esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Reference Point Group Mobility (RPGM)  
#### 6.2.1 Packet Delivery Ratio  
In Figure 7, a detailed depiction of PDR dynamics within a mobile environment using the RPGM mobility model is provided, both with and without the Hello Flood attack. The \(x\)-axis illustrates the number of nodes, ranging from 10 to 60, while the \(y\)-axis represents the PDR percentage, spanning from 0% to 100%. The graph comprises eight sets of bars, each representing different percentages (10%, 20%, 30%, 40%) of malicious nodes. These sets are further divided into two conditions: the mobile environment without the Hello Flood attack and with the attack, color-coded for clarity and accompanied by corresponding  
Figure 6: APC without and with attack using CMM.  
legends. Notably, the lowest PDR values are observed when the network is under the Hello Flood attack, particularly with 50 and 60 nodes, and when 30% and 40% of nodes are dynamic and malicious. Several trends emerge upon analysis: Firstly, there is a discernible decrease in the PDR as the number of nodes increases, especially pronounced when nodes are under attack. Secondly, in the absence of malicious nodes, the PDR remains relatively high, indicating efficient packet delivery. However, as the percentage of malicious nodes increases, the PDR steadily declines, highlighting the negative impact of attack nodes on overall delivery performance. Lastly, the presence of attack nodes, particularly at higher percentages, significantly worsens the decline in the PDR, emphasizing the detrimental effects of the Hello Flood attack on network reliability.  
The RPGM model, when coupled with the Hello Flood attack, significantly diminishes the PDR within a network environment through several interconnected mechanisms. Firstly, the structured movements of nodes in the RPGM can lead to congestion in specific network areas as nodes move together within groups, increasing the likelihood of packet collisions and retransmissions, thus reducing the PDR. This congestion is further intensified by the Hello Flood attack, which floods the network with excessive traffic, saturating communication channels, and disrupting packet delivery. Secondly, the movement patterns defined by the RPGM introduce instability in routing paths, leading to suboptimal routing decisions and increased packet loss, further decreasing the PDR. The Hello Flood attack exacerbates this instability by overwhelming routing protocols and disrupting packet delivery. Thirdly, as nodes compete for communication resources within RPGM groups, more resources are used. This causes bottlenecks and waiting times, which hurts network performance and lowers the PDR. The Hello Flood attack exacerbates resource consumption by flooding the network with excessive traffic, straining network resources further. Lastly, the RPGM's mechanisms for handling node movement at boundaries introduce additional delays in packet transmission, which, when exploited by the Hello Flood attack, further interfere with packet delivery, ultimately decreasing the PDR.
## 3 Results
### Simulations, Case Studies, and Real-World Applications
#### 3.3.3 Critical Analysis  
Despite significant progress in simulations and case studies, gaps persist between theoretical models and real-world applications [57]. Many simulations, such as those addressing hierarchical TCL scheduling in transactive energy systems, rely on simplified assumptions about user behavior and market dynamics. These idealized scenarios often fail to account for the complexities of real-world environments, limiting the scalability and adaptability of the proposed solutions [17]. Challenges related to data reliability and system resilience also emerge when transitioning from simulations to practical implementations. Blockchain-enabled frameworks for smart grids, for instance, offer robust solutions for enhancing security and transparency.  
However, these systems are susceptible to vulnerabilities such as cyberattacks and high computational demands, particularly in environments with advanced decentralized consensus mechanisms [58]. Similarly, decentralized algorithms for managing DERs in DC microgrids may underperform in the presence of communication failures or hardware limitations, which are often underestimated in simulated scenarios [59; 60]. The integration of emerging technologies and diverse stakeholders into energy management frameworks presents further challenges. While V2X-enabled P2P trading frameworks have shown potential in simulations, real-world applications must address issues such as EV battery longevity and the variability of vehicle arrivals and departures. These dynamic factors add complexity to system coordination, requiring robust strategies to fully realize the benefits of V2X technologies [21]. Likewise, achieving a balance between economic and environmental goals in urban smart grids requires more comprehensive strategies that consider regional variations in the energy demand and policy constraints [55].
## 3 Results
### Classification Complementarity: Combination of Clinical and RFI Data
#### 3.2.2 Application: Large Scale Retrieval  
In this case, for all the positive samples resulting on the clinical model, we run the VGG16 classifier approach to double check if it is CAC \(>\) 400. The ensemble only reports a positive case if in both models the samples result in a positive detection. The model minimizes the number of false positives (saving resources by avoiding the screening of patients that could be diagnosed CAC \(>\) 400). This protocol is depicted in Figure 3.  
This protocol may find utility in large scale database retrieval, typically hundreds of thousands of patients. In this cases it will not be possible to test with CT scan all the positive patients, and the priority should be to devote the resources to the most certain ones. As can be seen in Table 6, the experiments show a consistent reduction on the FP ratio (up to 90%), and a large improvement of the precision (91%), which shows that when the ensemble predicts a patient with CAC \(>\) 400, there is a 90% of chance of being correct. Notice that, globally, this restrictive setting underperforms the individual classifiers, as there is a significant increase in the false negative ratio (positive patients that where discarded).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Recall** & **Precision** & **F1** & **Conf Mat** \\ \hline CNN (RFI) & 0.72 & 0.52 & 0.77 & 0.62 & \begin{tabular}{c} 38 \\ 16 \\ \end{tabular} & \begin{tabular}{c} 5 \\ 17 \\ \end{tabular} \\ \hline Logistic Regression & 0.68 & 0.52 & 0.68 & 0.59 & \begin{tabular}{c} 35 \\ 16 \\ \end{tabular} & \begin{tabular}{c} 8 \\ 17 \\ \end{tabular} \\ Protocol & 0.67 & 0.27 & 0.90 & 0.42 & \
\begin{tabular}{c} 1 \\ 24 \\ \end{tabular} &
\begin{tabular}{c} 42 \\ 9 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy, Precision, Recall and F1-measure and confusion matrix \(\begin{pmatrix}TN&FP\\ FN&TP\end{pmatrix}\) for the large scale retrieval application.  
Figure 3: Description of the protocol used in the large scale retrieval application.  
Figure 2: Description of the protocol used in the clinical diagnosis application.
## 4 Experimental Results
### Selection of the Best Segmentation Algorithm
Figure 6: (**a**) Original image, (**b**) ROI segmented by an expert radiologist, (**c**) Overlap of the ROI over the original image, (**d**) Image segmented by the Otsu method, (**e**) Image segmented by NBA, (**f**) Image segmented by GA, (**g**) Image segmented by SA. Table 2 shows a descending error of the algorithms, which showed that Otsu has the highest error while NBA and GA have lower errors, with GA being the algorithm with the lowest error. Even if the Otsu method is a frequently used method for image thresholding, it can be observed that the Otsu method's error is 1.42 times higher than that of the NBA and 1.54 times higher than that of the GA. The error acquired by the simulated annealing algorithm was close to but lower than the Otsu method. In addition, the NBA's error is 1.08 times higher than the GA's; showing that the GA has the lowest mean error for the segmentation of breast cancer mammographies. The GA showed a lower error because of its capability to explore a higher number of candidate solutions, which resulted in a better segmentation compared to the NBA, which also explores a large number of candidate solutions. Run times for each algorithm were calculated, and the mean run time is shown in Table 3.  
According to the mean times, Otsu method had the lowest mean run time, in contrast to its higher error in the segmentation. The genetic algorithm had the lowest segmentation error, but it had the highest mean run time.
## 3 Results
### Understanding Local Farming Systems in the Case Study
#### 3.2.2 Adaptation Strategies in Responding to Climate Change  
Farmers mentioned several private AMs as their current responses toward perceived soil erosion. We selected a variety of these, both at the household level and at the community level. When AMs were identified in the interview process, we then asked the farmers to highlight which measures they were using and subsequently we recorded their explanations. Based on results from FGDs and in-depth interviews, these were grouped into four categories: (1) adjusting planting calendars, (2) adjusting planting techniques, (3) using native varieties, and (4) diversifying crops and varieties (see Table 7 for details).  
The farmers in our sample were able to plausibly justify their adaptive responses. For instance, to avoid storms and flood seasons (October and November), they use native rice variety \"_ba tring_\" with shortened growing seasons and earlier harvesting of cassava and wet rice. In addition, local rice varieties were intercropped with acacia during the early stages of acacia growth. They use the rice variety of \"_ba tring_\" when intercrop with the seed-planting of acacia at the same time (end of March). The planting of \"_mua_\" rice variety is combined with transplanting of acacia in the same fields, but at different times. Normally, acacia trees will be transplanted in May, then farmers will some rice seeds \"_mua_\" when intercropping with acacia fields after a two-week periods. In the past, farmers produced local rice in one season annually as a system of monoculture; and subsequently the land would be left for periods of time to allow for soil rejuvenation. Later, during the relaxed time after harvesting the rice, farmers started growing maize and cassava to prevent soil erosion caused by heavy rain. However, this method was inefficient because of low yield and because of the duration of time that was required. Subsequently, some farmers adopted acacia intercropping with local rice during the early growth of acacia. In this way, they have been able to expand the acacia area because the mono-rice fields have been replaced with acacia fields. However, they can still produce rice to meet their daily needs. This approach was an initiative of the \"Co Tu\" ethnic minority people that began around 10 years ago. In addition, a new finding in the study area is that the \"Co Tu\" people do not use any pesticides or fertilizers, even organic fertilizers such as cow manure. There are two reasons for this, the first relates to their agricultural culture, where they consider rain itself to be a sufficient fertilizer, the second reason is because of the lack of economic resources. Because of the fact that most households have a low collective income, their financial resources are used up in everyday concerns of shelter and survival, namely that of buying daily food. Hence, there is very little money left for investing in agricultural production.
## 5 Results and Discussions
### Average Number of Captured Data  
UAV remote sensing aims to collect, aggregate, and report the maximum number of sensory recordings (e.g., farming values). According to this, better performance is achieved if route-planning algorithms forward UAVs through best-fitted paths into target areas. By this, the UAVs are able to discover a greater number of SPs and collect maximized number of data samples. This results in increased data robustness and data collection accuracy.  
According to Figure 6, Q-learning addresses a better data collection performance if SPs are crowded. This is because Q-learning has the capacity to interact with the environment and discover SPs. It takes location-aware actions that forward UAVs through loop-free paths to explore the environment and update the Q-table. This allows remote sensing to capture a greater number of sensory data samples. However, the number of captured data is reduced in Q-learning as compared to TBID if SPs are rare. This is because TBID forms a tree-based infrastructure to forward the UAV through and collect the data. This links SPs as a tree just before stating UAV remote sensing mission. By this, TBID-enabled UAV routing can visit a greater number of SPs, especially if they are rare and difficult to find through environment exploration.  
IEMF underperforms Q-leaning and TBID when SPs are rare. This is because IEMF utilizes a heuristic route planning paradigm to find and visit SPs. This is unable to find (mobile) SPs if they are (or move) far from the current location (e.g., Farm's center). By this, IEMF wrongly forwards the UAV to the base station to finish the remote sensing task. However, IEMF has a better performance when SPs are crowded.
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 2 300 GHz Band Transceiver Using Silicon Integrated Circuits  
Here we introduce the features of silicon integrated circuits in comparison with compound semiconductor integrated circuits and introduce the technology of silicon integrated circuits which realizes a 300 GHz band communication transceiver.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Deployment Related Problems in WSNs
#### 3.3.1 Coverage Efficiency  
Optimal deployment of sensor network has been considered in the context of various preferences namely, coverage efficiency, network life time, energy conservation, efficient monitoring and minimum node density. In [103], the authors have tackled the problem of optimal deployment while considering the conflicting objectives of maximizing the coverage efficiency and network life time simultaneously. A PSO based multi-objective optimization formulation has been proposed in [104], which have optimized the coverage efficiency and the energy consumption of ad hoc wireless sensor networks. Maximization of coverage efficiency and minimization of cost through optimal sensor network deployment has been addressed in [105], where authors have proposed a multi-objective optimization technique based of PSO. Optimal deployment of heterogeneous WSN has been considered in [106] to optimize the coverage, average number of hops and network reliability. Deployment of WSN for smart grid communication has been addressed in [107] by optimizing coverage and end-to-end latency. In [108], a technique for optimal deployment of sensor network has been proposed to optimize the coverage efficiency and lifetime of the network.
# Investigation into the Yaw Control of a Twin-Rotor 10 MW Wind Turbine
## 1 Introduction
In the 1930s, the concept of multi-rotor wind turbines (MRWTs) was first introduced by [PERSON] as an alternative solution to the complications of single-rotor wind turbines (SRWTs) [28]. MRWTs replace the large single rotor with multiple small rotors on the same support structure. MRWTs with \"n\" number of rotors showed a reduction in mass by a ratio of \(1/\sqrt{n}\) compared with SRWTs with the same capacity [29]. Such mass reduction results in the use of less material, and hence, a reduction in production, transportation, installation, and maintenance costs. Several studies have been performed on MRWT designs, considering mass and cost analyses, power performance enhancement, and aeroelasticity. [PERSON] compared a 20 MW SRWT with two MRWT configurations: 4-rotor \(\times\) 5 MW and 45-rotor \(\times\) 444 kW. The results showed cost reductions of 20% and 30% for the 4-rotor and 45-rotor models, respectively [25]. [PERSON] also demonstrated a 37% mass reduction and a 25% cost reduction for a five-rotor WT compared with a 5 MW SRWT [30]. [PERSON] et al. reported reductions in mass of 25.6%, 16.9%, and 22.5% for twin-, tri-, and [PERSON] configurations, respectively, compared with an SRWT with a 5 MW capacity [31]. In light of this power production enhancement, an experimental study showed that two- and three-diffuser-augmented rotor configurations increased the power produced by 5% and 9% per rotor, respectively, compared with an SRWT [32]. In addition, a simulation study was performed on seven 2 MW rotors, usingCFD and vortex models, and the results showed an increase in power of 3% compared with seven non-interacting single rotors [33]. Aeroelastic simulations performed by [PERSON] and [PERSON] proved the advantage of MRWTs with regard to power production compared with SRWTs, along with the improved dynamic behavior of the support structure [34]. However, it was found that the twisting moment generated by the difference in thrust forces in each rotor could lead to structural issues and yaw misalignment.
## 4 Results and Discussion
### Exploratory Factor Analysis (EFA)
A detailed picture of the factor load matrix after rotation is shown in Table 4, along with the extraction of five groups. Variables with a load factor above or near 0.50 are suggested to be kept because they significantly contribute to the analysis of the group of factors [108; 109]. In addition, the variable CSF19 (automated assembly) was excluded due to cross-loading. As shown in Table 4, the remaining seventeen independent variables are divided into five significant groups with six variables of group 1, four variables of group 2, two variables of group 3, two variables of group 4, and two variables of group 5. To encourage further discussion, it is important to rename the five extracted groups based on the results of the analysis. The five underlying groups can therefore be named as follows:  
* Group 1--Productivity
* Group 2--Visualization
* Group 3--Coordination
* Group 4--Sustainability
* Group 5--Safety Improvement  
Despite an excessive emphasis on BIM in construction in many developed countries, its presence in developing countries is minimal. Malaysia has experienced difficulties and discrepancies in the building standards, as have many other developing countries. This shows that these challenges need to be addressed by BIM principles. Practitioners would significantly increase the decision of senior management to recognize BIM as an \begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline \hline \multirow{2}{*}{**Code**} & \multicolumn{6}{c}{**Component Loading**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** \\ \hline \multicolumn{7}{l}{**Group 1—Productivity**} \\ \hline CSF13 & 0.868 & - & - & - & - \\ \hline CSF14 & 0.843 & - & - & - & - \\ \hline CSF9 & 0.831 & - & - & - & - \\ \hline CSF12 & 0.793 & - & - & - & - \\ \hline CSF15 & 0.788 & - & - & - & - \\ \hline CSF16 & 0.710 & - & - & - & - \\ \hline CSF16 * & 0.648 & - & 0.510 & - & - \\ \hline \multicolumn{7}{l}{**Group 2–Visualization**} \\ \hline CSF3 & - & 0.808 & - & - & - \\ \hline CSF4 & - & 0.779 & - & - & - \\ \hline CSF7 & - & 0.744 & - & - & - \\ \hline CSF2 & - & 0.620 & - & - & - \\ \hline \multicolumn{7}{l}{**Group 3–Coordination**} \\ \hline CSF20 & - & - & 0.778 & - & - \\ \hline CSF8 & - & - & 0.665 & - & - \\ \hline \multicolumn{7}{l}{**Group 4–Sustainability**} \\ \hline CSF5 & - & - & - & 0.747 & - \\ \hline CSF6 & - & - & - & 0.646 & - \\ \hline \multicolumn{7}{l}{**Group 5–Safety Improvement**} \\ \hline CSF1 & - & - & - & - & 0.589 \\ \hline CSF10 & - & - & - & - & 0.521 \\ \hline \end{tabular}
\end{table} 
Table 4: Factor loading after rotation in exploratory factor analysis. integrated platform/feature for their high-rise building projects by implementing BIM. As the analysis results clearly shown that Malaysia has at level three (moderately aware) for awareness of BIM. It is an alarming situation because, with more emphasis on the usage of BIM, the construction of high-rise buildings will become more economical and increase sustainable development. The following section discusses the five groups derived from EFA and validated by SEM.
# Old-Fashioned, but Still a Superfood--Red Beets as a Rich Source of Bioactive Compounds
## 5 Minerals  
Beetroot contains natural minerals that are essential for the proper functioning of the human body. [PERSON] et al. analyzed the mineral composition of seven different popular beetroot varieties grown in Upper Austria [49]. Obtained values were in a range similar to those reported in previous studies [70]. However, as the authors highlighted, data regarding the concentration of trace elements and minerals in beet juices are still limited. It is also crucial to note which part of the plant is tested, as the content of individual elements varies among leaves and tubers [8]. The concentration of copper ions was more than two times higher in leaves, in comparison to tubers (0.191 and 0.075 mg per 100 g of beets, respectively) [8]. A greater difference was observed for iron, for which 2.57 mg was found in leaves and 0.80 mg in tubers (values per 100 g of beets). Also, calcium, sodium, potassium, and magnesium were detected in much higher concentrations in leaves. The concentrations of zinc, phosphorus, and manganese did not significantly differ from each other.  
Recent studies showed that beetroots and beet products are a great source of selenium [71; 72]. Selenium is an important trace element for humans and animals, as it plays a key role in several major metabolic pathways, such as thyroid hormone metabolism and immune functions [73; 74]. Its deficiency has been linked with cardiovascular and inflammatory diseases and cancer [71]. The major selenium-containing amino acids occurring naturally in dietary sources are selenomethonine (SeMet) and methyloselenocysteine (MeSeCys), which were also detected in beetroot juice [26; 72]. A study involving five juices from conventionally grown beetroot and one from an ecological cultivar reported that all juices differed from each other in terms of selenium content and antioxidant activity, which is shown in Figure 5. Overall, the squeezed juice was lower in selenium compounds and polyphenols, perhaps because commercially available juice is made with a higher weight of beetroot.  
An important aspect is the instability of selenium compounds, which is particularly visible in freshly squeezed juice that has not been pasteurized, especially SeMet, which was oxidized to selenomethionine oxide (SeMetO) [26]. This process resulted in a decrease in the antioxidant activity of the studied juices. The antioxidant activity of selenium species in the DPPH assay decreases in the following order: MeSeCys > SeMet - Se(VI) > Se(IV). The values of all species were lower than that of \(\alpha\)-tocopherol at the same concentration [75]. From the consumer's point of view, the best way to consume beetroot juices is to drink them immediately after preparation or opening the bottle.