esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Acetin (Glycerol Esters or Acetyl Glycerol)
### Mechanism of Acetylation of Glycerol  
Various articles report the plausible reaction mechanism in the acetylation of glycerol with acetic acid [58, 83, 84, 85, 86, 87]. The mechanism proceeds via the activation (protonation) of the carbonyl group of the acetic acid by a strong acid catalyst (usually Bronsted-type acid). The resultant acylium ion makes it more susceptible to nucleophilic attack by the oxygen atom of the hydroxyl molecule attached to glycerol, thereby forming a tetrahedral intermediate and leading to the transfer of hydrogen to the next hydroxyl molecule (attached to the carbonyl carbon) to form a water molecule. On the loss of the water molecule, a monoacetin (monoacetyl glycerol) is formed, and on further reaction with acetic acid following a similar mechanism, diacetin (diacetyl glycerol) is formed, and finally, triacetin (triacetyl glycerol) is formed as shown in Scheme 5.  
The acetylation of glycerol with acetic anhydride in a strong acidic medium proceeds via two possible mechanisms [88, 89, 90]. The first plausible mechanism, which is less energetic, involves the protonation of the carbonyl oxygen atom, which provides room for nucleophilic attack leading to the formation of a tetrahedral intermediate (quaternary carbon atom) which requires space. This is exemplified in the reaction Scheme 6. While in the second plausible mechanism, the protonation occurs on the oxygen atom attached to the carbonyl group leading to the formation of acylium ion (acylation) due to the presence of a bulky group as shown in Scheme 7.  
The conversion of glycerol to acetic can be influenced by several experimental parameters, just like most reactions. The main parameters include the nature of the catalyst and the catalyst amount (load), temperature, reactants and their mole ratios, as well as the time of the reaction. These parameters are reviewed below.  
**Scheme 6.** Plausible reaction mechanism for the acetylation of glycerol with acetic anhydride showing the nucleophilic attack leading to the formation of a tetrahedral intermediate and the subsequent formation of mono-, di-, and triacetin.
## 4 Simulation and Experimental Results  
The objective of the simulations was to analyze the behavior of the IT/OT convergent SDN-based architecture when it was under attack, particularly in the presence of denial of service (DDoS) attacks. The current section first presents the set-up of the simulation environment and then a significant selection of experimental results obtained in our lab deployment scenario. We analyzed the experimental results to measure the latency introduced by the attack that involved the IoT service and the degradation of the channel quality between the client and the server. In this way, the results obtained were a reasonable metric to how SDN security issues affect IIoT networks.
## 6 Simulation Results
### Performance of Effectiveness  
The effectiveness properties of the end-to-end delay and power consumption efficiency are investigated in this section. As shown in Figure 8, it is observed that the expected end-to-end delay degrades with increasing number of episodes. Figure 8(a) compares the delay performance for four kinds of protocols in the network with 10 SU nodes. It is found that the end-to-end delay of Q-routing and FP-Q-routing suffers a sharply drop in the beginning, and it retains almost steady state after about 400 episodes. While the delay of two DQN based schemes falls slowly before 400 episodes, and after that the convergence speed becomes faster than Q-routing and FP-Q-routing. The performance of four protocols is almost the same in the end. As shown in Figure 7(a), the convergence speed of Q-routing and FP-Q-routing is faster than DQN-based schemes before 400 episodes, and then the curves become flatter, which results that the delay of Q-routing and FP-Q-routing falls faster than it of two DQN based algorithms at first but loses ground in the end. In addition, the latency of FP-Q-routing is longer than Q-routing because FP-Q-routing fails to transmit data with high power when the communication condition is better so that it has longer time delay in total. Figure 8(b) shows that the performance of natural DQN based method is superior to Q-routing and FP-Q-routing in the network with 50 SU nodes, and PM-DQN achieves the least end-to-end delay. It illustrates the benefits of deep Q-learning and prioritized memories method in the network with large state space.  
Figure 10 depicts expected PCR as a function of amount of episodes. With the increasing number of episodes, the trend of PCR is very similar to that of end-to-end delay shown in Figure 9. That is, in Figure 9(a) the PCR of Q-routing and FP-Q-routing falls sharply in the beginning, is seemingly steady after 400 episodes, and achieves almost the same performance as DQN-based methods in the  
Figure 8: Average steps vs. the number of episodes.  
end. In addition, the PCR of FP-Q-routing is larger than Q-routing because FP-Q-routing fails to adjust transmission power adaptively and it may still transmit data with high power in poor communication conditions, which causes very low energy efficiency. In Figure 9(b), energy efficiency of DQN-based methods is significantly improved comparing to Q-routing and FP-Q-routing. The PM-DQN-based scheme achieves the least PCR, which is 25% less than it of natural DQN. Consequently, in the small scale network PCR performance of four protocols is relatively close. Furthermore, in the network with large state space, DQN based methods consume less power comparing to Q-routing and FP-Q-routing, and prioritized memories experience replay further enhances the power efficiency.
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 2 Related Work
### Density Estimation and Clustering Systems  
[PERSON] et al. [20] utilized an approach based on the Gaussian sum filtering model for tracking vessels on the high sea, along with a two-dimensional kernel density estimator for learning a probability density function from vessel tracks. [PERSON] et al. [21] proposed an approach for learning maritime traffic patterns based on the identification of turning points. A data model for vessel routes was presented, which defines data classes for vessel objects, turning point objects, ports and offshore platforms, entry and exit points, sea lanes and routes. The vessel objects store information such as the MMSI, call sign, name, or size. Turning points, which are inferred by a proposed algorithm, identify regions on which vessels tend to change their course over ground (COG). Ports and offshore platforms represent locations on which the vessels often have zero velocity. These areas are classified as ports or offshore platforms depending on their distance from the coast. Entry or exit points represent locations on which vessels have entered or exited a specific marked region. Sea lanes correspond to straight-oriented paths which connect two waypoints, which can be ports, offshore platforms, entry or exit points or turning points. Routes are defined as a series of connected sea lanes on the sea. [PERSON] et al. [22] propose an incremental DBSCAN approach to merge stationary objects and entry/exit points. This approach calculates synthetic routes, derived from the found route clusters. These route clusters are based on an entropy value, which is calculated per route. This approach is then used for vessel pattern knowledge discovered, in the later published work [23]. [PERSON] et al. [24] presented two models based on different types of training data. The first proposed approach is based on a time series model, which associates each timestamp to the latitude, longitude, and speed, used in the Bayesian network. This approach can be used in online analysis. The second approach learns track-based anomalies, based on the full track, which is out of scope for the present work. The method uses a Markov chain Monte Carlo method for searching and scoring the trade-off between data simplicity and goodness of fit. [PERSON] et al. [25] propose a system based on clustering line-based trajectories, combined with a naive Bayes classifier, which models normal vessel trajectories. The number of clusters is selected by using the Calinski-Harabasz index. [PERSON] and [PERSON] [26] presented a methodbased on DBSCAN for finding traffic patterns and an LSTM neural network. The authors utilized the elbow method for selecting the optimal number of clusters. The neural network receives as input the trajectories found by DBSCAN, in which a window size is set for the number of points that constitute each trip.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 3 Proposed Method
### Component Extraction  
A 3D model usually contains a large number of faces, which can generate a huge amount of computation if the entire mesh model is segmented directly. Therefore, the mesh sub-module is constructed using the method proposed by [PERSON] et al. [36] to over-segment the 3D mesh into several small patches. Since the number of vertices contained in each patch is inconsistent, Principal Components Analysis (PCA) is used to reduce the dimensionality to make the number of vertices contained in each patch equal. After that, the Average Geodesic Distance (AGD) feature of each patch is calculated, and the sub-model is aggregated into components with dynamic clustering. Finally, the improved Hausdorff distance is used for component matching to extract the component pairs.
## 1 Introduction
The present investigation uses a hybrid fuzzy technique for order preference by similarity of the ideal solution (TOPSIS) methodology (FTOPSIS) to analyze the coverage of a hypothetical EU ecolabel for FAPs based on the mentioned public consultation and it extends the previous research in two important aspects: (1) using a synthetic indicator, it identifies the differences of opinions on the level of acceptance for the inclusion of different issues in an EU ecolabel for FAPs, considering various types of stakeholders and segments of the sample according to their opinion on whether public bodies/goverments should be involved in the control of ecolabels (governmental intervention segments); and (2) it estimates the elasticities related to the synthetic acceptance indicator for each stakeholder and governmental intervention segment to discuss some policy implications based on the results. Thus, the present investigation sets out three main objectives: (1) to extend the current literature proposing a methodology that jointly analyzes the importance of including different types of information in a hypothetical ecolabel for FAPs; (2) to compare the values of the synthetic indicator by type of stakeholder and by respondents' opinions on whether public bodies/goverments should be involved in the control of ecolabels; (3) to determine the sensitivity of the synthetic indicator for each group of interest with respect to each type of information apart from environmental issues (animal welfare, health and safety, food quality and social-ethical issues) included in the ecolabel scheme. To our knowledge, this study contributes to the eco-labeling literature in two important aspects that have not been analyzed up to now. First, it is an attempt to provide more insights on a topic that has not been analyzed at the EU scale. Second, two important segmentation variables are selected to analyze the main results regarding the level of acceptance for the inclusion of different types of information apart from environmental issues in the ecolabels: stakeholder type and the support of the respondents to the fact that public bodies and government should be more involved in the control of eco-labeling. The rest of the paper is organized as follows: Section 2 presents the relevant literature review of ecolabels highlighting more features on FAPs; Section 3 details the data used for the estimation of the synthetic indicators and the elasticities; Section 4 describes the proposed methodology; Section 5 describes the main results based on the application of the methodology; Section 6 discusses the results and Section 7 offers some conclusions.
## 8 RAN Decomposition Scheme
### Performance Requirements
#### 8.3.1 Bandwidth Requirements  
The data transport bandwidth for Option 2 is virtually equivalent to that of Option 1 but for the UL signaling transmission bandwidth that should be considered in the formal. Note that the UL signaling is proportional to the number of UEs that report the UL request and the report packet contents. Unlike Option 2, Option 6 split introduces additional bandwidth overhead that is due to the associated PHY schedule signaling. Therefore, apart from the modulation mode, the UL data from UL-PHY to MAC, in addition to UL-PHY response to the schedule, majorly constitute the UL data/signaling. Furthermore, UL bandwidth for Option 7-2 comprises PRACH, PUSCH, and MAC information. Consequently, the required bandwidth can be estimated from different parameters such as RB assignment, number of sub-carrier, OFDM symbol rate, MIMO layer, IQ bit width. The UL bandwidth estimation for Option 7-1 is similar to that of Option 7-2. The notable differences are the required number of antenna port/MIMO layer and the associated overhead that accounts for scheduling/control signaling [430].  
In line with the basic 5G assumptions given in 3 GPP TSG RAN WG3 [430; 431; 432] and parameters listed in Table 14, we evaluate and simulate the bandwidth requirements of the UL transmission focusing on options 2, 7-1, and 8 (for benchmarking). The required MFH bandwidth for each option concerning the system bandwidth is depicted in Figure 29. The required MFH transmission rate for a 40 MHz system bandwidth for Option 2 is 1.224 Gbps. At 80 MHz RF bandwidth, the required MFH bandwidth doubles for the same option. This shows that the required bandwidth depends on radio configuration. Moreover, it can be inferred that the bandwidth significantly depends on the specific split option. For instance, the required MFH bandwidths at 80 MHz system bandwidth for options 7-1 and 8 are 90.92 Gbps and 125.8 Gbps, respectively. This indicates that the required MFH transport bandwidth increases and becomes more stringent as the split point goes farther down the PS towards the LLS. For example, the Option 8 split requires additional 123.378 Gbps bandwidth at 80 MHz compared with Option 2.
## 5 Performance Evaluation
### Multihop D2D Broadcasting in Urban Vehicular Networks
In any case, Figure 30 shows that the reliability of the dissemination is above 99%. Note that in this case vehicles are moving, hence they may enter/leave the target area while a broadcast is ongoing (although this effect is negligible in practice, given the short time it takes for a broadcast to cover the area). To maintain consistency, we count in the delivery ratio only the vehicles that are within the target area at time \(t_{0}\). Figure 31 shows that the average number of RBs allocated for each broadcast, which is around 280 for a target radius of 1000 m. In this case, too, the number of RBs used is of the same order of the number of UEs, which is quite low. Figure 31: Allocated RBs per broadcast. Figure 29: Example of broadcasting. The dashed circle delimits the broadcast area. Figure 30: Average delivery ratio.
# Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
## 3 Results
### Qualitative Parameters Analysis  
Analysis of variance (two-way ANOVA) revealed that the interaction between 1 AM model and trampling level significantly affected color (\(p<2.2\)\(\times\)\(10^{-16}\)), quality (\(p<2.2\)\(\times\)\(10^{-16}\)), and NDVI (\(p<0.01\)) (Table 6).  
Figure 4: Colorimetric maps showing the number of passages and the trajectories of the two different autonomous mowers (different replications). Dark areas mean higher trampling.  
Figure 5: Mean value of height in function of autonomous mower model (Robot) and trampling level (Level) and trampling level (Level) and position. Error bars represent standard errors. Data were averaged over days and repetitions.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**AM Model** & **Trampling Level** & **Electric Energy Consumption (kWh)** \\ \hline \multirow{3}{*}{535 AWD} & Control & 0.0053 \\  & Low & 0.0160 \\  & Medium & 0.0320 \\  & High & 0.0480 \\ \hline \multirow{3}{*}{450X} & Control & 0.0047 \\  & Low & 0.0140 \\  & Medium & 0.0280 \\  & High & 0.0420 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Electric energy consumption for each cutting session level and for the two different autonomous mowers.  
The mean value of qualitative parameters in the function of AM model and trampling level revealed how all parameter values decreased when the trampling level increased. General observation makes it evident how color, quality, and NDVI values are higher in plots moved by AM 535 AWD.  
As for the color analysis, more significant differences were detected in plots moved by AM 535 AWD between each trampling level, and the lowest color value (3.50) was detected in the plot moved by AM 450X with a High trampling level. No differences in the quality values were detected between Medium and High trampling levels in plots moved by AM 535 AWD, as in the plots moved by AM 450X.  
An evident variability, instead, was detected in the NDVI analysis, where all values were different from each other (Table 7).  
To better visualize the variation of NDVI and color, Figure 6 illustrates their values evolution along the studied period for the different treatments.
## 3 Results and Discussion
### Optical Properties Investigation  
UV-visible diffuse reflectance spectra (UV Vis DRS) of the prepared samples are shown in Figure 6a. It is obvious that pure TiO\({}_{2}\) sample only exhibits light absorbance in the UV region (200-400 nm), with a band appearing around 390 nm. However, upon doping with nitrogen, the NT samples exhibit a clear red shift in light absorption thus supporting the doping of pure TiO\({}_{2}\). It is well established that upon doping of external element such as nitrogen, new energy levels are created above the valence band which might results in the extension of light absorption and narrowing of the band gap. The band gap energy (\(E_{g}\)) of pure TiO\({}_{2}\) and NT samples are also estimated utilizing [PERSON]'s equation [15; 51]. [PERSON]'s plot is generated by plotting the Kubelka-Munk function \(\left(\alpha hv\right)^{2}\) against band gap energy \(\left(E_{g}=hv=\frac{hc}{\lambda}\right)\), where \(\alpha\) is absorption coefficient, \(h\) is the Plank's constant, and \(v\) is the frequency of radiation. The estimation of band gap is then done by extrapolation of the linear portion of curve to the zero value of the \(y\)-axis. The [PERSON]'s plots for pure TiO\({}_{2}\) and NT samples are shown in Figure 6b. It is noticeable that pure TiO\({}_{2}\) possesses a band gap of 3.4 eV, however upon N doping the linear portion of band edge intercepts at \(x\)-axis value of 2.75 eV, 2.60 eV, and 2.50 eV for NT-1, NT-2 and NT-3 samples, respectively. Hence based on UV Vis DRS measurements and band gap estimation it can be assured that the narrowing down of band gap and extension of light absorption is mainly due to N doping of TiO\({}_{2}\)[29; 30; 31; 34; 35; 36; 38; 40].  
Figure 7 displays the PL spectra of all as-prepared samples. A sharp peak appearing within the range of 500-530 nm can be attributed to the band to band recombination, whereas a broad hump observed around 600 nm is mainly associated to the defects induced by foreign elements [52; 53]. It can be observed that band to band peak is quenched for NT samples as compared to pure TiO\({}_{2}\) thus indicating the efficient separation of photo-excited charges, whereas the later broad peak for NT samples is higher as compared to pure TiO\({}_{2}\) thus again supporting the recombination of photo-excited charges, trapped by defects which were induced by doping process, and are less on the surface of pure TiO\({}_{2}\).
## 3 Results and Discussion
### Analysis of the Used Data Sets
The most striking result is an accumulation of low errors for the blue-colored highlighting of the enhanced ANNs in the graph on the bottom right. In order to achieve an exemplary comprehension of the comparison of the applicability of these approaches, an ANN from the enhanced, as well as standard category, is selected and compared in the following paragraphs. Figure 6: Examples of slopes for welding distortion progression over bead count. Figure 7: Implemented settings for the creation of ANNs.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.3. Mangrove Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**Resolution**} & \multirow{2}{*}{**Data Source**} & \multirow{2}{*}{**Application**} \\  & & **Spatial** & & & \\ \hline \multirow{3}{*}{Sentinel-2 images} & October & & & [[https://scihub.copernicus](https://scihub.copernicus)]([https://scihub.copernicus](https://scihub.copernicus)). & Estimating GPP \\  & 2015–December & 10 m & 5 day & eu/dhus/\#/home & Inversing mangrove \\  & 2020 & & & & parameters \\ \hline \multirow{3}{*}{landsat 8 OLI images} & March & & & [[https://code.eartheengine](https://code.eartheengine)]([https://code.eartheengine](https://code.eartheengine)). & Comparing the status \\  & 2013–December & 30 m & 16 day & google.com/ & Pre/Post- disturbance \\  & 2020 & & & & \\ \hline \multirow{2}{*}{Jilin-1 images} & June 2022 & 0.5 m & - & [[http://www.jll.cn/](http://www.jll.cn/)]([http://www.jll.cn/](http://www.jll.cn/)) & Mapping mangrove \\ \hline \multirow{2}{*}{Inner Mongolia images} & \multirow{2}{*}{2013–2020} & \multirow{2}{*}{0.25\({}^{\circ}\)} & \multirow{2}{*}{Daily} & [[https://cds.climate](https://cds.climate)]([https://cds.climate](https://cds.climate)). & \\  & (Tmax) & & & & \\ \hline \multirow{2}{*}{mean air temperature (Tem)} & \multirow{2}{*}{2013–2020} & \multirow{2}{*}{0.25\({}^{\circ}\)} & \multirow{2}{*}{Daily} & [[https://cds.climate](https://cds.climate)]([https://cds.climate](https://cds.climate)). & \\  & & & & & \\ \hline \multirow{2}{*}{sea surface temperature (SST)} & \multirow{2}{*}{0.05\({}^{\circ}\)} & \multirow{2}{*}{Daily} & \multirow{2}{*}{[[https://code.eartheengine](https://code.eartheengine)]([https://code.eartheengine](https://code.eartheengine)). & \\  & & & & & google.com/ \\ \hline \multirow{2}{*}{sea water salinity (SAL)} & \multirow{2}{*}{2013–2020} & \multirow{2}{*}{0.08\({}^{\circ}\)} & \multirow{2}{*}{Monthly} & [[https://code.eartheengine](https://code.eartheengine)]([https://code.eartheengine](https://code.eartheengine)). & \\  & (VPD) & & & & \\ \hline \multirow{2}{*}{photosynthetically active radiation (PAR)} & \multirow{2}{*}{2013–2020} & \multirow{2}{*}{0.05\({}^{\circ}\)} & \multirow{2}{*}{Monthly} & [[http://www.glass.umd](http://www.glass.umd)]([http://www.glass.umd](http://www.glass.umd)). & \\  & & & & & \\ \hline \multirow{2}{*}{Mangroves Map} & \multirow{2}{*}{1996–2022} & \multirow{2}{*}{-} & \multirow{2}{*}{Yearly} & https://www. & Mapping mangrove \\  & & & & globalmagnrovewatch.org & distribution \\ \hline \multirow{2}{*}{Tropical cyclones data} & \multirow{2}{*}{1949–2021} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & [[https://tcdata.tyhoon.org](https://tcdata.tyhoon.org)]([https://tcdata.tyhoon.org](https://tcdata.tyhoon.org)). & Screening interfering \\  & & & & & events \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the datasets used in this study.