esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Demonstration of Terahertz-Wave Signal Generation for 6G Communication Systems
###### Abstract  
Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates--on the order of terabits per second--while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of \(3.8~{}\times~{}10^{-3}\). To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb.  
6G; optical frequency comb generator; photonic technologies; THz-wave signal generation; uni-traveling carrier photodiode (UTC-PD)
# Accumulative Roll Bonding--A Review
## 4 Properties
### Strength and Ductility
#### 4.1.1 Sheets with Similar Materials  
As mentioned earlier, improved strength and reduced ductility usually occurs after the ARB process; however, [PERSON] et al. [66] investigated ARBed AA8011 at elevated temperatures wherein the YS and UTS only increased up to 2 cycles and then decreased; in contrast, the elongation diminished up to 2 cycles and then increased. Strengthening AA8011 alloy can be attributed to solid solution hardening, precipitation hardening, grain refinement and strain hardening. It was reported that dynamic recovery and static recovery easily takes place in Al-Fe alloys and the microstructural evaluations of AA8011 disclosed that the grain refinement contributes to strength enhancement only after the first cycle. The precipitation of Si particles was found to be an effective strengthening factor for AA8011 at 200 \({}^{\circ}\)C, nonetheless, a reduction in the concentration of precipitates may lessen the strength after a specific amount of precipitations probably leads to a decrease in strength with increasing ARB cycles. Having the ability of precipitation hardening, dynamic and static recovery may be considered as weakening factors for high cycle ARB, confirmed by these researchers later in another research work where they scrutinized the ARB of AA8011 at room temperature and observed that increasing the number of cycles resulted in the increased strength [48]. Hence, increasing or decreasing strength or ductility by increasing the cycles is affected by the competing effects of the strain hardening and grain refinement on one side and the recovery on the other side.  
Similarly, with an increase in tensile strength after each ARB cycle, abrupt decreases in elongation after the first cycle are reported for alloys like AA6061 [67; 68], AA1100 [69], AA5083 [70], AA1050 [71] and Mg alloy [72]. Similar to Al8011, however, a gradual increase in elongation after initial cycles is reported, suggesting that the strain hardening can not solely explain the mechanical properties variation observed for the ARBed metals and alloys; instead, it is caused by dynamic and probably static recovery of a heavily deformed structure. In addition, the aforementioned behavior is also reported for Cu and its alloys [73; 74], AA5052 [75], AA6014 [76], AA7075 [77], AA2024 [78] and pure Ti [79; 80; 81]. It is noteworthy that few articles have also reported simultaneous improvement in strength and ductility. [PERSON] et al. [82] proposed a new strategy for the simultaneous increase in both strength and ductility of AA2024 where after each ARB cycle, an annealed AA2024 strip is laid between the two roll-bonded strips resulting in a composite with a bimodal microstructure consisting of coarse and ultrafine elongated grain structures.
# Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
###### Abstract  
Every day, hundreds of thousands of new malicious files are created. Existing pattern-based antivirus solutions have difficulty detecting these new malicious files. Artificial intelligence (AI)-based malware detection has been proposed to solve the problem; however, it takes a long time. Similarity hash-based detection has also been proposed; however, it has a low detection rate. To solve these problems, we propose k-nearest-neighbor (kNN) classification for malware detection with a vantage-point (VP) tree using a similarity hash. When we use kNN classification, we reduce the detection time by 67% and increase the detection rate by 25%. With a VP tree using a similarity hash, we reduce the similarity-hash search time by 20%.  
similarity hash; classification; deep learning; malware detection +
Footnote †: journal: Applied Sciences
## 4 Performance Evaluation of Improved SOSK-Means
### Experiment 2  
In Experiment 2, an extensive performance evaluation of the ISOSK-means algorithm on 18 synthetic datasets was demonstrated. Table 6 shows the computational results from the experiment under the DB validity index, showing the best, the worst, the mean, and the standard deviation scores. The results of the ISOSK-means were compared with other existing hybrid algorithms from the literature [61] for the same problem in Table 7. The ISOSK-means recorded a better overall mean value for the 18 synthetic datasets, compared with the other hybrid metaheuristic algorithms. This showed that the mean performance of the improved hybrid algorithm was superior, compared with the competing hybrid metaheuristic algorithms, in solving the automatic clustering problem.  
Figure 9: Convergence curves for the 12 datasets under the CS index.
## 2 Materials and Methods
### Statistical Analysis  
A descriptive analysis of the selected population was performed for demographic, hematologic, and pathological variables. The normality of the quantitative variables was assessed using the Kolmogorov-Smirnov test. Based on the results, variables are presented as mean \(\pm\) standard deviation if the data follow a normal distribution, as geometric mean (95% CI) if the distribution is log-normal, or as median (Q1, Q3) if the data are nonparametric. Logistic regression and odds ratios were selected as the primary methods for investigating the association between pre- and postoperative indices and complications. These analyses were initially adjusted using univariate logistic regression for each variable in the dataset. Subsequently, a multivariate analysis was performed, including significant and clinically relevant variables.  
The differences and proportions between pre- and postoperative measurements in various hematologic values and derived indices (platelet-lymphocyte ratio [PLR], neutrophil-lymphocyte ratio [NLR], monocyte-lymphocyte ratio [LMR], and systemic immune-inflammation index [SII]) were examined. Hematologic variables were log-transformed before being entered into the model to account for their distribution. Logistic regression was employed to evaluate the relationship between the derived ratios and indices and the occurrence of complications.  
This analysis utilized ROC (Receiver Operating Characteristic) curves to assess the discriminative capacity of these indicators to predict complications. Additionally, the area under the curve (AUC) was calculated as a quantitative measure of discriminative power: an AUC close to 1 indicates excellent predictive ability, while an AUC of 0.5 suggests a performance equivalent to chance.
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Machine Learning Algorithms
#### 7.2.6 Federated Learning  
FL is an emerging model that is used for enhancing data privacy in DL through distributed learning and centralized aggregation. This means that in FL, the learning algorithm is trained across multiple decentralized edge devices and servers containing local data samples without exchanging information among the various entities [172]. However, FL enables advanced devices to collaboratively train a shared model but without sharing their local data. Confidentiality and security of both central and local parameters are important when developing applications in FL. Different privacy algorithms are used for this. The application of FL enables the development of secure ML models, especially in sectors such as health, education, and banking, where data privacy is important.  
FL can be divided into three different phases. These phases usually start with a basic model that is trained on a central server. In the first step, this generic model is sent to the client devices of the application. These local copies are then trained on the local data generated by the client systems, and the performance of the ML model is improved in the desired direction. In the second step, the parameters learned by the ML models are sent to a public server. These processes occur periodically on a specific schedule. In the third step, the central server collects these parameters. After the parameters are collected, the central model is updated and shared with the clients again [173; 174].  
Privacy is the most important advantage of FL. In addition, the reduction or elimination of network delays while working on various devices shows the importance of the FL approach in terms of efficiency. In addition, the costs of sharing the data with the server are eliminated. However, the FL approach has the challenges of synchronizing multiple clients as well as maintaining data homogeneity. Therefore, to implement FL, ML practitioners need to adopt new tools and a new way of thinking regarding model development, training, and evaluation without direct access to or classification of raw data while considering communication cost as a limiting factor [175].
## 2 Related Work
### Singular Spectrum Analysis
#### 2.4.2 Decomposition  
The second step in SSA is decomposition. The Trajectory matrix is decomposed using SVD. It is classified into three categories that constitute \(X\). \(U\) is the unit matrix of \(L\times L\) containing the left-specific vector orthogonal set of \(X\) as a column. \(\Sigma\) is an \(L\times K\) rectangular diagonal matrix containing an \(X\) value of \(L\), and is aligned with the largest and smallest values. Finally, \(V\) in Equation (3) is a \(K\times K\) unit matrix including an orthogonal set of right-specific vectors of \(X\). The equation for \(X\) is as follows:  
\[X=U\Sigma V^{T} \tag{3}\]  
Using a SVD equation for the Trajectory matrix is expressed as follows:  
\[\begin{split} X&=\sum_{i=0}^{d-1}\sigma_{i}U_{i}Vi^{T} \\ &\equiv\sum_{i=0}^{d-1}X_{i}\end{split} \tag{4}\]  
Here, \(\sigma_{i}\) represents to the \(i\)-th singular value, and \(U_{i}\) and \(V_{i}\) are vectors representing the \(i\)-th column of \(U\) and \(V\), respectively. \(d\leq L\) is the rank of the trajectory matrix. It is the \(i\)-th elementary matrix. \(U_{i}\), \(\sigma_{i}\) and \(V_{i}\) denotes the \(i\)-th eigentriple of the SVD.
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 4 Verification of Numerical Examples  
### Example One  
A fixed-end beam with a span of 20 m is considered in this example, as shown in Figure 5. The elastic modulus of the concrete (E) and the section moment of inertia (I) of the beam are modeled as a 1D Gaussian random field, whose statistical characteristics are presented in Table 1. The purpose of this case is to calculate the deflection reliability of a beam subjected to a distributed load q = 1 kN/m, and the performance function can be defined as:  
\[G=1.05\overline{\delta}-\delta_{0}, \tag{16}\]  
where \(\overline{\delta}\) represents the deflection at the middle site of the beam when the mean values of the random variables are taken; and \(\delta_{0}\) denotes deflection at the initial point under loads.  
Based on the method of local average discretization, the meshes of the random fields, \(E\) and \(I\), are the same as those of the FE model of the beam. The deflection reliability of the beam is calculated by using the proposed method considering the effects of the random field, and the results are compared with those in [32,33], as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Random Field** & **Mean** & \begin{tabular}{c} **Coefficient of** \\ **Variation (COV)** \\ \end{tabular} & **Distribution** & \
\begin{tabular}{c} **Correlation** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Correlation** \\ **Length** \\ \end{tabular} \\ \hline E (MPa) & 10 & 0.01 & Normal & Exp & 10 m \\ I (m\({}^{4}\)) & 0.6667 & 0.01 & Normal & Exp & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical properties of random fields.  
Figure 5: Fixed-end beam subjected to a distributed load.  
As seen in Figure 6, the reliability indexes calculated by this proposed method are in agreement with the results obtained from [PERSON] [32] and [PERSON] [33], which reveals that the proposed method in this paper is effective and correct. Specifically, [PERSON] adopts the perturbation stochastic finite element (PSFE) method to calculate structural reliability, and solving the gradient vector of the implicit function to random variables is the key step of the PSFE method. This process is more complicated because the existing FE program needs to be reformed. However, the calculation procedure of the proposed method is relatively simple.  
Compared with the method adopted by [PERSON] [33], the method proposed in this paper has the advantage of a faster convergence speed and can achieve higher accuracy with fewer discrete elements. As shown in Figure 6, the number of discrete elements of a random field has a certain influence on the structural reliability, and the reliability index tends to converge with the increase in the number of elements.
## 2 Materials and Methods
### Properties of DSD Material  
The chemical composition of the studied sediment was studied by X-ray fluorescence (XRF) spectroscopy. The structural characteristics of the DSD adsorbent were determined by X-ray diffraction (XRD) using a \"Bruker D4 ENDEAVOR\" diffractometer operating with monochromated CuK\(\alpha\) radiation (at 30 mA and 40 kV). The radial scans were acquired over the (20) range of 4-60\({}^{\circ}\) with a scanning rate of 2\({}^{\circ}\). min\({}^{-1}\).  
FTIR Affinity-1 Shimadzu apparatus was used for the Fourier Transformed Infrared (FTIR) analysis. Measurements were collected in the scan range of 400-4000 cm\({}^{-1}\) using the KBr disc method (1/100 \(wt\) %). The porous properties of adsorbent were obtained by N\({}_{2}\) adsorption-desorption technique using a Quadrasorb apparatus (Quantachrome, Boynton Beach, FL, USA) at 77.35 K. The specific surface area (S\({}_{\text{BET}}\)) and volume of pore (V\({}_{\text{Total}}\)) were calculated according to the Brunauer-Emmett-Teller method [18], and the pore size distribution was calculated by fitting the Barrett-Joyner-Hallender (BJH) model. The acidic-basic surface characteristics of the DSD adsorbent were studied according to the solid addition method as described by [PERSON] et al. (2020) [19]. A total of 50 mL of 0.01 M NaCl solution was kept in series of flasks (100 mL); the initial pH (pH\({}_{0}\)) of each sample was adjusted to a pH range of 3-11. Then, 50 mg of DSD material were added to each flask and the mixtures were stirred for 24 h. Finally, the final pH (pH\({}_{\text{F}}\)) was measured for all solutions and the pH\({}_{\text{PZC}}\) value was determined graphically.
# Intrusion Detection Model Based on Improved Transformer
## 3 Materials and Methods
### Data Preprocessing Strategy
#### 3.2.2 Dimensionality Reduction  
After numericalization and normalization, the dataset's increased dimensionality results in longer training times and sparse data, leading to a decrease in model accuracy. Therefore, dimensionality reduction becomes necessary. This paper proposes to use the encoding layer of the stacked auto-encoder for dimensionality reduction, based on the reconfigurability of the original distribution of data features by the auto-encoder. This ensures that the amount of information contained in the data features remains unchanged after reduction.  
The stacked auto-encoder (SAE) is an unsupervised learning model that utilizes multiple layers of pre-trained auto-encoders. The training process involves a layer-by-layer greedy training strategy, where one layer is trained at a time, and training only starts for the next layer after the current layer is trained. This approach initializes each layer with a reasonable value, leading to faster convergence and better accuracy.  
The principle of SAE is to utilize the input data \(X\) as a reference to guide the neural network to learn a mapping relationship that can reconstruct the data \(X^{R}\), where \(X^{R}\) is an approximation of \(X\). Hence, the feature \(h_{2}\) resulting from the dimensionality reduction of the encoding layer of SA  
This paper proposes to utilize the encoding layer of the stacked auto-encoder (SAE) for dimensionality reduction, which offers several benefits. Firstly, it provides control over the dimensionality reduction features, as each layer is initialized with a reasonable value after separate training. Secondly, intrusion detection classification tasks typically involve a large number of neurons in the neural network and more trainable parameters, and using the encoding layer of SAE for dimensionality reduction can simplify the complexity of the problem and facilitate the task. Additionally, the reconfigurability of SAE for data features ensures that the amount of information contained in the data features remains unchanged after dimensionality reduction.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 5 Summary and Outlook
### Future Research Directions
#### 5.1.3 Array Material Design Will Be Closely Integrated with Metamaterial Technology  
At the engineering level, in order to adapt to the new development situation of emerging fields and expand the new uses of sparse area arrays in satellite communication scenarios, the material design of the array component part is a key factor influencing the performance and cost of the array in optimization design. Currently, the design of feed networks, substrates, and other materials in the optimization design of sparse area arrays is one of the hot research fields in recent years. Metamaterial technology has gradually developed from theory and trial to application. Many devices in the world are often used in complex environments, having high requirements for performance indicators; thus, they are often combined with metamaterial technology. In satellite communication scenarios, sparse surface arrays are required to have the characteristics of ultra-wideband, low profile, low cost, low array element coupling, light weight, and radiation resistance. Therefore, in the future, combining metamaterial technology with array material design will become an important research direction for sparse area array material design.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Dynamic Detection Module
#### 3.3.1 Homography Calculation  
In order to accurately determine the homology matrix between adjacent frames, the matching relationships located on dynamic objects are eliminated as much as possible when inter-frame matching is performed. When the first inter-frame matching is performed, the dynamic feature points in \(k_{1}\) are eliminated based on the semantic information from the dynamic semantic recognition module. When the nth inter-frame matching is performed, the dynamic feature points in \(k_{1}\) are eliminated in the \(n-1\)th inter-frame matching based on the dynamic region information. Therefore, the dynamic feature points in \(k_{n-1}\) are not involved in the calculation of the homology matrix of the current two adjacent frames. Based on this, the matching relationship is constructed using the static feature points in \(k_{n-1}\) and the feature points in \(k_{n}\). Finally, the RANSAC algorithm is used to obtain the results of robust homology matrix between adjacent frames \(k_{n-1}\) and \(k_{n}\).
## 3 Classification
### Communication Technologies
#### 3.6.5 Bluetooth  
[PERSON] et al. [39] have developed a Bluetooth-based V2P safety system for bicyclists. This system uses iBeacon as a VRU device in order to communicate with the vehicle directly. It achieves a communication range of up to 50 m which may be enough for a particular pre-crash scenario e.g., Figure 3d. However, due to its limited communication range, Bluetooth may not be able to support V2P in its entirety. For example, it may support only urban scenarios with slower speeds.