esa-sceva/satcom-chunk-collection huggingface dataset:
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
## 2 Leakage Protection Method Based on 3 AC Detection Circuit
### Validation of 3 AC Mathematical Model  
Simulink is used to build a simulation model of the power supply system incorporating additional three-phase asymmetric capacitors. The parameters defined in the mathematical model are utilized in this simulation. The simulation diagram is shown in Figure 3:When the system is under normal operation, the \(\dot{I}_{\text{C0}}\), its waveform, effective value, and phase angle are shown in Figure 4:  
Through the simulation results, it can be observed that under the normal operation of the system, the amplitude and phase angle of the artificially manufactured zero-sequence \(\dot{I}_{\text{C0}}\) remain basically constant, with no significant fluctuation or change. Specifically, in real operational conditions, the RMS value of the zero-sequence current is maintained at approximately 25 mA, and the phase angle remains steady at around 90 degrees. This phenomenon shows that the system using the 3 AC method can effectively maintain the stability of the zero-sequence current under normal operating conditions and is not influenced by external factors. Such consistency ensures the reliability of this leakage protection method, providing a dependable safeguard against potential electrical hazards.  
Taking the phase A leakage fault as an example, the insulation resistance is set to 20 k\(\Omega\), and the simulation is carried out. The results are shown in Figure 5.  
Figure 4: The simulation results of \(\dot{I}_{\text{C0}}\) under normal operation of the system.  
Figure 3: Simulation diagram of power supply system using 3 AC method.  
A phase A leakage of 1.5 s occurs, and its phase angle and amplitude change significantly, that is, leakage fault occurs.  
The formula script of \(\hat{I}_{\text{C0}}\) is written in Matlab script, and the relationship between insulation resistance, \(\hat{I}_{\text{C0}}\) amplitude, and phase under different leakage faults is drawn.  
As shown in Figure 6, the colors in the upper right corner correspond to their respective fault modes. The dark blue line in the figure is the amplitude and phase of the \(\hat{I}_{\text{C0}}\) of the phase A leakage fault. Observing the point of 20 k\(\Omega\), it can be seen that the amplitude is about 25 mA, and the phase angle is about 137 degrees. The calculation results are basically consistent with the data obtained by simulation. Then, the \(\hat{I}_{\text{C0}}\) under different leakage conditions is simulated, and the calculated \(\hat{I}_{\text{C0}}\) is compared. The results are largely consistent, thereby validating the correctness of Formula (5).
## 1 Introduction  
\"Fall\" is defined by World Health Organization (WHO) as an event which results in a person coming to rest inadvertently on the ground or floor, or other lower level [1, 2]. A backward fall can lead to serious bodily injury. The consequence of the fall may be contusions of the body as well as bone fractures, which are especially dangerous for the elderly due to possible problems with bone fusion. However, it is the head hitting against the ground [3] that is particularly dangerous, as this can result in fatalities. Falls are also the cause of dangerous injuries of the cervical spine [4, 5].  
Some scientists have found that proper movement habits in a fall can protect against certain bodily injuries to some extent. The scientific literature reports that most countries do not teach in their schools appropriate movement habits during falls [6, 7]. This work analyses backward falls of which a person is aware and can respond with specific motor reactions during the fall. Such movement reactions are possible if the values of the forces causing the fall do not exceed certain values [8].  
Most research has been concerned with fall prevention by means of the elimination of external factors leading to a fall at work or through research on ways of improving people's reaction to their balance disturbances [9, 10]. Accelerating treadmills are traditionally used to analyze people's reactions to fall-generating forces causing them to lose their balance [11]. Platforms [12] and foot-clamps [13, 14] can also be used to study backward falls.  
Some researchers claim that, under certain conditions, falls are unavoidable, so it is quite reasonable to study what happens to a human body in a fall [6, 15]. To assess the risks of falling backwards research methods have been devised which examine body movements on hitting the ground [15; 16; 17; 18]. These nonapparatus methods describe fall movement patterns similar to those performed during a gymnastic backward roll. One can also come across falls performed backward with side aligning of the body. This kind of fall is taught in some martial arts [6; 19; 20].  
The aim of this article is to investigate the susceptibility to head injuries in physical education students who do not train a specific sports discipline and those who use the fall performed backward with side aligning of the body technique. The second goal is the biomechanical analysis of the impact of the fall technique on the likelihood of head injury. The rotating training simulator (RTS) was used in the course of the research as it forces a backward fall utilizing inertia forces [6; 21]. The loss of balance is induced by the inertia force leading to the person's falling down, which is a step forward compared to the previously described nonapparatus methods [22]. During the validation of this method, it was found that physical education students reveal certain motor habits when falling backwards [6]. The simulator itself is devised for people whose work or sports discipline involves falling. Its creator thus limits the research to adults with a high degree of physical fitness.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 4 Experimentation
### Time to Generate the Keys  
In this experiment, the amount of time needed to produce the key was measured to draw an assessment regarding the multi-key's effect on the encryption and decryption process. When it comes to encryption and decryption, the process and equation set that is utilised to derive the keys are exactly the same. Hence, in order to facilitate analysis, the delay that was computed for the encryption process is implemented in this section.  
This section compares the key generation technique proposed in this work with the method proposed in [13]. The proposed method uses the equation set for dynamic key generation, and in [13], only the ECC equation is used.  
The amount of time necessary to generate each key is illustrated in Figure 5. The public key of the receiver, in addition to some of the video data, is utilised as the foundation for the key that will be used to encrypt the first video chunk. The receiver details, the receiver's public key, and the previously computed key are used to acquire the remaining keys. Because the duration of the parameters remains the same throughout this process, the amount of time that passes between each key is not subject to much variation. When contrasted with the multi-key approach described in [13], the findings that were obtained are discussed. As a result of the utilised equation set, the delay consumed by the proposed approach is slightly increased. The key for each video chunk is generated with the help of an equation that was chosen at random.  
Figure 4: The length of time it takes the video processing module to divide the video into chunks (Compared with Multi-Key [13]).
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 5 Experimental Results and Analysis
### Comparison of Algorithms and Indicators  
LFU: This strategy prioritizes replacing the least frequently used content when storage space is low.  
LRU: This strategy prioritizes replacing the least recently used content when storage space is low.  
ECC [31]: This strategy uses the neural cooperative filtering algorithm to obtain popular content and then uses the greedy algorithm to optimize the caching strategy.  
Distributed: This strategy caches a portion of top-ranked popular content and does not cache duplicate content, so distributed policies can cache more content items.  
P_scheme [36]: This strategy establishes vehicle state and mobility models in the IoV network of ICN and performs active caching.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline residual network depth & 10 \\ dilation interval & 1, 2, 4, 8 \\ kernel size & 2 \\ sliding window & 2000 \\ sliding step length & 200 \\ input length & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter table.  
This article shows how THCS improves system performance through two metrics, namely, cache hit ratio (HR) and average content delivery latency (ADL).  
HR represents the hit content as a percentage of the request content. Specifically, \(HR=B/G\), where \(G\) represents the total number of requests received during period \(\Delta t\), and \(B\) is the number of request hits.  
ADL indicates the average delay of the request, expressed as:  
\[ADL=min\sum_{m=1}^{M}\sum_{i=1}^{F}P_{t}(r_{m},e_{i})d_{r_{\varphi},r_{m},i} \tag{26}\]
# Reconstructing Polarization Multiplexing Terahertz Holographic Images with Transmissive Metasurface
## 4 Results and Discussion
Figure 5: (**a**) The designed metasurface with merged phase profiles to reconstruct polariza-tion-multiplexing holographic images. (**b**) Retrieved phase profile for the hologram patterns with LP light illumination. The reconstructed images on the focal plane to present the polarization multiplexing holograms with the incident (**c**) LCP, (**d**) RCP, and (**e**) LP lights for working fre-quency of 0.85 THz. It is noted that the design is feasible because the proposed metalens could be fabricated on silicon wafer by standard electron beam lithography and etching process [19], which can follow the following steps: (a) clean the Si wafer in ultrasonic bath; (b) the photoresist is spin-coated onto the cleaned Si wafer; (c) the resist film is exposed by using a mask aligner and developed; (d) the resist film is etched; (e) the photoresist is removed, and the Si meta-atom is finally obtained. On the other hand, the successful demonstration of the terahertz multifocal polarization-controlled metalens shows that it is feasible to use the near-field scanning terahertz microscope system to perform the characterization of the designed metalens samples in this paper. A femtosecond fiber laser is used to generate a laser beam with a pulse width of 50 fs and a central wavelength of 1550 nm, and the laser beam is divided into two parts by a beam splitter. A part of the light is transmitted to the emitter of the commercial photoconductive antenna, which can generate 0.2\(\sim\)1.5 THz broadband terahertz radiation and irradiate the metalens sample. Another part of the light will be converted in free space, and then the second harmonic will be generated in periodically poled lithium niobate (PPLN). When it is irradiated on the terahertz probe used to detect the electric field distribution, the terahertz beam can be detected with high-resolution pixels. In front of the metalens sample, a group of terahertz polarizers are placed to ensure that the incident terahertz beam has the needed polarization (i.e., LCP, RCP, and LP). After passing through the metalens sample, the hologram pattern can be observed with an objective lens in the confocal plane with the metalens sample, and the polarization profile of the hologram pattern can be detected with an analyzer and collected by a terahertz probe. The terahertz probe is installed on the three-dimensional translation platform, which can realize the three-dimensional electric field scanning. The terahertz probe scans the electric field distribution of the terahertz beam in the imaging area to collect the target focal points at the focal plane of the metalens.
## 3 Methodology
### 3 DGS-Based Dense Scene Representation
For a 2D point \(p^{2D}\) in frame \(i\), its projection onto frame \(j\) is expressed as  
\[p_{i\to j}^{2D}=f_{warp}(\xi_{ji},p_{i}^{2D},D(p_{i}))=KT_{ji}\left(K^{-1}D(p_ {i}^{2D})p_{homo_{ij}}^{2D}\right), \tag{16}\]  
where \(K\) is the camera's intrinsic matrix, \(T_{ji}\) is the transformation matrix from frame \(i\) to frame \(j\), and \(p_{homo_{ij}}^{2D}=(u,v,1)\) represents the homogeneous coordinates of point \(p\). This formulation provides an accurate method for projecting 2D points across frames while accounting for both camera pose changes and depth information. Because the edge features exhibit strong geometric invariance in the scene, we precompute a distance transform map \(D_{j}\) in the target frame \(j\) to measure the distance between the projected points and their nearest edges. This step allows for precise edge matching, improving feature alignment during the scene optimization process. Based on this, we define the edge projection loss as follows:  
\[\mathcal{L}_{edge}=\sum_{p_{i}^{2D}\in E_{i}}\rho\left(D_{j}(f_{warp}(\xi_{ji},p_{i}^{2D},D(p_{i}^{2D})))\cdot M_{j}\right) \tag{17}\]  
where \(E_{i}\) represents the set of detected edge points in frame \(i\), \(\rho\) is the Huber loss function [26] used to reduce the influence of outliers, and \(M_{j}\) is the dynamic region mask. This robust formulation ensures that the contribution of outliers such as points with high projection errors is minimized, thereby improving the quality of the edge projection alignment. When the projection distance error exceeds a threshold \(\delta_{e}\), the corresponding point is treated as an outlier and removed. For non-keyframes (\(j\
otin K\)), the pose optimization problem is formulated as follows:  
\[\xi_{ji}^{*}=\arg\min_{\xi_{ji}}\lambda\mathcal{L}_{edge}. \tag{18}\]  
This optimization helps to refine camera poses and scene structure by incorporating geometric constraints from edges. Edge features provide stable geometric constraints, offering rich appearance details for 3 DGS scene reconstruction. In this way, the edge projection constraint enhances the system's reconstruction accuracy, especially in challenging environments with intricate structures or occlusions. Incorporating this loss function further improves the quality of the 3D map, ensuring that fine geometric details are preserved throughout the reconstruction process.
## 2 Materials and Methods
### Device Setup and Data Collection
#### 2.2.3 Video and Image Processing  
Image and Video Capture: High-resolution images and videos were captured using a USB digital microscope (Dino-Lite Digital Microscope AF4515 ZTL). To ensure the highest image quality, a smart cellphone was positioned underneath the device with a white light screen, optimizing the illumination for image and video capture. The prepared device was placed over the white screen light. The microscope was focused and positioned on top, as shown in the third stage in Figure 3. The experiment was then recorded with a sequence of images at 0.1 s of exposure. The video was recorded and saved in the repository for further analysis.  
Software Utilization: The DINO CAPTURE 2.0 software was employed to save the image and video files, ensuring standardized and organized data storage. Additionally, it facilitated the real-time observation of the microfluidic processes. The experiment was recorded and digitized through images.  
Contact Angle Measurement: The contact angles at the fluid interfaces were quantified using images obtained from the USB microscope. Multiple measurements (n > 3) were performed for statistical reliability. The analysis was carried out using the DINO CAPTURE 2.0 software, allowing for the accurate determination of the contact angles.  
Channel size: To determine the real size of the channel, a perfilometer (Bruker brand, model Dektak XT) was used. It was validated by image correlation with the Dino-Lite Digital Microscope (AnMo Electronics Corporation, Taipei, Taiwan).
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 2 Materials and Methods
### Orchard Robotic Vehicle Design
#### 2.1.2 Navigation System Design of the Orchard Robotic Vehicle  
The framework of the automatic orchard navigation system is shown in Figure 2, which mainly focuses on the positioning unit, decision unit, and control unit. The localization unit consists of RTK-GNSS, LiDAR, and the inertial measurement unit, and provides the central control unit of the robot with localization information and RTK-GNSS signal status information. RTK-GNSS includes a wireless transmission radio, GNSS receiver, GNSS antenna, and GNSS base station. The decision unit is connected to each module through the serial port to process the positioning information. Firstly, based on RTK-GNSS status  
Figure 1: Robotic vehicle overall hardware diagram. Note: 1. LiDAR; 2. GNSS antenna; 3. signal receiver; 4. industrial control unit; 5. GNSS/INS receiver; 6. mobile chassis; 7. mobile power supply.  
information, the system selects the appropriate navigation positioning mode. Next, it calculates the navigation path and determines the speeds of the left and right wheels using the PID control algorithm. Finally, the system issues control commands to the motion control module.  
The system software of the orchard mobile platform runs on the Linux operating system, using ROS as the software integration platform for the autonomous positioning and navigation system. The robot vehicle acquires and publishes the satellite-based self-positioning information and positioning status, as well as the LiDAR-based self-positioning information and positioning status, through the ROS-driven sensors. The information processing module subscribes to the sensor information through ROS and analyzes and processes the data. When the GNSS status, which indicates satellite positioning status information, is at 2, meaning the GNSS differential signal is good, the system navigates using GNSS positioning information. If the status is not 2, it switches to using laser odometer positioning information for navigation and positioning. After the information is processed and calculated, the system sends the robot's motion control signals to the motion control module. Upon receiving the command, the drive controller activates the left and right brushless DC motors, causing the main control wheel to rotate and thereby controlling the movement of the robot vehicle. The overall architecture of the software system is depicted in Figure 3.
## 3 Nanoparticles in EOR
### Zinc Oxide  
Zinc oxide (ZnO) has been widely studied for wettability change in EOR in the past few years. [PERSON] et al. [124] proved that ZnO has a better performance in oil recovery than Al\({}_{2}\)O\({}_{3}\) (72% ROIP for ZnO vs. 53% for Al\({}_{2}\)O\({}_{3}\)) with lower IFT values. They found that a larger particle size of ZnO gives about a 145% higher recovery than smaller sizes. Similarly, [PERSON] et al. [133] investigated oil recovery by 0.2 wt % ZnO NPs over heavy oil-saturated sandstone and carbonate core samples. Improved oil recovery with both carbonate (8.89% of OOIP) and sandstone (up to 20.68% OOIP) core plugs was achieved. ZnO NP was able to reduce the viscosity of heavy oil and alter the wettability of the grain surface. [PERSON] et al. [134] successfully recovered 26% of the remaining oil in a glass micromodel saturated with heavy oil (viscosity 12.31 cP) through 30 min of electromagnetic (EM) exposure with a simultaneous ZnO nanofluid injection.  
However, [PERSON] et al. [12] found that ZnO dispersed in brine and water caused permeability reduction due to the agglomeration of ZnO NPs into larger particles, which blocked the pore throats of the core. Since stability is one of the key factors for a successful EOR application, [PERSON] et al. [135] performed further studies on the stability of ZnO NPs dispersed in water by using different anionic surfactants as stabilizers with various NPs concentrations. They found that 0.1 wt % ZnO NPs stabilized with 0.025 wt % sodium dodecyl benzenesulfonate (SDBS) have the highest stability at 95 \({}^{\circ}\)C with viscosity enhancement up to 11%. The combination of surfactant, pH, and ultrasonication for ZnO nanofluid preparation was also suggested to affect the nanofluid mobility properties.
## 7 Classification of Mobile Sink based Data Collection Schemes
### Path Constrained Sink Mobility-Based Schemes
In the reassessment process, one of the following three choices is made: (1) the mobile sink is still within reach of the old neighbor node; (2) forwarder nodes are discovered; (3) rerouting is required. This process continues until the mobile sink arrives at a terminal position. Using the first choice, the sink instructs the last hop node to adjust its radio to cover the sink's next move if its residual energy is sufficient. When the residual energy is not enough, the sink exercises the second choice of appointing a forwarder. Finally, if no reasonable forwarder is available, then the sink exercises the last option to set up new routes. Rerouting is also triggered if the current paths to sink do not satisfy the end-to-end delay bound. A good feature of this scheme is that when the sink moves closer to its next stop, it can still overhear the communication between its new and old neighbor nodes and thus receives it directly. This results in reduced overall delay of that packet, thereby reducing the number of multi-hops. The main limitation of this scheme is the assumption that sink's navigational map and schedule are known, which may not be realized in many application environments. The aforementioned path constrained-based data collection schemes are summarized in Table 1. It illustrates each scheme in terms of any constraints it imposes on sink mobility, the number of sinks involved for data collection, data reporting modes (proactive, reactive, query-driven, periodic, or wait-and-upload), network architecture, network overhead control mechanism, an estimate of network control overhead, together with the main goal(s).