esa-sceva/satcom-chunk-collection huggingface dataset:
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 3 Related Work
### Node Scheduling and Random Access for Massive MIMO Systems  
There is also some, albeit limited, existing work on node scheduling for massive MIMO networks. Because massive MIMO systems serve multiple nodes simultaneously, such scheduling inherently requires the creation of node groups, although the groups do not necessarily need to be disjoint. In [4], joint node scheduling and transmission power control was performed for nodes with heterogeneous traffic demands. However, in that study, information about the traffic queues at each node was required, which increases the signaling needed and thus the energy cost. The authors in [22] examined fair scheduling in massive MIMO systems, but provided an analysis of the achievable fair rate, rather than a solution for actually performing scheduling.  
Some work has also investigated random access for IoT devices in massive MIMO systems [23; 24; 25; 26; 27]. Random access is a promising solution for IoT scenarios with sporadic and unpredictable traffic. However, in cases where there is a high level of contention, random access can lead to collisions, which induce retransmissions and increase energy costs, while further increasing the contention for the channel. Scheduled access also provides more predictable performance, which can be important for some applications, and is more suitable for predictable and/or periodic traffic.  
Nevertheless, random access protocols could be combined with our node grouping method, by allowing entire node groups to wake up during a limited period, in which they would be served through random access. Using an intelligent node grouping such as we propose thus also reduces the collision probability within each group and increases the chances of distinguishing colliding transmissions through techniques such as successive interference cancellation. Alternatively, our node grouping method could be used as the precursor to any scheduling algorithm. In both cases, node grouping simplifies network control, since devices can be orchestrated at the group level instead of addressing each individual device, and using node groups for channel access provides for predictable and extended sleep opportunities.
# Go Source Code Vulnerability Detection Method Based on Graph Neural Network
## 5 Experiments and Results  
### Dataset  
We collect a real-world Go program vulnerability dataset [44] from two sources: the GitHub Security Advisory Database and open-source projects on GitHub. The GitHub Security Advisory Database contains source code vulnerabilities associated with CWE identifiers. For open-source projects, we focus on high-star repositories, from which we extract CVE vulnerability files, patch files, and diff files based on commit information.  
In the raw vulnerability dataset, we collected a total of 630 CWE-labeled vulnerabilities and, after preprocessing, segmented them into 129,978 Go code snippets, with an equal number of positive and negative samples (64,989 each). During data splitting, we strictly followed a project-level division strategy to ensure that code from the same GitHub project does not appear in both the training and test sets. The final dataset was divided into training, validation, and test sets in an 8:1:1 ratio. Table 1 shows examples of selected CWE vulnerability samples in our dataset.
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 3 Simulator Software  
To facilitate the design of the chromatic confocal sensor, a custom Python module was developed specifically for this purpose. Simulations were conducted considering the actual surfaces of the experimental setup. This module offers a straightforward approach to optical design for the chromatic confocal sensor; it would also help other authors to develop their own sensors, provided the open-source nature of the software. Figure 6 illustrates the optical design along with ray tracing for the minimum and maximum wavelengths utilized in the experiment. Due to the design specifications of the optical setup, including the beam splitter and achromatic lenses, the simulations were limited to a spectrum of up to 800 nm, as the system was optimized for wavelengths ranging from 300 to 800 nm.  
To simulate the propagation of light rays through an aspheric optical system, we developed a ray-tracing module using Python 3.12. This module leverages numerical optimization techniques and performance enhancements to accurately model ray interactions with complex lens surfaces. The core of the simulation is based on solving for the intersection points between light rays and the surfaces of aspheric lenses, as well as computing the subsequent refraction according to [PERSON]'s law. All of the lenses are defined as aspheric surfaces. Those that are not aspheric will have the conic constant set to zero. The simulation workflow is detailed as follows:
# Effect of a Sensorimotor Training Program for Aerial Maneuvers in Junior Surfers
###### Abstract  
The purpose of this study was to examine the impact of a sensorimotor training program on maximum ankle dorsiflexion (ankle DF), coordination, dynamic balance and postural control, and lower-limb muscle power, in competitive junior surfers, and its relation to parameters of sensorimotor control required to perform aerial maneuvers. Twelve junior competitive surfers followed a 7-week sensorimotor training program, being assessed pre- and post-program with the knee-to-wall test (KW), Y-Balance test--lower quarter (YBT-LQ), and the countermovement jump test (CMI). Post-training assessment revealed positive effects on the KW (ankle DF) distance, which increased approximately 2 cm (\(p<0.001\)) for both ankles, and all scores for the YTB-LQ (coordination, dynamic balance, and postural control) variables increased, being significant (\(p<0.005\)) for some reach distances (YBT-LQ--Anterior Left, YBT-LQ--Postero-medial Left, and YTB-LQ Anterior Right). YBT-LQ Anterior Reach Asymmetry also improved by decreasing 1.62 cm (\(p<0.001\)) and the CMI) height (lower limb muscle power) increased 2.89 cm (\(p<0.001\)). The training program proved to effectively enhance parameters of physical performance for this sample, including ankle DF, coordination, dynamic balance, postural control, and lower limb muscle power. This tailored-made task approach can help to optimize surfing performance capabilities and contribute to reducing the risk of injuries while performing aerials.  
surfing; sensorimotor training; aerial maneuvers; lower limbs; injury prevention +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl42210159](https://doi.org/10.3390/appl42210159)]([https://doi.org/10.3390/appl42210159](https://doi.org/10.3390/appl42210159))
## 5 Experimental Results with AFC for FSM Motion System  
The control input was calculated based on the control law in Equation (7). The phase angles of the AFC algorithm at \(\omega_{1}\) = 59 Hz and \(\omega_{2}\) = 73 Hz were estimated to be \(\phi_{1}\) = 81\({}^{\circ}\) and \(\phi_{2}\) = 76\({}^{\circ}\), respectively, based on the update laws in Equations (8) and (9). The experimental results for FSM control using the PI controller, both with and without the AFC algorithm, indicate that the target rotational position was successfully maintained at 0 \(\mu\)rad (i.e., the  
Figure 16: Open\(-\)loop frequency response of FSM motion system (AFC at 73 Hz).  
Figure 17: Open\(-\)loop frequency response of FSM motion system (AFC at 59 Hz and 73 Hz).  
neutral position). As shown in Figure 6, the residual CES of the PI controller without the AFC algorithm was 12.23 urad. When the AFC algorithm was used with 59 Hz and 73 Hz disturbances separately, the corresponding root-mean-square (RMS) values of the CES were 7.30 and 9.42 urad, respectively. These can be observed in Figure 18a,b. When the AFC algorithm was applied with multiple frequency components, i.e., both 59 Hz and 73 Hz concurrently, the RMS of the residual CES was 4.11 urad, as shown in Figure 18c.  
Figure 19a-c demonstrates the power spectra of the AFC algorithm for all the disturbance frequencies. The overall experimental results for the FSM control system with and without the AFC algorithm are compared in Table 3. For fixed-frequency sinusoidal disturbances, the AFC algorithm evidently improved the control effectiveness compared to the PI controller. These results indicate that the AFC algorithm performed efficiently in terms of suppressing periodic disturbances in the FSM system [45; 46].  
Figure 18: Performance of AFC algorithm for FSM motion system (residual CES): **(a)** 59 Hz; **(b)** 73 Hz; **(c)** 59 Hz and 73 Hz.  
Figure 19: Performance of AFC algorithm for FSM motion system (power spectrum): (**a**) 59 Hz; (**b**) 73 Hz; (**c**) 59 Hz and 73 Hz.
# Entropy-Guided Distributional Reinforcement Learning with Controlling Uncertainty in Robotic Tasks
## 4 Experiments
### Generality and Scalability Results  
We evaluated the TQC algorithm using the Hindsight Replay Buffer on the FetchRobot of Gymnasium-robotics to assess its generality and performance in more complex environments. Owing to the complexity of the environment, the algorithm performed better with a smaller discount factor. Therefore, EG-TQC was applied variably based on the uncertainty provided by entropy, with the discount factor value ranging from \(\gamma_{\text{min}}=0.95\) to \(\gamma_{\text{max}}=0.99\). The TQC was evaluated with \(\gamma\) values of 0.95, 0.97, and 0.99.  
The robotics environments in the Gymnasium for this study are goal-oriented and feature sparse rewards, making it complex to balance exploration with exploitation. The success rate is determined by whether the robotic arm's gripper or the object being moved is accurately placed at the target location; each environment has different objectives. As illustrated in Figure 4, the frames depict the amount of data the agent has learned, while the success rate reflects the proportion of tasks completed. The success rate ranges from 0 to 1, where 1 indicates that all attempts were successful.  
Figure 3: Comparison of the learning performance for six robotic tasks between three TQC algorithms with fixed discount factors and the TQC algorithm using our proposed method. All curves are averaged over five random seeds, and the shaded regions represent standard deviations. All settings, except for the discount factor, were kept consistent across the experiments.  
The experimental results clearly show that our EG-TQC method surpassed the TQC method with a fixed discount factor (0.95) in the FetchPickAndPlace task, achieving an impressive success rate of 0.962 compared to 0.85. This reflects a notable improvement of about 11%. This enhancement not only increases sample efficiency but also allows for seamless adaptation to additional changes, such as the implementation of a hindsight replay buffer in the Gymnasium robotics environment. These findings robustly affirm the generality and scalability of our proposed approach.
## 3 Potential of Intelligent Algorithms for Hydrodynamic
### Heuristic Optimization Algorithms
The combination of the ACO algorithm with other intelligent algorithms enables it to address nonlinear and multi-objective optimization demands, while also demonstrating strong adaptability in source optimization and control strategy enhancement for fluid noise. Particle Swarm Optimization (Particle Swarm Optimization, PSO)  
Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that draws inspiration from the foraging behavior of birds. This algorithm is employed to address optimization problems. The PSO model simulates the process of a group of particles flying and cooperating in the search space. The flight path of each particle is updated according to its historical position and the optimal position of the entire population. This iterative process is repeated until the global optimal solution is approached [89]. Each particle in the swarm represents a potential solution, and the iterative process of the entire swarm can effectively explore the search space. This method has been shown to be particularly effective in addressing high-dimensional and nonlinear problems. In the domain of fluid noise research, PSO has been predominantly employed for the optimization of noise source geometry and the prediction of flow field characteristics. Thisapproach has been shown to enhance design efficiency and reduce resource consumption through parallel computing. For instance, [PERSON] [90] proposed an adaptive decision feedback equalizer (DFE) based on PSO for noise control in underwater acoustic communication (UWAC). Notwithstanding the high computational complexity of PSO, it has been demonstrated that PSO-DFE surpasses conventional methods in addressing specific noise environments and can markedly enhance signal processing. Furthermore, PSO has demonstrated its potential applications in the domains of aviation and machinery, particularly in the context of fault diagnosis for complex equipment. The identification of faults in high-performance hydraulic pumps is a complex process, primarily due to the nonlinear characteristics of these pumps and the noise interference they exhibit. In this context, [PERSON] [91] investigated the potential of the PSO algorithm for fault identification. Through the implementation of online measurement and system modeling, the local search capability and convergence stability of the algorithm were enhanced. This optimization process enables PSO to deal with the problem of fault prediction more accurately and quickly. This approach offers a reliable technical foundation for intelligent maintenance.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 4 Analysis of Track Circuit Misoperation Test
### Unbalanced Current Test during Driving  
Based on the simulation test of the interference source applied to the track circuit, the track circuit with voltage fluctuation is monitored. The field test wiring diagram is shown in Figure 19.  
Figure 17: Receiver choke transformer waveform in case of interference and misoperation. (**a**) Receiver choke transformer waveform in case of interference; (**b**) Receiver choke transformer waveform in case of misoperation.  
Figure 20 records the waveforms of electrical quantities at the monitoring points on the monitoring road sections and other adjacent track sections during driving. In Figure 20a,b, the unbalanced current with small value is generated in the track circuit. It can be seen that the 25 Hz signal current contains a 50 Hz interference current. Figure 20c,d are the unbalanced impulse current generated in the track circuit. The current has a short time and a large amplitude, which makes the choke transformer magnetic saturation and makes the secondary side equipment disturbed. In Figure 20e,f, it can be seen that when there is traffic in other sections, the unbalanced impulse current will also bring interference to the monitored area, indicating that the unbalanced effect brought by other track traffic will affect the track, which may cause the relay track coil voltage to decrease, resulting in track circuit misoperation.  
Figure 19: Layout of field measurement equipment.  
Figure 18: Receiver relay waveform in case of interference and misoperation. (**a**) Receiver relay waveform in case of interference; (**b**) Receiver relay waveform in case of misoperation.
## 3 Results and Discussion  
### Evaluation of the Accuracy of Stress-Strain Models  
The accuracy of stress-strain models for material C45 was evaluated by comparing the yield curve at different individual strain rates and considering both elevated temperature and strain rates. The forming force during open-die forging of simulated billet tubes was compared with experimental results to calculate the deviation between the experimental value (\(P_{E}\)) and the simulated value (\(P_{S}\)) in flange-forming force (\(\Delta P\)(\(\%\))) using Equation (2). Figure 5a,b show images of simulation and experimental results at strain rates of 1.0 S\({}^{-1}\) and 10 S\({}^{-1}\), respectively. Figure 5c represents the simulation and experimental results considering both elevated temperature and strain rates (Equation (1)). The comparison of the flange-forming force graph between simulation and experiment is displayed in Figure 5d.  
\[\Delta P(\%)=\frac{\left|P_{S}-P_{E}\right|}{P_{E}}\times 100 \tag{2}\]  
Additionally, the table labeled Table 4 provides a comparison of the flange-forming force when simulating the flange forming of C45 billet tubes using different stress-strain curve inputs and experimental data.  
Table 4 presents a comparative analysis of the forming force obtained from simulations using flow models at strain rates 0.1 S\({}^{-1}\) and 10 S\({}^{-1}\) and considering both elevated temperature and strain rates, compared with the test results. The largest deviation from the experimental forming force is observed at strain rate 0.1 S\({}^{-1}\), with a value of 21.36\(\%\), followed by 14.54\(\%\) for strain rate 10 S\({}^{-1}\), and the smallest deviation of 4.19\(\%\) is observed for the flow model considering both elevated temperature and strain rates (Equation (1)). Therefore,  
Figure 4: (**a**) Experimental system and (**b**) forming punch with varying tilt angles (\(\alpha\) = 0\({}^{\circ}\) to 20\({}^{\circ}\)).  
the flow model considering both elevated temperature and strain rates (Equation (1)) will be used in numerical simulations to optimize process parameters during the flange forming of C45 billet tubes.
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 4 Model Solution  
As mentioned before, a single-objective problem is formulated, which is an NP hard nonlinear combinatorial optimization. Two problems will be faced when the traditional mathematical method is adopted. Firstly, the calculation amount increases exponentially with the scale of distribution network increasing. Additionally, there is likely to be a combinatorial explosion problem [18], which is difficult to be solved by the computer. Secondly, the normal reliability analysis of distribution network depends on the search process of topology structure (such as the process described in Sections 2.1 and 2.2). It makes difficulties to express the reliability of system or load point as an explicit function of decision variables, which limits the application of mathematical optimization algorithm to solving the optimization model in this paper. A large error would be resulted in if the normal reliability algorithm is replaced by the reliability estimation algorithm, making the estimation algorithm incapable of being used to calculate the reliability of the high reliability distribution network with the average outage time of customers in minute level.  
Therefore, intelligent algorithms which have fewer restrictions on the optimization model are used to solve the satisfactory solution. By imitating the physical phenomena or group biological behaviors in nature, the intelligent algorithm provides a set of optimization mechanism, which can obtain the satisfactory solution of the optimization problem in acceptable computing time and space. The model-free nature of intelligent algorithms makes it hard to figure out which one has the best performance for a certain optimization problem. Therefore, this paper adopts GA [19], ACO [20] and DPSO [21] to solve the FTU configuration model, and the algorithm with the best performance for the proposed model is found through comparisons from different perspectives. The combination of the three algorithms and the optimization model is elaborated in Appendix A.
## 3 The Constitutive Relationship of Cellular Medium
### Phenomenological Constitutive Model
\tag{11}\]  
\[\left[ \begin{array}{cccc}1&\varepsilon_{1}&\varepsilon_{1}^{2}&\varepsilon_{ 1}^{3}\\ 0&1&2\varepsilon_{1}&3\varepsilon_{1}^{2}\\ 1&\varepsilon_{2}&\varepsilon_{2}^{2}&\varepsilon_{2}^{3}\\ 0&1&2\varepsilon_{2}&3\varepsilon_{2}^{2}\end{array} \right]\left[ \begin{array}[] {c}\text{c}_{1}\\ \text{c}_{2}\\ \text{c}_{3}\\ \text{c}_{4}\end{array} \right]=\left[ \begin{array}{c}\text{a}_{1}+\text{b} _{1}\varepsilon_{1}\\ \text{b}_{1}\\ \text{a}_{2}+\text{b}_{2}\varepsilon_{2}\\ \text{b}_{2}\end{array} \right] \tag{12}\]  
where \(\text{a}_{1}\), \(\text{a}_{2}\), \(\text{b}_{1}\) and \(\text{b}_{2}\), are obtained by fitting the experimental stress-strain data at strain rate of 0.1 s\({}^{-1}\). c\({}_{1}\), c\({}_{2}\), c\({}_{3}\) and c\({}_{4}\) are determined by solving the linear simultaneous Equation (12). In the dynamic load, cellular media will inevitably encounter the impact of shock waves. Therefore, different shock wave models of cellular media are proposed to fit different types of stress-strain curves [46; 87], as shown in Figure 4a. [PERSON] and [PERSON] [88] proposed a one-dimensional shock model. They assumed that the constitutive relationship of cellular media was independent on strain rate and obeyed the rigid-perfectly plastic-locking (R-PP-L) relationship. There were only yield stress and densification strain in the model. [PERSON] [89; 90; 91] considered the effect of the elastic section of a cellular medium on the mechanical properties at low velocities on the basis of R-PP-L model, and proposed an elastic-perfectly plastic-rigid (E-PP-R) model, which took into account the elastic modulus. [PERSON] [92] proposed a rigid-linear hardening plastic-locking (R-LHP-L) model. A shock model under high-velocity impact and a transition model under medium-speed impact were established. In addition, [PERSON] [93] used the rigid-softening-hardening (RSH) model and elastic-softening-hardening (ESH) model to fit the impact load on oak tree. [PERSON] [94; 95] and [PERSON] [96] proposed a simple rigid-power-law hardening (R-PLH) model to better evaluate the stress-strengthening level of impact enhancement. This model gave a closed or semi-closed solution of the physical quantities on the shock wave front. Although the phenomenological constitutive models can fit the compressive stress-strain curves of cellular media well, they cannot explain the mechanism of the compression process.