esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Based Design of an Aircraft Wing Using a Fuzzy-Based Metaheuristic
## 1 Introduction
This directly affects aeroelastic characteristics and the safety of flight. Uncertainty in aeroelastic aircraft wing design usually derives from various sources such as, material properties, load, tolerance in manufacturing processes, and flight conditions. The uncertainty sources can be classified in two categories, namely aleatory uncertainty and epistemic uncertainty. The first group occurs due to random physical variation while the second group is caused by lack of knowledge [2]. It is known that aleatory uncertainty can be handled by the probabilistic model, but epistemic uncertainty is preferably quantified by a convex set--a fuzzy set method [9], and anti-optimization [1; 10]. It means that the uncertainties of aeroelastic aircraft wing design cannot be quantified by theory alone. Composite aircraft wing structures that have been studied in design optimization are laminate layup in upper and lower skin [12], laminate ply thickness [13] and ribs and spars dimension [14], which resulted in both reduction in structural weight and flutter speed. Probabilistic reliability-based aeroelastic design of wings for aeroelastic tailoring in layup optimization of ply angles uncertain for composite wings [15] when reduce of the probability of failure make to expanding the design speed and stability margin. Uncertainties of material properties and ply thickness for robust optimization design of wings [16] have been used the polynomial chaos expansion (PCE) and gaussian processes (GP) method. This procedure is used to estimate the mean, variance and the probability density function (PDF) of uncertainties and flutter speed in optimal deterministic design for critical speed in flutter/divergence. It successfully used to obtain robust designs rather than the method in group of non-probabilistic, which has only few successive in solving such the problem. From the literature review, in this paper, the PSIBDO with fuzzy uncertainties is used to design a composite aircraft wing structure. The technique is used to quantify epistemic uncertainty due to the lack of knowledge in material properties and allowable transverse displacement. The present study expects to fill the gap left when utilizing the non-probability method for aircraft wing design. In the aeroelastic design, the maximum transverse displacement on the wing, wing lift effectiveness and flutter speed are considered as the design criterion. The optimal aircraft wing design of the composite wing considers an effect of aeroelastic response to design variables of aircraft wing structure that are thickness and composite ply orientations. It leads our research aiming to study aircraft wing design based on the PSIBDO, which expects to make it more realizable in practical. The remaining of this paper is organized as follows: the details of RBDO using PSIBDO approach are presented in Section 2. The teaching-learning based optimization (TLBO) is described in Section 3. The design demonstration is performed in Section 4. The design results and conclusions are detailed in Sections 5 and 6, respectively.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.11 Other Problems
Predicting the propagation characteristics accurately is needed for optimum cell design. Thus, the authors proposed a deep learning model to learn propagation loss from the map of a geographical area with high accuracy. The authors in [76] considered the multiuser detection problem in an SCMA system. A deep learning model was used to mimic the message passing algorithm (MPA), which is the most popular approach to implement multiuser detection with low complexity. The deep learning model was designed to estimate the probability that a user is assigned into a resource block from a pool of resource blocks, taken the signal sent by the users as input. In [3], an intelligent beamforming technique based on MIMO technology was proposed using reinforcement learning. The proposal builds a self-learning system to determine the phase shift and the amplitude of each antenna. The reinforcement learning algorithm can adapt the signal concentration based on the number of users located in a given area. If there are many users in a given small area, the solution may produce a more targeted signal for users located at that area. However, if users are spread out over a wide area, a signal with wide coverage will be sent to cover the entire area. In [77], [PERSON] et al. proposed a reinforcement learning-based solution in order to choose the best configuration of uplink and downlink channels in dynamic time-division duplexing (TDD) systems. The main goal was to optimize the mean opinion score (MOS), which is a QoE metric. This metric has a direct relationship with the system throughput. The optimization problem was formulated as one that maximizes the MOS of the system by allocating uplink and downlink traffic for the time frames. Thus, a set of downlink and uplink configurations was defined by the authors and, for each frame, these configurations are chosen for each base station.
## 3 Load Curve Clustering under DPVS Influence  
### Load Characteristics under DPVS Influence  
The load, due to its close association with residential and industrial activities, exhibits significant periodic characteristics in the field of electrical engineering. Additionally, the daily load scenario is significantly influenced by various factors such as the date features, temperature, and humidity. As shown in Figure 2, in regions with a large-scale integration of DPVS, the impact of solar power generation results in a pronounced dip in the load during sunny midday hours. The uncertainty in photovoltaic output increases the difficulty of load forecasting, as traditional deep learning methods struggle to identify the load curve fluctuations caused by photovoltaic generation. Further processing of input features is required to enhance the neural network's ability to recognize different influencing factors.
## 2 Materials and Methods  
The system designed in this work is composed of three different parts: first, a set of sensors is grouped into a single device, called a beacon. A central system then supports data storage and applies machine learning algorithms for data processing. Finally, a UAV collects the data from the beacon and transmits it to the central system.  
Figure 1 shows a scheme of the developed system. First, sensors take measurements of the considered variables. Once the data are retrieved, values are stored in the beacon. When the UAV connects to the beacon, the information is sent through LoRa communication technology. Finally, when the UAV returns home, all the information is moved to the central system, where it is permanently stored, and machine learning algorithms can be applied for data processing and classification.
# Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities
## 5 Conclusions  
This paper proposed a mobility support 5G architecture with real-time routing for sustainable smart cities. Its main factors are highlighted as follows:  
* It supports the delivery of online data with a high level of security and network continuity for mobile networks. Unlike other traditional approaches, it leads to few data delays and decreases the processing cost with the availability of higher bandwidth.
* It also secures the 5G ecosystem with a nominal risk rate and supports trustworthy communication. The mobile sink collaborates with both gateway nodes and medical storage centers to gather the IoT data, which explicitly increases the success rate of sensitive data with optimum delay.
* In addition, instead of only securing the boundary points for data routing, the proposed architecture performs risk analysis for the IoT nodes and links. The proposed architecture was tested and evaluated against existing work in terms of various experiments and it was seen to have significantly better performance. The multi-level security secures the routing for next-generation networks without imposing additional resource usage.  
However, it was observed that the proposed architecture still lacks intelligence in distributing the IoT data on established routes and leads to communication complexity when network nodes increase. Therefore, we aim to introduce some machine learning schemes to train the proposed architecture with nominal latency and support emergency communications.  
Conceptualization, A.R. and K.H.; methodology, A.R.; software, A.R.; validation, A.R., K.H. and J.L.; formal analysis, J.L.; investigation, T.S.; resources, T.S.; data curation, Z.A.; writing--original draft preparation, A.R.; writing--review and editing, T.S.; visualization, J.L.; supervision, J.L.; project administration, A.R.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.  
There is no funding for this research.  
Not applicable.  
All data is available in the manuscript.  
This research is technically supported by Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia. Authors are thankful for technical support.  
The authors declare no conflict of interest.
## 6 Experimental Results and Simulations
### Comparison with State-of-the-Art Approaches
It has been demonstrated several times in the literature that SCA's exploitation is its main strength, so the accuracy of results obtained in this work are due to the use of SCA in conjunction with WOA. Despite high exploitative, the algorithm perform well on high-dimensional data sets too, which are very challenging due to the large number of locally optimal solutions. This is due use of chaotic maps and different transfer functions what allow the proposed method to show diverse exploratory behaviours.
Sensors_**2011**, _11_, 4438-4461; doi:10.3390/s110404438  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Article  
1. **Abstract:** Mobile _Ad Hoc_ Networks (MANETs) are multihop wireless networks of mobile nodes without any fixed or preexisting infrastructure. The topology of these networks can change randomly due to the unpredictable mobility of nodes and their propagation characteristics. In most networks, including MANETs, each node needs a unique identifier to communicate. This work presents a distributed protocol for dynamic node IP address assignment in MANETs. Nodes of a MANET synchronize from time to time to maintain a record of IP address assignments in the entire network and detect any IP address leaks. The proposed stateful autoconfiguration scheme uses the OLSR proactive routing protocol for synchronization and guarantees unique IP addresses under a variety of network conditions, including message losses and network partitioning. Simulation results show that the protocol incurs low latency and communication overhead for IP address assignment.  
**Keywords:** Mobile _Ad Hoc_ Network (MANETs); IP address assignment; autoconfiguration; dynamic host configuration; stateful protocol; synchronization; OLSR proactive routing protocol
## 2 Authenticity Management in Normative Systems and Food Safety Standards
In the honey sector, particular challenges include the detection of blends with similar chemical profiles and the precise determination of geographical origin. These issues arise from similarities in pollen composition, chemical variability, and the complex matrix of honey, which can complicate data interpretation, even when using advanced techniques such as spectroscopy or DNA analysis [39; 47]. Moreover, many of these methods are costly, require specialized equipment and expertise, and are often inaccessible to small- and medium-sized enterprises (SMEs). The lack of formal accreditation (e.g., ISO/IEC 17025 [51]) and the need for extensive reference databases also pose significant barriers to the widespread adoption of these methods. An overview of these methods, along with their advantages, limitations, and certification requirements, is presented in Table 2. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method/Technique/Tool** & **Advantages** & **Limitations** & **Accreditation and Validation Requirements** \\ \hline \multirow{4}{*}{Pollen Analysis (Classical)} & Established, low-cost, allows assessment of botanical origin & Subjective, time-consuming, requires experienced specialists, prone to interpretative errors & Typically does not require certification, but validation is recommended \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and limitations of selected methods for honey authenticity assessment.
### Moderate Resolution Imaging (MODIS)
Mid- to high-level clouds are derived from radiance ratios of bands located within the broad 15 \(\upmu\)m CO\({}_{2}\) absorption region by applying the CO\({}_{2}\) slicing technique [53]. The theoretical basis of the CO\({}_{2}\) slicing technique is that as the wavelength increases from 13.3 to 15 \(\upmu\)m, the atmosphere becomes more opaque due to the absorption of CO\({}_{2}\), resulting in the radiation obtained from these spectral bands being sensitive to different layers in the atmosphere. CTP retrieval is derived from ratios of differences in radiances between cloudy and clear-sky regions at two nearby wave lengths [63]. Once the CTP is derived, it is converted to cloud-top height and temperature through gridded temperature profiles provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Differences between model-derived and measured clear-sky radiances are mitigated with a radiance bias adjustment to select the optimal CTP. [53]. Globally, when compared to the CALIOP 1-km products, MODIS underestimates the CTH relative to CALIOP by 1.4 \(\pm\) 2.9 km; when only high clouds above 5 km are considered, the differences are found to be greater than 4 km. Some differences in CTH between MODIS and CALIOP are expected since MODIS sees into the cloud to an optical thickness of approximately 1, while CALIOP senses the cloud top [35].
## 5 Deformation Control Measures  
### Determination of Deformation Control Scheme  
Due to its unique construction process, the top-down method significantly reduces the duration of road closure associated with construction activities and offers the advantage of rapidly restoring traffic flow. As discussed in Section 3, the top-down method demonstrates superior capabilities compared to the cut-and-cover method in terms of controlling foundation pit deformation and minimizing the impact of construction on the surrounding environment. In practical applications of the top-down method, the roof or floor slab of the main structure functions as a structure element that resists deformation. Analysis in Section 3.4 reveals that the roof or floor slab of the main structure experiences minimal deformation during construction, which effectively restricts the deformation of the diaphragm wall located above the floor slab. Given that this project features a maximum storey height of 9 m, there would inevitably be extended periods without support during the excavation of subsequent foundation pit layers. This condition could lead to the excessive deformation of the retaining structure beneath the foundation pit floor slab, as shown beneath in Figure 7a. Consequently, the deformation associated with the top-down method may not satisfy the requirements imposed in regions that necessitate strict deformation control. In this study, during the top-down construction process, each excavation height was reduced, and a construction method involving segmental pouring of the main structure, along with the addition of internal supports, was implemented, as shown in Figure 12. Plan 2, depicted on the left side of Figure 12, incorporates one steel support between each floor slab. Plan 3, presented on the right side of Figure 12, includes two steel supports between each floor slab. This method meets the functional requirements of structural deformation control while also taking economy into account. All internal supports utilize ordinary pre-stressed steel supports with specifications of \(\varphi\)609 mm \(\times\) 16 mm. They have a horizontal spacing of 3 m and a pre-stress of 300 kN. Comparing the new schemes with the original construction method facilitates a discussion of their advantages in controlling the deformation of the retaining structure, the vertical displacement of the ground surface, and the differential settlement between the diaphragm wall and the lattice columns.  
Figure 11: Final comparison diagram of bending moments of first floor and fourth floor of main structure for cut-and-cover and top-down methods of construction process.
# Experimental Study on Shear Strengthening of RC Beams with an FRP Grid-PCM Reinforcement Layer
## 3 Experimental Study on Reinforcement Amount
### Results and Discussion
#### 3.2.3 Load-strain (\(P\)-\(\varepsilon\)) Responses  
The load-strain (\(P\)-\(\varepsilon\)) responses for each specimen are presented in Figure 11. The values plotted in each case correspond the strain measured with strain gauges located at the positions indicated in the inserts of each panel, labelled as S for the cases with no CFRP reinforcement and G for the CFRP reinforced specimens. The S and G positions are given in respect to the bearing and the load application points. In panel (a), the results show that the ultimate stirrup strains of the control beam (i.e., N0') greatly exceed their yielding strain. For specimen RWHP, which is reinforced by PCM only, the stirrup strains increase gradually with the increase of the loading values but are not greater than the yielding strain at the end of the test. In addition, the \(P\)-\(\delta\) responses of the CFRP grids present similar behavior to those of stirrups; meanwhile, the stiffness curves are larger than those of the control beams before the stirrups reach their yielding strain, so it can be concluded that the synergistic effect between the grids and concrete is obvious. Therefore, the shear force is primarily resisted by the stirrups in combination with the CFRP grids. In terms of specimens RHWC4, RHWC6, and RHWC8 (panels a, b, and c, respectively), the corresponding ultimate strain of the CFRP grid increases with the reduction of the cross-sectional area. On the other hand, the ultimate strain climbs as the spacing of the vertical grids increases, which can be found through comparing specimens RHWC4 and RHWC4' (panels b and e, respectively), demonstrating that better deformation performance and CFRP grid efficiency are obtained when the reinforcement ratio of the CFRP grid is reduced. From this perspective, the conclusion can be drawn that delamination or debonding of the CFRP from the surrounding PCM (a second possible phenomenon) can be excluded, implying that local debonding occurred at the interface between FRP grids and the concrete substrate, rather than between the FRP grids and PCM. Similar results were also obtained by other studies [65; 66]. Overall, none of the FRP grids' strain reached rupture strain, as measured by uniaxial tensile tests when various load levels of strengthened beams arrived at ultimate states. In addition, the greatest number of cracks appear in RHWC4' due to the minimum spacing of the CFRP grid in comparison with the others. Furthermore, the partial grid strains abruptly and dramatically become small (the ultimate strain is equal to the debonding strain at this time), and it can be concluded that the debonding failure occurs at the interface between the CFRP grid and the concrete substrate prior to the rupture of the CFRP grid.  
Figure 10: Load–deflection (\(P\)–\(\delta\)) responses of Series B.
## 4 Discussion
### Stress Distributions  
The plot of the cumulative distribution of stress values highlights the fundamental difference between the LBM and PSM. The differences in the high-stress (above 1 MPa) range's distribution indicate that the application of patient-specific bone materials yields substantially different internal mechanical properties. The difference in the slope between the distribution curves is the largest for axial rotation, which agrees with the findings of [PERSON]. et al. [52], i.e., the stress distribution of finite element models is most sensitive to axial rotation. The analysis of the minimum principal strain values also concludes that the two models show significant differences in internal mechanical parameters under the same loading conditions. In the LBM model, the strain values cluster around a peak with only a few outliers, while in the PSM, no such strain peak is observed. The clustering effect explains why the LBM gave lower median values than the PSM; since investigating the outliers, one can see that the PSM contains bony tissues with higher compression. Based on these findings, using medical imaging-based bone material properties could be beneficial in biomechanical investigations where it is essential to analyze internal mechanical parameters,i.e., vertebral fracture risk or vertebral strength assessment [11; 17; 18]. These conclusions confirm the findings of [PERSON] et al., who found a good correlation between the fracture loads estimated by QCT-based FE models and in-vitro fracture loads [85]. Moreover, a biomechanical study by [PERSON] has shown how QCT combined FE simulations can estimate fracture outcomes in single vertebral models [86]. Analogously, literature-based linearly elastic homogeneous material models can only be used carefully in cases where bone material quality significantly influences the parameters to be investigated.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 5 System Limitations
### Tomographic Formations  
The previous analysis indicates that the main difficulty for using loose formations for a proficient SAR design comes from the narrowness of the orbital tube needed, as seen in the previous section. Indeed, lower frequency bands like S or L would lead to lesser prerequisites, but then the dimensions of the antennas would grow making less likely the hypothesis of nanosatellites. Still, the selection of S band for the nanosatellites CIRES [29] indicates that these solutions should not be discarded at all.  
Then, one could extremize the message coming from the previous considerations, and hint that formations of small satellites could best fit for tomographic applications, i.e., enhancing the role of the unwanted across track baseline, rather than reducing it at most. In other words, an interesting application for these formations could be derived from the availability of simultaneous multi-baseline data that would substantially abate the temporal decorrelation effects. In this view, the formation could be used for another purpose, renouncing to the extended swath and improved resolution, and exploiting the multeplicity of the satellites to analyze a layered medium, provided that the frequency used allows that penetration. Supposing that \(N\) receivers are available, \(N_{H}\leq N\) layered medium could be studied.  
Let the across track baseline be \(B_{ij}\), and \(D_{ij}\) the distance between the midpoint of the transmitter j and the receiver i and the formation center and with \(x_{h}\), \(H_{h}\) the complex amplitude of the return and the height of the \(h-th\) layer. \(n_{ij}\) is the noise.