esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multivariable Polynomial Signature Schemes
### LUOV Scheme
#### 3.2.1 The Reference Implementation
For all parameter choices, as shown in Table 1, the secret key consists of a 32-byte seed. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \(r\) & \(o\) & \(v\) & **Length of** & **Length of** & **Lengths of** & \(\mathcal{H}\) & \(\mathcal{G}\) \\
**Name** & & & & private\_seed & \(\Upsilon\),\(\mathcal{L}\),\(\mathtt{Q}_{1}\) & public\_seed,\(\mathtt{Q}_{2}\) & \\ \hline LUOV-7-57-197 & 7 & 57 & 197 & 32B & (1576B,2032B,8B,245856B) & (328,11778B) & SHAKE128 & SHAKE128 \\  & & & & & & & & & ChaCha8 \\ \hline LUOV-7-83-283 & 7 & 83 & 283 & 32B & (3113B,4026B,118,700425B) & (328,36168B) & SHAKE256 & SHAKE128 \\  & & & & & & & & & ChaCha8 \\ \hline LUOV-7-110-374 & 7 & 110 & 374 & 32B & (5236B,6776B,148,1557710B) & (328,83944B) & SHAKE256 & SHAKE128 \\  & & & & & & & & & ChaCha8 \\ \hline LUOV-47-42-182 & 47 & 42 & 182 & 32B & (1092B,1344B,6B,145782B) & (328,4741B) & SHAKE128 & SHAKE128 \\  & & & & & & & & & & ChaCha8 \\ \hline LUOV-61-60-261 & 61 & 60 & 261 & 32B & (2088B,2568B,898808B) & (328,13725B) & SHAKE256 & SHAKE128 \\  & & & & & & & & & & ChaCha8 \\ \hline LUOV-79-76-341 & 79 & 76 & 341 & 32B & (3410B,4170B,108,842270B) & (328,27797B) & SHAKE256 & SHAKE128 \\  & & & & & & & & & ChaCha8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recommended parameters for LUOV and lengths in bytes of the arrays generated during the key generation algorithm. The signature generation algorithm, on input m and private_seed, uses private_seed to recalculate public_seed and T, C, L and Q1, as done in the key generation algorithm. It then uses them to compute the augmented matrix \(A\in\mathbb{F}_{2^{r}}^{o\times(o+1)}\) that represents the linear system with \(o\) equations and \(o\) variables obtained from \(\mathcal{F}((v_{1},v_{2},\ldots,v_{v},x_{v+1},\ldots,x_{n}))=\mathcal{H}( \mathbf{n}||0x00||\mathtt{salt})\), where \(v_{1},v_{2},\ldots,v_{v}\) are randomly chosen values from \(\mathbb{F}_{2^{r}}\), represented as a byte array of length \(\lceil\frac{\varpi}{8}\rceil\); m is a byte array representing the message; salt is an random 16-byte array; 0x00 is a byte (0); and \(\mathcal{H}(m||0x00||\mathtt{salt})\in\mathbb{F}_{2^{r}}^{o}\) is represented as a byte array of length \(\lceil\frac{\varpi}{8}\rceil\). Once \(x=(v_{1},v_{2},\ldots,v_{v},v_{v+1},\ldots,v_{n})\) is found, the signature \(s\) is obtained by calculating \( \begin{pmatrix}I_{p}&-T\\ O&I_{o}\end{pmatrix} x\) and finally \((s,\mathtt{salt})\) is returned as a byte array of length of length \(16+\lceil\frac{\varpi}{8}\rceil\). Similarly, the signature verification algorithm, on input \((\mathtt{public\_seed},\mathtt{Q}_{2})\), \((s,\mathtt{salt})\) and \(m\), uses public_seed to recalculate C, L and Q1. It then computes the matrix Q \(\in\mathbb{F}_{2}^{o\times(\frac{\varpi+1}{2})}\), the Macaulay matrix of the quadratic part of \(\mathcal{P}\), by concatenating Q1 and Q2, and finally it makes use of Q, C, L to calculate \(\mathcal{P}(s)\) and compare it with \(\mathcal{H}(m||0x00||\mathtt{salt})\).
###### Abstract  
Cold atmospheric pressure plasma (CAP) is a novel non-thermal technology that is gaining increasing importance as a decontamination method. Stainless steel is a widespread food contact surface used in food-processing environments. In this study, for the first time, a low-voltage piezoelectric CAP device that uses ambient air was assessed for its antimicrobial efficiency against _Salmonella_ and _Listeria monocytogenes_. These inoculated on stainless steel at different exposure times (0-300 s), two different distances (10 and 20 mm), and two different cleanliness levels (clean and protein-soiled). Two inactivation models were compared to study the inactivation kinetics of the pathogens. The results showed that CAP treatment effectively reduced _L. monocytogenes_ and _Salmonella_ levels. The Weibull + tail model showed better goodness of fit than the Weibull model. Protein-soiled coupons showed a protective effect to cold plasma inactivation achieving lower reductions compared to clean stainless-steel coupons for both _L. monocytogenes_ and _Salmonella_. Longer distances from the plasma source decreased the decontamination efficiency of CAP; however, the difference in pathogen reduction was less pronounced at longer exposure times. This study demonstrates the capacity of a low-voltage piezoelectric CAP device to effectively reduce the levels of both foodborne pathogens on stainless-steel surfaces and the potential to adopt this technology by the food industry as a disinfection process of surfaces to reduce cross-contamination and thus increase safety.  
piezoelectric cold atmospheric plasma; stainless steel; foodborne pathogens; _Listeria monocytogenes_; _Salmonella_
## 4 Experimental Results and Discussion
### Comparison of Results  
To assess the proposed SEResNet-BiGRU model's interpretation, we begin by comparing it to baseline classification models (CNN, LSTM, and BiGRU). Summary hyperparameters of all models conducted in this study are described in Appendix A. Table 6 summarizes the experiment's findings. SEResNet-BiGRU outperforms other models with an overall accuracy of 98.03% and 98.92% on the HAPT and MobiAct v2.0 datasets, respectively. These findings demonstrate that the SEResNet-BiGRU combination outperforms the transitional HAR issues.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Acc.**} & \multicolumn{3}{c}{**Gyro.**} & \multicolumn{3}{c}{**Acc. and Gyro.**} \\ \cline{2-10}  & **Accuracy (\%)** & **Loss** & **F1-Score (\%)** & **Accuracy (\%)** & **Loss** & **F1-Score (\%)** & **Accuracy (\%)** & **Loss** & **F1-Score (\%)** \\  & **(\(\pm\)SD (\%))** & **(\(\pm\)SD)** & **(\(\pm\)SD (\%))** & **(\(\pm\)SD (\%))** & **(\(\pm\)SD (\%))** & **(\(\pm\)SD (\%))** & **(\(\pm\)SD (\%))** & **(\(\pm\)SD (\%))** & **(\(\pm\)SD (\%))** \\ \hline ResNet & 96.97\% & 0.103 & 92.53\% & 89.16\% & 1.185 & 87.55\% & 97.87\% & 0.088 & 93.89\% \\  & (\(\pm\)0.44\%) & (\(\pm\)0.017) & (\(\pm\)0.39\%) & (\(\pm\)6.39\%) & (\(\pm\)1.460) & (\(\pm\)5.13\%) & (\(\pm\)0.125\%) & (\(\pm\)0.009) & (\(\pm\)0.31\%) \\ \hline ResNetSE & 96.45\% & 0.107 & 92.62\% & 84.73\% & 1.895 & 85.59\% & 97.89\% & 0.095 & 93.81\% \\  & (\(\pm\)0.79\%) & (\(\pm\)0.016) & (\(\pm\)0.55\%) & (\(\pm\)7.31\%) & (\(\pm\)1.970) & (\(\pm\)5.28\%) & (\(\pm\)0.33\%) & (\(\pm\)0.018) & (\(\pm\)0.43\%) \\ \hline ResNeXt & 96.38\% & 0.114 & 91.99\% & 96.05\% & 0.137 & 91.63\% & 97.43\% & 0.093 & 93.33\% \\  & (\(\pm\)0.35\%) & (\(\pm\)0.014) & (\(\pm\)0.72\%) & (\(\pm\)0.36\%) & (\(\pm\)0.018) & (\(\pm\)0.83\%) & (\(\pm\)0.30\%) & (\(\pm\)0.017) & (\(\pm\)0.66\%) \\ \hline SEResNet- BiGRU & 97.01\% & 0.112 & 92.76\% & 93.69\% & 0.290 & 91.77\% & 98.03\% & 0.080 & 94.09\% \\  & (\(\pm\)0.30\%) & (\(\pm\)0.010) & (\(\pm\)0.48\%) & (\(\pm\)1.95\%) & (\(\pm\)0.114) & (\(\pm\)0.59\%) & (\(\pm\)0.17\%) & (\(\pm\)0.006) & (\(\pm\)0.55\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition effectiveness of DL models on the HAPT dataset by using different sensor data.
## 5 Conclusions  
Accurate RUL prediction is especially important for reducing lithium-ion battery maintenance costs and improving equipment safety. This paper proposes a transferable RUL prediction approach based on small samples. The RT-TrAdaBoost model for RUL prediction we established not only overcomes the problem of a few battery aging data samples but also achieves transfer prediction between different working conditions and different types of batteries. The CART is chosen as the base learner improves the feature recognition ability of RT-TrAdaBoost. The well-characterized lithium-ion battery charge voltage and temperature curves are applied as input for RUL prediction. It has certain engineering practicability to mine useful information from BMS directly measurable data to predict RUL. Finally, the RT-TrAdaBoost model is deployed on computer and edge computing modules, and the accuracy and efficiency of the model are verified using two public datasets. The MAE and RMSE of transfer prediction under different working conditions ranged from 0.36% to 1.04% and 0.46% to 1.32%, respectively. In the migration experiment from LFP battery to LCO battery, the MAE and RMSE are 1.24% to 1.92% and 1.50% to 2.24%. Also, the values on MAX and MAPE are lower. In the future, there will be more comprehensive research conducted on the degradation of lithium-ion battery life, and data-driven methods will be explored to address issues such as dynamic perception and fault detection of lithium-ion battery aging. This paper can also provide ideas for the remaining life prediction of batteries made of other materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Batteries** & **Time** & **CNN-TL** & **AdaBoost** & **RT-TrAdaBoost** \\ \hline \multirow{3}{*}{g1c38 \(\rightarrow\) Cell1} & Training & 241.77 s & 7.776 s & 10.33 s \\  & First testing & 0.18 s & 0.002 s & 0.025 s \\  & Second testing & 0.19 s & 0.007 s & 0.091 s \\ \hline \hline \end{tabular}
\end{table}
Table 9: The runtime of lithium-ion battery RUL prediction methods.  
**Author Contributions:** Conceptualization, H.Q. and Y.F.; methodology, H.Q.; software, H.Q.; validation, H.Q., X.F. and Y.F.; formal analysis, Q.S.; investigation, R.W.; resources, D.Z.; data curation, H.Q.; writing--original draft preparation, H.Q.; writing--review and editing, Y.F.; visualization, H.Q.; supervision, X.F.; project administration, Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52007196.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results
### The ppk/pptA/SLI_4382 Operon Belongs to the Pho Regulon
At last, proteins known to be involved in the biosynthesis of phosphate free teichulosonic acid cell-wall glycopolymer (SLI_5146-SLI_5156) in replacement of phosphate containing teichoic acid, in condition of phosphate limitation [28], were also up-regulated in the _pptA_ mutant (Figure 6b). Some of the proteins mentioned above were also up-regulated in the _ppk_ mutant strain [29]. Altogether, these data indicated that the _pptA_ mutant, as the _ppk_ mutant suffers from phosphate limitation that resulted into energetic stress. The ppk-pptA-SLI_4382 Region Is Highly Conserved in Streptomyces Species But Not in Other Prokaryotes  
Out of 163 fully sequenced _Streptomyces_ genomes from the RefSeq representative genomes database [30] that we analyzed using tlastn [31], we identified only 20 (12.3%) that did not have the _ppk-pptA-SLI_4382_ gene arrangement found in _S. coelicolor_. In 7 of these genomes (4.3%), the _pptA_ CHAD-encoding gene was missing and the _ppk_ gene was immediately followed by the _SLI_4382_ NUDIX hydrolase encoding gene. In the other 13 (8.0%), _SLI_4382_ was missing but _pptA_ was present downstream of _ppk_; in 2 of those cases, a likely _SLI_4382_ orthologique was found elsewhere in the genome, far from _ppk_. No _Streptomyces_ species were identified that lacked both _pptA_ and _SLI_4382_, or _ppk_. The genetic context of _ppk_ in other prokaryotes was investigated using COGNAT [32], PSAT [33] and MicrobesOnline [34]. These analyses revealed that in the vast majority of genomes, _ppk_ is not followed by a CHAD-encoding gene. Instead, the genes most  
Figure 6: Heatmap representation of proteins of the Pho regulon involved in phosphate scavenging and up-take (**a**) as well as in the biosynthesis of phosphate free teichulosonic acid biosynthesis in replacement of teichoic acids (**b**) with significant abundance change (ANOVA, adjusted \(p\) value < 0.05) between _S. lividians_ (TK24) and its _SLI_4383/_pptA_ deletion mutant. The strains were grown at 28 \({}^{\circ}\)C for 48 and 60 h on solid R2 YE medium containing glucose (50 mM) as main carbon source and 5 mM (Pi+) or 1 mM (Pi-) phosphate (conditions of phosphate proficiency or limitation, respectively). Protein identifiers are indicated as SLI numbers for both strains and by predicted functions. commonly found downstream of _ppk_ correspond to genes encoding PPX/GppA phosphatases [35], NUDIX hydrolases [36; 37], and related enzymes such as MutT [38] and histidine phosphatases [39].
## 2 Results
### Electroantenography Analyses
The mean EAG response to dodecanal at 0.01 ug was significantly higher than those recorded for decanal, \(\beta\)-myrrene, and 1-decanol, but not significantly different from that to decanoyl acetaldehyde. At the 0.1, 10, and 100 ug doses, the mean EAG response to dodecanal was significantly higher than those to decanal, \(\beta\)-myrrene, and decanoyl acetaldehyde. At the 1 ug dose, the highest mean EAG response was recorded on stimulation with \(\beta\)-myrrene among the different stimuli tested. Behavioral Responses of Franklin's occidentalis to Houttuynia cordata Volatiles in a Six-Arm Oflactometer  
In these bioassays, compared with the mineral oil control, _F. occidentalis_ was repelled by decanal at all concentrations ([PERSON] test: \(\chi^{2}\) = 22.56, \(df\) = 5, \(p\) < 0.001; [PERSON] tests: \(p\) = 0.042-0.043), with the 50 ug/ul concentration eliciting a significantly higher repellent effect than the other concentrations (Figure 3). Similarly, compared with the mineral oil control, _F. occidentalis_ was also repelled by dodecanal ([PERSON] test: \(\chi^{2}\) = 14.05, \(df\) = 5, \(p\) < 0.015; [PERSON] tests: \(p\) = 0.042-0.043) and decanoyl acetaldehyde ([PERSON] test: \(\chi^{2}\) = 22.56, \(df\) = 5, \(p\) < 0.001; [PERSON] tests: \(p\) = 0.042-0.043) at all concentrations, and there were no significant differences in repellent effect among the concentrations of each of these two compounds. \(\beta\)-Myrrene was attractant to _F. occidentalis_ at various concentrations ([PERSON] test: \(\chi^{2}\) = 24.00, \(df\) = 5, \(p\) < 0.001; [PERSON] tests: \(p\) = 0.042-0.043), with the most attractant dose being 5 ug/ul. For 1-Decanol, thrips showed attraction only to the concentration of 5 ug/ul ([PERSON] test: \(\chi^{2}\) = 14.45, \(df\) = 5, \(p\) < 0.001; [PERSON] tests: \(p\) = 0.039-0.043). Figure 2: Electroantennography responses of _Franklinella occidentalis_ females to decanal, 1-decanol, dodecanal, \(\beta\)-myrrene, and decanoyl acetaldehyde. Mean values are shown. For each dose, different letters indicate significant differences at \(p\) < 0.05 ([PERSON]’s honest significant difference test). Behavioral Responses of Predator Orius similis to compounds of Houttuynia cordata Volatiles in a Six-Arm Olfactometer  
In the six-arm olfactometer bioassay, \(O\). _similis_ showed no attraction to decanal ([PERSON] test: \(\chi^{2}=3.44\), \(df=5\), \(p=0.632\)), dodecanal ([PERSON] test: \(\chi^{2}=3.33\), \(df=5\), \(p<0.649\)), or decanoyl acetaldehyde ([PERSON] test: \(\chi^{2}=6.39\), \(df=5\), \(p=0.270\)) at any concentration (Figure 4). There were no significant differences in the number of \(O\). _similis_ that entered into each arm for each concentration of these three compounds. Figure 3: Olfactory responses of _Franklinella occidentalis_ to different compounds from the volatile profile of \(H\). _cordata_ in a six-arm olfactometer. The control was mineral oil. Each box plot represents the median and its range of dispersion (lower and upper quartiles and outliers). Above each box plot, different letters indicate significant differences (Wilcoxon test, \(p<0.05\)).
## 8 Cost Implications  
The SensoTube architecture can significantly influence the cost associated with the most critical aspects of a WSAN application. Both direct and indirect costs can be decreased. The benefits from the reduction of costs can be found in the following three areas of interest:  
1. _Cost reduction at the development phase_: The use of comprehensive functional building blocks, in order to build a WSAN system can reduce the traditionally long development periods, especially in the cases where diverse expertise and skills are required. Thus, the labor cost is decreased and the time-to-market could be shortened too. Towards this direction, the ability of SensoTube to allow designers to use established as well as new innovative tool chains can also help. Moreover, the decomposition of the system into its functions, which are implemented in the form of independent shields, can enable the reduction of the NRE costs associated with the development of the prototype systems because the changes in design can be focused just on particular system's parts.
2. _Cost reduction at the systems level_: The increased flexibility, reconfigurability, and reusability ensured by the proposed architecture can positively influence the system's total cost. In particular, the introduction of centralized provisions for the power supply and the programming and debugging, can release hardware subsystems from integrating superfluous circuits. On the other hand, the ability to add and remove functional building blocks can increase the value of the investment during the lifetime of the application, because any future change can be limited just to the purchase of a single expandable shield and avoid the need to change the whole system. Additionally, the adoption of the plastic PVC tubes for the final encapsulation of the system, instead of special expensive electrical boxes, can help to minimize the cost per WSAN node.
3. _Cost reduction at the application domain_: A serious problem in the agricultural domain is the theft of the WSAN nodes due to their metallic support poles which can be sold for scrap. This can be prevented by the proposed plastic tubes-based encapsulation, which is simultaneously used as the support pole of the system. Moreover, the proposed approach can significantly help the handling and transportation of the WSAN nodes due to the reduction of the systems weight.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 3 Craft
### Vertex Expansion Method
Upon expanding a new vertex \(x_{new}\), \(x_{new}\).\(CAND_{j}\) (where \(j\) is the candidate point index \(j=0\), 1, 2) is pre-assigned to maintain each vertex's edge angle at 120 degrees. Additionally, if a vertex's candidate point set is vacant, it is classified as a discarded vertex and removed from array \(V\). Figure 3 shows that every vertex logs its corresponding candidate expansion points \(x_{i}\).\(CAND_{j}\) (where \(i\) is the vertex index in the growing tree \(T\), \(i=0\), 1,..., n). If \(x_{i}\)'s candidate point set is void, it is deemed a discarded vertex and excluded from array \(V\). The expansion process of the CERRT is conveyed in Figure 3. After sampling \(x_{rand}\) randomly, the closest vertex \(x_{nearest}\) is selected from array \(V\) as the starting point. Since \(x_{1}\) is a discarded vertex and has been eliminated from array \(V\), \(x_{3}\) is chosen as the nearest vertex. Subsequently, vertex \(x_{new}\) is selected from the candidate expansion points of \(x_{3}\) based on its proximity to \(x_{rand}\). After calculation, \(x_{3}\).\(CAND_{2}\) is chosen as \(x_{new}\) for expansion and \(x_{3}\).\(CAND_{2}\) is removed from the candidate set of \(x_{3}\). As \(x_{6}\).\(CAND_{2}\) coincides with \(x_{3}\).\(CAND_{2}\), it is also removed from the candidate set of \(x_{6}\). Since the candidate set of vertex \(x_{6}\) is now empty, \(x_{6}\) becomes a dead vertex and is deleted from array \(V\). If edge \(x_{new}\)-\(x_{nearest}\) collides with obstacles, the algorithm advances to the environment awareness stage. Otherwise, \(x_{new}\) is added as a new vertex to tree \(T\), and a set of candidate expansion points is pre-allocated for \(x_{new}\). It is worth noting that the candidate points must not overlap with any existing points in tree \(T\). This process is illustrated in Figure 3.  
```
1:\(ang_{0}=\) GetCartesianAngle(\(x_{nearest}\), \(x_{new}\));
2:\(ang_{1}=ang_{0}+120\); \(ang_{2}=ang_{0}+240\);
3:\(new_{1}=x_{new}+d_{step}\)*[\(\sin(ang_{1})\), \(\cos(ang_{1})\)];
4:\(new_{2}=x_{new}+d_{step}\)*[\(\sin(ang_{2})\), \(\cos(ang_{2})\)];
5:\(CAND=[]\);
6:if NoOverlap(\(new_{1}\), \(T\)) then
7:\(CAND(end+1)=new_{1}\);
8:elseif NoOverlap(\(new_{2}\), \(T\)) then
9:\(CAND(end+1)=new_{2}\);
10:endif
11:return\(CAND\);
```  
**Algorithm 2** SetCAND(\(x_{nearest}\), \(x_{new}\), \(d_{step}\), \(T\), \(V\))  
Figure 3: Improved vertex expansion process. (**a**) Before expansion. (**b**) Selection of \(x_{nearest}\) after sampling. (**c**) After expansion.
## 5 Instrumental Monitoring
### Monitoring of Volumetric Water Content and Matrix Suction
As observed in Figures 9 and 10, the start of rainfall measurement on 23 February 2021 in the landslide area enabled the comparison of the volumetric water content matric suction with rainfall after this date. Between 23 February 2021 and 7 May 2021, a total of 250 mm of rain fell on the hillslope, with a daily maximum rainfall of 28 mm and an hourly maximum rainfall of 10.2 mm. In this period, volumetric water content was around 47%, and the matric suction was around 8.6 kPa. Between 7 May 2021 and 14 September 2021, a total of 463 mm of rain fell in the study area, with a daily maximum rainfall of 96.8 mm and an hourly maximum rainfall of 50.6 mm. After May 2021, heavier and less frequent rainfall was observed. Prolongation of the rainfall intervals reduced the volumetric water content by allowing the soil to dry. The variations in the volumetric water content and matric suction at a 1.63 m depth are presented in Figures 11 and 12. It can be remarked that at this depth, the volumetric water content and the matric suction did not change remarkably and were not affected extensively by seasonal climatic conditions. During the observation period, the volumetric water content was 42-46%, and the matric suction was measured between 21.5 and 10.0 kPa, at this depth.
# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 3 Materials and Methods
### Tracking of Airport Lights
During an observation, the colour of the light may change, indicating the operational properties of the lights. PAPI (precision approach path indicator) lights provide visual descent guidance and change colour when the angle of observation reaches prescribed values. PAPI lights help pilots maintain the correct glide slope during approach and landing. The lights are observed by pilots during the descent path. There are 4 light units with different light change angles. The colour of the light changes from red to white. The ratio of white to red lights, seen by the pilot, indicates whether the airplane is on the right glide slope. At the optimum approach angle, the ratio of white to red lights is equal.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol  
The proposed protocol (AODV-EOCW) combines subjective and objective weighting methods to identify the optimal route. AODV-EOCW first calculates the subjective weights with AHP and a comparison matrix and then objectively corrects the subjective weights with EWM to obtain a more scientific weight for route measurement. The AHP and EWM methods are detailed in Sections 3.2 and 3.3, respectively.