esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Bidirectional Wireless Power Transfer System for Mobile Power Application
###### Abstract  
This paper presents a bidirectional wireless power transfer system for mobile power applications. A novel 2-switch bidirectional wireless power transfer system with dual-side control is proposed for mobile power applications. Although only two switches are adopted, the energy can be transferred from the transmitter side to the receiver side and vice versa. The term bidirectional means that the power-flow is bidirectional and also that the transmitter is also a receiver and the receiver is also a transmitter. The output energy can be easily controlled by the duty ratios of the two switches. Thus, the proposed bidirectional power transfer system uses only one circuit to achieve bidirectional power transfer. Hence, the system cost and volume can be reduced so that the system is small and convenient for mobile power systems, portable and/or wearable electronic devices. A prototype system is constructed and the experimental results verify the validity of the proposed bidirectional wireless power transfer system.  
bidirectional; wireless power transfer; transmitter; receiver; dual-side control; mobile power system +
Footnote †: journal: Applied  
_Article_  
A Novel Bidirectional Wireless Power Transfer System for Mobile Power Application  
[PERSON], [PERSON], Y.  
provides active power or reactive power to the grid whereas renewable energies such as solar energy or wind power are all in the power system. Bidirectional WPT is needed as well as unidirectional power transfer [16]. Beyond the magnetic coupling and WPT circuit, the closed-loop control schemes are also essential to realize the WPT converter function. Closed-loop control schemes can be classified into three control types, including transmitter-side control [17], receiver-side control [18; 19], and dual-side control [20]. Receiver-side control is better than transmitter-side control and dual-side control in maintaining the load requirement. The dual-side control is apparently more important for bidirectional power flow control.  
A novel bidirectional WPT converter with only two switches is introduced in this paper. Dual-side control is also proposed for bidirectional power flow. Compared with the 8-switch full bridge bidirectional WPT converter, as shown in Figure 1, which is often utilized in the EV charger system, the proposed 2-switch bidirectional WPT converter is more suitable for mobile power systems, portable and/or wearable electronic devices with low power applications. Figure 2 demonstrates the cell phone power supply using mobile power with wireless power transfer technology for charging and discharging. Up to now, there is usually unidirectional wireless power transfer application in the mobile power supply. A bidirectional wireless power transfer technology for low power applications using simple circuitry with only two switches is proposed.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 3 Materials and Methods
### Data Processing and Analysis  
This study utilized DJI Terra Mapping & Surveying Edition software for the three-dimensional reconstruction of slopes. The software is efficiently integrated with the DJI Matrice 300 RTK UAV and the DJI Zenmuse H20T camera used in this study, significantly improving the efficiency of aerial photography processing while ensuring the accuracy of the data. This study utilized the change detection module in LiDAR software to perform a comparative analysis of slope changes and generate DEMs.  
This study employed LiDAR360 5.2 software to extract a contour model from DEMs for analyzing elevation variations in the study area. This study employed the Iterative Closest Point (ICP) algorithm to register two-phase point-cloud datasets and conducted a comparative analysis of variations between two aerial photogrammetric point-cloud models using the change detection plugin within LiDAR360 software, thereby delineating regional changes during the interval between the two aerial surveys. This study employed ArcMap 10.8 software to extract slope gradient and curvature metrics from two-phase DEMs to facilitate slope stability analysis. DJI Terra, LiDAR360, and ArcMap 10.8 softwareensure cross-platform data consistency and compatibility by supporting common data formats (e.g., LAS/LAZ, GeoTIFF, Shapefile) and standardized coordinate systems (e.g., WGS84, UTM). The UAV imagery point-cloud and 3D model generated by DJI Terra can be exported to LAS or GeoTIFF format. After point-cloud classification and terrain analysis in LiDAR360, spatial mapping and information extraction are conducted using ArcMap 10.8. This process guarantees that data attributes, accuracy, and spatial references are preserved without loss [29,30]. This paper used the Analytic Hierarchy Process (AHP) to calculate the weights of each evaluation factor of the slope, and then finally adopted the hazard evaluation model to assess the stability of the slope.
## 4 ANN Modeling Procedure
### Traditional ANN Modeling Approach  
A schematic illustration of the structure of the implemented ANN model for the traditional direct-inputs approach is shown in Figure 2a. The network consists of an input layer with 7 input nodes where 5 inputs are geometry related, one input is material related, and one input is used to identify the configuration of the test panel. The geometric input parameters are the lead crack half-length, MSD cracks half-length, ligament length, panel width, and the stiffener cross-sectional area. The stiffener cross-sectional area (A\({}_{\text{stf}}\)) will have a value for the stiffened panels only, while its value will be zero for the unstiffened and lap-joint panels. The yield strength is the only material related input parameter that is used here. Unlike the previous study by [PERSON] et al. [27] where three material related inputs were used (yield strength, fracture toughness and material identification index) to account for the difference in ductility between the different materials, only one material-related input is used in this investigation. The yield strength is chosen to be used here rather than the fracture toughness since the material being considered here is ductile. It should be stressed here that the sheet thickness is not included among the geometric input parameters since the yield strength depends on the thickness (i.e., the thickness is accounted for indirectly through the yield strength). Also, since the stress value (rather than the load) is used for the residual strength, it becomes completely unnecessary to include the thickness among the ANN inputs. Finally, the last input parameter is the panel configuration identification number which is used to designate each of the different test panel configurations. Identification numbers from 1 to 4 are assigned to the four distinct test panel configurations that are being used here (1: unstiffened panel, 2: one-bay stiffened panel, 3: two-bay stiffened panel with broken stiffener, and 4: lap-joint panel).
# Sentiment Analysis of Online New Energy Vehicle Reviews
## 2 Related Works
### BERT Model  
Figure 1 illustrates the architecture of the BERT network [28; 29], a deep neural network that utilizes Transformer encoders to generate word embeddings, which are vector representations of text. Initially, BERT was pretrained by Google on extensive corpora, including datasets like BooksCorpus and Wikipedia. Google has made several pretrained BERT models publicly available in their code repository. In this study, an enhanced version of BERT called ERNIE is employed, incorporating improvements such as pretraining on larger corpora and the enhanced integration of entities and knowledge bases. The BERT-based version consists of 12 Transformer layers, each with 768 hidden units, 12 self-attention heads, and a total of 110 million trainable parameters [30].  
Figure 1: BERT model input structure.  
The BERT model uses the [CLS] token to indicate the classification model, although it can be omitted for non-classification tasks. The [CLS] token acts as a separator, denoting the start and end of each sentence. By employing multiple layers of Transformer encoders, the model integrates the three types of embeddings mentioned earlier as inputs. These embeddings are subsequently processed to generate output embedding vectors, representing either a single sentence or a pair of sentences.  
However, BERT has limitations when dealing with specialized vocabulary in Chinese new energy vehicle comments, as it may not encompass all domain-specific terms, and its context-independent representations may not accurately capture the contextual dependencies of specialized vocabulary. Therefore, additional domain-specific optimizations and adjustments may be necessary when handling such specialized vocabulary.
# A Robust Adversarial Example Attack Based on Video Augmentation
## 5 Discussion  
**Q:** Gaussian noise, video shape transformation, and video color transformation are the three distinct varieties of video alteration that are taken into consideration in version 3a. Defenders, on the other hand, have the ability to develop one-of-a-kind transformations that are inaccessible to attackers. Will the presence of unknown transforms reduce the efficacy of v3a?  
**A:** There may be many different algorithms behind the various video enhancements and transformations. The influence of various transformations, however, may be simply broken down into a rise or a reduction in the value of a given pixel when viewed from the perspective of a single pixel. Because of this, v3a is capable of being transferred across several transformations.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems  
In this section, we review some specific applications of graph theory for blockchain network analysis along five major dimensions--decentralization, security and privacy, scalability, simulations and manipulation [56].  
Analyzing decentralization, metrics derived from the connectivity patterns and community structure within blockchain graphs determine whether decision-making power and asset ownership concentrate among a few dominant players. Skewed degree distributions or highly interconnected hubs highlight centralization risks [57].  
Evaluating security: simulating attack mechanisms like Sybils and Denial-of-Service within graph models of blockchain systems allows evaluating consensus resilience even before deployment. Graph metrics also uncover network vulnerabilities [58].  
Preserving privacy: Advanced graph analysis can compromise anonymity by uncovering patterns in transaction histories to uniquely identify entities within the network. Clustering and classification techniques quantify re-identification risks [59].  
Managing scalability: Graph algorithms localized performance bottlenecks, enabling targeted optimizations to improve transaction throughput like sharding and structured peer networking. Comparisons between baseline and stressed system graphs determine breaking points [60].  
NFT manipulation mapping: Utilizing graph theory to analyze the patterns and transactions of NFTs on blockchain networks sheds light on wash trading activities, where individuals artificially inflate the value of NFTs through self-directed trades [61]. By examining the graph's edges and nodes representing transactions and participants, respectively, we can identify cyclical trading patterns that indicate possible manipulation [62]. This analysis not only enhances the transparency and integrity of the NFT market but also assists in the development of regulatory and monitoring tools to combat fraudulent activities [63].  
The subsequent sections elaborate on these applications, and provide mathematical proofs and network simulations to demonstrate the problem-solving efficacy of graph theoretic approaches within the blockchain domain.
## 1 Introduction  
Electromyography (EMG) has been popularly used as a neurophysiological tool for detecting the electrical activities of muscles [1]. EMG data have been used to not only diagnose muscular disorders such as amyotrophic lateral sclerosis (ALS) and muscular dystrophy (MD) but also to measure the extent of muscle fatigue [2; 3; 4]. EMG recordings can be obtained by two methods: surface EMG recording (non-invasive method) and intramuscular EMG recording (invasive method). Surface electromyography (sEMG) does not require needles and has some advantages because of its convenience and non-invasiveness. Rather, a thin and flexible electrode is required for sEMG, since the high mechanical flexibility allows the electrode to follow the uneven skin surface, resulting in good electrical connections in the recording area.  
An EMG signal can be considered as the set of motor unit action potentials (MUAPs) produced by motor units (MUs), which are made up of motor neurons and skeletal muscle fibers, typically from an area of several thousand square microns [5]. Conventional sEMG electrodes have a limited resolution because of their large electrode diameters of 1-20 mm [6], which make them incompatible for small-area sEMG recording. To date, better spatial resolution sEMG recordings have been achieved by introducing micro-size electrodes, which have rapidly proved to be promising research tools [7; 8; 9; 10; 11]. Further, invasive EMG recordings using an implanted flexible microelectrode array in a moving subject (caterpillar) demonstrated a high-resolution recording capability [12]. However, the reduced EMG signal amplitude and signal-to-noise ratio (SNR) due to the small electrode size are potential drawbacks of the microelectrode EMG. In order to better understand small-area sEMG recording, further research on sEMG with a flexible microelectrode array must be conducted and the effect of the electrode size should be investigated.  
Herein, we report Parylene C-based flexible microelectrode arrays for small-area sEMG recording on curved skin surfaces. The characteristics of the microelectrodes were assessed by various means, including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and equivalent circuit modeling. In addition, the recorded sEMG using 8-channel microelectrode arrays were used to verify the viability of multiple sEMG recordings in a small area. Finally, by analyzing the effect of the size of the microelectrode, the relationship between the signal amplitude and the electrode size was studied. This study is expected to benefit investigators who are exploring potential approaches to record small-area sEMG using flexible microelectrode arrays.
## 2 Materials and Methods
### Test Methods  
#### 2.3.1 Electromagnetic Parameters Measurement  
The electromagnetic parameters of materials mainly include the relative complex permittivity (\(\varepsilon_{r}\)) and relative complex permeability (\(\mu_{r}\)), which are defined as Equation (1) and Equation (2), respectively.  
\[\varepsilon_{r}=\varepsilon^{\prime}-j\varepsilon^{\prime\prime} \tag{1}\]  
\[\mu_{r}=\varepsilon^{\prime}-j\varepsilon^{\prime\prime} \tag{2}\]  
where \(\varepsilon^{\prime}\) and \(\mu^{\prime}\) are the real part of \(\varepsilon_{r}\) and \(\mu_{r}\) respectively, reflecting the energy storage capacity of materials; \(\varepsilon^{\prime\prime}\) and \(\mu^{\prime\prime}\) are the imaginary part of \(\varepsilon_{r}\) and \(\mu_{r}\), respectively, representing the loss ability of materials; and \(j\) is imaginary unit.  
The loss angle tangent value (tan\(\delta\)) reflects microwave absorption ability, which can be calculated by Equations (3)-(5). The larger the tan\(\delta\), the better the microwave absorption ability.  
\[\tan\delta_{E}=\frac{\varepsilon^{\prime\prime}}{\varepsilon^{\prime}} \tag{3}\]  
\[\tan\delta_{M}=\frac{\mu^{\prime\prime}}{\mu^{\prime}} \tag{4}\]  
\[\tan\delta=\tan\delta_{E}+\tan\delta_{M} \tag{5}\]  
where tan\(\delta_{E}\) is permittivity loss angle tangent and tan\(\delta_{M}\) is permeability loss angle tangent.  
The N5234B type vector network analyzer produced by Agilent Technologies was employed to measure complex permittivity and complex permeability of limestone filler and magnetite filler through the coaxial method in the frequency range of 2 GHz to 18 GHz. The inner diameter, external diameter, and height of coaxial ring are 3.04 mm, 7 mm, and 2 mm, respectively.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 3 Proposed Methodology
### Data Compression and Network Communication  
1. Data compression Our proposed protocol is used for captured data, primarily images and videos. So, to reduce the data transmission energy, we need to use a compression algorithm. Our proposed protocol uses MRCQ (multi-resolution compression and query) image-based compression approach [26]. Sensor nodes are organized in a hierarchy to establish multiresolution summaries of sensed data in the network [11]. Lower-resolution summaries are transmitted to the sink, whereas high-resolution outlines remain in the network and can be accessed for further analysis [32]. As a result, MRCQ has lower implementation costs and may be used with low-cost sensor systems [33; 34].
2. Node Movement and Network Communications Communication and data transfer between nodes begin when clustering is complete. Whether a node inside a cluster [35], a node across the cluster, or the base station is the intended destination for the data, the CH is responsible for getting it there [36; 37]. EECP-MFO adheres to the RPGM [38] reference point group mobility model. There is a point of reference for all nodes in RPGM that they all will follow. EECP-MFO considers a reference point for the CHs, and all CMs adjust their positions under how their respective CHs move.  
Figure 8: Flowchart of K-means sorted fitness.
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 3 Methodology and Workflow
### Step 4: Seismic Modeling (Optional)  
Seismic modeling, as suggested by [PERSON] [9], is essential for establishing a clear connection between seismic lithology, geomorphology, and facies while also identifying potential pitfalls. Unlike the intricate structural and rock-physical models often discussed in the SEAM project of the Society of Exploration Geophysicists, geological and seismic models do not need to be overly complex for many sedimentary interpretations. Simple acoustic and convolutional models, which geologists can create and interpret, are typically sufficient unless otherwise specified. When elastic and wave-equation-guided modeling is required, staff geophysicists can provide critical assistance.  
Although modeling is generally case-sensitive and should be tailored to specific geological and geophysical conditions, several important lessons can be easily summarized from simple, convolutional models:  
1. For a typical, interfingered sand-shale profile, a 90\({}^{\circ}\)-phase trace is a reasonable estimation for acoustic impedance and, therefore, for lithofacies (Figure 4);
2. Maximum amplitude at \(\lambda/4\) defines the seismic resolvable limit and, at the same time, illustrates the amplitude tuning phenomenon that is a pitfall to porosity mapping (Figures 3 and 4);
3. Seismic onlap or downlink patterns do not necessarily indicate the true pinch-out points of lithofacies [51];
4. Seismic reflections do not necessarily follow geologic-time surfaces [7; 52; 53]; striatal slices are better choices for sedimentary facies imaging [7; 54];
5. For a thin and shingled procafadational sequence common in shallow-water deltaic systems or prograding carbonate platforms (Figure 8a), seismic clinoforms are challenging to recognize in the vertical section (Figure 8b) but more visible in a horizontal view (Figure 8c), showing the power of horizontal seismic resolution [23].  
It should be pointed out that, in practice, no models are complex enough to cover true geologic realities and some simplifications in modeling are always necessary. When making interpretations of seismic models, we should always consider multiple possible scenarios to avoid misleading, over-stretching conclusions.  
Figure 7: An optimal spatial resolution (1 m) is indicated by a striatal slice created from a 50 Hz dominant-frequency 3D volume in a lacustrine shallow-water deltaic system. The thickness (m) of sandstone measured in gamma ray logs in wells shows a linear relationship with seismic reflection amplitude. Modified from Zeng [12].
## 5 Prototype and Grasping Experiments  
In order to verify the design, analysis, and the basic function of the robot hand, a prototype of the VGS hand was manufactured, as shown in Figure 10.  
The prototype was controlled by Arduino and it was fixed on a robot arm to conduct the grasping experiments. The basic material used for the manufacture is the polylactic acid (PLA), which is small in density and has relatively good force properties. Both quantitative and qualitative experiments were conducted.  
In practical experiments, both rigid and soft objects are used for grasping. In order to test the linear-parallel pinching function, no additional displacement from the robot arm was provided for the pinching process. In addition, for some pinching experiments, the two fingers have different rotational angles around the shaft, which can also help prove that the end of the finger moves along a straight line. The details of the experiments are shown in Figure 11.  
As shown in Figure 11, the hand can perform hybrid grasping modes for different situations. It can grasp objects with different shapes and sizes and has a very good grasping ability to meet the requirements for industrial use, such as assembly or painting. Meanwhile, during the pinching process, the distal phalanx moves along a straight line as expected, which is suitable for grasping thin objects on a flat surface and overcomes the bottleneck of traditional hands. The prototype verifies the design and analysis to be reasonable. Some basic data and test results for the prototype are listed in Table 1.  
Figure 10: The prototype of the VGS hand.