esa-sceva/satcom-chunk-collection huggingface dataset:
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 3 Multi-Airport System for Cooperative Optimization of Flight Schedules
### Multi-Airport System for Cooperative Optimization of Flight Schedules  
To construct an optimization model that meets the actual operation of the multi-airport system, this study makes the following assumptions:  
**Assumption 1:**: _A slot is a period of time during which an aircraft may use airport facilities off-block or in-block. All time slots within the same airport are of the same length and are usually set at 5 min._  
**Assumption 2:**: _Airports have the same authority to adjust the timings of arrival/departure flights. Typically, an airport has more power to adjust departure times for departing flights. In this chapter, no distinction is made between arrival and departure flights, and the range of slot deviations depends on the constraints of the model._  
**Assumption 3:**: _Airlines have a maximum acceptable deviation for their requested slot; the maximum acceptable slot deviation is considered to be the same for all airlines, and it is assumed that the maximum deviation threshold can be adjusted based on different levels of resource constraints._  
**Assumption 4:**: _Aircraft transit time is the time interval between the start of an aircraft's parking in a parking position, acceptance of passenger departures, refueling, maintenance, passenger boarding, etc., and the time until takeoff again, which satisfies the minimum transit time._
## 5 Results and Discussions
### System 2: The IEEE 69-Bus
#### 5.2.2 Case 2: Simultaneous Minimization of APL and VD  
The simultaneous minimization of both APL (OF\({}_{1}\)) and VD (OF\({}_{2}\)) is considered in this case. Thus, as for the IEEE 33-bus system, the weighting factors in Equation (18) are taken as \(\omega_{1}=0.5=\omega_{2}~{}=~{}0.5,\omega_{3}=0\). The QODELFA is applied for three different values of power factor: Unity, 0.95 lag, and 0.82 lag. The results obtained by applying the proposed algorithm are outlined in Table 7.  
It may be remarked that, the APL and VD are reduced to 72.154 kW and 0.00150 p.u. by using QODELFA with a unity power factor DGs (Case 2.1); to 20.806 kW and 0.00014 p.u. with a 0.95 lag power factor DGs (Case 2.2); and to 4.302 kW and 0.00010 p.u. with a 0.82 lag power factor DGs (Case 2.3). By comparing the results of cases 2.1 and 2.2 to those from both methods in [28], it might be easily noticed that the solutions provided by the QODELFA have a better compromise than those of SIMBO-Q and QOSIMBO-Q considering both OFs. As also demonstrated in Table 7, significant enhancement of APL as well as VD is obtained when the power factor is set to 0.82 lag (Case 2.3). The small SD of the obtained solutions is listed in Table 7 as well. In addition, the fast convergence of the proposed algorithm to the optimal solution for Case 2.1, 2.2, and 2.3 is depicted in Figure 12.
## 4 Failure Mechanisms and Design Recommendations  
### Damage Observation  
During the PDTs, the specimen sustained slowly accumulative damage when subjected to the seven PGA levels. The initial cracks were observed on the bottom of column when PGA equaled to 0.20 g. During the same PGA condition but later loading steps (311 th step), new cracks formed firstly on the lower part of corbel. When PGA increased to 0.60 g, new cracks were observed on the upper part of corbel. In general, cracks mainly concentrated on the columns while no damage was observed on the steel truss, A-type bracket and diagonal braces during the PDTs. And with increase of PGA, cracks formed and developed on the bottom of column, lower part of corbel and upper part of corbel gradually.  
During the following QSTs, cracks on the columns further propagated and extended. Specim yielded when the amplitude of control displacement added up to 50 mm. Spalling of concrete was observed on the base of column when the amplitude of control displacement equaled to 110 mm. When the amplitude of control displacement increased to 130 mm, the concrete at the base of column crushed severely and the welding slag of truss-to-column connection began to spall, as shown in Figure 15. Finally, after the first displacement cycle at the amplitude of 190 mm, the lateral load decreased to about 85% of the peak load, test was terminated. Figure 16 shows the crack distribution patterns of column after the tests. It was found that the cracks mainly occurred on the bottom of the column and lower part of the corbel. Besides, some cracks were observed on the upper part of the corbel.  
Figure 16: Cracks distribution patterns of column: (**a**) upper part of column and (**b**) lower part of column.  
Figure 15: Local failure modes of the specimen: (**a**) Crushing of concrete on the base column and (**b**) cracking of weld joint on the truss-to-column connection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Yield Drift Ratio (\%)** & **Yield Load (kN)** & **Peak Load (kN)** & **Peak Drift Ratio (\%)** & **Ultimate Drift Ratio (\%)** & **Ductility Factor** \\ \hline
0.65 & 170.65 & 223,71 & 1.61 & 2.33 & 3.60 \\ \hline \end{tabular}
\end{table}
Table 5: Summary of QST results.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results
### Aggregated Throughput Verses Different UEs  
Figure 7 shows the aggregated throughput for various applications. As the result, the proposed method does not show better performance inside of aggregated throughput compared with the others because in non-real time applications, the change in the weight does not have any impact. The choice of data flow is improved according to channel quality indicator (CQI) with low traffic demand. Since video transmission depends on both QCI and CQI, changes in \(\alpha\) value lead to variation in the video transmission. Figure 7 shows the throughput performance for various applications by varying the value of \(\alpha\). Improvements of the proposed method will be further investigated in future works.  
Figure 6: Packet loss ratio versus different UEs.  
Figure 7: Aggregated throughput versus different UEs.
## 2 Numerical Approach  
### Engineering Context  
The Nantong Haitai River-Crossing Project in Jiangsu Province, China was chosen for analysis; its total length was 11,360 m, including a shield section of 9250 m. The cross-section of the shield tunnel is illustrated in Figure 2. It possessed an outer diameter of 14.5 m and an inner diameter of 12.7 m, categorizing it as an ultra-large-diameter underwater shield tunnel. The thicknesses of the segment and the secondary lining were 600 mm and 300 mm, respectively. Figure 3 provides an overview of the geological conditions along the longitudinal axis of the tunnel. As delineated in the figure, the tunnel traversed the expanse of the Yangtze River, with a tunnel segment extending for a distance of 7200 m beneath the water surface. The maximum depth of the river reached 24.5 m. At the midpoint of the river crossing, the tunnel structure encountered the highest hydraulic head, measuring at 71.63 m, while the maximum hydraulic pressure was documented at 0.72 MPa. These conditions imposed substantial demands on the tunnel's waterproofing measures. Moreover, the tunnel was located within an intricate geological setting. It traversed a range of soil layers, encompassing silty clay, silty fine sand, and muddy silty clay, with significant variations in the mechanical properties of these soil layers. Along the tunnel's trajectory, there were marked fluctuations in both overburden thickness and water pressure, initially increasing and subsequently decreasing. These factors had the potential to induce non-uniform longitudinal deformations within the tunnel, thereby jeopardizing its safety. Consequently, this river-crossing shield tunnel project holds considerable significance as a point of reference for the investigation of the longitudinal mechanical characteristics of a double-layered shield tunnel.
## 4 Performance Results
As in Figure 6, due to the existence of interference that is not cancelled by the SIC (lower power user, i.e., the one with power 0.5), the performance obtained with conventional NOMA is quite poor, as it contaminates the correct estimates of the high-power users. Moreover, due to the residual interference, results obtained with the iterative MRC receiver are worse than those obtained with the ZF. Nevertheless, when comparing Cooperative NOMA against conventional NOMA, we observe a performance gain, due to its ability to cancel all interfering users (not only those with higher power) and due to the existence of diversity. However, in this case, the interfering user, with lower power level, retransmits a copy of the signal of the reference user (clean of interference), which is combined with the one received directly. Once again and considering Cooperative NOMA, the MRC receiver outperforms the ZF. It is worth noting that Cooperative NOMA brings added value for users that present higher power levels, which corresponds typically to users further from the base station. Figure 9 shows the performance results for conventional NOMA against those of Cooperative NOMA, for the situation of five users sharing the spectrum using NOMA, with powers [2 1 0.5 4 8] (2 stands for the power of the reference user, while 1, 0.5, 4 and 8 stands for the power of the interfering users). Due to the higher number of interfering NOMA users and due to the existence of two lower power interfering users that are not cancelled by the SIC of Conventional NOMA, such performance is limited due to a noise floor. As before, in this scenario, the MRC is not able to achieve an acceptable performance, presenting lower performance than the ZF. Nevertheless, when observing the results of Cooperative NOMA, a high-performance improvement is registered as compared with Conventional NOMA and the iterative MRC receiver similar to the ZF receiver. Figure 8: Results for 4 NOMA users with powers [1 0.5 2 4], with 4 \(\times\) 32 MIMO. Figure 10 shows results in the same scenario as those of Figure 9, making a comparison between 4 \(\times\) 128 MIMO against 4X32 MIMO, with 5 NOMA users with received power levels [2 1 0.5 4 8] (2 is the received power of the reference user). As expected, due to higher level of diversity, 4 \(\times\) 128 MIMO outperforms 4 \(\times\) 32 MIMO. Therefore, the use of higher MIMO diversity can be viewed as a mechanism to mitigate the degradation of performance that results from a higher number of NOMA users. In other words, the increase of NOMA users can be compensated by employing a higher number of MIMO receiving antennas. Figure 10: Results for 5 NOMA users with powers [2 1 0.5 4 8], with 4 \(\times\) 128 versus 4 \(\times\) 32 MIMO. Figure 9: Results for 5 NOMA users with powers [2 1 0.5 4 8], with 4 \(\times\) 32 MIMO.
# Hybrid Improved PSO Algorithm for Soil Property Parameter Estimation
## 3 Improvement of the PSO Algorithm  
### Traditional PSO Algorithm  
Particle Swarm Optimization (PSO) [17] is an evolutionary computation technique proposed by Dr. [PERSON] and Dr. [PERSON] in 1995. The algorithm has garnered significant attention for its simplicity and minimal control parameters. The idea of PSO is inspired by the foraging behavior of bird flocks, where the birds are abstracted into particles. Each particle's position represents a solution, and the process of solving the problem within the search space simulates the way birds share information to find the best food source.  
Assume the search space of the particle swarm has \(D\) dimensions, and the total number of particles is \(N\). The position of the \(i^{th}\) particle is defined as follows:  
\[X_{i}=[X_{i1},X_{i2},X_{i3},\dots,X_{iD}],\]  
and its velocity as follows:  
\[V_{i}=[V_{i1},V_{i2},V_{i3},\dots,V_{iD}].\]The current position of the \(i^{th}\) particle is represented by:  
\[P_{i}=[P_{i1},P_{i2},P_{i3},\ldots\ldots,P_{iD}].\]  
The PSO algorithm begins by randomly initializing the position \(X_{i}\) and velocity \(V_{i}\) for each particle. At each iteration, the historical best position of each particle is updated as follows:  
\[\mathit{Pbest}_{i}=[\mathit{Pbest}_{i1},\mathit{Pbest}_{i2},\mathit{Pbest}_{i3 },\ldots\ldots,\mathit{Pbest}_{iD}],\]  
while the global best position of the entire swarm is as follows:  
\[\mathit{Gbest}_{i}=[\mathit{Gbest}_{i1},\mathit{Gbest}_{i2},\mathit{Gbest}_{ i3},\ldots\ldots,\mathit{Gbest}_{iD}],\]  
The position and velocity of each particle are then updated using the following equations:  
\[\left\{\begin{matrix}V_{ij}^{k+1}=\omega V_{ij}^{k}+c_{1}r_{1}\left(P_{ij}-X _{ij}^{k}\right)+c_{2}r_{2}\left(G_{ij}-X_{ij}^{k}\right)\\ X_{ij}^{k+1}=X_{ij}^{k}+V_{ij}^{k+1}\end{matrix}\right. \tag{17}\]  
where \(k\) denotes the current iteration, \(\omega\) is the inertia weight, \(c_{1}\) and \(c_{2}\) are acceleration coefficients, and \(r_{1}\) and \(r_{2}\) are random numbers uniformly distributed in \([0,1]\).
# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
###### Abstract  
Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.  
cultural heritage; crowdsourcing; recommendation system; word embeddings; ontology; classification
## 2 Materials and Methods
### Spectrophotometric Determination of Antioxidant Activities of Alkaloid Extracts  
2.4.1. Metal Ions Chelating Activity  
Alkaloid extracts of varying concentrations (0, 50, 100, 150, 200, and 250 ug/mL) were mixed with 0.05 mL of 2 mM iron(II) chloride (FeCl\({}_{2}\)) (Sigma-Aldrich) and 0.1 mL of 5 mM ferrozine (Sigma-Aldrich). To adjust the total volume, 2 mL of solvent was added. After vigorous mixing, the solution was allowed to rest at room temperature for ten minutes. Post-incubation, the absorbance of the samples was determined at 562 nm using a microplate reader, compared against blind samples. The ability of the samples to inhibit the formation of the ferrozine-Fe\({}^{2+}\) complex was assessed using the following equation: inhibition (%) = [(A\({}_{\text{control}}-A_{\text{sample}}\))/A\({}_{\text{control}}\)] \(\times\) 100. Here, A\({}_{\text{control}}\) represents the absorbance of the Ferrozine-Fe\({}^{2+}\) complex and A\({}_{\text{sample}}\) represents the absorbance of the test compound. Ethylenediaminetetra-acetic acid (EDTA)-sodium (Na) (0.5 mg/mL) (Merck) was used as a reference control [29; 30].  
2.4.2. 2,2-Diphenyl-1 Picrylhydrazil (DPPH) Radical Scavenging Activity  
Alkaloid extract solutions of varying concentrations (0, 50, 100, 150, 200, and 250 ug/mL) were combined with a 0.004% DPPH (Sigma-Aldrich) solution and allowed to sit at room temperature for 30 min. After this incubation period, the absorbance of the samples was measured at 517 nm using a microplate reader, with reference to blind samples. The capability of the samples to inhibit the DPPH radical formation was calculated using the following equation: inhibition (%) = [(A\({}_{\text{control}}-A_{\text{sample}}\))/A\({}_{\text{control}}\)] \(\times\) 100. In this equation, A\({}_{\text{control}}\) represents the absorbance when all reagents except the test compound are present, and A\({}_{\text{sample}}\) indicates the absorbance of the test compound itself. Ascorbic acid (0.5 mg/mL) (Sigma-Aldrich) was used as the benchmark control [31].
## 2 Related Works  
### Federated Learning  
In traditional centralized machine learning methodologies, all data generated by various devices or institutions must be uploaded to a central server or cloud, which can result in significant privacy concerns. To address these privacy concerns, federated learning, which is a paradigm that enables AI to be trained across devices or institutions without the direct exchange of data, has been proposed. Rather than transmitting raw data, each client exchanges model parameters or other model-associated information, thereby protecting the privacy of the user data [10].  
Federated learning was initially introduced in 2016 [11] and has emerged as an efficacious approach for collaboratively training machine learning models utilizing distributed resources [3]. As data privacy has become a paramount concern in AI research, federated learning has evolved into a critical domain of research and has been implemented in various fields, such as medical image segmentation, object detection, and medical image classification [5]. In federated learning, a global model is constructed based on local model parameters from each client, which are subsequently redistributed to the clients. Each client employs a deployed model to make predictions, and newly acquired data from the client is used to retrain the model. The updated model parameters are sent back to the central server, which employs them to generate a new global model. This iterative process was repeated, facilitating the gradual enhancement of the global model through continuous refinement.
### Prior Work
However, despite the remarkable capabilities of PD-NOMA, only a few studies has been performed to address this issue with clustering methods to achieve improved user association, system capacity, and sum rate in ultra-dense heterogeneous networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Contribution** & **Sum Rate** & **Capacity** & **Clustering** & **Ultra-Dense** & **User** \\
**Work** & & & & & **HetNet** & **Association** \\ \hline \multirow{4}{*}{[11]} & Proposed a unified NOMA framework & & & & \\  & for user association and resource & & & & \\  & allocation to achieve massive & & & & \\  & connectivity. & & & & \\ \hline \multirow{4}{*}{[12]} & Energy efficiency maximization is & & & & \\  & achieved by proposing a joint & & & & \\  & optimization framework of PD-NOMA & & & & \\  & and beam forming. & & & & \\ \hline \multirow{4}{*}{[17]} & System level performance analysis with & & & & \\  & hybrid PD-NOMA and OFDMA & & ✓ & ✓ & \\  & schemes. & & & & \\ \hline \multirow{4}{*}{[24]} & PD-NOMA, game theory algorithms are & & & & \\  & proposed based on QoS threshold to & & & & \\  & improve user association and power & & & & \\  & allocation. & & & & \\ \hline \multirow{4}{*}{[22]} & Proposed a cluster specific & & & & \\  & beam-forming algorithm to maximize & & & & \\ \cline{1-1}  & sum-throughput. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing literature performed with PD-NOMA.
## 2 Numerical Analysis Model  
The rule of mixture (ROM) is a well-established analytical method for evaluating the elastic properties of materials. The following equation is utilized to evaluate elastic properties using the ROM [23; 24]. There are two methods of determining the mechanical properties of composite materials: using the weight fraction (W%) or volume fraction (VF%) of the reinforcement and the matrix for the composite material. The ROM calculation is performed based on the volume fraction of the components of the composite. In practical applications at industrial sites, the weight fraction method is easier because most manufacturing equipment for composite materials employs a gravimetric system for material supply. However, to perform numerical analysis for the elastic properties of the reinforcement and matrix, the volume fraction is used for calculation. The equations for calculation of the elastic properties are as follows:  
\[E_{c}=E_{R}\times V_{R}+E_{M}\times V_{M} \tag{1}\]  
\[V_{R}+V_{M}=1 \tag{2}\]  
\(E_{C}\): : prediction modulus of elasticity of composite material; \(E_{R}\): : modulus of elasticity of reinforcement material; \(V_{R}\): : volume fraction of the reinforcement; \(E_{M}\): : modulus of elasticity of matrix material; \(V_{M}\): : volume fraction of the matrix.  
In addition, numerous effective medium approximations have been developed to predict the effective elastic moduli of two-component composite materials consisting of particles embedded in a matrix [25]. [PERSON] used the volume fraction of reinforcement and developed an effective medium approximation for the predicted modulus of elasticity (\(E_{c}\)) of a two-component mixed composite material based on the moduli of elasticity of the reinforcement and matrix.  
\[E_{c}=E_{M}\bigg{[}1+\frac{2V_{R}(E_{R}-E_{M})}{(E_{R}+E_{M})-V_{R}(E_{R}-E_{ M})}\bigg{]} \tag{3}\]  
In this study, the results of the two numerical analysis methods described above were compared with the FEA results.
## 3 Experimental Results and Analysis
### Verification
The first reason is the selection of the HV-night model coefficient. Since there is no real-time high-resolution data, only the meteorological data of that day were used to estimate the wind speed, near-surface turbulence intensity, and boundary layer turbulence intensity, resulting in the difference between the model and the actual detection results. Moreover, due to the overlap of the field of view, the turbulence intensity in the low-altitude range was different from the theoretical value. Another reason is that the turbulence has internal and external scale changes, but the change in the internal and external scales with height was not considered in the inversion process. It is assumed that the inner scale is 0 and the outer scale is infinite for the entire detection path, thus resulting in measurement errors. The \(C_{n}^{2}\) profiles of Kolmogorov turbulence and the \(\widetilde{C}_{n}^{2}\) profiles of non-Kolmogorov turbulence obtained by inversion are not entirely accurate. Figure 13: Comparison of atmospheric turbulence profiles detected by Mie-scattering lidar and the HV-night model. However, in general, the detected atmospheric turbulence intensity profile is basically consistent with the trend of the HV-night model, and the detection results are distributed near the theoretical value, indicating that it is feasible for the \(C_{n}^{2}\) profile and the \(\bar{C}_{n}^{2}\) profile to be detected by Mie-scattering lidar.