses 70 and 72 do not meet the characteristics of peak and belong to misjudgment results, and analyze the process in detail.  
Conversion function \(C=[-0.24\), 0.11, 0.26, 0.11, \(-0.24]\). The spectral intensity at address 70 is 21.02, the intensity of the five address around it is \(y_{68-72}=[5.26\), 12.50, 21.02, 25.00, 21.02], and the conversion result at address 70 is 3.28.  
After threshold processing, \(T(70)=2.7>T_{0}\), so address 70 is misjudged as the peak, and other misjudged situations are similar.  
Figure 3b shows the results of ISZAC, where the conversion function set consists of five Gaussian linear functions with \(m=2\), 3, 4, 5, 6, respectively, and a comparison function with \(cmpN=2\). The \(H_{G}\) of all Gaussian linear function is equal to 6. The number of iterations of background deduction SNIP method is 15. By using ISZAC, misidentified addresses are effectively eliminated.  
Derive the result group \(rSet(70)=[0.59\), 0.55, 0.52, 0.48, 0.46, 1, \(-1]\) of address 70. The first five values are the results of symmetric zero-area conversion, the sixth value is the result of intensity screening, and the seventh value is the result of the comparison method. By comparing SZAC results, it can be seen that after adjusting parameters \(m\) and \(H_{G}\), the symmetric zero-area conversion result of address 70 is still positive. The key to eliminating address 70 in the subsequent fusion process is that the result of the comparison method is negative. By introducing the comparison method, ISZAC ensures the accuracy of peak identification and improves the limitation of failure of the original SZAC in specific scenarios.
## 2 Overview of the Human Gut Microbiota
### Factors Influencing the Gut Microbiota
#### 2.3.1 Exposure to Antibiotics  
The effects of antibiotic use depend on their spectrum of activity, pharmacokinetics, and pharmacodynamics, as well as the route of administration, dosage, and duration of treatment. The oral administration of antibiotics is often the preferred route, directly exposing the intestinal lumen and microbiota to their bactericidal and/or bacteriostatic action [80]. Antibiotics' effects include alterations in the composition and function of the gut microbiota, increased susceptibility to infections, and the development of antibiotic resistance [51].  
Figure 3: Short-chain fatty acids (SCFAs) transport and immunomodulatory pathways in the intestine. SCFAs, produced by the gut microbiota through the fermentation of dietary fibres, diffuse or are transported across the intestinal epithelium via monocarboxylate transporters (e.g., MCT1, SMCT1). In the lamina propria, SCFAs interact with immune cells by binding to G-protein-coupled receptors (e.g., GPR43) or through histone deacetylase (HDAC) inhibition, promoting anti-inflammatory responses and the maintenance of the intestinal barrier.  
Disturbances in the composition and functions of the microbiota are often characterised by an imbalance, typically associated with a reduction in microbial diversity. This does not necessarily imply a decrease in the total number of microorganisms; in fact, microbial load may increase as antibiotic-susceptible bacteria, such as members of the _Bacilota_ phylum, are eliminated, providing an opportunity for resistant bacteria, including some _Pseudomonad_ species, to proliferate. Additionally, the administration of a particular antibiotic can impair the metabolic functions of the microbiota by reducing the production of key microbial metabolites, such as SCFAs, which play a role in maintaining gut homeostasis. This disruption can lead to immune dysregulation, characterised by a decreased frequency and function of regulatory T cells (Tregs) and an increased differentiation of pro-inflammatory effector T cells, such as T helper 17 (Th17) cells, tipping the balance towards inflammation and impaired immune tolerance [81; 82; 83].  
In terms of susceptibility to new infections, antibiotic use can lead to a reduction in the thickness of the mucus layer, facilitating bacterial invasion by newly acquired pathogens, or from the overgrowth and pathogenic behaviour of opportunistic organisms present, as observed in antibiotic-associated diarrhoea, the most severe form caused by _Clostridioides difficile_[81; 84].
## 3 Results and Discussion
### Fatty Acids Profile  
The fatty acid profiles of the analyzed samples are presented in Table 3. The predominant saturated fatty acid was palmitic acid in all samples. Their amounts ranged from 33.36% to 39.64%. To a lesser extent, the stearic (2.86-7.32%) and lignoceric (2.33-4.956%) were also found in samples. The predominant unsaturated fatty acids in _E. planum_ were linoleic acid (35.43-35.79%) and \(\alpha\)-linolenic (7.94-8.63%). Statistical analysis revealed no significant differences in the levels of these fatty acids between the EPF and EPT parts  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Organic Acid**} & \multicolumn{2}{c}{_E. billardieri_} & \multicolumn{2}{c}{_E. planum_} \\ \cline{2-5}  & **EBT** & **EBF** & **EPT** & **EPF** \\ \hline Citric acid & 4.7 \(\pm\) 0.4 b & 7.2 \(\pm\) 0.1 a & 8.6 \(\pm\) 1.0 a & 8.3 \(\pm\) 0.1 a \\ Tartaric acid & 0.7 \(\pm\) 0.1 c & 0.6 \(\pm\) 0.1 c & 2.7 \(\pm\) 0.4 a & 1.9 \(\pm\) 0.2 b \\ Malic acid & 12.2 \(\pm\) 0.9 c & 13.8 \(\pm\) 0.6 b & 15.6 \(\pm\) 0.3 a & 12.8 \(\pm\) 0.1 b \\ Quinic acid & 12.0 \(\pm\) 2.6 c & 16.3 \(\pm\) 0.5 a & 16.0 \(\pm\) 0.3 a & 13.3 \(\pm\) 0.0 ab \\ Sugars & & & & \\ Sucrose & 23.8 \(\pm\) 1.9 c & 22.8 \(\pm\) 0.54 c & 49.6 \(\pm\) 1.2 a & 33.8 \(\pm\) 0.7 b \\ Maltitol & 62.1 \(\pm\) 0.9 c & 62.4 \(\pm\) 1.7 c & 70.7 \(\pm\) 1.0 a & 66.3 \(\pm\) 0.8 b \\ Glucose & 14.8 \(\pm\) 1.1 c & 19.2 \(\pm\) 0.5 b & 24.1 \(\pm\) 0.6 a & 23.5 \(\pm\) 0.5 a \\ Fructose & 44.7 \(\pm\) 2.8 d & 56.4 \(\pm\) 0.9 c & 79.6 \(\pm\) 1.2 a & 72.5 \(\pm\) 0.4 b \\ Total & 145.6 & 160.9 & 224.2 & 196.3 \\ \hline \hline \end{tabular}  
* Values are mean \(\pm\) standard deviation (n = 3). Different letters in the same rows indicate significant differences (_p_ < 0.05). EBF: _E. billardieri_ flowers; EBT: _E. billardieri_ thorny; EPF: _E. planum_ thorny.  
\end{table}
Table 2: Organic acids and sugars profiles of two studied plants (mg/gdw) *.  
(\(p\) < 0.05). Whereas, _E. billardieri_ showed the highest level of oleic acid EBT (16.55%) and EBF (20.70%). Unsaturated fatty acids play an important role in body functions such as neuroprotection, antioxidant, and anti-inflammatory properties [20]. According to the findings of [PERSON] et al. [21], oleic and palmitic acids are the primary fatty acids present in _E. billardieri_. Additionally, oleic acid has also been detected in the fatty acid compositions of _E. maritium_ and _E. foetidum_[22; 23]. [PERSON] et al. [24] reported that linoleic acid (24.4%) and palmitic acid (19.9%) were the two main constituents of the volatile fraction of the chloroform extract from the aerial parts of _E. palmatum_.  
Furthermore, the aerial parts of both _E. maritium_ and _E. campestre_ were found to be rich in fatty acids, with _E. maritium_ containing 5.26% palmitic acid and 41.88% oleic acid and _E. campestre_ comprising 5.25% palmitic acid and 41.97% oleic acid [17].