esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal and Physical Properties of Crude Palm Oil with Higher Oleic Content
## 3 Results and Discussion  
### Chemical Characterization of the Oils  
Table 1 presents the physicochemical characterization of HOPO and APO. Total caroten content was 830 and 524 mg/kg for HOPO and APO, respectively, practically within the range reported for African palm oil (500-1000 mg/kg) [27]. In APO, the \(\beta\)-carotene concentration was 380 mg/kg, while in HOPO it was 504 mg/kg. The \(\alpha\)-carotene concentration was 51 mg/kg in APO and 60 mg/kg in HOPO. The concentration of \(\alpha\)- + \(\beta\)-caroteness combined corresponded to 69 and 82% of the total carotenes for HOPO and APO, respectively. According to the literature, all quality parameters and caroten contents met the requirement expected for palm oil [12; 28].  
Regarding tocopherols, HOPO and APO presented 136.4 and 167.5 mg/kg of \(\alpha\)-tocopherols, respectively. On the other hand, \(\beta\)-, \(\gamma\)- and \(\delta\)-tocopherols were not detected in both oils, as usual for palm oil [12]. All classes of toctortienols were present in HOPO and APO, i.e., 128.6 and 117.1 mg/kg of \(\alpha\)-tocotrienol; 603.5 and 413.6 mg/kg of \(\beta\)-/\(\gamma\)-tocotrienols; and 15.2 and 29.8 mg/kg of \(\delta\)-tocotrienol, respectively. Thetocopherol/tocotrienol ratio of HOPO and APO was 18% and 29%, respectively. A similar ratio of about 20% was previously reported for an oil obtained from palm trees [29]. Moreover, [PERSON] et al. [12] described similar results for HOPO, in which toctortienols were found in high incidence, and the \(\alpha\)-isomer was the most abundant tocopherol. Tocotrienols possess neuroprotective, anti-cancer and cholesterol-lowering properties that are often not ascribed to to tocopherols [30].  
The unsaturated fatty acids with the highest occurrence in both oils were oleic, linoleic, and linoleic (Table 1). Those correspond to 65.6 and 53.7% of total fatty acids, corroborated by previous reports [6]. Palmitic acid is mostly responsible for completing the fatty acids composition [5; 7; 12; 28]. This fatty acid composition is vital to explain the differences in vegetable oils' physical properties described hereafter. Additionally, vegetable oil composition may vary slightly with cultivation conditions, raw material maturation, and storage time. Thus, this complete chemical characterization is important to guarantee the authenticity of studied oils and register the composition at the analysis moment.
## 1 Introduction
Additionally, a weighted fusion method is employed to integrate multiple contextual information and calculate the final POI representations, ensuring that the model can capture the nuanced relationships between POIs and users; (3) Based on geographical and temporal factors, we develop a novel strategy to estimate user preferences. This strategy leverages the learned POI representations to construct a more comprehensive model of user behavior, integrating both geographical and temporal factors. The main contributions of this paper are summarized as follows:  
* We introduce a new method (MCGRec), which employs a super node-based PPR sampling strategy to extract information from graph-structured data. This method transforms the sampled data into a grid-like feature matrix, which is then processed by a CNN-based backbone to learn POI representations. * We propose a novel user preference estimation method that comprehensively leverages the influence of geographical and temporal factors to calculate user preferences. This method integrates these contextual factors to enhance the accuracy of user preference modeling. * We conduct extensive experiments on real-world datasets to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of MCGRec in the POI recommendation task, showing superior performance compared to representative POI recommendation approaches. The remainder of the article is organized as follows: In Section 2, we review related studies for POI recommendation. In Section 3, we introduce the necessary preliminaries, including commonly used notations and definitions. In Section 4, we detail the proposed method. In Section 5, we describe the experimental setup and report the experimental results. Finally, in Section 6, we summarize the entire paper.
## 3 Results and Discussion
### Strong \(B_{0}\) Field Limit Case  
For the strong magnetic flux density field limit case, the time evolution of \(\varphi(t)\) and \(\omega(t)\) in Figure 1a,b is shown, and it is clear that the viscous history acceleration torque as part of the arbitrary viscous torque starts to have a significant effect. The involvement of the history acceleration torque term in the SIDE and IDE models results in a reduction in the amplitude of the angle and angular velocity at the early stage of alignment in comparison with the SDE and ODE models. For \(B_{0}=5\) T (Figure 1a), this correction of the \(\varphi(t)\) amplitude caused by the history acceleration torque is a few percent. The correction of the \(\omega(t)\) amplitude is \(>\)10%. Furthermore, the stochastic Brownian torque effect is minimal and, for the temperature considered, can be neglected.  
For a better illustration of the significance of each torque in the strong \(B_{0}\) field limit for the complex SIDE model (i.e., inertial, magnetic, arbitrary viscous, and stochastic Brownian torques) during the whole time evolution, see Figure 2.  
The observed oscillatory character of the \(\varphi(t)\) and \(\omega(t)\) time evolution in the strongest \(B_{0}\) field has its origin in the involvement of the inertial torque in the description of the models. In contrast, its diminishment, for instance, at a low Reynolds number limit, results in a loss of this oscillatory behavior.  
As much as the usual inertia contribution, the non-local Basset force is usually negligible at the macroscopic observation time scales considered in standard tracking experiments, but its effects have been shown to be prominent at short time-scales in [51, 52]. This finding is an analogue to the behavior of our studied system with the non-Basset kernel of the history acceleration torque for arbitrary rotating sphere.
## 4 Simulation Results and Analysis
### Simulation Scenario II  
In this simulation scenario of a large network, we consider a network topology of 60 edge data centers, which contains thirty edge computational resource suppliers and two edge data centers for each. The number of AI services is set to 100, which consists fifty users and two types of AI distributed model training tasks. The backlog threshold of AI-distributed model training tasks in edge data centers is set to 50. A series of simulations in simulation scenario II of a large network are conducted to evaluate performance comparisons among different proposed DRL-based algorithms, MILPs, and benchmarks in terms of multi-objective optimization, profit, backlog, and running time.  
* Multi-objective optimization vs. number of AI services  
Figure 5 shows that our proposed DRL-based algorithm significantly outperforms benchmarks and approaches to MILP in terms of multi-objective optimization in a large network. Notably, MILP can obtain the exact solution to our discussed problem. In Figure 5, the average error between the DRL-based algorithm and MILP is 30.34%, and DQN increases its average 34.25% joint optimization of profit and backlog than the benchmark, as the ratio of weight coefficient between profit and backlog is 100:1. Simulation results in Figure 5,5 show that the respective average error between DRL-based algorithm and MILP  
Figure 4: Performance comparisons in small network in terms of (**a**) profit vs. No. of services, (**b**) backlog vs. No. of services, (**c**) running time vs. No. of services.  
is 25.19% and 29.18%, and DQN increases its respective average by 12.83% and 86.28% of joint optimization of profit and backlog than the benchmark, as the respective ratios of weight coefficient between profit and backlog are 0.8:0.2 and 0.9:0.1. This is because under diversified types of AI service requests and changing workloads on edge data centers, DRL performs more adaptable to changes than MILP with fixed accurate solution and benchmark with random assignment strategy. Another reason is that the proportion of profit is larger than the backlog of distributed model training tasks in multi-objective optimization. Overall, the DRL-based algorithm increases by an average of 28.24% more than the benchmark in a large network.
## 4 Conclusions  
This paper emphasizes the crucial importance of integrating communication and sensing systems in modern technology, demonstrating their key role in various fields such as defense applications and autonomous vehicles. The study focuses on developing an integrated system that leverages innovative spread spectrum and RoF transmission technologies based on photonics. Simulation findings concerning ODSB transmission reveal a degradation in the performance of both radar and communication systems whenever they operate close to a fiber null. However, notable performance is still achievable at distances up to 50 km. This makes the proposed approach particularly suitable for applications like border protection and securing restricted areas. Simulation results at a carrier frequency of 28 GHz, with a received optical power of \(-3.8\) dBm at the transmitter, show an exceptionally low BER of 0 for the communication system. The detected radar signals demonstrate an 18 dB PSL ratio. Additionally, promising results were obtained from laboratory experiments conducted to validate the ISAC system design.  
Conceptualization, S.A.A. and A.A.; methodology, E.M.A. and S.A.A.; software, A.K.A. and M.A.S.; validation, A.K.A., M.A.S., E.M.A. and S.A.A.; formal analysis, E.M.A. and S.A.A.; investigation, A.K.A. and M.A.S.; resources, A.K.A., M.A.S., E.M.A. and A.M.R.; data curation, A.K.A. and M.A.S.; writing--original draft preparation, A.K.A., M.A.S. and S.A.A.; writing--review and editing. A.K.A., M.A.S., E.M.A. and S.A.A.; visualization, A.K.A., M.A.S. and E.M.A.; supervision, A.M.R., A.A. and S.A.A.; project administration, A.M.R., A.A. and S.A.A.; funding acquisition, A.M.R., A.A. and S.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under grant RSP2024R46.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Hybrid Routing Protocols
#### 2.3.1 Zone Routing Protocol (ZRP)  
This routing protocol is best suited for the dissimilar mobility patterns of UAVs and is basically based on the concept of \"zones\" [34]. In the ZRP, each UAV is placed into a zone, and the zones of the neighboring UAVs intersect. The size of the zone is measured by a radius \"R\" which is the number of UAVs to the perimeter of the zone. By fine-tuning the transmission power of the UAVs, the number of UAVs in the zone can be controlled. The routing of the data packets within the zone is called intra-zone routing, whereas the inter-zone routing is used to send data packets outside of the zone. Intra-zone routing is carried out through proactive routing to maintain the paths, and inter-zone routing utilizes reactive routing mechanisms in order to maintain and find the optimal paths. The delay introduced with the route discovery process is reduced by using border-casting [35]. The border UAVs in a zone only generate reply messages. Inter- or intra-zone routing is decided by the selection of the border UAVs.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
###### Abstract
In this research work, we have selected the trust metrics through critical analysis and investigated their suitability for improving security in RPL routing protocol in mobile IoT environment [14]. SMTrust is integrated into RPL to provide secure routing with nodes mobility and enhanced network performance. The trust-based approach is applied to secure IoT networks, specifically routing, by detecting and isolating malicious nodes. SMTrust routing algorithms are embedded into RPL, and the protocol is evaluated for network performance, in terms of topology stability, packet loss rate, throughput, and power consumption. Overall, the performance of SMTrust is significantly better as compared to standard RPL objective function (OF), which is Minimum Rank with Hysteresis Objective Function (MRHOF) [28], and existing trust-based approaches, such as SecTrust [25], Dynamic and Comprehensive Trust Model (DCTM) [27], and Metric-based RPL Trustworthiness Scheme (MRTS) [26]. Following are the objectives of this research work:  
1. To analyse and quantify the trust metrics and present an integration of SMTrust, proposed in [29], with RPL. 2. To provide algorithmic details for the integration of SMTrust into RPL. 3. To evaluate the network performance metrics via simulation. 4. To validate the simulation results and proposed protocol through mathematical analysis in accordance with the requirements of a routing protocol. The summary of the main contributions of this research work includes the analysis and quantification of trust-based metrics to develop a trust and mobility-based routing protocol for secure routing in IoT. It is demonstrated that the selected trust metrics promote secure routing and improve the packet success rate, throughput, and routing topology stability. The proposed trust and mobility-based protocol will help in enhancing the defence against routing attacks in IoT. In general, this research will extend support to research community of IoT networks and routing security. This research article is organized as follows. Section 2 provides the background knowledge of IoT networks, security, routing, and the significance of trust-based solutions. Section 3 refers and describes the related work. Section 4 presents the design and implementation of SMTrust, along with its integration with RPL. Section 5 elaborates the simulation results and analysis. Section 6 provides the validation and mathematical analysis of the proposed model, and Section 7 presents the discussion. Finally, the last section concludes the paper with some future directions of the research work.
## 3 The ASLM Architecture
### Problem Formulation  
Let \(\mathcal{U}\) denote a set of users and \(\mathcal{V}\) denote a set of items, where \(|\mathcal{U}|\) and \(|\mathcal{V}|\) are the total numbers of users and items, respectively. In this work, we concentrate on extracting the user contextual intent and preference from sequential user-item interaction events (e.g., commenting to a thread or listening to music). For each user \(u\in\mathcal{U}\), the sequential sessions are denoted as \(S^{u}=\left\{S^{u}_{1},S^{u}_{2},\ldots,S^{u}_{T}\right\}\), where \(T\) is the total number of time steps and \(S^{u}_{t}\subseteq S^{u}(t\in[1,T])\) represents the item set related to the interaction of user \(u\) at time step \(t\). In each session, there is a set of events \([e^{u}_{t,i}\in\mathbb{R}^{m}]i=1,2,\ldots,|S^{u}_{t}|\), where \(e^{u}_{t,i}\) represents the event \(i\) in the session \(S^{u}_{t}\). For each event, the user \(u\) interacts with an item \(v_{i}\in\mathcal{V}\). Both \(S^{u}_{t}\) and \(e^{u}_{t,i}\in S^{u}_{t}\) are ordered by time sequence. For a time step \(t\), the session \(S^{u}_{t}\) implies the \(u\)'s short-term intent at time \(t\), while the sessions before time step \(t\), defined as \(L^{u}_{t-1}=S^{u}_{1}\cup S^{u}_{2}\cup\ldots\cup S^{u}_{t-1}\), implies user \(u\)'s long-term preference. As in [7], our task is to predict each successive item in a session \(S^{u}_{t}\). That is, for a sub-session \(\{e^{u}_{t,1},e^{u}_{t,2},\ldots,e^{u}_{t,i},\ldots,e^{u}_{t,n}\}\) of \(S^{u}_{t}\), the system is to predict \(e^{u}_{t,i+1}\). This is repeated for \(i=1,2,\ldots,|S^{u}_{t}|\)-1. A recommendation \(R_{i}\) is a rank ordered list of \(k\) recommended items. The goal of the system is to make the next item, \(e^{u}_{t,i+1}\), be located as close to the top of the ranked list as possible.
## 3 Noise Source Localization Method Using GIB with 'p+v' Joint Processing  
In order to further improve the resolution of a virtual extrapolated array (EA) for the localization of sound sources in the low frequency range, a GIB beamforming algorithm is adopted in this paper. Considering the advantage of a vector microphone in the low frequency range, a GIB based on 'p+v' joint processing is proposed.
# Control Strategies for Piston Trajectory in Ionic Compressors for Hydrogen Storage
## 3 Results and Discussions
### Energy Consumption and Isothermal Efficiency Results When Using Different Control Methods  
A \(p\)-\(V\) diagram of the compressor, comparing the different control methods, is provided in Figure 10. It can be seen directly that the expansion of the gas finished the earliest in the system utilizing the Position-S control method, while the latest was observed in the case with the Dual-PS control method. This was because the bottom center position of the piston was reached the earliest with the Position-S control method, while it was achieved the latest when the Dual-PS method was used, as presented in Figure 6. It was found that the smallest stroke volume of the compressor was 1.28 \(\times\) 10\({}^{2}\) cm\({}^{3}\) in the system using the Position-S control method, while in the cases using the Position-P or Dual-PS methods, it was found to be around 1.31 \(\times\) 10\({}^{2}\) cm\({}^{3}\). In all three simulation cases, the compression factors were found to be almost the same because the curve variation trends during the compression process were observed to be similar across the results.  
Figure 11 displays the energy consumption and the isothermal efficiency for the compression of the hydrogen after one cyclic operation for the various control methods. After a single compression cycle, a relatively high energy consumption of the compressor when using the Position-P control method was found: 2724.59 J. However, the maximum isothermal efficiency was observed to be 50.28% with this control strategy. The lowest single-cycle energy consumption, 2655.17, was obtained with a relatively low isothermal efficiency of 49.77% when the hydraulic drive system was designed with the Dual-PS control method. After one operating cycle, the system based on Position-S control was found to have the highest energy consumption and the lowest isothermal efficiency values of 2726.54 J and 49.72%, respectively. The maximum difference in the energy consumption of the compressor system after one cycle operation was observed to be 71.37 J, while the maximum difference in the isothermal efficiency of the compressor was 0.56%. Although  
Figure 9: Variations in control error for the piston’s speed, using different control methods.  
the lowest isothermal efficiency was obtained in the system using the Position-S control method, it was significantly close to the case with Dual-PS control.
# Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering
## 5 Bio-Inspired Optimization Methods
### Particle Swarm Optimization  
Particle swarm optimization (PSO) is a population based stochastic optimization technique that was put forward by [PERSON] and [PERSON] in 1995, which was inspired by social behavior of bird flocking or fish schooling. However, in the algorithm birds or fish are viewed as particles, which is a more general abstraction.  
PSO shares many similarities with evolutionary computation techniques, such as with the genetic algorithm (GA). The system is initialized with a random population solutions and searches for optima by updating generations. However, unlike the GA, the PSO has no evolution operators, such as crossover and mutation. Instead, parameters of social and individual behaviors are used in PSO, and velocity and position of the particles. In PSO, the potential solutions, called particles, fly through the problem search space by following the current best particles [1; 11; 16].  
Another reason that PSO is attractive is that there are few parameters to adjust. One version, with slight variations, works well in a wide variety of applications. Particle swarm optimization has been used for approaches that can be applicable across a wide range of problems, as well as for specific applications focused on a specific requirement.
## 4 Engine Test Results and Discussions
### Engine Combustion Characteristics
#### 4.2.2 Effects on the Combustion of the Main Injected Fuel  
The combustion processes of the main injected fuel shown in the figures are quite similar when the engine is running on different fuels at different loads. There are no significant differences in ignition delay or the shape of the HRR. The autoignition and combustion processes are affected mainly by the cylinder temperature and elevated further by the combustion of the preinjected fuel, while the fuel properties like CN and viscosity play a minor factor [22].  
Tables 6 and 7 list the main injection timing, injection pressure, and other conditions at different loads. It can be seen that most fuel injection is completed very early, and there is little difference between the ignition delay and the start of the combustion of the main injected fuel. By comparing the HRR curves in Figure 8, it is clear that most fuel is burned in the premixed form only under full load conditions, i.e., above 1600 Nm, the combustion typically shows premixed and diffusion characteristics.  
Nevertheless, it is clear that the addition of heptane can improve the main combustion process in middle- and high-load cases. With additions of both 20% and 50%, the engine has a higher cylinder pressure and HRR, as shown in Figures 8-10. The low viscosity has positive effects on atomization and the mixing process of air fuels [23], resulting in the higher peak values of in-cylinder pressure for HE20 and HE50. Moreover, the low cetane number of tetrain affects the combustion duration [21; 24], which causes the lowest peak values of in-cylinder pressure of TE20 and TE50.