esa-sceva/satcom-chunk-collection huggingface dataset:
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 9 Future Works  
This work investigated the upload channel using the ACK-on-Error mode of the SCHC protocol. Future work will apply the same approach to investigating the efficiency for the downlink using the ACK-Always mode over LoRaWAN(tm).  
Conceptualization, R.M. and S.C.; methodology, R.M. and S.C.; software, J.S.H., F.C. and R.M.; formal analysis, R.M.; writing--original draft preparation, R.M.; writing--review and editing, S.C. and D.D.; supervision, S.C. and D.D.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research has been funded in part by ANID Project FONDECYT Regular 1201893, CONICYT + FONDEF Concusro FONDEF IDeA I+D 2019 Folio ID91I0363, and the Advanced Center for Electrical and Electronic Engineering (AC3E) Basal Project FB0008 ANID. Other sources of funding were the CYTED AgIoT Project (520 rt0011), CORFO CoTH2O \"Consorcio de Gestion de Recursos Hidricos para la Macrozona Centro-Sur\", and Proyecto Asociativo UDP \"Plataformas Digitales como modelo organizacional\". The APC was funded by ANID Project FONDECYT Regular 1201893.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# UAV-Assisted Hybrid Scheme for Urban Road Safety Based on VANETs
###### Abstract  
Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a serious issue of all time since the nodes have high mobility and transmission range is limited, resulting in an interruption of communication. One of the significant technical challenges faced in implementing VANET is the design of the routing protocol, providing adequate information and a reliable source for the destination. We proposed a novel mechanism unmanned aerial vehicle (UAV)-assisted ad hoc on-demand distance vector (AODV) routing protocol (UAVa) for current-time traffic information accumulation and sharing to the entire traffic network and to control congestions before it happens. The UAV-assisted (UAVa) protocol is dedicated to urban environments, and its primary goal is to enhance the performance of routing protocols based on intersections. We compared the overall performance of existing routing protocols, namely ad hoc on-demand distance vector. The simulations were done by using OpenStreetMap (OSM), Network Simulator (NS-2.35), Simulation of Urban Mobility (SUMO), and VanetMobiSim. Furthermore, we compared the simulation results with AODV, and it shows that UAV-assisted (UAVa) AODV has significantly enhanced the packet delivery ratio, reduced the end-to-end delay, improved the average and instant throughput, and saved more energy. The results show that the UAVa is more robust and effective and we can conclude that UAVa is more suitable for VANETs.  
VANET; UAV; AODV; NS-2; SUMO; congestion control Article
## 3 Results and Discussion
### Analysis of Vegetative Indices
In order to test the effectiveness of the proposed method, the correlation was estimated between the obtained values of the vegetative indices ARI and mARI and the anthocyanins concentration value in the basil leaves obtained by the chemical method (Figure 8). The determination coefficients R2 = 0.82 for ARI and R2 = 0.85 for mARI indicate a high degree of correlation, which indicates the acceptability of this method for the anthocyanins content assessment in plant leaves. For the carotenoids, a weak correlation was observed (R\({}^{2}\) = 0.0479), which is explained by the shielding effect of the anthocyanins as far as the CRI\({}_{700}\) index is determined at the reflection band of 510 nm, which coincides with the maximum absorption of the anthocyanins [32]. The weak correlations of the R\({}_{750}\)/R\({}_{700}\) and MND\({}_{705}\) indices obtained for the chlorophylls can be explained by the large unevenness of the reflection spectra over the leaf area (Figure 6), which is typical for the purple basil. After all, on the crops with the green leaves, the strong correlations of these indices with the chlorophyll content were revealed [34]. Additionally, the deviations present in the results are influenced by such factors as the geometry of a leaf, the uneven lighting, the morphological features (thickness and density of the sheet), etc. Figure 8: Evaluation of correlation between pigment concentration and vegetation indices obtained on 30 th day of cultivation. (**a**) Correlation between CRI\({}_{700}\) index and carotenoids concentration; (**b**) correlation between the R\({}_{750}\)/R\({}_{500}\) index and a+b chlorophylls concentration; (**c**) correlation between MND705 index and a+b chlorophylls concentration; (**d**) correlation between ARI index and anthocyanins concentration; (**e**) correlation between mARI index and anthocyanins concentration. For a more accurate assessment of the stress factors' impact on the concentrations of the chlorophylls and the carotenoids in the purple-leaved crops and on the overall productivity, it is necessary to conduct some additional research and develop such reflection indices that take into account the shielding effect of the purple pigments.
## Numerical Example
### Sensitivity Analysis  
The comparison of the model's performance for different numbers of feeder buses is shown in Table 5. As the number increases, vehicles starting at the depot and ending at the rail station result in the number of demand points covered by each vehicle being reduced, but there is an increase of invalid travel distance and time. However, the ride time for the vehicles from the demand points to the rail station is reduced. This also reveals that the proposed algorithm is efficient and feasible in solving the speed and solution quality. In the case of the three scenarios of three, four, and five vehicles, the calculation time of the proposed algorithm is 5.6 times, 28.6 times, and 326.2 times more than that of Cplex, respectively. In Figure 7, we also analyse the differences between the proposed model and the DRTNSCRT models under three scenarios of three, four, and five vehicles. The results are consistent with the results of Figure 6; i.e., the total travel time of the proposed model is better than that of the DRTNSCRT models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Demand Point** & **Boarding Time** & **Ride-Time** & **Wait-Time** & **Vehicle** \\ \hline C3 & 6:12 & 17 & 4 & \\ C9 & 6:18 & 11 & 4 & \\ C11 & 6:23 & 6 & 4 & V1 \\ C12 & 6:16 & 13 & 4 & \\ C14 & 6:26 & 3 & 4 & \\ \hline C1 & 6:19 & 6 & 0 & \\ C5 & 6:15 & 10 & 0 & \\ C6 & 6:16 & 9 & 0 & V2 \\ C10 & 6:23 & 2 & 0 & \\ C13 & 6:09 & 16 & 0 & \\ \hline C2 & 6:21 & 8 & 0 & \\ C4 & 6:19 & 10 & 0 & \\ C7 & 6:24 & 5 & 0 & V3 \\ C8 & 6:26 & 3 & 0 & \\ C15 & 6:14 & 15 & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The time of the vehicle visiting the demand points.  
Figure 6: Result comparison of the proposed model and traditional DRTs.  
Additionally, the comparison of the improved GA and the standard GA is shown in Table 6. The improved GA is better than the standard GA in solving the quality problem. As the test scale increases, the differences between the algorithms also gradually show upward trends of 3.7%, 6.4%, and 11.3%, respectively. This is because each sub-problem has fewer chromosome genes, in which the crossover and the mutation are operated more efficiently, and it is easier to jump out of the local optimum when the problem is divided into several sub-problems. Similarly, since each sub-problem operates independently, the more sub-problems there are, the faster the solution converges.
###### Abstract  
Herbicide resistance is an emerging phytosanitary threat, causing serious yield and economic losses. Although this phenomenon has been widely studied, only recently has the role of epigenetic factors in its occurrence been considered. In the present study, we analyzed the microRNA-mediated regulation in _Echinochloa oryzaeicola_ (Vasinger) Vasinger (late-watergrass) of the expression of cytochromes P450, glutathione S-transferase (GST), and eIF4B, all of which are enzymes involved in profoxydim (AURA(r)) detoxification. Before and after profoxydim application, the expression profiles of microRNAs (miRNAs) were selected for their ability to target the genes considered, and their targets were assessed by means of RT-qPCR. Susceptible and resistant biotypes showed different responses to this herbicide. After profoxydim application, in resistant biotypes, _osa-miR2099-5p, ath-miR396b, os-miR395f, os-miR8396a-5p, os-miR1664-5p, os-miR1664-5p, gra-miR8759_, and _gma-miR8396f_ were not triggered, allowing the expression of CYP81A, _GSTF1_, and _elF4B_ genes and the herbicide's detoxification. Meanwhile, the transcription of _ata-miR166c-5p, ath-miR847, osa-miR85538_, and _gra-miR7487c_ was triggered, down-regulating _CYP71 AK2_, _CYP2A254_, _CYP72A2122_, and _EcGST_ expression. In susceptible biotypes, the herbicide stimulated _ata-miR166c-5p, ath-miR847, osa-miR85538, gra-miR87487c, osa-miR1664-5p_, and _gra-miR8759_, down-regulating their respective target genes (_CYP72A122_, _CYP1 AK2_, _EcGST_, _CYP72A254_, _CYP81A12_, and _elF4B_). A better understanding of the role of miRNA-mediated epigenetic regulation in herbicide resistance will be useful in planning more targeted and sustainable methods for controlling this phytosanitary threat.  
late-watergrass; profoxydim resistance; microRNA; CYP450; GST; eIF4B; CYP81A
## 6 Unlocking the Power of Wavelet Transforms
More regular and symmetric wavelets are, respectively, useful for gradual artifacts and for signals with symmetric characteristics as well as influencing the ability to detect transient or extended events. Daubechies wavelets are known for their regularity and rough symmetry, ideal for highlighting significant details in EEG signals, with the order determining their sensitivity to signal variations. Coiflets, offering greater symmetry, are suitable for transient signals such as EEG artifacts. Symlets, with improved symmetry compared to Daubechies, are effective in removing artifacts while maintaining the characteristics of biological signals.
## 2 Results
### Identification of SSRs  
SSRs, or microsatellites, are widely distributed in eukaryotic genomes [41]. We analyzed the 40,850 unigenes in _A. paniculata_ transcriptome using MISA 1.0 software and identified a total of 29,751 SSRs. More than 1 SSR was found in 7264 unigenes, and compound SSRs were found in 4129 unigenes (Figure 10A). There were 9817 mono-nucleotide repeats, 11,294 di-nucleotide repeats, 7051 tri-nucleotide repeats, and 1589 hexa-, penta-, and tetra-nucleotide repeats. The number of SSR units in the _A. paniculata_ transcriptome were distributed between 5 and 32 times. There were 27,333 SSR units with repeat lengths between 5 and 16 times, accounting for 91.87% of the total repeat units. There were  
Figure 9: Biosynthesis pathway of flavonoid biosynthesis and the heatmap analysis of their DEGs. (**A**) Predicting the biosynthetic pathway of flavonoids. The steps and enzymes shown in the diagram are known. (**B**) Heatmap of flavonoid pathway of DEGs.  
2241 SSRs with repeat lengths between 17 and 32 times, accounting for 7.53% of the total SSRs. There were also 177 SSRs with repeat lengths greater than 32, with mono-nucleotide repeats being the most common. This SSR landscape provides a rich basis for developing microsatellite markers to study genetic diversity, population structure, and linkage mapping in _A. paniculata_.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### Nh\({}_{3}\)-Tpd  
Figure 3 shows the NH\({}_{3}\)-TPD spectrum of the prepared catalyst. The figure shows the temperature of the acid center of ammonia desorption. Three desorption peaks were found at about 77 \({}^{\circ}\)C, 161.1 \({}^{\circ}\)C, and 371.1 \({}^{\circ}\)C, representing the different types of acid sites on the catalyst surface. In general, the intensity of the acid sites was proportional to the temperature of the desorption peak, and the number of acid sites was proportional to the area of the desorption peak. The desorption peaks at about 77 \({}^{\circ}\)C and 161.1 \({}^{\circ}\)C were attributed to weak acidic sites bound to NH\({}_{3}\), while the desorption peaks at 371.1 \({}^{\circ}\)C were attributed to strong acidic sites bound to NH\({}_{3}\)[20]. Compared with the desorption peaks of 4 Cu-4 Ce/SAPO-34, 4 Cu-4 Ce-4 Zr/SAPO-34, and 4 Cu-4 Ce-8 Zr/SAPO-34, the number of weak acid sites at 77 \({}^{\circ}\)C decreased, and the intensity increased with the increase in the Zr mass fraction. In addition, the number of acidic sites of 4 Cu-4 Ce/SAPO-34 was the largest. The number and intensity of weak acid sites at 161.1 \({}^{\circ}\)C increased first and then decreased, and the number and intensity of acidic sites of 4 Cu-4 Ce-4 Zr/SAPO-34 were the largest. The catalysts with the strongest acidic sites at about 371.1 \({}^{\circ}\)C were 4 Cu-4 Ce/SAPO-34 and 4 Cu-4 Ce-6 Zr/SAPO-34. In conclusion, the total number of acidic sites of 4 Cu-4 Ce/SAPO  
Figure 1: NO\({}_{x}\) conversion of catalysts with different Zr loadings.  
34 was the largest, and thus the denitration efficiency was the highest. This shows that the Zr loading reduced the total number of acidic sites of 4 Cu-4 Ce/SAPO-34, thus reducing the denitration efficiency. The denitration efficiency of 4 Cu-4 Ce-4 Zr/SAPO-34 was only lower than that of 4 Cu-4 Ce/SAPO-34, which could be attributed to the fact that the number and intensity of weak acid sites at 161.1 \({}^{\circ}\)C increased due to the introduction of the appropriate amount of Zr. Similarly, the poor denitration efficiency of 4 Cu-4 Ce-6 Zr/SAPO-34 could be attributed to the excessive metal Zr, which led to a reduction in the number of acidic sites. The type of specific acid sites at each temperature needs to be further concluded through in situ DRIFTS tests.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 4 Proposed Scheme
### Holding Time Estimation
Upon the receiving duplicate packet from node A, it simply drops the packet. The following equation must be satisfied to avoid duplicate packets:  
\[t_{1}+T[HH_{A}]<t_{2}+T[HH_{B}]+t_{12}. \tag{14}\]  
Substituting Equation (10) in Equation (13) results in:  
Figure 7.0  
Figure 7.1 Holding time scenario. According to the the triangle inequality theorem that the length of each side is less than sum of lengths of the other two sides, and greater than the difference between these lengths, thus \((t_{2}-t_{1})-t_{12}\) is always less than 0, and, as \(HH_{A}>HH_{B}\), thus k is always a negative number. The above two inequalities will be satisfied if:  
Figure 7.2  
For the worst case, the value of k will be :  
Figure 7.3  
where \(V_{0}\) is the propagation speed of acoustic signal and R is the maximum transmission range. The value of \(k\) varies between 0 and R, as it depends on \((HH_{A}-HH_{B})\) and \(HH_{A}\)\(\epsilon\)[0 R]. \(k\) cannot always satisfy the above inequality in Equation (14) as \(k\rightarrow-\infty\) when \((HH_{A}-HH_{B})\to 0\). If we replace the \((HH_{A}-HH_{B})\) by a global variable \(\delta\) such that \((HH_{A}-HH_{B})\leq\delta\), then it guarantees that node A will forward the packet before node B. Hence,  
Figure 7.4  
To find \(\beta\), we consider that the node having the minimum Fitness Function (HH) value will have the holding time approximately zero; therefore, from Equation (10),  
Figure 7.5  
By solving the equation and putting the values in (10), we have :  
Figure 7.6  
The node having the highest fitness function (HH) value will be selected as a next forwarder. For instance, node A will calculate the holding time and start timer. When the timer is expired, then node A will forward the packet. If the other nodes in the range of A, i.e., B receives the duplicate packet during the holding time, it will drop both the packets because it means the original packet is already transmitted. The holding time is inversely proportional to \(\delta\), if we select larger \(\delta\), then the holding time will decreases and therefore end-to-end delay will also reduce. Along with this improvement, there is also reduction in energy consumption that has been noticed and this is due to optimal forwarder selection at each.
## 3 Analysis and Discussion of Results
### Fitting of Artificial Neural Networks  
With the aim of studying the fitting of the UCS by using models based on ANN's, different architectures were generated, and their fitting was studied using the indicators of goodness of fit indicated above. Three artificial neural networks architectures were considered for training and testing, with network architectures of 1-2, 1-3 and 1-4 hidden layer and neurons per hidden layer, respectively. The statistics of the percentage of relative errors between the predicted responses and the measured values have been summarized in Table 6.  
From the analysis of the goodness-of-fit statistics of the different ANN configurations presented in Table 6, it can be checked that the model presenting the best fit in is the simplest configuration (one hidden layer with two neurons), which is validated by the highest determination coefficient (0.6975), a high accuracy (85%) and low errors (MAD and MSE). The increase of the coefficient of determination through the training and testing periods is presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture/Statistic** **(Layer, Neurons)**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \cline{2-9}  & **MAD** & **MSE** & **ACC (\%)** & **R\({}^{2}\)** & **MAD** & **MSE** & **ACC (\%)** & **R\({}^{2}\)** \\ \hline
1 layer, 2 neurons & 0.0712 & 0.0469 & 85.00 & 0.6975 & 0.0856 & 0.0536 & 77.77 & 0.6646 \\
1 layer, 3 neurons & 0.0816 & 0.0523 & 85.00 & 0.6732 & 0.0975 & 0.0618 & 77.77 & 0.6371 \\
1 layer, 4 neurons & 0.0902 & 0.0587 & 85.00 & 0.6271 & 0.1012 & 0.0729 & 66.67 & 0.5582 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Goodness-of-fit statistics for artificial neural network (ANN) architectures.  
Figure 5: Predicted UCS vs. experimentally determined UCS for simple multiple regression (**a**) (Equation (22)) and second degree multiple regression (**b**) (Equation (23)).  
Figure 6: Variation of the coefficient of determination R\({}^{2}\) vs. epochs for neural network architecture of 1 hidden layer, 2 neurons.
## 5 Program Customizability and Adaptability
### Manual Control  
An adaptive program requires realtime analysis of the responses of a participant during a program in one or more aspects and decides on the most appropriate next step autonomously. To a large degree, there is still a gap towards this goal. Hence, a psychologist or a trained clinician is often required to provide their subjectively evaluation based on their observation during the execution of a program. Sometimes, the professional is asked to not only evaluate the response of the participant, but also to control the flow of the program as well, which is true especially for social robotic based studies, where a social robot is controlled manually by the professional based on his/her judgment. This scheme is often referred to as Wizard of Oz scheme [3; 152]. The professional evaluation can also be used to assess the effectiveness of the program.  
In [PERSON] et al.'s study of interactive avatars [38], the system output was verbal utterances by a human actor that tele-operated the avatar to talk on the screen. The avatar tried to elicit responses (verbal statements and gestures) from the participants and occasionally called on those who raised their hands (and their parents) to engage in one-on-one conversation. In [64], a carton-like avatar was controlled by an actual person. The motivation for doing so is to gain more flexibility during the interactive session because the current state-of-the-art technology is still incapable of handling spontaneous conversations.  
In [85], a technician was used to monitor the eye gaze of the participant both for the purpose of calibration of the eye tracking system and for identifying when the participant looked at the robot and the object as directed by the robot. When the participant performed as instructed within 7 s, the technician also triggered a reward for the participant by pushing a button in the system. Otherwise, a miss event was recorded and the next task was administered.  
In [PERSON] et al.'s study [106], the system output consists of simple motions of the robot parrot following the commands of the operator, as well as voice from the speaker uttered by the operator. This involves the use of body motor to control the motions of the wings, the legs, and the neck, and the use of head motor to control the motions of the eyes and the beak. The operator (ideally a therapist) can use the camera on the cage to check the validity of the execution of commands sent to the robot parrot, use the speaker and microphone to verbally communicate with the ASD child, and adjust both the digital commands and verbal output based on what the child says or does.  
In [95], the robots were also controlled by a trainer using a laptop running a custom program. The trainer is responsible for deciding when to greet a participant, warming up a game with body stretching activities, playing various games, and deciding when to say goodbye. The same scheme was used in [96].  
In motion-based touchless serious games [22; 25], a therapist was also tasked to observe the behaviors of participant and to intervene when necessary.