esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study proposes a brand-new method called SFO-CORP. It tackles the issue of short-lived sensor nodes close to the base station. The high traffic volume between the many CHs and the sink causes the low-life problem. SFO-CORP uses a system for choosing energy-efficient cluster heads that considers various factors, including closeness, cost, residual energy, and coverage. SFO-CORP was compared to the existing optimization-based routing methods. The strategy was successfully developed and validated in a MATLAB Tool simulation. Compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, the suggested protocol, SFO-CORP, enhanced the network lifespan by 19.6%, 13.63%, 11.13%, and 4.163%, respectively. Compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, the suggested protocol SFO-CORP outperformed them regarding PDR, network lifetime, E2 ED, energy consumption, and computational efficiency.  
Conceptualization: V.C. and T.T.; methodology: V.C.; validation: S.A., S.A.-O. and P.A.; formal analysis: T.T. and R.S.; investigation: S.A. and S.A.-O.; resources: S.A.-O., V.C. and T.T.; data curation: R.S. and P.A.; writing--original draft preparation: V.C. and T.T; writing--review and editing: S.A.-O. and S.A.; visualization: V.C. and S.A.-O.; supervision: V.C., T.T. and S.A.-O.; project administration: S.A., R.S. and S.A.-O.; funding acquisition: S.A.-O. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node Counts** & **LEACH** & **EECRP** & **FEEC-IIR** & **CL-IoT** & **SFO-CORP** \\ \hline
5 & 120 & 110 & 95 & 98 & 65 \\
7 & 131 & 120 & 115 & 105 & 78 \\
9 & 144 & 128 & 120 & 112 & 83 \\
11 & 149 & 135 & 138 & 118 & 90 \\
13 & 153 & 140 & 145 & 127 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Timing analysis for CH selection.  
Figure 10: Number of cluster heads vs. time for CH selection.  
**Funding:** Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R136), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 5 Epigenetic Factors  
VOC release is highly dependent on the environment [58] and is also influenced by the biological clock, as well as epigenetic factors such as DNA methylation and histone modification. These factors can regulate the release of VOCs by controlling genes and TFs. In a sense, the biological clock is also epigenetic. This changes the daily VOC emissions of fruit [59]. Epigenetic regulation produces changes in gene function that can be inherited during mitosis or meiosis without the need for DNA sequence polymorphisms [60]. Methylation and histone modification have been shown to epigenetically regulate the various nonvolatile phenylpropane-like biosynthesis pathway genes [59].  
Related studies [2; 4; 49] have shown that promoters of the _NAC_ and _AAT_ genes related to the synthesis of volatile compounds are widely bound by the inhibitory methylation marker H3K27 me3 during the ripening process of peach and apple. The levels of _H3K27 me3_ decrease with fruit ripening, thereby activating the expression of the _NAC_ and _AAT_ genes. The synthesis of volatile ester compounds can be promoted [49]. During peach fruit ripening, the DNA methylation level of the _PPTPS3_ gene promoter decreases, which is associated with increased _PPTPS3_ gene expression and increased linalool content [25]. In a subsequent study, [PERSON] et al. [46] reported that, during the ripening process of peach fruit, the expression of the transcription factor PpERF61 was negatively correlated with the DNA methylation level of its promoter region (R = \(-\)0.892, \(p\) < 0.01). That is, a decrease in the DNA methylation level is also correlated with an increase in PpERF61 expression and the linalool content [46]. In addition, RNA-based epigenetic mechanisms can also modify chromatin and silence transcription, but these mechanisms are poorly understood in fruit trees.
## 3 Dispersion Relations, Group Velocities and Frequency Range of Backward Surface Waves
In principle, this scenario is able to occur only if the tangential wave vector component of the Gaussian beam equals to the propagation constants of the SWs. Figure 7a displays the magnetic field distribution of the backward SW with the propagation constant and related frequency acquired at Point A of the dispersion curve in Figure 2a. By properly adjusting the thickness of the partner dielectric medium, the light beam of the SW can be on the same side of the normal with the incident beam, which is similar to the negative refraction phenomenon, revealing the fact that the direction of the energy flow is opposite to the wave vector of the SW along the \(x\) axis. Moreover, according to the magnification of the white rectangle, as shown in Figure 7b, the field distributed in the MMDM is far stronger than that in the partner dielectric medium, which verifies the fact that the energy flow is mainly in the MMDM for the backward SW, indicated by Figure 5. As a contrast, Figure 8a displays the forward SW with the propagation constant and related frequency acquired at Point C of the dispersion curve in Figure 4a. In this case, the light beam of the SW and the incident beam are on different sides of the normal, indicating that the propagation direction of the energy flow is in the same direction with the wave vector component of the SW along the \(x\) axis. Furthermore, in the light of the magnification of the white rectangle (see Figure 8a) depicted in Figure 8b, the field is mainly distributed in the partner dielectric medium; thus, the energy flow in the partner dielectric medium is dominant under this circumstance. Figure 6: Sketch of a forward or backward SW excited by a TM-polarized Gauss beam based on a prism-coupled configuration. The beam waist \(G_{w}=2000\) nm and the incident angle \(\theta=\arcsin\left(\frac{\beta}{n_{p}k_{0}}\right)\), where \(n_{p}\) is the refraction index of the prism, and the thickness of the partner medium is \(h\). The blue arrow represents the direction of energy flow of the backward SW while the green arrow represents the direction of energy flow of the forward SW. SW: surface wave. Figure 8: Forward SW in the UML case with \(f=0.7\), \(L=0.1\lambda_{p}\), \(\Omega=0.2942\), \(\beta=0.6628\times 10^{7}\)m\({}^{-1}\), \(h=700\) nm and \(n_{p}=2\). **(a)** Distribution of the magnetic field of the whole calculation area. **(b)** Magnification of the white rectangle in **(a)**. The region between the two dashed lines is the partner medium. Figure 7: Backward SWs in the UDL case with parameters \(f=0.7\), \(L=0.1\lambda_{p}\), \(\Omega=0.299\), \(\beta=2.392\times 10^{7}\)m\({}^{-1}\), \(h=100\) nm and \(n_{p}=6\) **(a)** Distribution of the magnetic field of the whole calculation area. **(b)** Magnification of the white rectangle in **(a)**. The region between the two dashed lines is the partner medium. The color bar represents the magnitude of the magnetic field.
## 3 Results and Discussion
### Adsorption Equilibrium and Kinetics of SiO\({}_{2}\) and the rGO/SiO\({}_{2}\) Composites

Table 2: Pseudo-second-order kinetic parameters for the adsorption of neutral red at various initial concentrations. To further test the adsorption property of SG 0.30, the concentration of NR was increased to 25 and 50 mg/L (pH = 5). For comparison, SiO\({}_{2}\) was used as a reference. Figure 9a shows the removal efficiency and saturated amount of adsorbed NR at equilibrium for SiO\({}_{2}\) and SG 0.30 versus the initial NR concentration at room temperature. At the same time, it was obvious that the removal efficiency of SG 0.30 was greater than that of SiO\({}_{2}\). Figure 9b displays the saturated amount of adsorbed NR at equilibrium versus different initial NR concentrations (10, 25, and 50 mg/L) at room temperature. When compared, the experimental adsorption capacities of SG 0.30 were 1.876, 3.791, and 1.353 times higher than those of SiO\({}_{2}\). In Table 3, the maximum adsorption amounts of the composites toward the adsorption of NR dye from the aqueous solution are compared with those of other adsorbents in the literature at room temperature. As clearly seen, the rGO/SiO\({}_{2}\) nanocomposites in this study exhibited a superior adsorption capacity and have potential for use in the treatment of wastewater containing cationic NR dye. Figure 8: Effect of the ratio of GO and SiO\({}_{2}\) on the adsorption rate of NR (C\({}_{0}\) = 10 mg/L). Figure 9: **(a)** Removal efficiency of SiO\({}_{2}\) and rGO/SiO\({}_{2}\) with different initial concentrations of NR. **(b)** Plot of the saturated amount of adsorbed NR at equilibrium versus the initial concentration at room temperature.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 6 Discussion
### Contributions  
This study's primary contribution is a systematic and comprehensive review of the state-of-the-art methods and algorithms used in predicting object trajectories within the realm of robotics and automation. This review is specifically focused on applications within warehouse management systems. Through the detailed analysis of 39 identified research papers, this study has drawn attention to the following major categories: deep learning methods, probabilistic methods, methods for solving the Travelling Salesman Problem, algorithmic methods, and framework systems.  
An additional significant contribution of the study is the classification and evaluation of these methods regarding their applicability in production-warehouse systems. This study offers crucial insights that benefit both practitioners and researchers, acting as a consolidated source of knowledge. Furthermore, it serves as a stepping stone for future investigations into trajectory prediction methods in warehouse management and automation systems.
## 1 Introduction  
Vertical root fracture (VRF) is a fracture which originates in the root of a tooth and can propagate either coronally or apically. Vertical root fracture (VRF) is a root-canal treatment complication [1] and is considered the third leading cause for extraction among the root-canal-treated teeth after caries and periodontal disease. The prevalence of extracted vertically fractured teeth among those who were endodontically treated is between 11% to 20% [2; 3; 4].  
This complication of the endodontically treated tooth is often frustrating, both for the dentist and the patient alike. Firstly, because often it is difficult to diagnose this complication accurately and in a timely manner, and secondly, because in most cases the tooth or the involved root has to be extracted. In some cases, it is possible to save the tooth or the involved root during endodontic surgery [5; 6].  
The etiology of this complication is multifactorial and consists of predisposing factors and a variety of operating factors. For this reason, it is hard to prevent this complication, resulting in both a destructive inflammatory process in the supporting tissues and bone loss, which at times can spread easily. This inflammatory process can be looked at as an endodontic-periodontal entity [7].  
An incorrect and/or delayed diagnosis of the vertical root fracture usually leads to more bone loss and to inappropriate management [8]. As long as the infection stays in the supporting tissues, more bone is resorbed. Therefore, there is the need for not only timely diagnosis but meticulous clinical and radiographic examination, coupled with the highly important relevant information of the tooth history.  
The largest clinical diagnosis issue is that these chronic, longitudinal root fractures often imitate other dental pathological conditions [9]. The signs and symptoms in these cases, such as acute apical periodontitis, dull pain or pain on mastication, tooth mobility, presence of a sinus tract, deep probing defects (a periodontal pocket), a periodontal-type abscess or periapical and lateral radiolucencies, are often similar to those found in cases of endodontic treatment failure or imitate periodontal disease [1; 8; 10; 11; 12; 13].  
Based on the literature available until 2008, the AAE Communique [14] has published a statement declaring that there is a combination of signs and symptoms that, when present in a specific tooth, is considered \"pathognonomic\" for the presence of a VRF. This can be called \"the pathognonomic combination\". These entities include a presence of deep pocket and a sinus tract in root-canal-treated teeth [15].  
The aim of the present study was to evaluate the correlation between the clinical signs considered pathognonomic for the diagnosis of VRF and the actual reference standard, through systematic review of the literature.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 4 EVSP Process
### HostRSU Algorithm  
The HostRSU algorithm determines whether the RSU itself should be responsible for the approaching emergency vehicle. As shown in Figure 8, when the RSU receives a message, it first checks the EV approaching angle (\(\theta OBU_{(GPS,\ t)}\)) and determines if it falls within the RSU service angle. The message will be ignored if the moving angle of the approaching EV does not fit into the RSU service range, which means that this EV may not move on sections of this intersection. The algorithm then double confirms the EVSP request by checking if the received OBU packets is more than a threshold (\(C_{threshold}\)). The third check is the service range: the distance from the vehicle to the signal controller (\(\delta\)) should fall into the service range predefined in the RSU; otherwise, it ignores the message. The RSU confirms it is the host RSU only if all these checks are passed, it then returns true and starts the EVSP process.  
Figure 7: Vehicular network communication protocol.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 2 Experimental Program
### Preprocessing of Data  
Since the input and output data of different features vary in range and units, the features with a bigger number would steer the model performance. As shown in Table 3, the range of FA varied from 641.16 kg to 816.03 kg, but the range of SF varied from 0 kg to 174.86 kg. Similarly, the range of the crack width varied from 0 \(\upmu\)m to 135.47 \(\upmu\)m, as shown in Table 4. To eliminate this potential bias, the experimental data were preprocessed through min-max normalization in order to scale the range of all features into [0, 1] with the following equation:  
\[x^{\prime}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where \(x^{\prime}\) is the scaled value of variable \(x\). \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of variable \(x\), respectively.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 2 Related Work
### Explicit Feedback  
Explicit Feedback is known as a receiver-driven rate adaptation scheme where the receiver takes a decision that is based on its estimation of the channel conditions and relays it back to the sender via different approaches using control frames, such as Clear To Send (CTS) and Acknowledgements (ACK).  
On Demand Feedback Rate Adaptation (OFRA) [11] is a receiver based rate adaptation algorithm (RAA), where the channel quality is estimated at the receiver based on SINR values. The receiver selects the optimal bit rate from a lookup table created previously. It contains a set of thresholds at which data rates should be changed. This information is fed back to the sender on demand while using ACK frames. In case of an ACK-less traffic, OFRA uses a specially designed feedback frame. OFRA presents some limitations, such as modifying the ACK frame that is a violation of the standard, introduction of additional overhead with the special feedback frame, which is sent at the lowest data rate.  
SINR-aware Intra-frame Rate Adaptation (SIRA) [12] selects two rates for a single Aggregate MAC Protocol Data Unit (A-MPDU) transmission. It finds the starting symbol \"I\" when the rate should be changed. When the condition \(SINR_{i}<SINR_{th}\) is met, the symbol \"I\" is found. \(SINR_{th}\) is the minimum SINR at which the theoretical Bit Error Rate (BER) of the primary rate is less than \(10^{-4}\). Subsequently, \"I\" is fed back to the sender via the BlockAck. The main drawback of SIRA is that it only determines two rates for an aggregated frame, which may not be enough for a fast-changing channels.  
An Ideal RAA is implemented in the famous network simulator NS-3. This RAA initially creates a table of SINR and MCS pairs. The SINR thresholds in this table ensure selecting an MCS that leads to a BER below a certain value. For example, the default value is \(10^{-5}\)), the SINR is fed back from the receiver to the transmitter via a perfect out-of-band mechanism. The main drawback of this mechanism is the use of an out-of-band channel for sending back the feedback which is not available in Industrial, Scientific, and Medical (ISM) bands used by IEEE 802.11.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 4 System Model
### Communication Model  
For the communication model, a wired connection is assumed between the charging segments and the pad owners, to provide a reliable and high-speed communication link. A 5G cellular communication link is used to exchange information between the EVs and the CSC, and between the EVs and the PO, through 5G base stations, also known as 5G g-Node-B (gNB). By using 5G communications, EVs benefit from the wide cellular coverage, ultra-low latency, high capacity, and the support for unicast, groupcast and broadcast messages [51; 52; 53]. This enables each EV to simultaneously initiate multiple dedicated communication sessions with different entities within the charging infrastructure. For the direct communication between the EVs and the charging segments, DSRC is utilized to benefit from the short operation range of DSRC links and avoid interference from adjacent channels. Utilizing DSRC between the EV and the CS also reduces the physical-layer latency due to the small air gap between the EV and the underlying segment. A summary of the different communication technologies utilized in the proposed model is shown in Figure 3.
# Reinforced Concrete Slab Optimization with Simulated Annealing
## 1 Introduction
Structural systems with reinforced concrete slabs are a common structural solution. Their structural behavior is complex and is currently being studied. Fatigue [29], punching shear [30; 31; 32] resistance, aging [33], and cracking properties [34; 35] have been discussed in the current literature. Flat slabs have several advantages such as a reduced and simple formwork as well as versatility and easy space partitioning, thus making them an economical and efficient structural system. Slab optimization has been thoroughly discussed in the literature. The case of flat slabs with an arbitrary configuration is presented in [36], where materials and construction costs were taken into account. The yield lines method is usually adopted to assess the slab collapse load with low computational cost. The discontinuity layout optimization (DLO) procedure [37] can be used to systematically automate the method to be applied to a wide range of practical slab problems. A hybrid genetic algorithm was proposed for slab optimization in [38]. A very interesting example of design optimization of simply supported slabs using finite elements was described in [39]. An emerging trend in this kind of problem is the analysis of the whole life-cycle of the structure, and consequently the optimization of the total costs of the building from construction to demolition. Various papers have been published on this specific topic in recent years. In particular, it is worth highlighting the results of the work dealing with flat slabs as presented in [40] as well as the sensitivity analysis of the life-cycle assessment in [41], which proves how transport and material waste are significant parameters. CO\({}_{2}\) emissions were the focus of [42; 43], which dealt with slab life-cycle optimization. More recently, in [44], deep learning neural networks were used to optimize slab design. Finally, in [45; 46], criteria to optimize structural and energy retrofitting in order to optimize greenhouse gas emissions and economic cost were discussed. Simulated annealing (SA) applications to slab optimization are not common in the literature. In this work, the cost optimization of rectangular anisotropic RC slabs was developed in an innovative way using limit analysis, based on yield lines theory and the SA algorithm. The main target of this research was to demonstrate the effectiveness of this new optimization method for a flat slab with a given set of boundary conditions. The rest of this paper is organized as follows. Section 2 presents the structural model based on limit analysis and yield lines theory and the equations that will be the target of the optimization. The main characteristics of the SA algorithm are presented in Section 3. The results of the specific case study are shown in Section 4, which also reports a comparison with finite element analysis. Finally, conclusive remarks are made in Section 5.
###### Abstract
Compared with the numerical analysis method, the test study could obtain the stress performance of airport pavements under a complex environmental load action, which was an important method to study the performance of airport pavements. [PERSON] and [PERSON] investigated the influence of temperature, humidity, frost heave, and snow melting on the dynamic response of airport pavements and showed that the total frost-heaving quantity in the pavement shoulder area was greater than that of the pavement area [14; 15]. [PERSON] studied the effects of temperature on the deflection and load transfer capacity of airport pavement; the analysis showed that variations in daily expansion and contraction strain cycles were more evident near the top surface of the pavement slab, where the ambient temperature affects it directly [16]. [PERSON] used field tests to study differential settlements and pavement cracking along the joint between the new subgrade and the old embankment and proposed three indexes for new and old subgrades, which should be viewed as the basis for the design and construction of new subgrades and old embankments [17]. Due to the inhomogeneous compaction of subgrade filling and long-term environmental loading action, runways are usually under inhomogeneous settlement conditions during service periods. However, few studies focus on the inhomogeneous distributed subgrade effects on runways. To ensure the safety of aircraft operation, it is necessary to study the time-varying effect of inhomogeneous subgrade on the flexural tensile stress and thickness of concrete pavement slabs to provide a basis for airport long-life pavement structure design methods. In this paper, a three-dimensional finite element analysis model of an aircraft load pavement with an inhomogeneous, horizontal subgrade structure is established. Considering factors such as aircraft wheel load type and rigid pavement thickness, the time-varying effect of an inhomogeneous subgrade support on the mechanical response of pavements under aircraft loads is analyzed. A scale model test of an airport pavement structure supported on an inhomogeneous subgrade is developed.