esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 2 Related Work  
### Motivation  
The most important component of the IoT is the networking that facilitates communication and interconnectivity. Particularly, routing holds a prominent place in networking, which involves building traffic routes for transmitting a packet from source to destination. Moreover, the security issue is crucial in networks, particularly routing, when the billions of devices are connected with each other, and the number of devices is predicted to be exponentially increasing in the coming years, as mentioned in a report by Statista [4].  
Network security becomes challenging when a packet is routed through heterogeneous networks from resource-constrained devices to a server over the Internet. The IoT is a hybrid network that involves a number of heterogeneous networks, thus requires security solutions against network intrusions and disruptions [22]. In the IoT applications, there are different devices and computers connected with each other and use different operating systems and protocols. Hence, with the widespread IoT applications involving routing via RPL [23], it is imperative to address the related attacks. Out of several security solutions proposed for secure routing, a trust-based approach possesses the significance and viability for IoT networks and routing. A robust security solution will not only enhance protection against attacks but also facilitate the overall widescale adoption of IoT applications.
## 3 Nonthermal Methods
### Ozone Using Method  
The application of ozone is another effective method, an environmentally friendly solution classified as generally recognized as safe (GRAS), being appreciated for its ability to reduce microorganisms due to its strong oxidative properties. The use of ozone in the dairy industry is considered a clean technique, capable of eliminating microorganisms and limiting enzymatic activity without leaving chemical residues and increasing efficiency in controlling microbial loads. Ozone can also be used to sanitize equipment, helping to prevent contamination and ensure food safety. Studies have shown that the application of ozone in the food industry, including in the cleaning and sterilization processes of equipment, can significantly reduce the microbial load without affecting the quality of the products [14].  
In a recent study, the use of gaseous ozone at 50 ppm on food-associated strains of _L. monocytogenes_ resulted in a significant reduction in bacterial load, with a decrease of more than 3 log10 cfu/mL after 10 min. Also, after 6 h of treatment, a complete inactivation of planktonic cells and a considerable reduction in biofilm biomass were observed [77]. Thus, gaseous ozone is a promising method for controlling _L. monocytogenes_ contamination on food contact surfaces and in finished products [78].  
The effectiveness of ozone can vary depending on factors such as temperature, pH, humidity, and the presence of organic matter. Also, the inactivation of microorganisms by ozonation is a complex process that depends on the cellular structures and composition of the microorganisms. In the case of _L. monocytogenes_, a food pathogen with a high potential to contaminate dairy products, ozone has been shown to be effective in reducing microbial loads. In a study conducted in a cheese factory, ozonation significantly reduced the presence of _L. monocytogenes_ in the samples tested. Ozone was used in both gaseous and aqueous forms, demonstrating its effectiveness against a wide range of pathogenic microorganisms [79].  
A study by [PERSON] (2011) [35] showed the complete elimination of _L. monocytogenes_ in raw and processed milk after 15 min of ozone treatment. [PERSON] et al. (2013) [80] also observed a reduction in bacteria, including _Listeria_ and _Enterobacteriaceae_, in ozone-treated milk. [PERSON] et al. (2020) [81] applied ozone gas to various cheeses and observed a significant reduction in _L. monocytogenes_.  
However, some studies suggest that although ozone reduces microbial loads, its effectiveness may be limited in certain cases, requiring combination with other methods. As research continues, it is anticipated that ozonation will become a standard method of food sterilization and preservation due to its efficiency and low environmental impact [14].
# SDToW: A Slowloris Detecting Tool for WMNs
## 5 Results
### Experiment 3: Comparing SDToW with Snort
Two factors directly enhanced our tool's performance. The first is the distributive operations by the modules. Our tool uses three modules on different machines, hence distributing the resource consumption between the modules. The second factor is the strategy of blocking the attacker on its first hop. Our tool blocks the attacker on the first AP, where it is connected. Therefore, it reduces the amount of malicious traffic running on the network. Snort does not block the attacker near its origin. Therefore, by using Snort, Slowlois will keep sending connection requests to the network. These requests will pass through the APs until they reach Snort, where they will be dropped. Figure 10: Experimental scenario using Snort. After the resource consumption analysis, we measured the number of false positive errors; see Figure 13. We conducted the tests with five clients. One of the five clients was an attacker, and the other clients made legitimate HTTP requests. Figure 11: Processing consumption comparison for Snort and SDToW. Figure 12: Memory consumption comparison between Snort and SDToW. Snort presents weaknesses considering the occurrence of false positive errors. These weaknesses rely on the number of ongoing connections permitted by each user. In our tests, we used a Snort rule that verifies if the number of connections exceeds 20 parallel connections per user. Therefore, whenever a user exceeds this limit, Snort will drop packets from this user. Considering the page with 10 and 20 objects per page, Snort did not create any false positive blocks. However, when we increased the number of objects per page to 30, Snort blocked all the clients. Snort blocked the clients because they exceed the maximum limit of 20 parallel connections per user. Therefore, Snort erroneously blocked four of the five clients that were not attackers, creating 80% false positives. The same number of false positives occurred with the 40 objects per page. It is possible to increase this limit for parallel connections, but at any point, this limitation will create a challenge between the number of false positives and accuracy. Therefore, raising the limit of ongoing connections reduces the number of false positives, but it permits Slowloris to consume a higher number of connections. Conversely, if we reduce the limit of ongoing connections for each user in the Snort rule, it will increase false positive errors. Considering a server hosting a page with many objects per page, it will raise the number of ongoing connections per user. Therefore, if the page has more objects than the connection limit, it will create a false positive error in Snort detection. The connection limit does not affect SDToW because its detection mechanism does not use this limitation parameter. Hence, raising the number of objects per page does not lead our tool to create false positive errors.
## 3 Results and Discussion
### Adsorption Isotherm
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Temperature/\({}^{\mathrm{c}}\)C**} & \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Adsorbate**} \\ \cline{5-7}  & & & **Aniline** & \(p\)**-MA** & \(p\)**-CA** & \(p\)**-\(ABC\)** \\ \hline \multirow{10}{*}{Langmuir Model} & \multirow{4}{*}{25} & \multirow{4}{*}{20} & \(K_{\mathrm{L}}\) (L/mg) & \(9.32\times 10^{-3}\) & \(6.84\times 10^{-3}\) & \(1.99\times 10^{-3}\) & \(1.29\times 10^{-2}\) \\  & & & \(q_{\mathrm{max}}\) (mg/g) & 163.25 & 226.44 & 292.50 & 753.81 \\  & & & \(R^{2}\) & 0.968 & 0.971 & 0.824 & 0.995 \\  & & & \(K_{\mathrm{L}}\) (L/mg) & \(1.31\times 10^{-3}\) & \(6.74\times 10^{-3}\) & \(1.98\times 10^{-2}\) & \(2.67\times 10^{-3}\) \\  & & & \(q_{\mathrm{max}}\) (mg/g) & 152.93 & 183.54 & 254.13 & 666.40 \\  & & & \(R^{2}\) & 0.993 & 0.996 & 0.838 & 0.998 \\  & & & \(K_{\mathrm{L}}\) (L/mg) & \(1.52\times 10^{-3}\) & \(5.74\times 10^{-3}\) & \(3.13\times 10^{-2}\) & \(7.08\times 10^{-2}\) \\  & & & \(q_{\mathrm{max}}\) (mg/g) & 140.25 & 171.56 & 210.18 & 579.27 \\  & & & \(R^{2}\) & 0.996 & 0.997 & 0.848 & 0.998 \\  & & & \(K_{\mathrm{f}}\) (L/mg) & 1.71 & 16.62 & 48.74 & 61.42 \\  & & & \(n\) & 3.03 & 2.70 & 3.57 & 2.50 \\  & & & \(R^{2}\) & 0.995 & 0.994 & 0.982 & 0.945 \\  & & & \(K_{\mathrm{f}}\) (L/mg) & 1.95 & 14.81 & 40.98 & 11.89 \\ Freundlich Model & 25 & & \(n\) & 1.58 & 2.78 & 3.57 & 1.56 \\  & & & \(R^{2}\) & 0.992 & 0.938 & 0.989 & 0.998 \\  & & & \(K_{\mathrm{f}}\) (L/mg) & 1.90 & 11.28 & 22.48 & 24.67 \\  & & & \(n\) & 1.64 & 2.61 & 4 & 1.26 \\  & & & \(R^{2}\) & 0.996 & 0.959 & 0.998 & 0.997 \\  & & & \(\beta\) & \(4.22\times 10^{-4}\) & \(2.89\times 10^{-4}\) & \(1.41\times 10^{-5}\) & \(3.99\times 10^{-5}\) \\  & & & \(q_{\mathrm{m}}\) (mg/g) & 129.87 & 163.25 & 240.89 & 476.56 \\  & & & \(E_{\mathrm{a}}\) (K/Jmol) & 34.44 & 41.58 & 91.08 & 63.09 \\  & & & \(R^{2}\) & 0.892 & 0.848 & 0.808 & 0.846 \\  & & & \(\beta\) & \(1.15\times 10^{-3}\) & \(4.84\times 10^{-4}\) & \(4.36\times 10^{-5}\) & \(2.15\times 10^{-4}\) \\ D-R Model & 25 & & \(q_{\mathrm{m}}\) (mg/g) & 97.46 & 135.75 & 210.67 & 396.24 \\  & & & \(E_{\mathrm{a}}\) (K/Jmol) & 21.45 & 32.69 & 78.13 & 49.47 \\  & & & \(R^{2}\) & 0.725 & 0.918 & 0.737 & 0.768 \\  & & & \(\beta\) & \(1.35\times 10^{-3}\) & \(6.26\times 10^{-4}\) & \(3.93\times 10^{-5}\) & \(4.15\times 10^{-4}\) \\  & & & \(q_{\mathrm{m}}\) (mg/g) & 86.59 & 123.47 & 186.31 & 372.94 \\  & & & \(E_{\mathrm{a}}\) (K/Jmol) & 19.74 & 28.24 & 64.09 & 35.88 \\  & & & \(R^{2}\) & 0.772 & 0.898 & 0.766 & 0.748 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated parameters of Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) Models for the adsorption of anilines. Figure 9: Isotherm model curves for the adsorption of aniline (**a**), \(p\)-MA (**b**), \(p\)-CA (**c**) and \(p\)-ABC (**d**) on the CDM-HCP; solid line is the Langmuir model and dashed line is the Freundlich model.
## 4 Results
### Generation of Winter Wheat Maps  
To assess the classification accuracy at different spatial resolutions, we classified the time-series PROBA-V 300-m and the predicted 100-m images. The NDVI profiles of the 10 classes are shown in Figure 6. Combined with Figures 3 and 6, Classes 1-5 were considered to be wheat, whereas the remaining classes were considered to be other types. Classes 1-5 and Classes 6-10 were then merged into two larger classes to evaluate the accuracy of the classification. An interesting finding that is inconsistent with the previous assumption is that almost all 10 profiles followed a similar trend  
Figure 5: Comparison between the observed and predicted PROBA-V 100-m NDVI and LSWI. The 1:1 line is blue, and the regression line is black.  
though the shapes were different, mixed pixels of the images and misclassifications of the clustering methods can well explain this phenomenon. The classification results are provided in Figures 7 and 8. Some relatively small objects such as roads and rivers can be clearly identified in Figure 7 when compared with the classification shown in Figure 8 due to the higher spatial resolution of the 100-m data. The classification accuracy assessments were conducted using the ground investigation data (92 points for wheat and 104 points for the other types at Site 1; and 126 points for wheat and 154 points for the other types at Site 2).  
The accuracy assessments, including the overall classification accuracies, producer's accuracies, user's accuracies and kappa coefficients, are presented in Tables 7 and 8. The overall accuracies and kappa coefficients of classification for the time-series blended data were 84.69% and 0.7198 at Site 1, respectively, and 78.57% and 0.5708 at Site 2, respectively. These values were noticeably higher than those of the 300-m data (for which the overall accuracies and kappa coefficients were 68.88% and 0.3795 at Site 1, respectively, and 67.86% and 0.3562 at Site 2, respectively), the existence of more mixed pixels in the 300 m data might explain the lower accuracies. Nevertheless, the classification accuracies were acceptable no matter for the 100-m or for the 300-m data. The producer's accuracy of wheat at Site 1 was higher than that at Site 2 because a mountainous region at Site 2 led to many more fragmentized fields that were prone to misclassification. Moreover, the results also showed that the misclassification of wheat was lower than that of the other land-cover features, which probably occurred because the farmlands in the study areas were typically much larger and more homogeneous. This probably because the BRDF effects in the mountainous regions cause additional variations on the slopes. However, wheat fields are not inclined, so the BRDF effects are smaller there.
## 4 Experiments
### Ego-Motion Estimation  
For ego-motion estimation, the matching result of the VGICP is experimentally verified on SemanticKITTI, and the matched pose matrices are obtained and compared with the ground truth pose which is given by the dataset. The comparisons are made by decomposing the pose matrix into a position matrix and a pose angle matrix, where the pose angle matrix is expressed through Euler angles, and the deviations of the VGICP displacements and pose angles from the ground truth are obtained as shown in Tables 2 and 3. It can be seen that the ego-motion estimates computed by VGICP are less than 0.02 m offset from the ground truth value in all three X, Y, and Z axes, which is a relatively small difference, and the standard deviation is also within 0.015 m. For the pose angular deviation, the largest deviation is the pitch, which is also within 0.0018 rad, or about 0.2\({}^{\circ}\).  
The comparison between VGICP and other point cloud matching methods (NDT, ICP, and GICP) is shown in Table 4. The fitness score is an evaluation metric of the matching errors [24] and the \(\downarrow\) indicates that smaller metrics are better. It can be seen that compared with ICP, NDT, and GICP, the FitnessSocre of VGICP is comparable with other methods and the displacement and angular errors are kept at the same level, while the VGICP method has the fastest speed in processing time, especially when use multi-thread technology.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CPU** & **GPU** & **CUDA** & **cuDNN** & **OS** & **Python** & **Pytorch** \\ \hline i9-10850K & RTX 3090 & 11.1 & 8.0.4 & Ubuntu 18.04 LTS & 3.8 & 1.10.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental setting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Euler Angle** & **Average Error (rad)** & **Standard Deviation (rad)** \\ \hline Yaw & 0.000226 & 0.000734 \\ Roll & 0.000686 & 0.000335 \\ Pitch & 0.001800 & 0.000645 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The error of pose angle between VGICP and the ground truth on SemanticKITTI.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cyclagement Axis** & **Average Error (m)** & **Standard Deviation (m)** \\ \hline X & 0.0127 & 0.0099 \\ Y & 0.0108 & 0.0146 \\ Z & 0.0166 & 0.0149 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The error of position displacement between VGICP and the ground truth on SemanticKITTI.
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 6 Cost Evaluation
Regarding the group of the most studied nanomaterials, nanosilica, and nanoclays, the cost analysis suggests that nanosilica may be more promising as it had the lowest cost. Nevertheless, the three-fold increase in cost might be justified by the improvements obtained in mechanical performance and aging resistance. At current prices, for the nanomodifications to be cost effective, a significant improvement in durability has to be attained. It is expected that the production of nanoparticles in bulk quantity and the use of alternative sources will significantly reduce the costs. For example, rice husk, an abundant waste biomass with high content of silica was identified as a potential low-cost resource for the production of nanosilica particles [138].
## 3 Results
However, further increased deposition time (10 h and 12 h) of AgNPs on PDA/ZnO@GMF suppressed the rate constants of RhB degradation, because excessive AgNPs accumulated on ZnO nanorods and suppressed the UV-induced electron transport--resulting in decreased photocatalytic activity. Therefore, the deposition time of 8 h was sufficient to drive efficient catalytic degradation of RhB. We further measured the elemental content of the AgNPs and ZnO using inductively coupled plasma mass spectrometry (ICP-MS) for different AgNP deposition times on PDA/ZnO@GMF. Table 2 shows the Zn and Ag content of the substrate at 0, 2, 4, 6, 8, 10, and 12 h deposition times as measured by ICP-MS (unit: micrograms of element per milliliter of nitric acid). It can be clearly observed that the amount of AgNPs increased with deposition time. This result further confirms our assumption that the amount of AgNPs increased with deposition time and that excessive deposition leads to AgNP accumulation on the ZnO nanorods (deposition time of 10 h and 12 h) and hinders electron transfer between Ag and ZnO--leading to reduced photocatalytic activity. After confirming strong SERS activity and photocatalytic degradation of RhB on the constructed SERS substrate, we wanted to check whether repeated cycles with simultaneous removal of RhB can generate better SERS substrates. The optimum substrate with 8 h AgNP deposition time was chosen to test sustainability over repeated cycling of catalytic degradation of RhB. We examined the RhB photocatalytic degradation reaction cycle five times and plotted the number of cycles by photocatalytic degradation efficiency, as shown in Figure 1:3 We observed that the degradation reaction efficiency reduced below 80% by the third cycle and was about 70% by the fifth cycle. This is due to the accumulation of material in the process of repeated experiments, thereby reducing the surface area of the catalyst and thus the overall catalytic activity. This result indicates that there is still considerable photocatalytic activity after repeated testing and thus considerable potential and prospect for the development of recyclable sensing materials. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Zn (ug/mL)** & **Ag (ug/mL)** \\ \hline Blank & \(5.12\times 10^{-4}\) & \(0.07\times 10^{-4}\) \\
0 h & 42.5 & \(1.86\times 10^{-4}\) \\
2 h & 34.5 & 2.00 \\
4 h & 44.9 & 4.49 \\
6 h & 41.8 & 7.46 \\
8 h & 41.1 & 10.2 \\
10 h & 37.8 & 10.8 \\
12 h & 39.3 & 12.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ICP-MS of substrates with different silver-nanoparticle deposition times. Figure 8: Five repeated photocatalytic degradation reactions on the Ag-8h/PDA/ZnO@GMF substrate.
## 3 Results and Discussion
### Relationship between T/ET Ratio and Vegetation
However, when NDVI is higher than a threshold value, the potential canopy transpiration determined by physiological factors is much higher than the environmental factors. In such a situation, the potential canopy transpiration is constrained by environmental conditions rather than plant physiological factors and thus the T/ET ratio is insensitive to the variation in NDVI. A similar pattern was found by [PERSON] [50] during an assessment of the relationship between LAI and sap flow density (SFD), which concluded that there is a threshold effect associated with the controlling impact of LAI on SFD. The relationship between T/ET ratio and NDVI is relatively different in the case of the Daman and Xnier sites (Figure 10). The T/ET ratio is highly correlated to NDVI with an \(R^{2}\) value of 0.9 at the Daman site, whereas the T/ET ratio is irrelevant to NDVI at the Xnier site due to seasonal variation. This is because both sites are irrigated during the growing season but the type of irrigation and schedule are relatively different. At Daman, flood irrigation is applied about four times during the growing season and soil evaporation contributes a high portion of ET. Transpiration is confined to the amount of  
Figure 10: The relationship between NDVI and \(T/ET\) ratio. water that the crop absorbed from the soil, which depends on the density of roots and vegetation cover; therefore, transpiration is mainly determined by physiological factors. In contrast, Xinier is mainly governed by mulched drip irrigation, which is applied in the growing season, from June to August. As drip irrigation transports a very limited amount of water directly to the crop root zone, the soil water in the inter-film zone is very low; as a result, soil evaporation only contributes a small portion to ET when irrigation takes place. Transpiration is highly related to irrigation events, so, even with the same NDVI, transpiration could be quite different with or without irrigation, which leads to an inconsistent relationship between NDVI and T/ET ratio.
## 3 Method and Testing Architecture  
The testing architecture implies the implementation of three applications in Java, using IntelliJ IDEA [15] one for CouchDB, one for relational MySQL, and one for document-based MySQL. Even if all the applications contain similar information, structured in different forms, for the MySQL case, two different applications were needed due to the widely different structure of the application considered in these two approaches. In the case of the relational database, the application is built out of entities, repositories, and services besides the main class, according to the relational database structure from Figure 1. Queries in MySQL were executed from classes that have the Repository annotation and each method has a @Query where the command is placed [16].  
For the non-relational databases (CouchDB and document-based MySQL), each application contains the main class where the methods are implemented, and the objects are described in the structures from Figures 2 and 3.  
Figure 1: Relational database structure.  
Figure 3: CouchDB’s second structure.  
Figure 2: CouchDB’s first structure.  
For MySQL non-relational approach, commands were predefined by the MySQL Connector Java version 8.0.16 library [17]. For the non-relational approach, the CouchDB commands were either predefined by the Ektorp library [18] or by requests. For the relational approach, MySQL version 8.0.21 was used. As shown in Figure 1, a complex database structure with multiple joins was used in order to be able to highlight the possible differences between the two types of databases in case of a large and complex application and different query complexity. The deleted field was used to be able to mark an element as soft deleted, so in queries, we can ignore or not these elements, depending on its context. _Created_at_ and _updated_at_ fields were useful to see if an item has been updated or not, if the two dates are different, we can know that the item has been changed. For non-relational databases, Apache CouchDB version 3.1.0 was used. To transpose the structure of the similar database from MySQL in CouchDB we considered two possible structures (first and second).