esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Latency and Bandwidth
#### 8.1.1 Network Latency  
Network latency refers to the delay between a user's action and the response from the network in a digital environment [244]. In the context of the metaverse, which relies heavily on real-time interactions and seamless connectivity, latency can significantly impact user experience. High latency can cause noticeable delays in data transmission, leading to lagging or choppy movements, delayed responses from virtual entities, and disrupted interactions between users. These delays can break the immersion and fluidity of the metaverse, making it difficult for users to enjoy a smooth and interactive experience.  
The effects of network latency on the metaverse are particularly pronounced in activities that require real-time synchronization [245], such as multiplayer gaming, live events, and social interactions. When latency is high, users may experience out-of-sync movements, communication breakdowns, and inconsistencies in the virtual environment. Lack of synchronization can lead to frustration and disengagement, undermining the metaverse's overall effectiveness and appeal. Ensuring low latency is critical for maintaining the real-time responsiveness and immersive quality essential for a compelling metaverse experience. To address these challenges, advancements in network infrastructure, such as 5G technology and edge computing, are being leveraged to reduce latency and enhance the reliability of real-time interactions in the metaverse.
## 4 Numerical Experiments  
### Preparation of Head MR Images  
The institutional review board of the University of Tokyo Hospital approved the use of head MR images (T1 WI) taken in the hospital for the present retrospective study. From November 2006 to December 2017, high-resolution volumetric fast spoiled gradient-echo MR imaging was performed with a Signa EXCITE and a Discovery MR750 scanner (GE Healthcare Japan, Tokyo, Japan) (repetition time, 6.4 ms; echo time, 2.0 ms; inversion time, 450 ms; field-of-view, 25 cm; flip angle, 15 deg; acquisition matrix, 256 \(\times\) 256; number of excitations, 0.5; and voxel dimensions 0.98 \(\times\) 0.98 \(\times\) 1.0 mm\({}^{3}\)) using an 8-channel head coil. From January 2018 to April 2021, high-resolution volumetric magnetization-prepared rapid gradient-echo MR images were acquired with a Biograph mMR scanner (repetition time, 1660 ms; echo time, 2.4 ms; inversion time, 910 ms; field-of-view, 25 cm; flip angle, 8 deg; acquisition matrix, 256 \(\times\) 256; number of excitations, 1; and voxel dimensions, 0.98 \(\times\) 0.98 \(\times\) 1.0 mm\({}^{3}\)) using a 16-channel receiver coil (These protocols are the same as in [24].). We extracted only 1327 head MR images (volumes) from a large set (including both normal and abnormal cases) of images taken with the above protocols. The resolution of each two-dimensional MR image (sagittal slices) was 256 \(\times\) 256. We stacked these two-dimensional images to create a three-dimensional image of 256 \(\times\) 256 \(\times\) 256 size with padding. These head MR images were processed without skull stripping. The images were divided into training (1224) and test (103) datasets. Each image was normalized so that the whole pixel range of all the images was included. We then mapped the range of these images onto \([-1,1]\) for the training.
## 3 Results and Discussion
### Calorific Value Analysis
#### 3.2.2 Prediction of Unknown Samples  
Using the spectral data of an unknown sample, the calorific value can be predicted by the PLSR model. As shown in Figure 12, seven samples were tested. UO1 and UO2 are the unknown samples that were not used in the calibration of the previous PLSR model. The samples named from UBI to UB5 are the blended unknown samples. The predicted error in calorific values for the unknown samples are listed in Table 4 using the reference calorific value. The calorific values of UO1 and UO2 were known to be 6350 kcal/kg from air-dried basis analysis. As indicated in Table 4, the relative errors range from 0.0663% to 2.7629%. The relative error values of all samples were less than 3%.
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 5 Computational Simulations of Dynamical Systems  
This section explores physics-informed neural networks (PINNs) for parameter identification and solution in various dynamical systems. Parameter identification is crucial, especially when working with ordinary differential equations (ODEs), as each model is essentially unique. ODEs showcase their practicality when the parameters are correctly determined, emphasizing this methodology's significance. The identification of parameters becomes evident as we develop a model employing ordinary differential equations (ODEs) to elucidate the fundamental occurrences. These parameters include different characteristics of the modeled system, such as rate constants, initial conditions, and coefficients, defining the form, rate of change, and stability of the solution. The parameters that make up an ODE can also determine the computational techniques used to solve it and have a big impact on the quality of the solution. Consequently, parameter identification is crucial for making insightful predictions and adequately assessing the simulation of the modeled dynamical system. We consider five benchmark problems derived from the existing literature in our discussion. For each example, we will specify the choice of neurons, the number of hidden layers, activation functions, and other network parameters. Two of these five problems have an exact analytical solution, which will be leveraged to validate the model's accuracy. In addition, the relative error metric showing the fitting accuracy of each dynamical system using the PINN algorithm will be provided.
# Enhanced Proton Acceleration from Laser Interaction with a Tailored Nanowire Target
## 4 Conclusions  
In summary, a novel nanowire target with a corrugated front surface was proposed to improve the proton acceleration via TNSA. Two-dimensional particle-in-cell simulations demonstrated that this target design can significantly increase the cut-off energy, the laser-to-proton energy conversion efficiency and the number of accelerated protons, compared to an ordinary nanowire target. This enhancement can be attributed to suppressed reflectivity and enhanced absorption by the corrugated front surface. Furthermore, a series of simulations were carried out with various laser and target parameters to verify the robustness of the CNT. The simulation results show that the CNT always has better performance than the PNT and is robust with different simulation parameters. Given the technological advances  
Figure 6: (**a**) Energy spectra of protons at the end of simulation. (**b**,**c**) Density of electrons at \(t=22T_{0}\).  
Figure 7: Cutoff energies of protons versus fill factors for the PNT and the CNT.  
in the fabrication of nano-structured targets, our scheme may provide insight for obtaining high-energy proton beams without requiring higher laser intensity.  
Conceptualization, simulation, data analysis and writing--original draft preparation, Y.C.; writing--review and editing, L.C., C.Z. and Z.L.; project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 11875091 and 11975059.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The EPOCH code is used under UK EPSRC contract (EP/G055165/1 and EP/G056803/1). [PERSON] would like to thank [PERSON], [PERSON] and [PERSON] for useful discussions and help.  
The authors declare no conflict of interest.
# Minkowski Island and Crossbar Fractal Microstrip Antennas for Broadband Applications
###### Abstract  
The paper presents microstrip patch antennas, which are based on the fractal antenna concept, and use planar periodic geometries, providing improved characteristics. The properties of the fractal structure were used in a single-fractal layer design as well as in a design, which employs fractals on both the upper and bottom layers of the antenna. The final structure, i.e., a double-fractal layer antenna has been optimized to enhance bandwidth and gain of the microstrip antenna. The proposed geometry significantly improved antenna performance. The antenna could support an ultra-wide bandwidth ranging from 4.1 to 19.4 GHz, demonstrating higher gain with an average value of 6 dBi over the frequency range, and a radiation capability directed in the horizontal plane of the antenna.  
metasurfaces; microstrip antenna; fractal antenna 1
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 5 Results and Discussion
### Numerical Results
#### 5.3.1 Results of Scenario 1  
Since we used three different areas in our case study as explained in the network topology section. We need to evaluate the overall network performance by sending all the data from the lower layer (IoT-layer) to the upper layers (edge and cloud) in all three areas. The average latency for sending all data from the first layer to the edge server has been shown in Figure 10. We can notice the latency for the three areas is different due to the accessibility to the medium and the data size in which Area-1 recorded a low latency 15.5 mS. In comparison, Area-3 recorded the height value of the average latency (25.8 mS). Figures 11 and 12 show the average throughput used by each area during the transmission and the average packet delivery ratio (PDR) that offers the average success of packet delivery, respectively.  
Figure 10: Latency of areas for scenario 1.  
The relation between latency versus throughput is presented in Figure 13. We notice that throughput has a significant impact on both latency and PDR as increasing the throughput helps reduce the latency and increases the successful number of delivery packets. Considering power consumption as an essential metric for performance evaluation, we calculate the power consumption for edge servers and sensors. The amount of energy consumed for each device has been calculated based on four different models: low power mode (LPM), processing mode (CPU), receiving (Rx), and transmission (Tx). The total power consumption of sensors for each area (Area-1, Area-2, Area-3) is presented in Figure 14. Area-3 shows high power consumption because extensive data have been collected from that area, while Area-1 power looks small because it collected small data. The details of power consumption for Area-1, Area-2, and Area-3 including all modes are presented in Figures 15-17, respectively.  
Figure 16: Power modes for sensor in Area-2.  
Figure 14: Power consumption for sensors.  
Figure 17: Power modes for sensor in Area-3.  
Figure 15: Power modes for sensor in Area-1.  
On the other hand, the total power consumption for edge servers has been calculated using the same mods used with sensors. Figure 18 presents the total power of all edge servers (Area-1, Area-2, and Area-3). We can notice that the edge servers used a huge amount of power in this scenario because all the tasks of training and detection for the MLP model have been applied at the edge layer. Most of this power was consumed by the processing mode (CPU) that performed all training and detection tasks, and the rest of the consumed power was distributed between the other modes, high for the receiving mode (Rx). Figures 19-21 present the breakdown power consumption of all servers. These figures show in which mode the power consumption rate rises (LPM, CPU, Rx, Tx).
# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 3 Experiments and Analysis of Results
### The Results of Different Fine-Tuning Method  
AgriVLM realizes the recognition of crop growth stages, pests and diseases, and other tasks by fine-tuning a large amount of agricultural data to a visual language large model, where, in order to verify the effect of different fine-tuning methods on the experimental results, we also used the p-tuning method [33] for fine-tuning to compare it with LoRA. The key idea of p-tuning is to add some consecutive embedding to the embedding layer of the model and train only this part. This method greatly reduces the complexity and cost of the model because, during the fine-tuning process, only the added embedding parameters need to be trained, and the original model parameters remain unchanged. The accuracy of different fine-tuning methods on different tasks is shown in Figure 4.  
The results shown in this figure indicate that LoRA generally outperforms P-tuning. Although the results of the two fine-tuning methods are the same in poultry recognition, this is due to the relatively obvious differences between the two categories, which makes it easier for the model to recognize them. Secondly, in chili pepper disease recognition, the difference between the two is not obvious, only 0.82%. These results illustrate that in simple tasks such as binary classification or data with obvious differences, there is not much disparity in the learning effect between the two fine-tuning methods. However, in the multi-classification tasks in cabbage, strawberry growth stage recognition, and tomato disease recognition, the accuracy disparity between the two fine-tuning methods is constantly increasing, reaching 1.25%, 4.35%, and 5%, respectively, which shows that the stability as well as the adaptability of LoRA is better than P-tuning.
# The Impact of an Electric Machine Body on EM Wave Propagation in RTMS
###### Abstract  
The metallic components of an electric machine exert a notable impact on the electromagnetic transmission between an external recipient and an internal shaft sensor. The RTMS (rotor temperature monitoring system) is aimed at boosting power transfer in the machine, regardless of the considerable impact of its components. This research assesses the influence of the components of an electric machine on shaft sensor transmission and specifically addresses the packaging needs of the receiver antenna. The study includes a comparative analysis of two antennas. HFSS (high frequency structure simulator) simulation was utilized to identify the antenna with a superior propagation factor. The principal contribution of this paper is the assessment of how the electrical machine body affects the transmission and propagation of rotor sensor signals, establishing a connection between these effects and the packaging criteria for the receiver antenna. Moreover, the paper presents an antenna design that capitalizes on the electrical machine body to enhance the power transmission effectiveness. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, provides valuable insights into the propagation characteristics, and contributes to a comprehensive understanding of the electromagnetic dynamics within the system.  
rotor temperature monitoring system; IoT industry 4.0; electrical machine; wireless communication; antennae +
Footnote †: journal: _machines_  
Article
Token-based method is a static access control. After the user passes the identity authentication of the system, the system assigns a Token to the user. Each Token uniquely identifies a user, so in later operations, the system will no longer authenticate the user. The computing environment where the access topic is located typically caches the authentication token to increase authentication efficiency. However, there is a significant security problem with this method. On the one hand, if the security of the main environment deteriorates, the authentication token may be used illegally, resulting in the illegal use of system resources and services; on the other hand, consider a scenario when the system is currently in an emergency state (such as CPU and memory utilization are saturated), traditional access control will continue to accept access requests as long as the request is legitimate, increasing the risk of a system crash.  
Through the above analysis, this paper adopts a different strategy, starting from the likelihood that a request could compromise system security, along with the dynamic changes of the object environment, based on attribute-based access control. This paper proposes a set of thorough risk assessment indicators and quantitative analysis models to adapt access control decision-making to the effects of the environment's dynamic change.  
The remainder of this paper is organized as follows. A literature review is provided in Section 2. Section 3 introduces the proposed dynamic access model based on risk assessment. Experimental results are provided in Section 4. Finally, the content of this paper is summarized in Section 5.
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 2 Materials and Methods
### ShuffleNet V2 Model  
The ShuffleNet V2 network developed by [PERSON] et al. [13] is a lightweight model that builds upon the ShuffleNet V1 architecture. The following four key factors can significantly affect the speed of the ShuffleNet V2 network. (1) When the input and output channels of the convolutional layer are equal, the model operates at its maximum speed with minimal memory access time. (2) Excessive convolution operations can increase memory access time, resulting in a slower model speed. (3) The fewer the branches in the model network, the faster the speed. (4) Adding point-by-point operations can decelerate the model and the frequency of such additions can be minimized.  
The ShuffleNet V2 comprises two main components: the basic unit and the downsampling unit, as depicted in Figure 3a,b, respectively. In the basic unit, the input image features are divided evenly into two groups under a channel split operation. The right branch sequentially traverses the 1 \(\times\) 1 convolution, 3 \(\times\) 3 depth-wise convolution, and a 1 \(\times\) 1 convolution, while the left branch remains unprocessed. Subsequently, the left and right branches are concatenated, and channel shuffling is performed to enhance the information exchange between different groups. In the downsampling unit, the image features can directly enter both branches. The right branch undergoes sequential processing through a 1 \(\times\) 1 convolution, 3 \(\times\) 3 depth-wise convolution with a stride of 2, and a 1 \(\times\) 1 convolution, and the left branch first undergoes a 3 \(\times\) 3 depth-wise convolution with a stride of 2 and then a 1 \(\times\) 1 convolution. Subsequently, the left and right branches are concatenated and channel shuffling enhances information exchange between different groups.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
## 1 Introduction
In the experiments, the drop-weight impact test is the most common methodology [5; 7; 21] to measure the impact force between a falling weight and a beam to observe the failure mode and determine the dynamic response. On the other hand, the experiments could lead to numerical analyses for the validation and improvement of numerical models, ultimately resulting in more accurate and dependable simulations. Therefore, along with experimental approaches, most of the previous studies were especially intended to propose a numerical analysis due to its cost-effectiveness, rapid prototyping, reproducibility, and ability to predict and explore a wide range of scenarios, which are limitations of the experiments. Most researchers applied the finite element method (FEM) to perform the impact response analysis of RC beams [22; 23]. In addition, the smoothed particle hydrodynamics (SPH) method developed by [PERSON] and [PERSON], and [PERSON] in 1977 [24; 25], is a numerical technique and belongs to the category of mesh-free discretization techniques that could be used in continuum mechanics to simulate solid dynamics, including elasticity and plasticity [26]. Considering the advantage of SPH over FEM that it is easy to analyze the final fracture conditions accurately and realistically by taking advantage of the mesh-free method [27], SPH is often employed to analyze the impact behavior of structures [26; 28]. In the current study, the investigation of beam behaviors was conducted through experimental tests and numerical analyses to investigate the impact resistance of reinforced concrete (RC) beams strengthened with CFC panels. In the experiment, the drop-weight tests were performed to compare the impact resistance of conventional RC beams with that of specimens strengthened with different strengthening patterns of CFC panels, such as those attached either to the bottom or to the sides and bottom. Additionally, the study also presents an impact analysis using the SPH method to simulate the impact fracture behavior of RC beams. Considering the mechanical properties of adhesives to the SPH method, virtual particles were proposed and attempted to analytically reproduce the peeling between concrete and the CFC panels. Furthermore, a parametric study was conducted using a self-coded Java program that verified the accuracy by impact experiment results, and the fracture behavior of each test object under the impact load was analyzed.
## 5 Conclusions
### Discussion
In particular, the indispensability of optical networks to the development of 5G renders observations on the distribution of optical network technologies crucial. Presenting the whole picture of 5G optical network technologies, the current study filled this research gap by revealing the key technologies and development trends in 5G optical networks using a technology network analysis. In terms of policy suggestions, the technology map of 5G optical networks could provide valuable insights for the government. This technology network of 5G optical networks revealed the technical development focuses, which provided the government with relevant information, particularly that related to the shifts in key technology domains in recent years. The findings of the current study indicate that multiplex communication systems have become the key technology. With the ever-increasing demand for communications between people and objects as well as between objects, multiplex communication systems will play an increasingly crucial role in coming years. Therefore, the government should engage domestic hardware and software service providers in developing comprehensive solutions in response to the development trends of the optical communication industry to develop technologies that match market demands.
## 5 Experimental Results
### Evaluation on an Embedded System  
The evaluated classifiers and the proposed cascade approach have been implemented on Raspberry Pi 3B+ running a Raspbian 4.14 environment. Firstly, the accuracy and timing measurements done on the Raspberry Pi of the five classifiers are discussed. Secondly, the proposed approach is evaluated and compared to a traditional solution running on this embedded system. The libraries and the source code running on the embedded system is the same as the one used for the experiments on the computer. The execution time of each operation related to the feature extraction and to the sound classification has been individually measured. Our experiments are repeated 10 times in order to obtain the average timing and the standard deviation (SD) of the operations.