esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 5 Security Analysis of WoT
### WoT Threats
#### 5.2.3 T3: Service Unavailability  
This threat is particularly critical in environments where continuous availability is essential. It occurs when an attacker consumes excessive system resources, rendering the system unable to satisfy other legitimate user requests. This threat primarily affects nodes, things, and APIs, as they can be overwhelmed with traffic through denial of service (DoS) or distributed denial of service (DDoS) attacks. Vulnerabilities such as the lack of rate limiting and insufficient resource allocation make these components susceptible. The consequences include service disruptions, financial losses due to downtime, and damage to the service provider's reputation [46; 47; 48].
## 4 Discussion
### Iodine Content in Lettuce  
Even though iodine is not considered an essential nutrient for plants, several investigations have shown that the right doses of iodine can improve yields and that it is translocated to diverse tissues within the plant. Maximum concentrations of iodine were seen following application of KIO\({}_{3}\) (25 mg I kg\({}^{-1}\)) as a split dose. Possibly, iodine was rapidly absorbed by the roots due to the direct application to the substrate. Some authors have suggested that biofortification is most successfully with IO\({}_{3}\)\({}^{-}\) as it does not have phytotoxic effects and the biomass either increases or is not negatively affected [12; 47; 52].  
Regarding the original question of whether Cs-KI or Cs-KIO\({}_{3}\) complexes would increase iodine absorption, neither complex had an outstanding impact on iodine content when compared to the treatments without chitosan. For example, for I\({}^{-}\), the plant response was similar after single application treatments with and without Cs. The greatest concentrations were after SDA of KI treatments, even those changes were not significant. In contrast, KIO\({}_{3}\) and its Cs complexed form induced significant differences in iodine concentrations after two applications. Of the two forms, the KIO\({}_{3}\) alone led to a greater increase in iodine concentration. There are two potential explanations for these results. One, in order to be absorbed IO\({}_{3}\)\({}^{-}\) must first be reduced to I\({}^{-}\), a process that is carried out by iodate reductase enzymes [53]. So, when IO\({}_{3}\)\({}^{-}\) is complexed with Cs, the reduction process takes longer than when the free salt is applied and thus it becomes bioavailable slower. Two, the Cs-KIO\({}_{3}\) may become fixed in the ground, not volatilizing, which means crops with short cultivation cycles, like lettuce, would be unable to fully take advantage of its presence. Both I\({}^{-}\) and IO\({}_{3}\)\({}^{-}\) applied to soil are converted to organic iodine after only 14 days, and in that form become fixed in the ground and less readily soluble in water [54]. This could explain why there was no significant difference between I\({}^{-}\) and IO\({}_{3}\)\({}^{-}\) applied in a single treatment, with and without Cs, at either concentration (5 or 25 mg I kg\({}^{-1}\)).  
Several authors have reported high concentrations of iodine after biofortification with KIO\({}_{3}\)[15; 55]. Conversely, iodine content in lettuce after biofortification with high doses of iodide during two trials (summer and winter) was up to five times higher than with iodate. The iodine concentrations of inner and outer leaves reached 653 and 764 ug I kg\({}^{-1}\), respectively [45]. Doses of iodine below 2.5 mg I kg\({}^{-1}\) have no effect on biomass [55; 56], while at times doses between 5 and 25 mg I kg\({}^{-1}\) applied to soil have been found to not affect yields [57; 58]. Those previously reported results are consistent with the findings of the present study, with the exception of the SDA of Cs-KI (25 mg I kg\({}^{-1}\)) which ended up reducing biomass.
## 3 Results and Discussion
### Effect of pH on Enzymatic Transesterification (Second Stage)  
Figure 7 shows the effect of pH of the reaction media on transesterification process as key parameter for biodiesel production. The pH value of the enzyme solution at concentration of 14% (E1 experiment) was measured in 6.5. The pH = 7.0 reached the highest yield (\(91.49\pm 2\%\)), followed by pH = 6.0, which were in the effective range for the lipase (5.0-8.0). Similar results were reported by other contributions. For example, [PERSON] et al. [67] evaluated the production of biodiesel from waste cooking oil using free lipase and immobilized lipase. For the free lipase system, they reported that maximum lipase activity was reached at pH = 7.0 and above this value activity was reduced. [PERSON] et al. [36] also reported a maximum production yield of biodiesel from Jatropha oil (72%) at pH = 7.0 and the yield decreased when increasing pH beyond 7.0.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions
### End-to-End Delay Results  
#### 6.3.1 Random Node Distribution Results  
The movement patterns of attacker nodes significantly influence their effectiveness in delaying packets within the randomly distributed network. Both single and double attacker scenarios exhibit a strategic initial movement: approaching the root node first.  
It is crucial to recognize this initial starting movement pattern of the attackers when considering the impact on end-to-end delay from both dropping and combined attacks within the randomly distributed network. While one might expect delays to increase with attacker interference, the act of dropping packets removes them from the network entirely. This eliminates their contribution to the overall end-to-end delay calculation. Since the motion of the attackers primarily targets long-delay packets, their removal can lead to a reduction in the average end-to-end delay observed in the network, as seen in Figures 12 and 13.  
Figure 12: End-to-end results’ random distribution for all attacks.  
Figure 13: End-to-end results’ uniform distribution for all attacks.
## 2 Materials and Methods
### Three-Dimensional Digitization Method  
The dental casts were scanned, and 3D digital models were obtained through the CAD/CAM (Computer-Aided Design and Computer-Aided Manufacturing) system (Kavo Arctica, Kavo, Germany). This desktop scanner uses a combination of striped light scanning technology and highly sensitive 3D sensors for scanning and has a built-in blue filter to scan dental casts to an accuracy of 20 \(\upmu\)m. The generated data were stored in STL (Standard Triangle Language) format, which utilizes small triangles to describe the surface of digitally captured objects and is a standard format used by many CAD systems. The STL format files were imported to 3 Shape(r) Ortho Analyzer(tm) software version 2020 (3 Shape, Copenhagen, Denmark) for digital measurements. A single experienced and precalibrated investigator who was blinded to the gender selection of casts conducted all the measurements, including scanning and marking of the study casts to avoid interexaminer error. Each parameter was measured three times, and the average value was recorded as the final result. The same investigator repeated the measurements at a two-week interval on a total of 30 randomly selected digital casts to assess the intraexaminer variability using intraclass correlation coefficient (ICC) measurement agreement. Measurements on the dental casts included the mesiodistal crown (MD) width of maxillary amines and the intercanine distance (ICD). The greatest MD width of each maxillary canine was measured between the mesial and distal anatomical contact points. In the absence of contact points, the greatest MD width of the canine was measured and recorded. ICD referred to the linear distance between the cusp tips of both the maxillary amines or in the center of their wear facets should they be present (Figure 1a,b).  
The maxillary canine index (MCI) and standard canine index (SCI) were calculated using the formula given by [PERSON] et al. [10].  
Maxillary Canine Index = mesiodistal crown width of maxillary canine/maximillary intercanine width.  
Standard canine index = (mean male MCI - SD) + (mean female MCI + SD)/2  
Gender was then predicted based on the observed maxillary canine index and standard canine index for the Saudi population of the Eastern province. If the observed MCI was more than SCI, then the individual was considered to be male, and if the observed MCI was less than SCI, then the individual was considered to be female [11].  
The percentage of gender dimorphism was assessed using the [PERSON] et al. [12] formula:  
\[\text{Gender Dimorphism = [Xm/Xf]}-1\times 100\]  
where:  
Xm = mean value of tooth dimensions for males;  
Xf = mean value of tooth dimensions for females.
# Methods of Analyzing Microsized Plastics in the Environment
## 2 Microscopy Analyses
### Polarizing Microscopy  
Polarized light microscopy has been successfully used to identify polyethylene (PE) particles in toxicity experiments [47]. The crystal structure within the plastic can affect the transmission of polarized light that can be measured [48]. The degree of crystallinity depends on the type of polymer; it varies even within the same polymer depending on the manufacturing process. However, microplastic samples must be thin to allow sufficient polarized light to pass through. This method cannot be used for opaque microplastic samples.
## 2 Materials and Methods  
The objective was to develop a technique that could detect graffiti using images gathered by citizens. To enable automation, an orthophoto was generated at various intervals to aid in decision making and planning regarding community maintenance needs. By allowing the citizens to collect the data, key structures in the community could be readily and frequently monitored against graffiti markings. A step-by-step procedure is depicted in Figure 2. As a preliminary step, a robust graffiti detector was trained. In this study, we used a robust convolutional neural network (CNN)-based object detector, called Faster R-CNN, to train the graffiti detector [32]. A real-world database with various graffiti scenes was used to train the robust detector [33]. Actual steps of the proposed technique were as follows. In Step 1, visual data were collected periodically from key structures that needed to be monitored. Ordinary citizens (e.g., residents, tourists, and visitors) were engaged to collect photographs to document relevant visual scenes in the community. The recommended guidelines for data collection are outlined in Section 2.2. In Step 2, orthophotos were generated that contained full views of facades in a target structure. An orthophoto is a high-resolution image, that is generated using the data collected in Step 1. The subject of this orthophoto, denoted here as the target object in a community (TOC), was represented by several orthophotos that each maintained their original image quality, sufficient for observing graffiti markings. Such orthophoto generation was a key step to enable image-based graffiti monitoring by removing any repetition or overlap that may have existed in the individual images, and then the TOC was represented by a single view per its facade. In Step 3, graffiti detection was performed by applying the trained graffiti detector to the reconstructed orthophotos. Since this process was repeated on the orthophotos reconstructed on a regular basis, such digital documentation was useful for instantly assessing the overall presence of graffiti on the TOC over time.
# Mapping the Most Susceptible Regions to Fire in Portugal
## 3 Results  
### Historical Vegetation Activity and Burned Area Patterns in Portugal  
Portugal can be divided in two different altitude regimes due to the Montejunto-Estrela mountain range that crosses the country diagonally. The northern part of the country is characterized by higher elevations and steep mountains, especially in the central-eastern and north-eastern sectors, while the southern half of Portugal is mostly characterized by low-relief plains (Figure 1a). Nevertheless, two medium-high elevation areas are found in the southernmost part of the country.  
The northern and central regions are the most affected by fires (Figure 1b) with a higher propensity to burn, as several areas have burned two to three times between 2001 and 2021, and a small fraction of pixels burned more than three times in this period. In southern regions, it is also possible to find areas that have burned more than once over the last 21 years.  
The results in Figure 1c, that show the time elapsed (in months) since the last fire occurrence (TwB), allow the distinguishing of two main sets of large scars along the country: (i) one showing areas where previously burned vegetation remained free of fire events for more than 200 months (orange and reddish points); and (ii) another with recent fire occurrence, i.e., less than 50 months (purple and blue points). Over the areas with TwB of 100-150 months, the long-elapsed time allowed the burned vegetation to recover from the fire events and re-accumulate fuel. This is shown in Figure S1, where high accumulation of GPP, and thereby fuel accumulation, above 20 kg C/m\({}^{2}\) clearly overlaps areas with TwB > 150 months, while over recently burned areas the accumulation of GPP is about 5 kg C/m\({}^{2}\).
## 3 Results and Discussion
### Characteristic Band Improvement  
Because the CARS algorithm uses the PLS method as the fitness function, this linear model faces the problem of high dimensional data, which causes the problem of low accuracy. The CARS algorithm does not change the original data, and it is often impossible to use in the case of low-correlation data. It is therefore necessary to improve the characteristic bands. Through the SD, GF and GF again pretreatment methods, the characteristic bands can be effectively enhanced. By extracting [PERSON]'s rank correlation coefficients with absolute values of greater than 0.6 for the subsequent model building, the first \(k\) correlation-optimized bands were obtained as the characteristic band subset. It can be seen from Figure 7 that the spectral correlation is still low after the CARS rough selection, but the correlation coefficient is greatly improved by the pretreatment. Five enhanced characteristic bands (827.2 nm, 1000.3 nm, 1929.2 nm, 1949.2 nm, 2357.7 nm) were obtained for the Honghu area, and nine enhanced characteristic bands were obtained for the Daye area (402 nm, 420 nm, 566 nm, 577 nm, 689 nm, 690 nm, 697 nm, 2382 nm, 2393 nm), for use in the final modeling.
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 6 Discussion  
Once the results have been presented, in this section, we provide a further insight into the key facts deduced from the real data results.  
As an initial outline, Figure 13 demonstrates the reliability and consistency of the proposed robust localization. The results confirm the enhancement of the localization angles \(\beta\) and \(\phi\) (\(\sim\)40%), in comparison to the standard visual localization technique. Besides this, a relevant increase of the number of matching points is produced (\(\sim\)30%), a fact that implies a better final estimate for the pose, since the number of false positives is also reduced. This is an important outcome, as it allows us to get enough visual data to keep producing estimates even under some specific circumstances in which the standard localization technique may be compromised.  
Next, the evaluation of the robust localization proposal has been analyzed under real operation conditions within a novel omnidirectional SLAM approach. Figure 14 shows the performance of the approach in a dynamic and challenging scenario. These results highlight the capability to produce an acceptable performance, as a balanced tradeoff, for the final application requirements.  
Regarding the error assessment, the results presented in Figure 15 prove the ability of the proposal to keep the estimation error stable, the convergence of the SLAM system and also the uncertainty of the system.  
Finally, Figures 16 and 17 present comparison results with a well-acknowledged technique, such as the inverse EKF parametrization method. Here, the proposed approach confirms its validity to deal with large outdoor scenarios, but also to improve the final estimation accuracy and the scale estimate.  
Overall, the following aspects should be highlighted:  
1. Robust visual localization technique in terms of accuracy and performance.
2. Significant increase of usable visual data (matchings) when standard techniques fail to gather reliable visual data.
3. Acceptable tradeoff solution to produce real-time localization.
4. Consistent SLAM estimation with the proposed robust localization technique for real-time-oriented tasks.  
The most straightforward future improvements are oriented towards the 6 DOF extension outdoors, but also towards the computation enhancement. Regarding the uncertainty management,  
Figure 17: Histograms for the absolute position error (m) in Dataset 3: (**a**) proposed approach with alignment optimization; (**b**) inverse EKF approach with alignment optimization; (**c**) proposed approach without alignment optimization; (**d**) inverse EKF approach without alignment optimization.  
some other developments may be tackled in order to confer the ability to feature entire regions on the image, which would permit accounting for specific area-tracking and also for the associated uncertainty. This would also be interesting for discovering unexplored areas.
## 3 Results and Discussion
### Recovery of Tomato Seed Oil
#### 3.2.2 Lycopene Content of Tomato Seed Oil  
The lycopene content of the tomato seed oil obtained from different extraction methods was analyzed using HPLC-DAD, and the results are presented in Figure 6. The findings indicate significant variations in the lycopene content, depending on the extraction method. Among the methods tested, PLE proved to be the most efficient, yielding the highest lycopene concentration (44.0 \(\pm\) 2.0 mg/100 g of oil), followed by UAE-MAE, with a lycopene content of 38.0 \(\pm\) 2.0 mg/100 g of oil. Soxhlet extraction and conventional stirring were less efficient, resulting in a lycopene content of 28.0 \(\pm\) 1.0 mg/100 g of oil and 13.0 \(\pm\) 2.0 mg/100 g of oil, respectively.  
PLE emerged as the most effective method for lycopene recovery, combining a high yield, short extraction time, and preservation of lycopene stability. The use of pressurized conditions facilitated enhanced solvent penetration due to increased diffusion from the solid matrix to the bulk solvent and the reduction of solvent viscosity, improving extraction efficiency while minimizing the risks of oxidative and thermal degradation [34]. UAE-MAE also proved to be an efficient technique, achieving high lycopene yields while requiring less time and energy than traditional Soxhlet extraction. The ultrasound cavitation and  
Figure 5: Effect of the extraction method on the tomato seed oil yield. Different lowercase letters above the bars indicate statistically significant differences (\(p\) < 0.05).  
microwave heating effectively disrupted the plant cell matrix, enhancing mass transfer and, as a consequence, lycopene extraction [20]. While Soxhlet extraction is known for its ability to extract lipophilic compounds, its extended heating period led to lower lycopene yields than both PLE and UAE-MAE, likely due to thermal degradation of the compound [35]. Conventional stirring, on the other hand, was the least efficient method, yielding the lowest lycopene content. Its limited ability to break down cellular structures and enhance the solvent interaction resulted in poor lycopene recovery.
## 2 Method and Experiments
### Sessile Drop Test  
Powder bed recoating was performed using a sessile drop tester (Freeforms T150, SFS, Korea) to determine the powder bed formability. It was intended that the sessile drop tester make use of a layered surface, similar to a powder bed 3D printer. It consisted of a metal frame along the XY axis and steel rollers with a diameter of 35 mm, connected to a step motor. The axis movement and rotation of the metal roller were controlled using a driver and a speed controller, respectively, and it was installed on a rail without vibration to enable motion at a constant speed along the X axis. The recoating speed of the recoating device was 50-200 mm/s, which was fixed for a rotation speed of 20 rpm. In order to obtain the same packing density as an actual 3D printer, each powder was recoated to a thickness of 10 mm on a dense plastic plate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multirow{2}{*}{**Density (g/mL)**} & \multicolumn{3}{c}{**Particle Size (\(\mu\)m)**} \\ \cline{3-5}  & & **D10** & **D50** & **D90** \\ \hline CAC & 2.1 & 1.16 & 3.43 & 6.06 \\ Silica & 0.43 & 2.51 & 4.47 & 7.39 \\ sand & 1.37 & 2.22 & 4.34 & 7.11 \\ Alumina & 1.41 & 2.51 & 4.47 & 7.39 \\ sand & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density and particle size distribution data of materials.  
Figure 2: Cube molded bodies made from silicone molds.  
The sessile drop test of fixed-size binder droplets on the powder bed surface was performed by pipetting 30 \(\upmu\)L at a fixed height of 2 mm (slightly larger than the droplet size to avoid its contact with the powder interface). The droplet was positioned suitably such that the effect of gravity on the droplet was minimized.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 3 Network Model and Aggregation Rules  
### Network Model  
In this paper, we assume that all sensor nodes have the same sensing radius, denoted by \(R_{S}\), and the same communication radius, denoted by \(R_{C}\) (\(R_{C}>2R_{S}\)) [23]. The data collected by sensor networks may be periodic sensing information, such as the average temperature, or information triggered by specific events, such as fire alerts. Our scheme focuses on the periodic data collection, and assumes that the amount of information collected is the same for all the nodes, which is a common assumption about data collection [24]. The data collected by each node is denoted by \(d\). Since the sink usually has sufficient power, we only consider the energy cost of sensor nodes in our scheme. Because data sending (the amount of data sent out from node \(v_{i}\) is denoted by \(d_{i}^{T}\)) and receiving (the amount of data received by node \(v_{i}\) is denoted by \(d_{i}^{R}\)) consume most of the energy, small energy consumptions such as the cost of data processing are ignored. Therefore we focus on the transmission overhead in AggOR. For simplicity, we assume that total energy consumption of node \(v_{i}\) is \(E_{i}^{C}=d_{i}^{T}E_{T}+d_{i}^{R}E_{R}\), where \(E_{T}\) and \(E_{R}\) are the energy costs for sending and receiving per unit data, respectively. The primary symbols used in AggOR are listed in Table 1.  
Before presenting the details of AggOR, several definitions are introduced as follows. **Definition 1**. : _Transmission hierarchy diagram: The diagram is a directed acyclic graph, including all the sensors, the possible communication paths, and the hierarchy levels (denoted by \(L\)). It is similar to a tree structure rooted at the sink, but the parent node is not unique._  
For data collection in a dense network, we only care about those nodes which can communicate with the sink through one-hop or multi-hop transmissions. For two levels \(L_{i}\) and \(L_{i}+1\) in the diagram, \(L_{i}\) is called upper-level and \(L_{i}+1\) is called lower-level. In other words, the value of upper-level is smaller than that of lower-level. As shown in Figure 1, there are \(m\) sensor nodes in the network as well as \(v_{0}\) as the sink. The edges show the communication chances between nodes. Specifically, the solid lines indicate the possible parent-child relations between lower-level and upper-level nodes, while dotted lines show the neighbor relations between nodes at the same level.