esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The goal of this work is to validate the application of the 3-dimensional constellation that was introduced in the literature for the generalized SM with multiple active antennas (MA-SM). In this paper, we derived closed-form upper bound formulae of the pair-wise error probability of the MA-SM system as a function of the rotation angles used in the 3-dimensional constellation. To this end, the search problem is modeled as a multi-objective optimization. The obtained optimal set of angles are then evaluated against those which were proposed in the literature and the MA-SM system without the 3-dimensional constellation. We finally showed, using analytical and simulation results, that only a slight improvement of less than 0.5 dB in the error performance is achieved using the optimal angles. Therefore, we do not recommend the use of 3-dimensional constellation with the MA-SM system.  
**Author Contributions:** Conceptualization, S.M., M.M., K.-S.C.; methodology, S.M., T.H., N.M., M.M. and K.-S.C.; software, S.M., T.H. and N.M.; validation, S.M., T.H., N.M., M.M. and K.-S.C.; formal analysis, M.M. and S.M.; investigation, S.M., T.H., N.M., M.M. and K.-S.C.; resources, K.-S.C. and M.M.; data curation, S.M., T.H. and N.M.; writing-original draft preparation, S.M., M.M. and K.-S.C.; writing-review and editing, S.M., T.H., N.M., M.M. and K.-S.C.; visualization, S.M., T.H. and N.M.; supervision, K.-S.C. and M.M.; project administration, M.M.; funding acquisition, K.-S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the BK21 plus program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 4 V2X Communication Architecture
Highfrequency signals can transmit data quickly over short distances, while low-frequency signals can transmit data over longer distances but at a slower rate [86; 88]. * Range: The maximum distance over which radio communication can occur between the source and the intended recipient(s) while still maintaining the necessary level of reliability [86; 88]. * Network speed: The rate at which data can be transmitted between the various components of the system, such as the navigation device, server, and map data source. It can impact the latency or delay between data transmission and reception. For example, if the network speed is slow, data transmission may take longer, leading to outdated or incomplete guidance information being provided to the user [86; 88]. * Security and Privacy: it is important to secure V2X communication within the context of smart cities, integrating IoT devices, 5G networks, and autonomous vehicles. V2X technology facilitates seamless data exchange among vehicles, infrastructure, and other entities, enhancing road safety, traffic efficiency, and environmental sustainability. However, the increasing adoption of V2X systems raises significant security and privacy challenges, including data breaches, malicious attacks, and traffic manipulation. The findings of a study demonstrate the effectiveness of blockchain's decentralized and immutable nature in protecting V2X systems against data tampering, unauthorized access, and malicious threats [89]. Future research should focus on developing standardized protocols and frameworks for secure interoperability, improving scalability and robustness in security measures, and exploring AI-driven analytics to counter evolving cyber threats. To effectively accommodate more complex situations, it is needed to address stricter demands when it comes to latency, range, speed, reliability, location accuracy, and data rate. The various use cases for Vehicle-to-Everything (V2X) communication, including specific delays and data rates for each vehicle, are explained in detail in [88].
## 4 Discussion and Conclusions
### Algorithm Discussion
#### 4.1.2 Path Planning for Hybrid Networks  
The traditional heuristic function is designed for single network path planning, overlooking the complexity of real-life travel networks that often involve both driving and public transportation. The traditional heuristic function cannot solve the path planning problem of hybrid networks where various modes of transportation coexist. In this paper, a hybrid network that can be used for path planning analysis is constructed firstly by establishing the topological relationship between restricted and unrestricted networks, then, based on the multivariate heuristic function, the different needs of travelers in the hybrid network are realized by prioritizing the control of each evaluation criterion of constraint functions in the heuristic function.
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 2 Psychological and Neurobiological Basis of Visual Saliency  
Attention mechanism has always been an important subject of neuroscience and psychology. In the mid-1950s, cognitive psychology gradually emerged. Attention was regarded as an important mechanism of human brain information processing, and several influential attention models, such as the filter model (1958), attenuation model (1960), and response selection model (1963), among others, were produced. [PERSON] [25] proposed an important model called Feature Integration Theory (FIT) to vividly illustrate the selective role of visual attention. The visual process in this model was divided into a pre-attention stage and a focal attention stage. Feature integration was implemented after extracting the location-related features. [PERSON] and [PERSON] [26] enhanced FIT by integrating the return-inhibition mechanism to achieve a focus shift. Moreover, on the basis of criticisms of the early FIT model, [PERSON] [27] proposed the guided search model to explain and predict search results. These neurological and psychological studies have provided an important basis and criteria for calculating visual saliency, such as center surround antagonism, global rarity, or maximization of information.  
Visual saliency prediction mainly used mathematical models to simulate the human visual attention function and subsequently calculated the importance of visual information. The simulation of the human visual attention system mainly used some of the important achievements in visual physiology and psychology mentioned above. Notably, visual saliency prediction did not study eye movement strategies in visual attention but rather calculates the information pertaining to the different degrees of importance with respect to scenes for eye movement decision-making. These studies have played a guiding and standardizing role in the subsequent development of saliency detection models.
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 1 Introduction
### Organization  
The structure of this article is organized as follows. In Section 2, two preliminaries will be briefly introduced, including CLS and bilinear pairing, and a SM2-based CLS scheme will be reviewed. In Section 3, a new signature scheme with batch verification will be proposed, and in Section 4, the performance of these schemes will be evaluated through simulation experiments. Finally, a conclusion of the entire article is provided in Section 5.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Deep Learning Models
#### 3.5.2 Recurrent Neural Networks (RNNs)  
Recurrent Neural Networks are powerful in sequential data processing, where previous words' memory helps capture contextual dependencies [44]. However, RNNs are susceptible to the vanishing gradient problem, limiting their performance on lengthy texts.
# Numerical Modeling of Wave Disturbances in the Process of Ship Movement Control
## 1 Introduction  
A typical practice in designing an automatic control system is to build a mathematical model of an object (or process), and on this basis to make a synthesis of a control system (controller, algorithm). Then, before implementation, the created control algorithm is tested (if possible) by computer simulation and tuned in right after installation. Simulation is therefore the first step in checking the stability of the system and algorithm operation, so it should reflect reality as faithfully as possible, accounting for disturbances.  
The process of ship movement control is characterized by a high level of external disturbances. Due to the manner of their influence, disturbances can be divided into two groups. Disturbances of one type affect ship dynamics (changes in water depth or loading condition, etc.); the other type is additive disturbances, whose effects are observed as additional signals superimposed on a ship's heading, angular speed, drift angle, etc. The latter group also includes disturbances from currents, winds, and waves. Although they are correlated, these disturbances are usually divided into three different types of signals. This article focuses on disturbances from wind-generated waves, as they are the most essential from the viewpoint of the automatic ship movement control problem.  
Literature on the subject abounds, with publications presenting complex models of sea waves related to specific water areas [1; 2]. Other articles covering the topic under consideration address wave filters [3; 4; 5; 6; 7; 8], as a standard based on the Kalman filter. Similar topics are also discussed in other works [9; 10; 11; 12; 13].  
The article presents a numerical model of sea wave generation as a method alternative to the existing ones. The presented solution can be universally used in simulations of automatic ship movement control systems, tackling various problems. This is often difficult in the case of solutions presented in the literature on the subject. This restriction often results from the complexity of the models and the assumptions made. The proposed model is easy to implement and still takes into account all relevant components. The model was verified in simulations testing the influence of disturbances on the operation of the ship course stabilization system.
# A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions
## 3 Security Assessment for Web Application Tools
### Evaluation Methodology and Criteria  
In this subsection, the evaluation methodology has been explicitly defined. The black box penetration testing technique is used in this paper to evaluate open-source web application assessment tools. The real advantage of this approach is that it provides a uniform scenario for launched attacks. The black box approach evaluates web application scanners with no prior knowledge of web application internal structure. The major components of web application security perform three significant tasks: crawling, fuzzing, and reporting. For evaluation purposes, these three tasks are needed to be discussed.  
The OWASP [13] is an international non-profit organization primarily focused on web application security. OWASP project regularly maintains an updated list of severe security vulnerabilities in web applications. Being an open-source solution, all of their material is freely available and easily accessible on the internet. This material includes documentation, tools, videos, and a forum for user guidance. Their popular project is the OWASP Top 10.  
The OWASP Top 10 [13] is a frequently updated document focusing on web application security on the top ten critical risks. This document is composed of a team of security experts from all over the world. OWASP Top 10 is also known as \"Awareness Document\" it provides extensive statistical analysis of the most severe vulnerabilities in web applications. A list of Web application security vulnerabilities for OWASP Top 10 2021 [12] report is given below:  
1. Broken Access Control;
2. Cryptographic Failures;
3. Injection;
4. Insecure Design;
5. Security Misconfiguration;
6. Vulnerable and Outdated Components;
7. Identification and Authentication Failures;
8. Software and Data Integrity Failures;  
\begin{table}
\begin{tabular}{c c c c} \hline
**Tool Name** & **Not Available** & **Did Not Work** & **Proxy** & **Other** \\ \hline WSFuzzer & & & Only for SOAF Services \\ \hline RegFuzzer & & & Only for Regular Expressions \\ \hline RFuzz & & & Only for HTTP requests \\ \hline XSSFuzz & & & Only for CSS \\ \hline Watcher & & & Depends on another \\  & & & application (Fiddler) \\ \hline Falcove & & Could not be Installed \\ \hline NeXpose community edition & & Not for Web Applications \\ \hline SQLMap & & Only for SQL Injection \\ \hline XSS & & Not Automated, Only for CSS \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._Security Logging and Monitoring Failures;  
10. Server-Side Request Forgery.
## 4 Overview of Experimental Research in the Literature
### Effect of Slenderness Ratio and Eccentricity
The capacity reduction factor (CRF) of CCB and HCB masonry walls obtained experimentally was compared with the various national codes such as the Indian standard [95], the British code [93], the Eurocode [24], and the Australian standard [26]. The results showed that for both axially and eccentrically loaded cases, the CRF derived experimentally for walls built with CCB seems to be higher than the values prescribed in the various codes. A similar trend was also observed for HCB walls with a slenderness ratio of 14.9. However, HCB walls with a slenderness ratio of 11.3 indicate lower experimental CRF values when compared to code recommendations. Likewise, [PERSON] and [PERSON] [16] noted that the CRF prescribed in the masonry standard seems to underestimate for axially loaded brick/block walls and overestimate eccentrically loaded brick walls. Hence, it is of utmost importance to reassess the capacity reduction factor values by conducting more tests on URM walls and also to prescribe the values of the capacity reduction factor separately for concrete block and brick masonry.
## 2 Measurement System Architecture  
This system analyzes the measurement data, which are used to predict the lifetime of the gearbox. A fast Fourier transform is applied to the vibration data to derive the vibration spectrum, which shows the condition of the gears.  
The only gearbox with a data transmission function seen on the market is sold by Wittenstein (Igersheim, Germany). The product transmits temperature and vibration signals by wire using the IO-Link communication protocol. If the environment is limited in terms of space, a wired transmission method can be negatively affected, and without PLC or IO-Link hardware, it is impossible to receive data, so a printed circuit board with a wireless transmission function is embedded in the gearbox mechanism (as shown in Figure 2). The benefits over Wittenstein's product are described in the following.  
* Wired transmission requires spatial limits. The embedded sensor is directly powered by the power source and can be used in a wide range of applications, regardless of environmental constraints.
* The measurement data are wirelessly transmitted to the user directly through this printed circuit board using the microcontroller and the wireless module, without the need for PLC or IO-Link hardware.  
Figure 2: System architecture for the proposed gearbox.
# Multi-LEO Satellite Stereo Winds
## 4 Discussion
### Applications  
The multi-LEO method developed here improves upon existing operational methods in the polar regions and has important applications to strengthen our understanding of polar weather and climate.
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 3 System Testing
### Dynamic Range Testing  
The principle and environment for dynamic range testing are the same as those in Section 3.1. Using an input signal frequency of 18 GHz as an example, the input-output response curve of the system is shown in Figure 15.  
In the figure, the blue curve represents the measured data, while the red curve is the fitted curve for the linear region, with the following fitting equation: \(y=0.9959\)x \(+\) 14.0081. The line loss at 18 GHz is 4.65 dBm, resulting in a calculated linear region gain of 18.66 dBm for the system at 18 GHz. The saturation input power is approximately 10 dBm, and the saturation output power is around 20.5 dBm. Using the same method, the test results at 1 GHz, 4 GHz, 8 GHz, 10 GHz, and 15 GHz are shown in Figure 16.  
Finally, the system's gain, saturation input power, and saturation output power are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency/Hz** & **First Harmonic/dBm** & **Second Harmonic/dBm** & **Delta/dB** \\ \hline
1 G & 11.67 & \(-27.33\) & 33.00 \\
4 G & 8.00 & \(-28.33\) & 36.33 \\
8 G & 8.67 & \(-30.83\) & 39.50 \\
10 G & 9.00 & \(-30.83\) & 39.83 \\
15 G & 8.83 & \(-31.50\) & 40.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Higher harmonics at various frequencies with \(-10\) dBm of input power.  
Figure 15: Power response curve with 18 GHz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Linear Region Gain** & **Saturation Input Power** & **Saturation Output Power** \\
**(GHz)** & **(dBm)** & **(dBm)** & **(dBm)** \\ \hline
1 & 25.50 & 2 & 23.5 \\
4 & 24.05 & 3 & 23.00 \\
8 & 23.43 & 4 & 22.00 \\
10 & 22.80 & 6 & 22.33 \\
15 & 19.47 & 9 & 19.83 \\
18 & 18.66 & 10 & 20.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic range test results for the system.
## 3 Variable Selection and Methodology
### Methodology  
#### 3.2.1 Measuring UBLGUE: Super-SBM-DEA  
[PERSON] innovatively put forward the super-SBM-DEA assessment method to overcome the shortcomings of the traditional models by extending the traditional DEA [49; 50; 51]. The super-SBM-DEA model is a modified data envelopment analysis (DEA) method, which is an effective analysis means first proposed by American operational researchers in 1978 [52]. This method can almost ignore setting the function form deliberately. Each geographical unit is regarded as a decision-making unit (DMU) [53]. The DEA model adapts linear programming technology to measure the efficiency value of DMUs without considering the dimension and index weights of the input-output evaluation indicators [54]. This effectively avoids the error problem of unified standardization caused by the dimension problem of indicators. Thus, it has been widely applied by many scholars in various applications.  
At present, the conventional DEA methods, predominantly the CCR-DEA and BCC-DEA methods, do not consider undesirable outputs in input-output evaluation indicators, which results in a biased efficiency value. The former CCR-DEA model supposes that the scale income is constant, whereas the latter BBC-DEA model assumes that the scale income is variable. However, the traditional DEA method disregards the expected output and fails to reflect actual efficiency. The advantage of the super-SBM-DEA method over traditional DEA [55; 56] is its ability to rank the effective decision units in the production process [57]. In this study, the UBLGUE frontier is constructed for use as a DMU on the basis of a panel dataset for 30 cities from 2003 to 2020. The equation is defined as follows:  
\[\begin{split}\text{min}\rho&=\frac{\frac{1}{m} \sum\limits_{i=1}^{m}\frac{s_{i}^{-}}{x_{ik}}}{\frac{1}{s_{1}+s_{2}}\left( \sum\limits_{r=1}^{s_{1}}\frac{s_{i}^{g}}{y_{rk}^{g}}+\sum\limits_{q=1}^{s_{2 }}\frac{s_{r}^{p}}{y_{pk}^{b}}\right)}\\ s_{i}.t.&\left\{\begin{array}{l}\sum\limits_{j=1, \
eq k}^{m}\lambda_{j}x_{ij}-s_{i}^{-}\leq x_{ik}\\ \sum\limits_{j=1,\
eq k}^{m}\lambda_{j}y_{j}^{g}+s_{r}^{g}\geq y_{rk}^{g}\\ \sum\limits_{j=1,\
eq k}^{m}\lambda_{j}y_{j}^{b}-s_{r}^{b}\leq y_{qk}^{b}\\ s_{i}^{-}\geq x_{k},s_{i}^{g}\leq y_{k}^{g},s_{r}^{b}\geq y_{k}^{b},\lambda_{j }\geq 0\end{array}\right.\end{split} \tag{1}\]  
where \(\rho\) is UBLGUE. When \(\rho>1\) and \(s^{-}=s^{g}=s^{b}=0\), the efficiency of the city is effective. When \(0\leq\rho<1\), the efficiency of a city is ineffective. \(\lambda_{j}\) denotes the coefficient; \(m\), \(s_{1}\), and \(s_{2}\) are the numbers of input indicators, desirable output indicators, and undesirable output indicators, respectively; \(s^{-}\), \(s^{\otimes}\), and \(s^{b}\) are the slack variables; \(x_{ik}\) represents the \(i\)th input indicator value of city \(k\); \(y_{rk}^{g}\) represents the \(r\)th desirable output indicator value of city \(k\); and \(y_{qk}^{b}\) represents the \(q\)th undesirable output value of city \(k\).
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 4 Flying Ad Hoc Networks
### Applications  
Flying ad hoc networks are applied in various areas. In the following, we explain these applications, which are also shown in Figure 5.  
* **Search and rescue operations:** In this application, flying ad hoc networks can act as the first defensive line during natural disasters due to their fast and easy deployment capability. Furthermore, drones play the role of human relief forces in high-risk areas and pursue specific goals such as finding the precise location of survivors or victims.
* **Wildfire monitoring:** Flying ad hoc networks can be used to monitor temperature, diagnosis, and prevent fire in forests.
* **Traffic monitoring:** Highway traffic monitoring is one of the FANET applications. Drones can easily perform this monitoring task to detect gridlock and report traffic management data. This is a viable and economical option. Moreover, these networks can achieve different real-time security solutions to provide security on roads and trains [43].
* **Reconnaissance:** In aerial surveillance applications, drones fly statically to identify a particular area without human intervention. During surveillance operations, drones collect images of the desired goals and sites in a wide area. This information is quickly processed and sent to a smart control station. When drones oversee a particular target or area, they periodically patrol the target to inspect and monitor their security goals. For example, the border police can identify illegal border crossing through a flying ad hoc network [43].
* **Agricultural monitoring:** This application is known as precision agriculture. It includes all information technology-based solutions and methods that monitor the health of agricultural products. This application can be upgraded by FANETs to overcome the existing problems in this area. In this case, drones collect information on the quality of agricultural products, growth, and chemical fertilizers in a short period of time and analyze them based on precision scales and criteria [42].
* **Remote sensing:** Recently, flying ad hoc networks are used with other networks such as wireless sensor networks (WSN). It includes the use of drones equipped with sensors and other equipment. These drones automatically fly throughout the area to obtain information about the desired environment.
* **Relaying networks:** In this application, drones act as aerial relays to send information collected by ground nodes to base stations efficiently and securely; for example, sending data produced by ground nodes in wireless sensor networks and vehicular ad hoc networks. UAVs are also used to increase the communication range of ground relay nodes.