esa-sceva/satcom-chunk-collection huggingface dataset:
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 5 EEG-Based Approaches for Diagnosing CVD  
The selection of an EEG-based BCI design generally depends on the chosen paradigm. Currently, popular approaches in the field include Motor Imagery (MI), Event-Related Potential (ERP), and Steady-State Visually Evoked Potential (SSVEP) [63]. Although SSVEP belongs to ERP, it is often treated as a distinct category due to its unique characteristics and applications in most studies [64; 65; 66; 67; 63]. In the field of CVD, SSVEP has been utilized in several studies. SSVEP is considered a constant, regular response to a repeated visual stimulus [68]. This technology enables users to select from multiple commands by focusing on visual stimuli, displaying a steady response that occurs when individuals are exposed to repetitive visual stimuli [68]. Other reasons that make SSVEP a good choice in such studies include its high signal-to-noise ratio (SNR) and low artifacts [69]. Although many studies have discussed using SSVEP in the BCI field, most have focused on signal processing techniques to improve BCI performance without considering that parameters such as shape and color significantly affect SSVEP [70]. This highlights the importance of considering the properties of the stimulus in future studies. Before 2020, there had been no significant advancements in using SSVEP to diagnose CVD. This is a new development that deserves  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Conventional CVD Tests** & **EEG-Based Methods** \\ \hline \multirow{2}{*}{Behavioral Requirement} & Yes, requires active participation & No, relies on brain responses without \\  & (e.g., verbal response) & behavioral input \\ \hline \multirow{2}{*}{Subjectivity} & High, results can be influenced by & \\  & individual perception and experience & Low, results are based on objective EEG data \\ \hline \multirow{2}{*}{Training Required} & Yes, some tests require extensive training & Minimal, simple instructions \\  & (e.g., anomalouscope) & and sensor placement \\ \hline \multirow{2}{*}{Susceptibility to Manipulation} & Yes, possible through memorization & No, EEG signals cannot be \\  & (e.g., Ishihara plates) & consciously controlled \\ \hline \multirow{2}{*}{Speed} & \multirow{2}{*}{Fast, typically completed within minutes} & Slower, requires more time for setup \\  & & and signal analysis \\ \hline \multirow{2}{*}{Suitability for Disabled Individuals} & Limited, requires active participation & High, suitable for individuals with \\  & & disabilities or communication challenges \\ \hline \multirow{2}{*}{Use Case} & \multirow{2}{*}{Quick screening, large population testing} & Research, cases where traditional methods \\  & & are not viable or require \\  & & more detailed analysis \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of conventional CVD diagnostic tests and EEG-based methods.  
researchers' attention to improve the healthcare field. Table 3 provides a detailed summary of the existing studies that have utilized EEG signals for diagnosing CVD.
## 3 Methodology
### Road Region Extraction  
The road region is the most important area for perception, as objects in the road region may affect the passing of autonomous trucks. Road region extraction can provide the ROI for object detection, which can filter out invalid areas to reduce the computational burden. Most road region detection algorithms have been applied to structured environments [32, 33] where road features are obvious: the road surface is flat, and the sidewalks are higher on both sides. As roads in mining environments have large undulations, and the boundaries of the road are not clear, it is a challenge to accurately extract the road region. In this section, a novel road region extraction method for mining environments is proposed. The method mainly includes two stages: first, a semantic segmentation network based on a deep learning method [34; 35] is created to obtain the semantic information of point clouds. In this study, the semantic segmentation method is RangeNet++ [36]; then, a search-based strategy is applied for ROI point extraction.
## 4 Applications to Real Data
### Macroeconomics
Furthermore, CUR achieves the lowest value since the beginning of the financial crisis in 2008 (see [75]). Figure 8: Standardized macroeconomic time series of consumer price index (CPI), gross domestic product (GDP), federal government tax receipts (FGT), civilian unemployment rate (CUR), and economic policy uncertainty (EPU) (**left**) and pairwise distances applying the generalized causality algorithm (**right**). Numbers in bold symbol represent the lowest costs. Figure 9: Local costs and optimal causal path of gross domestic product (GDP) and consumer price index (CPI). The large bars on the x-axis represent structural breaks with corresponding 95 percent confidence intervals (small bars). To summarize Section 4.2, the generalized causality algorithm is able to detect interesting relationships between important macroeconomic variables. In both cases, the structural breaks go along with incisive economic and political events that might have had an influence on the lead-lag structure of the time series.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
###### Abstract  
The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.  
physical layer authentication; physical layer security; signal classification; wireless communication; machine learning; deep learning +
Footnote †: journal: _Sensors_**2023**, 23, 1814. [[https://doi.org/10.3390/s23041814](https://doi.org/10.3390/s23041814)]([https://doi.org/10.3390/s23041814](https://doi.org/10.3390/s23041814))
## 2 Measurement of the Membrane Penetration: A Novel Procedure
### Chassis Apparatus of the Triaxial Test  
Different measurement systems can significantly affect the accuracy of the triaxial tests. Due to the difference of apparatus size and loading system, the reliability of measurement results can be directly affected. For example, when the confining pressure is loaded, the specimen size is different in the increment of confining pressure loading. This will affect the drainage volume of the sample at the initial stage and have a great influence on the volume change of the sample. When the sample cannot be drained in time, it may produce pore water pressure and affect the test results. Therefore, in order to diminish the measurement error and to carry out triaxial tests of different diameters on the same triaxial apparatus, a chassis device for multiscale triaxial test apparatus, designed by Nanjing Hydraulic Research Institute, is shown in Figure 3. The chassis apparatus consists of a confining pressure import, a saturated water inlet, and a drain hole (measure the volume change of specimen). Shaped like a tower, this apparatus is divided into five layers. Each layer can be assembled into a specimen. The diameter-to-height ratio of the specimen is 0.5, and customized caps are designed to individually match the top of each specimen. A drain hole, located on the upper side of the specimen cap, is connected to the lower pipe of the drainage system. The bottom connection device of the chassis device is designed according to the size of existing pressure chamber, which can perfectly match the existing pressure chamber system. In this way, each specimen could be loaded in the same confining pressure increment. A proper confining pressure increment can ensure that each sample does not produce pore water pressure due to the excessive loading speed. The measurement error caused by different instrument size under different loading conditions is also handled in this apparatus. By installing rubber membrane on each corresponding size base and then filling the test materials, the isotropic consolidation drainage test can be carried out. When the triaxial test with a particular specimen size is finished, the base corresponding to the specimen size (for example, base 100 indicates sample diameter of 100 mm) can be removed, and the test with the following specimen size can be carried out.  
Figure 2: Relationship between \(\Delta V_{T}(P)/A_{m}\) and the diameter of the specimen.
## 5 Analysis of Influence Factors
### Influence of Anchor Cable Parameters  
To understand the effect of anchor cables on pit deformation, four prestress values (0, 50 kN, 100 kN, 150 kN, 200 kN) and four inclination angle values (\(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\), \(25^{\circ}\), \(30^{\circ}\)) were used to simulate anchor cables with other parameters held constant.
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 3 The Novel AVS/RS/ML System
### Description of the Movable Lift (ML)  
The ML (Figure 2) is the main innovation of the proposed novel design of AVS/RS. It consists of upper trolley (Figures 2 and 4), the lightweight vertical structure (Figure 2) and two lightweight lower beam trolleys (Figure 2). The design of the upper trolley is similar to the design of simple overhead crane's trolley with driving, braking, and lifting mechanisms, where instead of a large and expensive wire rope drum, only four sprocket wheels are implemented. The driving mechanism of the upper trolley has four wheels, running on the lower flanges of the U-shaped rails and two wheels running on the upper flanges providing exact and stable position of the trolley during ML's operations in standstill, during decelerations and accelerations of horizontal movements, and during constant velocity travel. There is a lightweight vertical structure which mainly consists of four vertical lattice columns. The top ends of these four columns are connected to the upper trolley and the bottom ends are connected to two lower beam trolleys. Lower beam trolleys are designed as horizontal beams equipped with guiding side wheels, brakes, and four return sprocket wheels. The guiding wheels and the brakes are acting on the lower rail, which is built-in into the aisle floor. Four lifting chains are tensioned over sprocket wheels on the upper trolley and return sprocket wheels on the lower beam trolleys.  
The ML is capable of performing horizontal travel to reach the target column location when it is empty. When it is in standstill, it is capable of lifting an AGV. The main and very significant difference between the ML and the stacker crane therefore is that ML is able to lift payload only when it is in standstill. In addition, the horizontal velocity of ML is smaller than that of the stacker crane. According to this, it can be assumed that the ML can be produced cheaper compared to the lifting mechanism in SBS/RS and mini-load stacker crane of an automated storage and retrieval system.  
The MLs are structured slender and lightweight and have relatively small accelerations and decelerations of horizontal travel velocity. Further, they are equipped with brakes on the upper and lower trolleys, enabling precise positioning of the ML and more rigid construction during standstill. More rigid construction is provided by also designing vertical columns as lattice girders. Keeping all this in mind, we assume that oscillations of the ML's columns fade out fast enough. To prevent excessive horizontal oscillations of vertical lifting chains, simple dumpers, connected to the ML's lattice columns can be employed.  
To test its performance, this proposed novel system's working principle is simulated under computer environment. Different operating policies are considered to observe how performance metrics of the system are affected.
###### Abstract  
The paper presents the range and applications of thematic tasks for ultra-high spatial resolution data from small unmanned aerial vehicles (UAVs) in the integral system of environmental multi-platform and multi-scaled monitoring of Lake Sevan, which is one of the greatest freshwater lakes in Eurasia. From the 1930s, it had been subjected to human-driven changing of the water level with associated and currently exacerbated environmental issues. We elaborated the specific techniques of optical and thermal surveys for the different coastal sites and phenomena in study. UAV-derived optical imagery and thermal stream were processed by a Structure-from-Motion algorithm to create digital surface models (DSMs) and ortho-imagery for several key sites. UAV imagery were used as additional sources of detailed spatial data under large-scale mapping of current land-use and point sources of water pollution in the coastal zone, and a main data source on environmental violations, especially sewage discharge or illegal landfills. The revealed present-day coastal types were mapped at a large scale, and the net changes of shoreline position and rates of shore erosion were calculated on multi-temporal UAV data using modified Hausdorff's distance. Based on highly-detailed DSMs, we revealed the areas and objects at risk of flooding under the projected water level rise to 1903.5 m along the west coasts of Minor Sevan being the most popular recreational area. We indicated that the structural and environmental state of marsh coasts and coastal wetlands as potential sources of lake eutrophication and associated algal blooms could be more efficiently studied under thermal UAV surveys than optical ones. We proposed to consider UAV surveys as a necessary intermediary between ground data and satellite imagery with different spatial resolutions for the complex environmental monitoring of the coastal area and water body of Lake Sevan as a whole.  
UAV; highly-detailed spatial data; thermal survey; coastal processes; shoreline position; coastal wetlands; eutrophication; multi-platform environmental monitoring 2020 IEEE/RSJ Web Conference (2020); 12, 3821; doi:10.3390/rs12223821
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 2 Related Work
In [22], the authors propose a heterogeneous communication architecture that communicates between moving agents in harsh conditions and uses a Ubiquitous Sensor Network (USN) consisting of Drone-Based Wireless Mesh Networks and Near Vertical Incidence Skywave technologies, able to reconnect the relocating agents that constitute it and spanning a large-scale area. Before their proposal, they first survey the emergency communications challenges, then they analyze the existing technologies for emergencies, and finally, they conduct an extensive review of the most common technologies for ECSs. They conclude that ECSs share many similarities with the USNs and that a dynamic set of actuators and heterogeneous sensors can be deployed into a large-scale area to address the adverse communication circumstances. Table 1 summarizes the most relevant surveys on ECSs described in this section compared to our proposed system. Each study contributes to a specific field and can be improved to be highly beneficial in its scientific area.
## 6 Evaluation of PGA Values for Specific Limit States by Pushover Analysis
### Detection of Specific Limit States
Accidental eccentricity equal to \(\pm 5\%\) of the maximum floor dimension is considered to model the non-regular mass distribution. Pushover analysis provides reliable results for the structures that oscillate predominantly in the first mode. In the presence of both horizontal and vertical irregularities, multi-modal non-linear static analysis can be applied [46]. Due to significant irregularities of buildings, a threefold lateral load distribution, namely uniform, linear, and modal, has been applied in this study. Considering the eccentricities in positive and negative directions, this resulted in a total of 36 analyses. The evaluation procedure of peak ground accelerations for three limit states is shown for the Cambi Tower (Figure 8). The floors were modeled as flexible. The walls' mechanical properties were taken according to [40] as follows: compressive strength of 3.20 MPa, tensile strength of 0.10 MPa, modulus of elasticity of 1700 MPa, shear modulus of 580 MPa, and specific weight of 21 \(\mathrm{kN/m^{3}}\). Seismic demand was deduced from the elastic response acceleration spectrum. A soil class A and type 1 response spectrum [37] have been adopted. Other assumptions include the importance factor \(\gamma_{1}=1.2\) and the design ground acceleration \(\mathrm{a_{g}}=0.22\) g. Figure 9 illustrates the results of the pushover analyses for x and y direction. Figure 8: Cambi Tower: (**a**) photo of the building; (**b**) ground floor plan; (**c**) section view; (**d**) structural model. Seismic capacity for two orthogonal directions was evaluated by a pushover analysis, comparing the displacement capacity and the displacement demand for the same control point. The calculation was repeated for all of the 36 loading cases. The critical peak ground accelerations associated with the DL, SD, and NC limit states were computed as follows: (a) x direction--PGA\({}_{\text{DL}}\) = 0.093 g = 0.422a\({}_{\text{g}}\), PGA\({}_{\text{SD}}\) = 0.116 g = 0.527a\({}_{\text{g}}\), PGA\({}_{\text{NC}}\) = 0.147 g = 0.668a\({}_{\text{g}}\); (a) y direction--PGA\({}_{\text{DL}}\) = 0.030 g = 0.136a\({}_{\text{g}}\), PGA\({}_{\text{SD}}\) = 0.059 g = 0.268a\({}_{\text{g}}\), PGA\({}_{\text{NC}}\) = 0.078 g = 0.355a\({}_{\text{g}}\). For completeness, the design ground acceleration a\({}_{\text{g}}\) = 0.22 g has been obtained based on the seismic hazard map for the return period of 475 years.
# A THz Passive Image Generation Method Based on Generative Adversarial Networks
## 5 Conclusions  
In this paper, a Res-WGAN-GP generation model and a quality evaluation method are proposed for THz passive image data augmentation application. Based on the framework of deep convolutional generative adversarial network, the generator and discriminator models based on residual structure are designed. The WGAN-GP loss function is used to ensure the stability of the training process. The generated images are evaluated in terms of visual quality, category consistency, diversity, and usability. The results show that the proposed model can improve the quality of generated images and meet the requirements of data augmentation applications. In the classification tests, the classification accuracy can be improved by applying the augmented dataset, so it is expected to apply the augmented data in the object detection task with more target categories.  
It is worth noting that the main contribution of this paper is the first attempt to use deep-learning methods for data augmentation of terahertz passive image datasets. This paper aims to provide a low-cost solution for poor detection performance caused by insufficient data volume.  
In the future, the network structure, training strategy and loss function need to be optimized, and the proposed method also needs to be verified with a dataset containing more categories. In addition, some effective clarity evaluation methods suitable for GAN models need to be investigated.  
Figure 9: Diversity evaluation by interclass SSIM.  
**Author Contributions:** [PERSON] performed theoretical study, conducted the experiments, processed the data and wrote the manuscript. [PERSON] designed the imaging system and revised the manuscript. [PERSON] and [PERSON] provided the experiment equipment and funds for the research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key-Area Research and Development Program of Guangdong Province (2019B010157001, 2020B0101110001); the National Natural Science Foundation of China (61988102, 61731020, 61671432); the National Key Research and Development Program of China (2017 YFA0701004,2018 YFF01013004); the Project of Equipment Pre-Research (WJ2019G00019); and the Key Program of Scientific and Technological Innovation from Chinese Academy of Sciences (KGFZD-135-18-029).  
**Data Availability Statement:** Not applicable.  
The authors would like to thank the editors and reviewers for their efforts to help the publication of this work.  
The authors declare no conflict of interest.