esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation
## 4 Implementation
### Program Implementation, Testing, and Analysis Approaches
Subsequently, the average value and the standard deviation were calculated and are presented in the following section. The dataset we used as input to calculate the DCT IV transforms is randomly generated by the rand() function from the C language and mapped to [\(-\)1, 1]. In a parallel application, the minimum execution time is always higher than the time required to execute the part of a program that cannot be executed in parallel. Accordingly, Amdahl's Law, the theoretical overall speedup of a program, is given by [46]:  
\[S_{overall}=\frac{1}{\left(1-portion\_time_{optimized}\right)+\frac{portion\_time_{optimal}}{S_{optimal}}} \tag{24}\]  
In relation (24), \(portion\_time_{optimized}\) is the portion of execution time of the part of the program that can benefit from parallel execution and \(S_{optimized}\) is the theoretical speedup of the part able to benefit from parallel execution due to the improved system resources, e.g., the number of cores. In the case of using two cores, the theoretical speedup, \(S_{optimized}\), is considered to be 2. However, working with two cores does not necessarily double the performance of the part of the application where they are used. Many factors prevent reaching this limit, such as the latency given by the data transfer to the parallel system or the additional code required to transform a serial program to be modified to run in parallel. The running time of the measurement cycle was calculated based on events, based on CUDA event API. Functions like cudaEventCreate (), cudaEventRecord (), and cudaEventElapsedTime () were used to measure the time. This time measurement approach has a resolution of approximately one-half microsecond [47]. This time measurement technique is extensively accepted for performance studies in the case of GPUs [47; 48; 49; 50]. Appendix C presents the modalities used to obtain the specific binary file associated with a particular GPU architecture. Also, the NVIDIA Nsight Compute package was used to profile the CUDA applications for GPU utilization or memory workload.
## 5 Numerical and Simulation Results
### Multiple SWIPT Relaying Systems
#### 5.2.1 BER Comparison  
Figures 6 and 7 show the BER performance comparison with respect to the direct link SNR and different parameters of \(R\) and \(\rho_{r}=\rho_{\text{opt}}\) for Ch. Model\(=[1,1,X]\) and Ch. Model\(=[1,X,1]\), respectively. From Figures 6 and 7, it is shown that BER performance can be improved as \(R\) increases. Furthermore, it is verified that the numerical results for 'Theory, Exact', 'Theory, Up' of (33), and 'Theory, AF-App' of (41) or (42) well match the simulation results regardless of SNR for both indirect and combined links. Although the larger \(R\) increases the discrepancy, we can additionally find that 'Theory, Asyn' of (35) shows asymptotic BER bounds in good agreement in the high SNR region.
## 5 Discussion
### Future Works  
The proposed EFDPN model heralds a promising frontier in the security landscape of WSN-IoT networks. In the future, the following trajectories can be pursued to further its potential:  
1. Conduct pilot studies to assess the model's adaptability and performance in real-time environments, with a focus on scaling the model to accommodate larger and more complex network infrastructures;
2. Further refine the F3S and TSO algorithms to enhance computational efficiency and accuracy, possibly integrating it with other optimization techniques to forge a more robust system;
3. Continually update and adapt the model to identify and counteract emerging and sophisticated attack vectors, fostering a dynamic security framework that evolves with the threat landscape;
4. Develop multi-layered security protocols within the EFDPN framework, which can work in synergy with existing security infrastructures, to provide a comprehensive security solution;
5. Explore the potential applications of the EFDPN model in other domains, such as industrial control systems and healthcare networks, tailoring the model to meet the unique security requirements of these sectors;
6. Engage with the user and broader community to gather feedback and insights, fostering a collaborative approach to further refine and enhance the model;* (7) Develop educational initiatives and training programs to foster awareness and skill development, equipping individuals and organizations with the tools to effectively deploy and manage EFDPN-based security systems. By pursuing these trajectories, we envision the EFDPN model evolving into a cornerstone of cybersecurity in WSN-IoT networks, setting a new standard in resilience, efficiency, and intelligence in the face of escalating cyber threats.
## 3 System Architecture  
The main concept of an OCC system is to control the intensity by which optical signals transmit and receive data; then, with a promising modulation scheme, the system communication performance can be enhanced. OOK is a well-known modulation scheme and has the simplest amplitude-shift keying modulation. It uses two statuses, \"on\" and \"off,\" to transmit data, represented by bit \"1\" and bit \"0,\" respectively. In this paper, we propose the details of an LED-array-based scheme, which is applied for an IoT-system-based OCC technique. By locating each LED in an LED array based on our proposed scheme's spatial frame format, the receiver side can easily detect and decode data based on RoI algorithms. Figure 1 presents the architecture of the 2D OOK-MIMO transceiver. The operation of the blocks is discussed in Section 3.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 3 Autonomous Features in UAV Networks
### Power Control and Energy Efficiency  
For drones and unmanned aerial vehicles (UAVs), power control and energy efficiency are other very critical realms. As these network nodes are not on the ground, an uninterrupted supply of power and efficient utilization of energy become very crucial. Many research studies have addressed this area and proposed several solutions, some targeting multiple power sources, including battery [87; 88], hydrogen fuel [89; 90], solar [91; 92], and hybrid [93; 94], that can be utilized by the UAVs. While others focus on how efficiently energy can be consumed. Energy efficiency affects all the operations of a UAV. Many studies have been conducted to explore UAV placement for optimal energy efficiency [48; 95].  
Another aspect explored in this domain is path planning. Various approaches are being used to explore this area, including sample-based [49; 96], space search [50], and biological searching techniques [97; 98; 99].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scope**} & **Autonomous** & **Computational** & **Channel** & **Interference** & **Security and** & **Reference** \\  & **Features** & **Intelligence** & **Modeling** & **Management** & **Safety** & **Reference** \\ \hline \multirow{2}{*}{Channel access} & Cyclic multiple & \multirow{2}{*}{\(\surd\)} & Free-space path & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[61]} \\  & channel access & & & loss with LOS & & & \\ \hline \multirow{4}{*}{MAC protocol} & Energy consumption, & \multirow{2}{*}{\(\surd\)} & Free-space path & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[62; 63]} \\  & Packet-error-rate & & & loss with LOS & & & \\ \cline{1-1}  & (PER) & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Performance evaluation of MAC \\ \end{tabular} } & \multirow{2}{*}{PER} & \multirow{2}{*}{\(\surd\)} & Rician fading & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{[64]} \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & Trajectory optimization & Trajectory planning & \(\surd\) & Rician fading, & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{-} \\  & & & Rayleigh & & & \\ \cline{1-1}  & & & fading, Rician & & & \\ \cline{1-1}  & & & K-factor & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of related survey: UAV multiple access and routing protocols.
## 3 Materials and Methods
### Hand Gesture Recognition  
For the detection of static hand gestures, the Hand Gesture Recognition Image Dataset (HaGRID) is utilized [52]. This dataset is a comprehensive collection of images specifically designed for hand gesture recognition tasks, suitable for both image classification and detection purposes. The HaGRID dataset has a total size of 723 GB and includes 553,991 FullHD RGB images categorized into 18 different gesture classes. The dataset features 37,563 unique individuals, with participants ranging in age from 18 to 65 years.  
The images in the dataset were primarily collected indoors under a variety of lighting conditions, including both artificial and natural light. Moreover, the dataset also comprises images captured in challenging scenarios, such as those facing directly toward or away from a window. To ensure diversity and robustness in gesture recognition, subjects were required to perform gestures at distances ranging from 0.5 to 4 m from the camera [52]. No additional modifications or techniques were employed beyond the original dataset. The only difference in the proposed approach is that for training, we specifically selected 13 static hand gestures from the original dataset.  
Communication between the PC and the robot is facilitated through Ethernet/IP. Many collaborative robots come with Ethernet ports, enabling direct connection to a local network. This setup allows communication with computers or other devices using standard network protocols such as Ethernet/IP or TCP/IP, thereby supporting data exchange, control commands, and real-time monitoring. In this configuration, the router must be set up to assign static IP addresses to both the control PC and the robot, ensuring a stable and consistent connection. The arrangement of the connected devices is illustrated in Figure 5. A server is implemented on each robot arm, developed using the RAPID programming language and configured within ABB RobotStudio. Each robot arm is programmed with its own movement control program. On the client side, the control software is developed in Python 3.  
The RAPID programs in RobotStudio are responsible for managing command stack settings, specifying the robot's IP address, and defining parameters for connection and command execution. The Python-based client software includes functions for calculating joint angles, managing robot parameters, and handling TCP/IP connections.  
Figure 4: Various camera placements: (**left** camera attached directly to the robot, **middle** camera stand in front of the robot, and **right** camera stand behind the robot).
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 4 Experiment Results
### BER Comparison According to Carrier Frequency and Distance
as the carrier frequency decreases, the frequency selectivity increases and SC-FDMA exploits frequency diversity by DFT spreading, whereas FBMC cannot exploit frequency diversity. Hence, in the NLoS environment where the channel is more frequency-selective when compared to LoS environment, the BER gap between FBMC and SC-FDMA further increases. On the other hand, LP-FBMC achieves almost the same BER as that of SC-FDMA over the entire considered region of distance and carrier frequency. This is because LP-FBMC has similar PAPR performance as that of SC-FDMA, and its signal generation also includes DFT spreading. All of these trends are common to LoS and NLoS environments.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 2 The Real-Time Metaverse
### Applications and Potential Impact  
As we approach the beginning of the digital transformation, the implications of the _real-time metaverse_ extend far beyond mere entertainment. The potential applications of the real-time metaverse are vast and diverse. In terms of entertainment, live events such as sports games or concerts [52] could be experienced virtually in real-time, allowing users to engage with the event from anywhere in the world. In urban planning and architecture, creating a real-time virtual replica of a city can help planners and architects test designs and simulate the impact of changes [53] before they are made in the physical world. Education can also benefit from real-time virtual classrooms that mirror real-world laboratories or simulations [54], giving students hands-on experience in science, engineering, and medicine. The real-time metaverse also has the potential to transform social interactions [33]. Instead of static avatars meeting in a fixed environment, people could interact dynamically with each other in spaces that reflect their real-world surroundings, allowing for more meaningful and immersive connections. For example, a person in one part of the world could invite a friend to virtually experience their real-world environment in real-time, whether a stroll through a park or a live museum tour.  
Figure 3 illustrates a hierarchical system for the real-time metaverse, consisting of three layers. At the physical layer, real-world actions and data are captured through sensors and devices in various domains, such as robotics, medical procedures, and fitness tracking. The data are transmitted via 5G networks to the technology layer, where cloud and high-performance computing (HPC) infrastructure processes and synchronizes it in real-time. The top metaverse layer represents digital twin environments, where the processed data are rendered into virtual replicas of physical actions, enabling immersive real-time interactions. This system ensures that real-world activities are mirrored seamlessly in the virtual world, supporting real-time metaverse experiences.  
Figure 3: Real-time metaverse hierarchical system.
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Extension (m2)** & **Overall Accuracy (with Respect to “Ground Truth”)** \\ \hline Norcia village & 232,000 m\({}^{2}\) & / \\ Damaged areas by “ground truth” & 17,600 m\({}^{2}\) & Reference (100\%) \\ COSMO-SkyMed Spotlight damage map & 15,900 m\({}^{2}\) & 90\% \\ COSMO-SkyMed Stripmap damage map & 7900 m\({}^{2}\) & 45\% \\ Sentinel-1 damage map & & Not determinable \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative evaluation of damage map in Figure 5.  
Figure 6: Cross-check among field surveys, COSMO-SkyMed Spotlight, and StripMap results. The white dotted line is the perimeter of the Norcia ancient walls used for the quantitative evaluation of the damage map reported in Table 4.  
As we can see, COSMO-SkyMed Spotlight returns a very accurate mapping, detecting of approximately 90% of the structures severely damaged, while COSMO-SkyMed Stripmap has been able to detect about the 45% of the damaged buildings.
## 2 Analysis of Failure Coal Seam Area on the Fully Mechanized Mining Face
### Division of Coal Seam Damage Areas on the Working Face  
During mining, due to the presence of a goaf area, the coal seam at the front end of working face formed a two-way stress, and the roof, hydraulic support, bottom plates, and coal seam of the working face reached a local balance after the dynamic pressure was stabilized. The coal wall failure in the mining direction was simplified into a two-dimensional plane strain problem. Due to the large thickness of the coal seam in Huoxi Coalfield, the pressure produced a plastic plateau, which formed a significant plastic zone.  
Figure 2: Axial compressive stress–strain curve of hard coal samples of different sizes.  
At the same time, owing to the different roof pressure, the area close to the deep coal seam without plastic deformation was the elastic zone, and the coal seam which endured a force exceeding the limit on the side of goat zone was the failure zone, where the intersection of the plastic zone and the failure zone was the failure boundary point. When determining the failure boundary point, the crack development and residual bearing capacity should be considered at the same time. Usually, it is the starting point of the force transmission path closest to the coal wall.  
After the failure boundary point was determined, the area that was considered without the endurance of the force of coal seam at all was defined as the failure zone; the area where there was fracture development but still served as the main bearing part was defined as the plastic zone; the crack zone of roof formed by the advanced working face was defined as the roof damage zone, and the area close to the original rock conditions without plastic changes is the elastic zone. The schematic diagram of the division of failure zones of the coal seam at the front end of the fully mechanized mining face is shown in Figure 3.  
The state of the failure zones directly reflects the degree of coal wall failure during the coal mining process. The coal wall failure in Huoxi Coalfield is often wedge-shaped. The larger the failure zone, the greater the depth of spalling and the more serious coal wall instability is; the force and crushing in the plastic zone reflect the distribution of roof pressure on the coal seam, which, in turn, reflects the carrying state of the coal wall. In the plastic zone, in addition to crack development due to normal vertical compression, when the pressure on the roof is severe and concentrated, there is also oblique crack development in the coal seam. When a large number of oblique cracks occur, this indicates that the roof is at risk of advance caving. After analysis of the state of cracks in the roof failure zone, countermeasures should be timely adopted to prevent the risk of sudden pressure on the support. In summary, a \"one point four zone\" comprehensive analysis of the front-end coal wall can assess the coal wall's stability.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 3 Results and Discussion
### Optimum Fiber Length Variation with Temperature
#### 3.4.1 Dependency of L\({}_{\text{opt}}\) on Input Signal Power  
The effect of input signal power on L\({}_{\text{opt}}\) variation with temperature is studied in Figure 5. Three different input signal powers are chosen: 1 \(\mu\)W, 5 \(\mu\)W, and 10 \(\mu\)W. The corresponding optimum fiber length is calculated using Equation (12) for an input pump power of 200 mW. For the case of the maximum gain amplification channel located at 1532 nm and pumped using a pump wavelength of 1480 nm, the signal and pump overlap factors are calculated using Equations (5)-(7), and are found to be 0.6986 and 0.7165, respectively. A decrease in the value of the optimum fiber length with the input signal power is observed. This is needed in the fiber design; otherwise, no maximum gain could be obtained. This result is in fair agreement with both the numerical and analytical solutions of [PERSON] et al. [21].  
It is also noticeable that while changing the environmental temperature, there is a certain temperature value at which the value of L\({}_{\text{opt}}\) is maximized. At lower temperature values, the L\({}_{\text{opt}}\) value increases with the increase in temperature, while an opposite behavior occurs at higher temperature values. Concerning this limiting temperature at which L\({}_{\text{opt}}\) is at its maximum, its value decreases with the increase in the driven input signal power, as illustrated in Figure 5.
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 2 Proposed Model  
In this paper, a hybrid transfer learning model is proposed. The input of the model includes historical air quality and meteorological data of source and target domains. Firstly, the source domain site selection method based on MMD is used to find the source domain site closest to the target domain. Then the data of the two sites are input into the improved DSTP model together. A feature extractor based on the DSTP model is used to extract the spatial-temporal features of training data from source and target site data. The obtained spatial-temporal features are input into the domain classification model and the regression prediction model, respectively. In this paper, a domain adversarial neural network (DANN) is used to find the domain invariant features between the source domain and the target domain through adversarial domain adaptation of DSTP feature extractor and domain classifier. Finally, the regression prediction model based on the fully connected layer is used to predict the values of the source and target sites. The test data of the target site are input into the pre-trained DSTP-DANN model for PM2.5 concentration prediction. The framework of the proposed model is shown in Figure 1, which will be described in detail below.  
**Remark 1**.: _The method presented in this paper is different from those that fine-tune by freezing the first few layers of the model. This method uses the domain adaptation of an adversarial neural network to conduct transfer learning. DANN combines domain adaptation and feature learning in a training process, so that the features of domain invariance can be predicted. Then, the proposed transfer learning-based model trained by source domain site data can be used to assist in predicting target site data without degradation of the prediction performance due to domain drift._
## 1 Introduction  
Temperature is one of the key parameters in the combustion process. It is important to understand the energy release rate and pollution formation during the combustion process. Many techniques for temperature measurement have been developed so far, but laser-based methods have been developed more rapidly because of their advantages of non-interference and high spatial and time resolution [1, 2]. In many optical thermometry techniques, CARS technology has the characteristics of strong anti-interference ability and high precision, but it is more applied to single point measurement [3, 4]. The Rayleigh scattering temperature measurement technique can achieve good results only if the Rayleigh scattering cross-section has little change in the flow field [5, 6]. PLIF is preferred for two-dimensional temperature measurements with high spatial resolution [7, 8, 9, 10]. Among the various molecular used in PLIF measurements, OH is one of the ideal targets, and the OH-PLIF thermometry method is more widely used [11, 12, 13, 14, 15]. The absorption wavelength, fluorescence emission wavelength, energy level distribution, and the quenching intensity of different energy levels of OH is described in detail [16, 17, 18], which provides the basic parameters for the measurement of OH fluorescence. Generally, a two-color PLIF measurement system is calibrated using only one point, and then the two-dimensional temperature distribution of the flow field can be obtained [19, 20]. It is assumed that the ratio of fluorescence yields from two different transition lines at different temperatures remains the same [12, 13]. In practical application, the fluorescence yield of those two transitions has a different response to temperature, and the temperature result is different under different calibration conditions [21; 22]. Therefore, a correction of the fluorescence yield is needed in the calibration model to resolve this problem. By two points calibrating at both high and low temperature region, an intrinsic calibration coefficient can be obtained, and the application scope of the calibration coefficient is improved.  
In a word, in PLIF temperature measurement, the single point calibration is relatively simple, and more suitable for the measurement in combustion with a small temperature range and low quenching effect. The two-point method requires two calibration points at high and low temperature, respectively. Although the two-point calibration increases the workload to some extent, the accuracy of temperature measurement is higher. Thus, the two-point calibration method is suitable for measuring in the flow field with a wide temperature range and strong quenching effect.