esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention
## 2 SARL and MARL  
SARL and MARL are defined by the number of agents a reinforcement architecture deploys on the environment. Most research works for traffic engineering (TE) and intrusion detections (IDs) in SDN have adopted the SARL architecture. The common SARL implemented algorithms includes Q-learning [25], SARSA algorithm [26], the Deep Q-Network (DQN) [25], and the Deep Deterministic Policy Gradient Algorithm (DDPG) [27]. DQN and DDPG algorithms have seen the integration of deep neural networks (DNNs) in optimizing the policy network of agents for higher efficiency and scalability. As shown in Figure 3, for the SARL architecture, the SDN controller collects network metrics as state for the RL agent. The agent based on a policy enforces an action on the forwarding devices through the controller and subsequently receives a reward. The SDN controller then installs new flow rules for the OpenFlow [28] switches in the forwarding plane.  
Figure 3: SARL.  
Figure 2: Transient load detection.  
With collaboration comes faster convergence in a cooperative and competitive environment among agents. Agents' cooperation increases the state space representation with exhaustive dimensionality, resulting in faster executions through parallel computation in the environment. Experience sharing and cooperation in IoT-related dynamic environment have necessitated a research discussion surge from SARL to MARL, especially with the advent of 5G and newer technologies.  
MARL involves interactive, autonomous systems acting on a common environment to accomplish a task within a shorter time. These lead to agents executing decentralized actions with centralized training. MARL systems have seen applications in diverse domains including game development, network management, distributed control, and decision support systems [29; 30; 31].  
As shown in Figure 4, multi-agents execute decentralized action on the stochastic environment using respective actor networks. A historic tuple using the replay buffer is used to store the state-action-reward of each agent after every episode. The centralized critic network is then used to evaluate the actions of the actor network of each agent through a policy gradient.  
Figure 4: MARL.
# Automatic Roadway Features Detection with Oriented Object Detection
## 4 Case Study  
### Greater Cairo  
GCR is the largest megacity in the MENA region with a population of 24.5 million (according to Central Agency for Public Mobilization and Statistics--CAPMAS), hosting 3.7 million registered vehicles (in 2017) resulting in almost 2.5 million daily trips (6). Statistics show that 3604 crashes occurred on GCR roads in 2016, among them 1390 fatalities occurred. An equally startling statistic is that there are 4 deaths in Egypt per 100 km roads [57] while the rates of death in the UK and US are 0.47 and 0.92 people, respectively [1].  
Despite the dire need for accurate crash data to methodically advance roadway safety research in Cairo/Egypt (and most developing countries), crash data, such as exact location, crash detailed report, and black spots, are largely missing. The proposed system in this paper can enable characterizing roadways by assigning a safety score which could support in screening roadway networks; such screening can further identify segments of low safety scores that either require further safety studies by decision makers or notify road users ahead of time when approaching such segments.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 4 Results  
This section presents the results obtained after the full processing of the collected data, incorporating both statistical analysis and vector-based modeling in the urban and suburban study areas. To evaluate the behavior of radio-frequency parameters across different spatial conditions, a comprehensive statistical framework was applied. This included the calculation of the mean, standard deviation (SD), standard error (SE), and 95% confidence intervals (CIs) for each cell in the dataset, which allowed for the identification of signal quality patterns and the detection of anomalies in the coverage zones.  
The analysis compared the power variation observed in the coverage area of a cellular network using two theoretical models--previously introduced in this study--and the experimental measurements collected in the field. Handover regions, specifically intra-handover and inter-handover events, were accurately detected using the methodology described in [4]. These identified zones were subsequently used to test the performance of the proposed vector-based theoretical approach to estimate the power variation as a function of the user terminal's direction of movement.  
Although the statistical evaluations and model validations were carried out in both urban and suburban environments, the initial focus of the discussion is on the urban scenario, as it presents more complex propagation conditions due to obstacles such as buildings and traffic density. Nonetheless, comparisons involving deviations between theoretical and experimental power estimates are provided for both environments in later sections, ensuring a comprehensive and balanced evaluation of the model's performance.  
The reference thresholds for classifying the quality levels of the main RF parameters analyzed in this work--RSSI, RSRQ, and RSSNR--are summarized in Table 2, serving as a benchmark for interpreting the results that follow.
# Quality-Aware Resource Model Discovery
## 3 Quality-Aware Resource Model Discovery
### Statistical Analysis on Resources
Algorithm 2 explains the proposed approach to identify mainline states that have a significant relationship with quality. Note that all statistical analysis needs user-defined significance level thresholds. ```
Input  An integrated model \(IM\)  A set of user-defined significance level thresholds for statistical testing \(\mathcal{A}\) Output  A set of states generating superior quality cases \(S\)  A set of states generating inferior quality cases \(I\)  A set of states generating moderate quality cases \(M\)  Let \(IM\) be the integrated model, i.e., \(IM=\)(\((S,E,T,A^{s},A^{t})\), \(BWS\), \(BWD\)). Let A be the set of activities in an event log \(L\). Let \(\mathcal{A}=\{\)\(\textit{ff}_{KSSI}\), \(\textit{ff}_{AVOVA}\),\(\textit{ff}_{IC}\),\(\textit{ff}_{KW}\),\(\textit{ff}_{MW}\}\) be the set of user-defined significance level thresholds for Kolmogorov-Smirnov one sample test, ANOVA, Linear contrast, Kruskal-Wallis H-test, and Mann-Whitney U-test. Average and Median are functions to calculate average and median values of a specific distribution, respectively. \(S\leftarrow\varnothing\), \(I\leftarrow\varnothing\), \(M\leftarrow\varnothing\) forall\(a\in A\)do \(TS\leftarrow\varnothing\) forall\(s\in S\)do ifs[0] is a thenTS\(\leftarrow\)TS\(\cup\) s  normality\(\leftarrow\)getNormality(TS, \(\textit{ff}_{KSSI}\)) if normality is True thenanova_result\(\leftarrow\)getANOVResult(TS, \(\textit{ff}_{ANOVA}\)) ifanova_result\(\leftarrow\)acceptthen forall\(s\in TS\)do\(M\leftarrow\)M\(\cup\) s elsel_result\(\leftarrow\)getLCSult(TS, \(\textit{ff}_{LC}\)) \(S\leftarrow\)S\(\cup\)l_result[0], \(I\gets\)I\(\cup\)l_result[1], \(M\gets\)M\(\cup\)l_result[2] else kw_result\(\leftarrow\)getKWResult(TS, \(\textit{ff}_{KW}\)) ifkw_result is acceptthen forall\(s\in\)TSdo \(M\gets\)M\(\cup\)s elsemw_result\(\leftarrow\)getMWResult(TS, \(\textit{ff}_{MW}\)) \(S\leftarrow\)S\(\cup\)mw_result[0], \(I\gets\)I\(\cup\)mw_result[1], \(M\gets\)M\(\cup\)mw_result[2] return S, \(I\), \(D\) Function getQuality(TS) \(Y\) = \(\varnothing\) forall\(s\in TS\)do \(Y\gets Y\cup\textit{ff}_{q}(A^{s}(s))\) return\(Y\) Function getNormality(TS, \(\textit{ff}_{KSSI}\)) \(Y\gets\)getQuality(TS), normality \(\leftarrow\)True forall\(y\in Y\)do \(p\)-value \(\leftarrow\)KSSI(y) ifp-value <\(\textit{ff}_{KSSI}\)then normality \(\leftarrow\)False
```  
**Algorithm 2**IdentifyingSignificantMachines(IM, \(\mathcal{A}\))  
On the basis of Algorithm 2, we can divide all states into three categories: \(S\), \(I\), and \(M\). As discussed in the above, these are utilized to simplify the discovered model.
## 4 Experimental Results and Analysis
### Test 2: Sooty Mold vs. Healthy  
In this test, 45 images of sooty mold and 45 healthy mango leaf images were classified. Table 4 shows that an accuracy of 95.5% was attained by using linear SVM. It proved to be the highest amongst all the other competing classifiers. Furthermore, 0.05, 0.95, 0.95,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Sensitivity** & **Specificity** & **AUC** & **FNR (\%)** & **Accuracy (\%)** \\ \hline Linear discriminant & 0.24 & 0.89 & 0.82 & 17.8 & 82.2 \\ Linear SVM & 0.22 & 0.91 & 0.85 & 5.6 & 94.4 \\ Quadratic SVM & 0.22 & 0.91 & 0.86 & 6.7 & 93.3 \\ Cubic SVM & 0.16 & 0.97 & 0.97 & 3.4 & 96.6 \\ Fine KNN & 0.22 & 0.91 & 0.84 & 11.2 & 88.8 \\ Medium KNN & 0.18 & 0.84 & 0.86 & 16.7 & 83.3 \\ Cubic KNN & 0.20 & 0.71 & 0.84 & 18.9 & 81.1 \\ Weighted KNN & 0.18 & 0.82 & 0.86 & 11.2 & 88.8 \\ Subspace discriminant & 0.2 & 0.82 & 0.86 & 12.3 & 87.7 \\ Subspace KNN & 0.18 & 0.84 & 0.86 & 16.7 & 83.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Powdery mildew vs. healthy mango leaves.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Classification-Class**} & \multicolumn{2}{c}{**Classification-Class**} \\ \cline{2-3}  & **Powdery Mildew** & **Healthy** \\ \hline Powdery mildew & 97.7\% & \textless{}1\% \\ Healthy & \textless{}1\% & 95.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of powdery mildew vs. healthy mango leaves.  
and 4.5 are the obtained values of sensitivity, specificity, AUC, and FNR, respectively. The confusion matrix of this test is also given in Table 5.
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 4 Numerical Results
### Cases Definition
#### 4.2.7 Case 7: Minimizing the Fuel Cost and Voltage Deviation  
The results cannot achieve the required network voltage profile if only the simple fuel cost function is considered. As a result, the goal function can be divided into two parts. As in Case 1, the first part is the fuel cost function. The second part is to reduce load bus voltage differences from the reference value of 1 p.u. to increase the network voltage profile. Generally, this optimization's objective function can be described as below [61]:  
\[OF_{7}=OF_{1}+\lambda v*\sum_{i=1}^{NPQ}|V_{i}-1.0| \tag{33}\]  
where the value of factor \(\lambda v\) is set to 100 [61].  
In Table 8, the minimum fuel cost (USD/h) and V.D. (p.u.) of the TLBO and AGTLBO algorithms are compared, and the findings are linked to those published in the recent literature such as MPSO (Modified Particle Swarm Optimization) [16], MFO (Moth-Flame Optimization) [16], MOICA (Multi-Objective Imperialist Competitive Algorithm) [62], NKEA (Neighborhood Knowledge-based Evolutionary Algorithm) [62], BB-MOPSO (Bare-Bones Modified Particle Swarm Optimization) [62], MNSGA-II (Modified Non-dominated Sorting GA-II) [62], and MOMICA (Multi-Objective Modified Imperialist Competitive Algorithm) [62]. The table's findings demonstrate AGTLBO's ability, as it was able to find appropriate solutions for this situation.
## 2 Materials and Methods
### Characterization Techniques  
#### 2.2.1 Compressive Strength Testing  
Compressive strength was determined using a concrete compression testing machine, as shown in Figure 4. This standard equipment fulfills the technical specifications for testing machines indicated in UNE-EN 12390-4 [28].  
Compression tests were developed following standard UNE-EN 12390-3 [29]. The testing machine applied compressive strength until the final breakdown of the specimens. The testing machine was used to determine the maximum compressive load supported. Compressive strength is determined using Equation (1), according to Standard EN 12390-3, where the standard test to determine compressive strength is detailed [29].  
\[f_{c}=\frac{F}{A_{c}} \tag{1}\]  
where:  
\(F\): maximum compressive load.  
\(A_{c}\): section of the specimen where the load is applied.  
Tests to determine the compressive strength of samples were performed following Spanish standard UNE-EN 12390-3 for hardened concrete characterization [29]. The specimen was used to determine the compressive strength of samples with a given sample.  
Figure 4: Compression testing machine following UNE-EN 12390-4 [25].  
Figure 3: (**a**) Cubic specimens (50 mm \(\times\) 50 mm \(\times\) 50 mm) of mixture type Zn-S-0.5. (**b**) The specimens submerged underwater.  
mens were loaded in a standard testing machine. A compression load was applied to the specimens, and the compressive strength was determined from the load supported until failure. In this work, cube specimens were manufactured as indicated in Section 2.1. The load-bearing surfaces were molded to be plane, ensuring the load was uniformly applied to the total surface of the specimen. The specimens were tested after the curing process at a room temperature of 21 \({}^{\circ}\)C. The compression load was applied at a constant rate of 0.6 MPa/s (1514.5 N/s in a square of 50 mm \(\times\) 50 mm). The load was continuously increased until failure when no greater load could be supported. The testing machine used was automatically controlled.  
The four cracked faces must show similar damage and cracking patterns. Satisfactory failures for cube specimens are shown in Spanish standard UNE-EN 12390-3 [29].  
Morphological and Compositional Characterization Techniques  
Morphological and compositional characterizations of ZnO nanoparticles and different samples were performed using a Scanning Electron Microscopy device (SEM, JEOL 5600, Akishima, Tokyo, Japan), equipped with an EDX microanalysis system (INCA, Oxford Instruments, Abingdon, UK).
## 2 Literature Review
However, it encounters a challenge regarding network lifetime (NLT) due to the utilization of a constant ring, which was intended to enhance routing path diversity, inadvertently resulting in reduced NLT. It is observed that the majority of these existing techniques do not prioritize the simultaneous improvement of both the safety period and network lifetime (NLT). Although these protocols exhibit a certain degree of improved privacy, they still show some degree of privacy vulnerabilities. Notably, there are shortcomings in terms of routing path randomization and diversity. Furthermore, it is evident that the majority of these solutions aimed at enhancing privacy did not consider the specific position of the source node within the network; consequently, this results in a privacy model that is depending on the source node's position within the network. The proposed scheme (SLP-E) utilizes a reverse random walk, followed by the walk on annular rings to establish a completely random and diverse routing path to confuse passive attackers. It then employs min-hop routing in conjunction with the walk on dynamic rings to relay the packets to the base station (BS). The routing path in each phase is strategically designed with the aim of enhancing both privacy and network lifetime (NLT) while simultaneously achieving uniform privacy across the network. Furthermore, there has been no research aimed at SLP which investigates the impact of sensor nodes' radio range on SLP schemes' important performance metrics. These performance metrics typically encompass the safety period, network lifetime (NLT), entropy, capture percentage, energy consumption, and delay.
# Enhancement of Wheat Flour and Dough Properties by Non-Thermal Plasma Treatment of Wheat Flour
## 3 Results and Discussion
### Effects of Non-Thermal Plasma-Treatment Reactor Filling on Wheat Flour and Dough Properties
#### 3.2.1 Hydration Properties of Flour  
Similar to the findings for moisture content, presented in Figure 3, the moisture content also remained close to 14% with varying flour masses and therefore the results are not discussed further here.  
Figure 6(a),b shows the dependence of WHC and WBC on wheat flour mass (type 550). Even though a stronger effect of NTP in a less full reactor would be expected, a general trend of increase in WHC and WBC values with increase in flour mass was observed. The WHC and WBC values generally increased for dough prepared with treated flour, with the maximal increase for the flour mass of 150 g, i.e., 6% filling of the reactor volume.  
The above observation could be attributed to the fact that, during the flour treatment, a proportion of the flour particles stick to the reactor wall and to the HV pins. According to [PERSON] et al. [41], sample surface area exposed to plasma treatment is an important factor that influences the hydration properties of flours. At a lower flour mass (50 g, equivalent to 2% of reactor volume), a bigger share of the treated flour mass is immobilized and thus the treatment is not uniform. At a moderate mass (150-350 g, i.e., 6-13% of reactor volume), a smaller proportion of flour particles are 'glued' to the reactor surfaces and thus a more intensive mixing takes place, contributing to more effective contact between the plasma species and the flour particles. At higher mass (450 g, i.e., 17% of reactor volume), the contact between the flour mass and the plasma species decreases, hence contributing to a decrease in hydration properties. In the case of type 1050, the change in hydration properties lay within the standard deviation range, hence no conclusion could be drawn and the results are not shown here.
# Effect of Food Emulsifiers on Aroma Release
## 2 Results and Discussion
### Influence of Xanthan Gum on Aroma Release  
As shown in Figure 4, there were no significant differences (\(p>0.05\)) in the peak areas of ethyl acetate and (\(E\))-2-hexenal at different concentrations of xanthan gum solutions. This indicated that the concentration of xanthan gum had little influence on these two aroma compounds. As shown in Figure 4a, when the concentration of xanthan gum solution is 0.1% and 0.3%, the quantities of carvone and perillaldehyde released are the lowest; whereas when the concentration was 0.2%, the quantities released were the highest. However the release of ethyl acetate and (\(E\))-2-hexenal had the lowest peak areas in 0.5% xanthan gum solution. In Figure 4b, the peak area was the lowest in 0.1% xanthan gum solution, which is approximately 0.5%. After 0.2%, the quantity of \(\alpha\)-pinene, 3-carene, and limonene released exhibited a decreasing trend.  
The effect of xanthan gum on aroma release was attributed to the variation of xanthan gum concentration at the oil-water interface, which resisted the transfer of most flavours to the headspace. This inhibition effect of xanthan gum resulted from its more distinctive hydrophobic character compared with other hydrocolloids; thus, the hydrogen bonding between xanthan gum and hydrophilic compounds would influence aroma release [18; 21]. This observation was consistent with the fact that xanthan gum is an ionic polysaccharide comprising a \(\beta\)-(1,4)-d-glucopyranosyl backbone with ionized trisaccharide side chains comprising two mannose residues and one glucuronic acid residue. Xanthan gum is a multifunctional biological polymer with high molecular weight and a good deal of free carboxyl groups. Because of these active sites, xanthan gum exhibits great water absorption and water-holding capacity. When dispersed in aqueous solutions, xanthan gum transforms from an ordered double helix to a complex polymer through the action of hydrogen bonds and polymer entanglement. Xanthan gum molecules form a tenuous three-dimensional network. [PERSON] _et al._[18] found that the ordered conformation of xanthan gum gives a structure homologous to a double helix, which may create a \"hydrophobic cavity\" and can entrap aroma compounds. This ordered conformation plays an important role in solution viscosity [22]. Increasing the xanthan gum concentration enhanced the viscosity and thus retarded the release of aroma compounds. Therefore, the minimal variation of xanthan gum content was able to induce aroma release because of the complex composition and structure of xanthan gum [18; 21; 23].  
Figure 3: Effects of sucrose fatty acid ester on the release of ethyl acetate, (\(E\))-2-hexenal, carvone (**a**) and perillaldehyde, \(\alpha\)-pinene, 3-carene, limonene (**b**). Significant differences (\(p<0.05\)) between different concentrations are performed by letters on the same polylines. Where the letters on the same polyline are the same, there is no significant difference between different concentrations.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 3 Results and Discussion
### Corrosion Inhibition
#### 3.3.4 Surface Analysis by SEM-EDX  
Figure 5a,b, representing the SEM images of mild steel without and with 600 ppm of _C. tagal_ extract in 1 M HCl, is yet another observation made that supports enhancing the role of _C. tagal_ extract on corrosion inhibition.  
The roughness, large pores, cracks, and pits on the surface of mild steel, shown in Figure 5a, indicate an aggressive acid attack causing corrosion, whereas the smooth and even surface (Figure 5b) are due to the protective film formed by plant extract. In addition to these results, numerous studies on mild steel have demonstrated the possibility of plant metabolites for protecting metal surface from corrosion [55; 56; 57]. The differences in the elemental composition in both uninhibited and inhibited samples were clearly identified in EDX. Figure 6a,b shows the variation in the percentage composition of elements in blank and inhibited that provides proof of the action of _C. tagal_ active compounds on mild steel surface [58].  
Secondary metabolites such as phenols, tannins, carbohydrates, and proteins are known for their shielding effects on metals and alloys, which is the bases for the reduction in corrosion rate by preventing oxidation [58]. It is clear that the differences in Table 4 and Figure 6a, with 71.54% Fe, 22.1% O, 4.34% C, and 1.51% Mn, and Figure 6b, with 84.21% Fe, 9.01% O, 6.37% C, and 0.07% Mn, represents the acid attack depleting iron atoms by oxidizing the mild steel. The latter with a lower percentage of oxygen and higher percentage of iron atoms shows a decrease in acid attack due to the protective film of plant extract [59; 60]. This assures that the decreased corrosion rate might be due to the active ingredient in plant extract.  
Figure 4: UV-visible spectrum of: (**a**) Pure _C. tagal_ extract; (**b**) inhibited _C. tagal_.  
Figure 5: SEM photographs of mild steel dipped in: (**a**) 1 M HCl; (**b**) 1 M HCl with 600 ppm of inhibitor.