esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line Condition Monitoring Intelligent Sensing Design
#### 2.3.1 Overall Design of Intelligent Sensing  
The overall structure of intelligent sensing includes the sensor nodes, microcontroller, and the modules for communication, information positioning, and energy extraction. Sensor nodes can be used to collect various status parameters in transmission lines, such as voltage, current, temperature and humidity, wire dance, insulator discharge, arc sag, wind deflection, etc. [24].  
The microprocessor collects and processes the corresponding data and later uploads it to the server side through the wireless communication module, thus, realizing the communication between the intelligent sensing terminal and the server side. Among the structure components, the sensor nodes collect data intermittently, and according to the varying demands for data, different data collection and sending cycles are set to reduce power consumption of the terminal as much as possible.
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 2 Related Work
### Low-Dimensional Embedding Learning of Information Networks  
Dimension reduction is the process of transforming the original high-dimensional attribute space into a lower-dimensional subspace data representation through mathematical transformations. At present, dimension reduction techniques are mainly divided into two categories: linear and non-linear methods.  
Classic linear dimension reduction methods include principal component analysis (PCA), multi-dimensional scaling (MDS), linear discriminant analysis (LDA), etc. PCA [20] projects data into a low-dimensional space representation through linear transformation. Its optimal objective function is to minimize Formula (6):  
\[\operatorname*{arg\,min}_{W}-trace(W^{T}XX^{T}W)\,s.t.\,W^{T}W=I. \tag{6}\]  
The goal of MDS is to construct a low-dimensional space where the similarity relationships between nodes can be represented. In this space, the distances are consistent with those in the original data, taking into account the global Euclidean distance of samples during sample dimension reduction. LDA minimizes differences between nodes in the same cluster and maximizes differences between nodes in different clusters through linear projection.  
Recently, neural information network learning has achieved significant success in various applications, including image classification, speech recognition, and natural language processing. [PERSON] et al.'s research [21; 22; 23; 24; 25] made training in deep architectural structures possible and effective. One of the strategies employed in their research was the greedy layered unsupervised pre-training strategy, whose aim was to learn a useful representation layer by layer and then set the output features as inputs for the next layer. Each layer in this process involves some form of non-linearity, such as non-linear activation functions (e.g., sigmoid or tanh) or regularization of features (e.g., sparse constraints). By stacking these non-linear layers together, deep learning can be used to generate improved representations. The process from the input layer to the hidden layer is called encoding, and the process from the hidden layer to the output layer is called decoding, as illustrated in the model process in Figure 2.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 6 Discussion of Simulation Results
### Impact of Traffic Load and Placement of Nodes
#### 6.1.5 Stability of the Network  
We asses the stability of the network by looking at the number of parent switches. Figure 15 illustrates the number of parent switches in function of traffic load. One can observe that, when the traffic increases above 5 packets per minute, in case of RBS Orchestra, both topologies become unstable. For our new scheduler, the increase in number of parent switches is less significant, and remains below 3, regardless of the traffic load or topology. For both schedulers, the average number of parent switches is higher for placement 2. In placement 2, the nodes have more direct neighbors, which leads to higher interference and link instability, directly related to the number of parent switches.  
In our simulations, RPL is in storing mode, which means that each parent switch represents a DIO-DAO message exchange. Considering this, the number of parent switches is correlated with the overhead. The overhead created by the control traffic is significantly higher in the case of RBS Orchestra.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion
### Wireless Link Budget Analysis  
The ability of implantable devices to communicate reliably and effectively with external devices is crucial for the monitoring and transmission of medical data. Therefore, the performance of the implant antenna in actual application scenarios largely depends on its ability to seamlessly transmit recorded real-time data. To determine the wireless telemetry capabilities of the proposed miniaturized broadband implantable antenna, the link budget was theoretically calculated and experimentally evaluated.  
The link budget was first theoretically calculated, and Table 3 lists the basic parameters for controlling the communication link survey. A dipole antenna with a gain of 3 dB was used as the receiver to calculate the link budget from the in-body transmitter to the external device scenario. To ensure the safety of the implantable device, according to ICNIRP regulations, the power of the implantable antenna was set to 25 \(\mu\)w (\(-\)16 dBm). Other relevant parameters related to the transmitter, receiver, propagation, and signal quality in the link budget calculation are shown in Table 3. Here, \(A_{P}\) represents the available power of the antenna, \(R_{P}\) represents the power required for antenna operation, K = 1.38 \(\times\) 10\({}^{-23}\) is the Boltzmann constant, and \(B_{r}\) represents the transmission rate in Mbps or kbps. The link margin (LM) in the wireless communication system is calculated using the following formula:  
\[R_{P}(dB)=\frac{E_{b}}{N_{0}}+KT_{0}+B_{r} \tag{1}\]  
\[A_{P}(dB)=P_{Tx}+G_{Tx}+G_{Rx}-L_{f} \tag{2}\]  
\[L(dB)=20\text{log}\bigg{(}\frac{4\pi d}{\lambda}\bigg{)} \tag{3}\]  
\[LM(dB)=A_{P}(dB)-R_{P}(dB) \tag{4}\]The calculated results of the Link Margin (LM) for the antenna at different data rates and transmission distances are depicted in Figure 16. The implantable antenna can easily achieve data transmission rates of 100 kbps and 1 Mbps within a 10 m range, with the calculated LM exceeding 20 dB. When the transmission rate is increased to 2 Mbps, the effective communication range of the implantable antenna can still reach 9 m. Since the implantable antenna is situated in the upper layer of human muscle tissue and the external receiver will be in contact with the skin, the wireless data transmission distance should be less than 5 cm [17]. As shown in Figure. 17, the LM is higher than 58 dB within a 1 m range, allowing for wireless biotelemetry of the implantable antenna.
## 6 Results and Discussion
### Visual Evaluation of EEG Signal Cleaning Quality
Figure 12 presents the difference in spectrograms between the original signal and the signal cleaned with CNN-LSTM. It can be seen that the CNN-LSTM method removed the artifact activity at seconds 4, 10, 16, 22, and 27. Notably, CNN-LSTM did not remove the 25 Hz frequency, indicating that the SSVEP was preserved.
## 7 Conclusions and Future Work  
This section provides an entire overview of the article, including its results and any weaknesses. The heterogeneous ACO-PT approach is proposed for efficient-route discovery, increasing throughput while decreasing energy consumption and end-to-end latency. The heterogeneous method combines the best aspects of two cutting-edge algorithms (pheromone termite and ant-colony optimization) to produce an efficient and optimum vehicle-pathfinding solution. The pheromone-termite algorithm is based on real-world termite behavior, in which termites utilize pheromones to locate the shortest path to food. The ACO algorithm, on the other hand, is a metaheuristic algorithm that employs the notion of artificial ants to discover the optimum solution to a given issue. It is based on the behavior of actual ants while looking for food.  
The first algorithm in our proposed ACO-PT technique, PT, provides an initial path from source to destination, whereas the second, ACO, optimizes the resultant path for accurate and optimal output. Vehicle congestion is a serious problem that has had a significant influence on performance. Congestion avoidance is intended to alleviate this issue by lowering energy usage and throughput. A module for automated vehicle detection is also provided to recognize vehicles at various phases (image preprocessing, contour detection, contour matching, and blob detection).  
The experimental findings demonstrate that in terms of throughput, energy consumption, and end-to-end latency, the proposed ACO-PT approach performs better than other approaches, such as ACO-FL, RACO, and AODV-ACO. In comparison to other algorithms, ACO-PT can determine the best path with better throughput, less energy utilization, and shorter end-to-end latency, making it the perfect choice for practical applications.  
In the future, we will evaluate the effectiveness of the proposed approach using quality-of-service indicators (minimum arrival time, average arrival time, loss rate, delay variation, and transfer delay). To determine whether this approach is practically appropriate for commercial purposes, the computational complexity will also be determined.  
Conceptualization, M.N. and A.R.; methodology, M.N. and A.R.; software, M.N. and A.R.; validation, M.N. and A.R.; formal analysis, M.N. and A.R.; investigation, M.N. and A.R.; resources, M.N. and A.R.; data curation, M.N. and A.R.; writing\(-\)original draft preparation, M.N. and R.R.; writing\(-\)review and editing, M.N., A.R. and R.R.; visualization, R.R.; supervision, R.R.; project administration, R.R. All authors have read and agreed to the published version of the manuscript.  
Research foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) (NRF-2021R1F1A1063640).  
Not applicable.  
The authors would like to thank the respectable reviewers who provided excellent comments to improve the manuscript.  
The authors declare no conflict of interest.
# A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities
## 4 A Survey of In-Band SDN Control in SDN
### Automatic Routing
[70] acknowledge the need for reliable in-band control in Low Earth Orbit (LEO) constellation systems and propose a dual-layer architecture where the upper-layer control plane provides reliable in-band control paths for the lower-SDN control plane. This plane, in turn, deploys virtual out-of-band control in the data plane, in spite of the constantly changing of network topology. In order to provide reliable control traffic transmission, a redundant path-planning algorithm is further proposed. They evaluate the framework with a simulation which proves much better satellites accessibility and has barely any inaccessible satellites under massive link faults. **[PERSON]** et al. [71] design an in-band control channel communication scheme for P4, in which control messages are piggybacked onto data packets. The target switch is based on the BMv2, and their evaluation shows performance degradation in data forwarding, which they envision to be solved as future research work.
## 2 Methods
### Analysis of Gtss Data  
The GPS raw code and phase observations on both L1 and L2 frequencies were acquired by each GPS antenna/receiver system (in the following referred to as \"receiver\") with a sampling rate of 2 Hz. The raw observations were stored on a flash-card by each receiver. The data analysis was carried out in post-processing after the recovery of the flash-cards. The analysis carried out in the post processing described in the following, however, can be carried out in real-time onboard of the sailboat. The post-processing was carried out by employing two different techniques: the Variometric approach and the Moving Base Kinematic.  
The Variometric Approach for Displacements Analysis Standalone Engine (VADASE) is an innovative GPS data processing approach proposed in the recent past [20; 21]. The approach is based on timing single differences of carrier phase observations continuously collected using a standalone GPS receiver and on standard GPS broadcast products (orbits and clocks) that are available in real-time. Therefore, one receiver works in standalone mode and the epoch-by-epoch displacements (equivalent to velocities) are estimated. In this work, a kinematic extension of the Variometric approach (Kin-VADASE) developed specifically for the navigation field was used.  
A second approach was used. Indeed, the most widely used technique in GPS kinematic positioning is based on the using of two devices: the rover moving receiver and a reference static receiver of known coordinates. To obtain high accuracy results, the distance between the rover and the reference receiver should not exceed few tens of kilometers. Thus, it is almost impossible to apply this technique in off-shore navigation, due to the lack of close reference receivers availability. Nevertheless, in this work, we present an innovative application of differential kinematic positioning applied to a reference moving (hereinafter referred to as Moving Base Kinematic, MBK). With respect to this reference moving receiver, it was possible to estimate, epoch by epoch, the positions of another receiver. This technique does not allow defining the absolute position of the receiver but only the relative one, which is very accurate, since the distance between each couple of receivers is known. The results of the GPS data post processing may then be used to reconstruct the six Degrees of Freedom (hereinafter referred to as 6 DOFs) boat motion and to estimate the wave parameters encountered by sailing boats (i.e., significant wave height and mean wave direction). Actually, only heave, roll and pitch motions are described as they are needed to estimate the waves parameters.
## 4 Experimental Results and Analysis
### Analysis of Validation Set Results  
The evaluation metrics obtained for the validation set on the final trained RBCN-Net are shown in Table 1. The total trajectory length of the validation set is 1968 m, the average RMSE is 1.912, and the RTE values are all around 1%. It indicates that the model has excellent generalization performance on the validation set, and the error rate is not high. Meanwhile, the error of the validation set itself is relatively stable, with little difference between the same indicator parameters, because after normalization of the IMU coordinate system, the way the pedestrian manipulates the phone during data collection has little impact on the accuracy of navigation. The RMSE of NO.3 is higher than the other validation sets because the magnitude of the RMSE value is inversely proportional to the length of the trajectory.
## 5 TSCH Link Scheduling Visualization and Data Processing
Since TLS-VaD only requires exe files, researchers do not need to be bound to one particular programming language; they only need to create an exe file capable of processing input in the form of an adjacency matrix and producing schedule matrices as output. These schedule matrices will then be processed by TLS-VaD to produce animations and data output. A description of the relationship between TLS-VaD and the exe file of the scheduling algorithm is presented in Figure 25.  
Figure 25: Flowchart of how the TSCH link-scheduling visualization and data processing (TLS-VaD) works.
## 4 Improved PSO-Optimized Fuzzy-BPNN Control Algorithm
### Improved Particle Swarm Algorithm  
The optimization search process of the Fuzzy-BPNN-PID controller is as follows: firstly, the fuzzy rules are used to preprocess the two input signals \(e\) and \(ec\); then, the fuzzy processed and normalized signals are fed into the neural network for training and learning; and finally, the optimal output control signal and the corresponding network weight parameters are obtained. However, the initial weight parameters for each layer of the BP neural network are usually chosen as random numbers of \([-1,\,1]\), and choosing random numbers as the initial values causes the algorithm to converge slowly, greatly affecting the training efficiency of the network and causing the algorithm to converge to local extremes [29]. Therefore, in order to obtain better network training accuracy, this paper used the particle swarm algorithm to calculate and find the optimal parameters as the initial network weights of the BP neural network, as to improve the iterative convergence speed of the network and the efficiency of finding the optimal control signal.
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 6 Simulation
### Error Analysis of the Proposed Method  
The longitudinal prediction error, lateral prediction error and total error within 100 s, 200 s and 300 s are shown in Figure 19. It can be seen that, in the early period of prediction, the error of the target is very small while the time is less than 80 s. However, due to the difference between the sign change rules and the real guidance logic, the lateral error of the target gradually increases. Since the intention of the target could be correctly predicted by the intention inference network, the lateral error increases slowly within 200 s and 300 s. The longitudinal error increases with time due to the accumulation of the error. The error  
Figure 19: Variation of prediction error: (**a**) represents the longitudinal prediction error; (**b**) represents lateral prediction error; and (**c**) represents the whole prediction error.  
Figure 18: Lateral prediction effect of case 2: (**a**) is the lateral prediction effect within 100 s; (**b**) is the lateral prediction effect within 200 s; (**c**) is the lateral prediction effect within 300 s.  
results are shown in Table 1. Our method obtains the result that the whole error is less than 9 km with a 100 s prediction time and it is less than 32 km with a 300 s prediction time, which shows a good effect on both short and long-term prediction accuracy.
## 4 Experiments and Results
### Attention Deficit Hyperactivity Disorder Diagnosis
#### 4.1.2 Accuracy and Analysis of the Network's Depth
For instance, there were 23.4 M parameters in \(A_{2}\). If we simply increased the depth of the network from \(A_{2}\) to \(A_{3}\), the increased parameters would quickly saturate the network (the accuracy dropped from 74.53% to 71.94% and the overfitting distance raised from 0.2807 to 0.2961). By introducing 3D-DSC, the number of parameters for each 3D kernels was significantly reduced, and we could construct \(C_{6}\) with less parameters and deeper architectures [31]. In addition, the employment of dense connections in the 3D-DSC module further improved the parameter utilization efficiency by encouraging feature reuse. (2) The stronger nonlinear representation ability was powered by the more activation layers integrated in 3D-DSC. By decomposing the 3D kernel into a series of concatenated 1D filters and inserting activation (nonlinear) layers between them, the effective depth of network, as well as its representational power could be considerably increased without increasing the number of parameters. The normal 3D convolution was usually followed by only one activation layer, whereas each 3D-DSC module could accommodate four or more activation layers. The effect of additional activation layers could also be observed in our ablation studies, as illustrated in Table 5.
## 1 Introduction  
Aging affects numerous systems in the human body; further, the concomitant decrease in muscle mass and muscle strength significantly affects the quality of life and independence of older adults [1]. Recent studies have demonstrated the role of skeletal muscle not only as a motor organ, but also as an endocrine organ that secretes various physiological substances (myokines) crucially involved in muscle growth, regeneration, metabolic regulation, and overall health [2, 3].  
Among the representative myokines, secreted protein acidic and rich in cysteine (SPARC) regulates muscle and fat development during the aging process. SPARC, which is secreted by skeletal muscles, facilitates muscle regeneration and growth while inhibiting fat accumulation [4]. Insulin-like growth factor 1 (IGF-1) strongly activates SPARC expression in muscle cells via the phosphatidylinositol 4,5-bisphosphate 3-kinase (PI3K) signaling pathway [5]. These myokines are crucial for preventing or improving age-related sarcopenia.  
Other myokines, including interleukin 6 (IL-6) and brain-derived neurotrophic factor (BDNF), also influence muscle health, metabolic processes, and cognitive function [6, 7, 8]. For example, IL-6 rapidly increases after exercise and is involved in immune and inflammatory responses, while BDNF contributes to neuroplasticity and improves cognition.  
Secretion of the aforementioned myokines is promoted during exercise, especially resistance training (RT). However, the effects of RT on myokine levels and functional fitness in elderly individuals remain unclear. Specifically, the comparative efficacy between low-intensity high-repetition (LI-HR) and high-intensity low-repetition (HI-LR) resistance exercises in elderly individuals remains unclear. Some studies have suggested that HI-LR RT is better for increased muscle strength and muscle mass [9; 10; 11], while others have suggested that LI-HR RT is better for improvements in muscle function and daily life activities [12; 13; 14]. Contrastingly, other studies have reported no significant differences between the two approaches [15; 16; 17].  
Despite these prior studies and public interest in the elderly population, the comparative efficacy of LI-HR and HI-LR RT is still unclear, especially the effect of such exercise intensity control on biomarkers such as myokines. Accordingly, this study aimed to compare the effects of LI-HR and HI-LR resistance exercises on functional fitness and myokine levels (IL-6, IGF-1, SPARC, and BDNF) in elderly individuals. In addition, we aimed to provide a better training approach to certain aspects, such as muscle strength and muscle mass in older persons, identify the optimal RT protocol for improving the overall quality of life, and provide a scientific basis for the prevention and management of age-related sarcopenia.