esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description  
The following equation can express the problem of optimizing the multi-objective of the antenna:  
\[\left\{\begin{array}{l}\min F(x)=[f_{1}(x),f_{2}(x),\ldots,f_{n}(x)]^{\text{ T}}\\ \text{s.t.}\ x\in X\\ X\subseteq R^{m}\end{array}\right. \tag{1}\]  
In the equation, a design variable \(x=[x_{1},x_{2},\ldots,x_{m}]^{\text{T}}\in X\subseteq R^{m}\) is a group of determined vectors. It represents \(m\) structural sizes of the antenna that need to be processed in this paper. \(X\) is the space of parameters that is decided by the boundary of the structural parameters of the antenna. \(f_{j}(x)\), \((j=1,2,\ldots,n)\) are \(n\) objective functions to be optimized. The main objective of antenna design is to reduce the reflection coefficient of a specific frequency band. Still, other indicators of the antenna have high demands in many cases. The different indicators will have clashed, making it difficult to reach the optimal solution simultaneously. At this time, we demand an answer in the middle to solve the problem, which is called the non-dominated solution set. All solutions in the non-dominated solution set constitute the Pareto front (PF) [32]. In this paper, PF is the solution we need to get. PF represents the intermediate answer to the multi-objective antenna, and we can design the antenna according to it.
# A Review of Research on Secure Aggregation for Federated Learning
## 5 Challenges and Future Directions
### Security  
In federated learning, three primary areas are susceptible to attacks, namely, input data, the aggregation process, and the aggregated model. While federated learning does not collect raw user data, other types of attacks can still hinder its deployment. For instance, category inference attacks [141] and poisoning attacks [142; 143] target data categories by either inferring original data through model access or degrading model performance for specific categories. Consequently, improving the security of secure aggregation algorithms remains an important area of research.  
Poisoning attacks degrade the performance of learned models by introducing noise into the federated learning system. To counter malicious client-side poisoning, model detection algorithms are employed to identify and filter out compromised models. For server-side poisoning, clients must verify the integrity of the aggregated model. In this context, integrating blockchain technology and verifiable secret sharing algorithms into secure aggregation methods represents a promising research direction.  
Inference attacks compromise privacy by recovering user data through the inference of model updates. While existing secure aggregation techniques protect local models to prevent such attacks, the global model remains exposed and susceptible. Protecting the global model, however, incurs significant computational and communication costs. Balancing secure aggregation with model utility thus poses a critical challenge.  
Although robust solutions for poisoning attacks have been developed [133; 132], and differential privacy techniques have been applied to mitigate inference attacks, federated learning systems still face unknown security vulnerabilities. Therefore, combining secure aggregation techniques with other privacy-preserving methods to enhance security continues to be a valuable research topic.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 7 Assessment of Measurement Uncertainties  
Using the methodology described in Section 4, differences between SLSTR-A and B measurements are compared to the modeled per-pixel uncertainties. For this purpose, we generated histograms of the differences normalized by the estimated uncertainty on the difference. The technique is applied to level 1 (Figure 8) and level 2 sea-surface temperature (SST, Figure 9) measurements. The statistics provide useful insights into the reliability of the uncertainties, the influence of which on the SLSTR view is used, and the consistency of the SSTs between sensors dependent on retrieval algorithm is used.
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
## 5 Planar Phased Array Design of the 26 GHz Quasi-Yagi Antenna  
The designed 26 GHz Quasi-Yagi antenna can also be used in a planar array form for 5G base station or channel measurement applications [33; 34]. Figure 19 illustrates the structures and the directional radiation beams of the designed planar phased array.  
As shown, 64 elements of 26 GHz Quasi-Yagi radiators are employed to form the proposed 8\(\times\)8 planar antenna array design. In other words, the planar array has been designed using eight rows of the linear phased array described in Section 3. The proposed planar array antenna works in the same operation band of the single element Quasi-Yagi (frequency range from 25 to 27 GHz). It is seen that the designed planar array has a compact size, high gain, sufficient efficiency, and beam-steering properties.  
In addition, the characteristics of the 26 GHz planar Quasi-Yagi antenna array with different numbers of the radiators (2 \(\times\) 2, 4 \(\times\) 4, and 8 \(\times\) 8) are studied in Figure 20. As shown in Figure 20a,  
Figure 19: Configuration and 3D beams of the Quasi-Yagi phased array at different angles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Param./Angle** & **0\({}^{\circ}\)** & **15\({}^{\circ}\)** & **30\({}^{\circ}\)** & **45\({}^{\circ}\)** & **60\({}^{\circ}\)** & **75\({}^{\circ}\)** \\ \hline Rad. Effic. (dB) & \(-1.52\) & \(-1.56\) & \(-1.78\) & \(-2.11\) & \(-2.56\) & \(-2.83\) \\ Tot. Effic. (dB) & \(-2.03\) & \(-2.08\) & \(-2.25\) & \(-2.78\) & \(-2.64\) & \(-4.5\) \\ Directivity (dBi) & 11.8 & 12.1 & 12 & 11.92 & 11.6 & 10.8 \\ Rlzd. Gain (dB) & 9.8 & 10 & 9.77 & 9.14 & 8.12 & 6.4 \\ \hline \end{tabular}
\end{table}
Table 3: The properties of the 5G antenna with the user’s hands at different scanning angles.  
Figure 18: SAR analysis: (**a**) top and (**b**) bottom placements of the 5G smartphone phased array antenna.  
the designed planar arrays exhibit directional radiation beams with high gain levels. Figure 20b,c illustrates the reflection coefficient and mutual coupling (S\({}_{\mathrm{nn}}\) and S\({}_{\mathrm{nm}}\)) characteristics of the middle elements for the planar arrays. As shown, similar performances of the S parameters with high isolation and low mutual coupling have been obtained for them. Reflection coefficients (S\({}_{\mathrm{nn}}\)) of \(-35\), \(-48\), and \(-43\) dB have been achieved for \(2\times 2\), \(4\times 4\), and \(8\times 8\) planar arrays. Furthermore, the designed Quasi-Yagi antenna arrays provide a low mutual coupling characteristic (less than \(-13\) dB at 26 GHz).
# Isoprene Emissions, Oxidation Chemistry and Environmental Impacts
## 6 Measurements of Isoprene and Model Validation
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Location** & **Time** & **Lat, Lon** & **Measurement Technique** & **Measured Isoprene** & **Modeled Isoprene** \\  & & & & **Mixing Ratio (ppb)** & **Mixing Ratio (ppb)** \\ \hline Peru (\(-2\) m) [122] & Jul & \(-5,-77\) & GC-MS/FID & 3.3 & 0.83 \\ Peru (\(91\) m-\(1.2\) km) [122] & Jul & \(-5,-77\) & GC-MS/FID & 1.4 & 0.48 \\ Venezuela, Baja [123] & Sep & \(9,-62\) & GC-FID & 3.3 & 0.96 \\ Venezuela, Ayanutepuy [123] & Apr & \(6,-62\) & GC-FID & 1.6 & 0.83 \\ Venezuela, Calaboo [124] & Sep-Oct & \(9,-67\) & PTR-MS & 0.8 (0.2-3.1) & 1.40 \\ Venezuela, Calaboo [124] & Mar-Apr & \(9,-67\) & PTR-MS & 0.5 (0.1-1) & 0.38 \\ Venezuela, Parupa [124] & Jan-Feb & \(6,-62\) & PTR-MS & 0.5 (0.1-1.2) & 0.48 \\ \hline Surinam (PRL) [125] & Mar & \(2\) to \(7,-54\) to \(-58\) & PTR-MS & 3.5 (0.5-7) & 0.87 \\ Australia, Sydney [126] & Feb-Mar & \(-34,151\) & PTR-MS & 0.76(0.09-7.1) & 0.15 \\ Australia, Sydney [126] & Apr-May & \(-34,151\) & PTR-MS & 0.63(0.01-4.63) & 0.12 \\ Australia, Wellington [126] & Dec-Feb & \(-35,151\) & PTR-MS & 0.28 (0.002-4.6) & 0.14 \\ Australia, New South Wales [126] & Nov & \(-36,148\) & PTR-MS & 0.15 (0.02-1.01) & 0.09 \\ \hline Borneo, Malaysia \(-426\) m [127] & Apr-May and Jun-Jul & \(4,117\) & GC-FID & \(10.60\pm 05.42\) & 0.66 \\ \hline Borneo, Malaysia \(-426\) m [128] & Jun-Jul & \(4,117\) & PTR-MS & \(1.4\pm 1.2\) & 0.42 \\ \hline Jimei District, Xiamen City, China [129] & Aug & \(24,118\) & GC-MS/FID & \(0.66\pm 0.50\) & 0.10 \\ \hline Jimei District, Xiamen City, China [129] & Sep & \(24,118\) & GC-MS/FID & \(0.14\pm 0.21\) & 0.09 \\ \hline Shanghai, China [130] & Apr-Sep & \(31,121\) & UV-DOAS & \(0.22\pm 0.20\) & 0.02 \\ \hline Nanjing, China \(-1690\) m [131] & Jul-Aug & \(24,112\) & GC-MS & \(0.38\pm 0.05\) & 0.01 \\ \hline Nanjing, China \(-1690\) m [131] & Jul-Aug & \(24,112\) & GC-MS & \(0.29\pm 0.03\) \({}^{*}\) & 0.01 \\ \hline Shenzhen, China [132] & Aug-Sep & \(22,113\) & PTR-MS & 0.75 & 0.51 \\ \hline Taipei, China [133] & Jul-Aug & \(25,121\) & GC-MS/FID & 1.26 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Impact of Vehicle Speed on V2X System Model  
Scenarios S2 and S5 are based on the NLOSv channel state and different traffic scenarios. These two scenarios are the best representatives of examining the impact of vehicle speed on the system performance, since both scenarios have common small-scale characteristics, except for the Doppler effect, which is affected by vehicle speed. In these two scenarios, the vehicle speed increases from 50 to 100 km/h, i.e., it doubles. In Figure 2(a), we show the mean percentage FER increase when the vehicle speed changes from 50 to 100 km/h. For our calculations, we took into account only the SOVA algorithm, considering it as the most representative of all, since it is the only one that has a non-zero FER in the entire range of SNRs between 0 and 10 dB. From Figure 2(a), our hypothesis that stronger Doppler effects create a worse channel, and therefore a worse FER, becomes apparent, and this information is also quantified since we can conclude that FER is worse at 100 km/h than at 50 km/h by 12.4% on average. From Figure 2(b),e, we can also observe that the FER difference gradually rises as the SNR increases. This phenomenon is analyzed in [37], where the authors determined that at the low SNR region, the influence of the Doppler effect is small since the bad large-scale features suppress the system performance. As the SNR becomes higher, the channel performance improves, and the small-scale differences intensify the FER difference between the two scenarios.
# Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms
###### Abstract  
Currently, fraud detection is employed in numerous domains, including banking, finance, insurance, government organizations, law enforcement, and so on. The amount of fraud attempts has recently grown significantly, making fraud detection critical when it comes to protecting your personal information or sensitive data. There are several forms of fraud issues, such as stolen credit cards, forged checks, deceptive accounting practices, card-not-present fraud (CNP), and so on. This article introduces the credit card-not-present fraud detection and prevention (CCFDP) method for dealing with CNP fraud utilizing big data analytics. In order to deal with suspicious behavior, the proposed CCFDP includes two steps: the fraud detection Process (FDP) and the fraud prevention process (FPP). The FDP examines the system to detect harmful behavior, after which the FPP assists in preventing malicious activity. Five cutting-edge methods are used in the FDP step: random undersampling (RU), t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), singular value decomposition (SVD), and logistic regression learning (LRL). For conducting experiments, the FDP needs to balance the dataset. In order to overcome this issue, Random Undersampling is used. Furthermore, in order to better data presentation, FDP must lower the dimensionality characteristics. This procedure employs the t-SNE, PCA, and SVD algorithms, resulting in a speedier data training process and improved accuracy. The logistic regression learning (LRL) model is used by the FPP to evaluate the success and failure probability of CNP fraud. Python is used to implement the suggested CCFDP mechanism. We validate the efficacy of the hypothesized CCFDP mechanism based on the testing results.  
fraud detection; fraud prevention; Big data analysis; t-SNE; PCA; SVD; LRL; RU; CNP
# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 4 Challenges Still Exist in the Field of Clustering Algorithms
### Missing Values  
During the process of collecting data from a wide variety of sources, including sensors, digital devices, machines, and people, these sources produce significant volumes of data in a very short period of time. However, collecting data is not always a simple operation, and in certain cases it might result in values being absent from the data [91]. Incomplete or missing data may obscure the true answers that lie under the surface. They are also capable of hindering the algorithms' overall performance.
# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
###### Abstract  
Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.  
cross-project defect prediction; manifold feature transformation; naive Bayes prediction model; F1 +
Footnote †: journal: Future Internet
## 3 Proposed Approaches
### Spectrum Attention Mechanism
#### 3.2.2 Define Frequency Domain Attention Calculation  
In the frequency range of primary focus (49.9 Hz-50.1 Hz), a linear transformation is applied to map the frequency domain signal to the attention space and calculate the attention weight for each frequency component. The specific formula is presented in (6), where \(W_{1}\) and \(W_{2}\) denote the weight matrices of the linear transformation, \(b_{1}\) and \(b_{2}\) represent the bias vectors, \(tanh\) serves as the activation function, and softmax is utilized for normalizing the weights. Subsequently, inputting the frequency domain signal, \(X_{f}\), yields the frequency domain attention weight matrix, \(A\).  
\[A=softmax\Big{(}W_{2}\cdot tanh\Big{(}W_{1}\cdot X_{f}+b_{1}\Big{)}+b_{2} \Big{)}\qquad\quad 49.9\;\mathrm{Hz}\leq x_{f}\leq 50.1\;\mathrm{Hz} \tag{6}\]
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 5 SDN-eINP Interplay for Application-Aware Network Traffic Management
### Control Plane
Then, the traffic flow steering takes place accordingly. Similarly, our traffic flow steering may affect multiple NDs in redirecting flows while considering their priority along the path.
## 4 Parametric Studies
### Pile Length  
The bearing capacity of the foundation is related to the imposed load on the base, which is proportional to the pile length (i.e., the thickness of the subsoil). Therefore, an increase in pile length signifies an enhancement in the bearing capacity of the foundation, and its effect on soil arching will not be repeated. [PERSON] and [PERSON] [44] have found that floating piles rely on end resistance and skin friction to sustain the embankment load. When the settlement of the subsoil exceeds that of the pile, negative skin friction occurs, whereas  
Figure 17: Variation in settlement with different properties of embankment fill: (**a**) cohesion; (**b**) friction angle.  
positive skin friction occurs when the settlement of the pile exceeds that of the surrounding soil. Changes in pile length led to variations in the distribution of end resistance and skin friction, thereby influencing the development of soil arching.  
Figures 18 and 19 show the distribution of pile axial forces and skin friction along the pile for different pile lengths. It has been found that the 12 m pile exhibits the highest skin friction, which is 56% greater than that of the 16 m pile and three times that of the 20 m pile. As soil consolidation progresses, the skin friction for three lengths of piles gradually increases. The neutral plane (where skin friction is zero) gradually moves downward. After consolidation, the neutral plane for the 12 m pile is located at a depth of 5 m, while for the 16 m pile, it reaches a depth of 9 m. For the 20 m pile, positive skin friction is almost negligible along the entire pile length, and the neutral plane appears at the pile tip when soil consolidation is accomplished.  
The axial force increases with soil consolidation for three piles, as shown in Figure 19. It exhibits a trend of initially increasing and then decreasing along the pile length, reaching its maximum value at the neutral plane. The 12 m pile exhibits the lowest axial force, being only 25% of that of the 20 m pile. It has been observed that rigid floating piles rely on end resistance and skin friction to sustain the superimposed load. Moreover, as the pile  
Figure 19: Axial force distribution along the pile with time for different lengths of the pile: (a) 12 m; (b) 16 m; (c) 20 m.  
Figure 18: Skin friction distributions with depth for different lengths of the pile: (a) 12 m; (b) 16 m; (c) 20 m.  
length increases, the skin friction decreases continuously while the axial force on the pile gradually increases.
# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
## 4 Application of Modal Tracking
### Analysis of Modal Tracking Results  
Based on modal tracking results, researchers have conducted extensive investigations into structural environmental dependencies [134; 135; 136; 137] and damage assessment frameworks [138], with particular emphasis on establishing temperature-frequency relationship models [139; 140; 141]. Most studies employ correlation analysis to identify dominant influencing factors, followed by environmental effect decoupling through regression modeling [142], principal component analysis [143], or artificial neural networks [144]. The compensated modal evolution can be subsequently utilized for structural state evaluation and damage detection [145; 146; 147]. Empirical evidence indicates that the dynamic characteristics of bridges generally exhibit pronounced sensitivity to environmental variables (e.g., temperature and wind speed), with marked seasonal frequency fluctuations [61; 148]. For instance, the Sutong Bridge manifests annual variation rates of 4.47% for its first vertical modal frequency and 8.32% for the fourth vertical mode [120]. Such environmentally driven variations may obscure frequency shifts attributable to structural damage, material deterioration, or other anomalies [149]. As summarized by [PERSON] et al. [91], (i) under invariant boundary conditions, temperature dominates frequency fluctuations during bridge operation and (ii) bridges' natural frequencies demonstrate a negative correlation with temperature. Notably, this empirical pattern is not universally valid. For example, the Voigt Bridge case study demonstrates positive correlations between three targeted natural frequencies and ambient temperature variations during the summer [150]. In contrast to bridge structures, wind loads, particularly typhoon effects, dominate the dynamic characteristics of super high-rise buildings with low stiffness and damping [151; 152; 153; 154; 155], whereas temperature exhibits negligible influence on their natural frequencies [127; 156]. This phenomenon may be attributed to two aspects: (i) the boundary conditions of super high-rise structures resemble those of cantilever columns, facilitating the prompt release of thermal stress, and (ii) curtain wall systems separate the interior and exterior environments of a skyscraper, thereby constraining the temperature impacts on the core tube [31]. Additionally, the non-destructive nature of modern SHM techniques has promoted the application of modal tracking in historical heritage construction (generally masonry structures) [157; 158]. Current implementations focus on three critical aspects: damage feature extraction [159; 160], environmental effect elimination [161], and post-seismic assessment [162; 163]. Particularly, the monitoring system of Milan Cathedral exemplifies advanced implementation in heritage construction due to its comprehensive sensor network [121]. Modal tracking also extends to other fields, such as dam engineering [112] and wind turbines [132].