esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Power-Flow Ratios in a PGU  
The PGU (see Figure 1) is composed of two mating gears and the gear carrier. Let us denote by \(x\), \(y\) and \(z\) the three links of the PGU.  
Angular speeds, torques and powers in a GPU are governed, respectively, by the well-known analytical conditions from [PERSON] equation, torque equilibrium and power balance:  
\[\omega_{z}+\tau_{1}\omega_{x}+\tau_{2}\omega_{y} =0 \tag{1a}\] \[T_{x}+T_{y}+T_{z} =0\] (1b) \[T_{x}\omega_{x}+T_{y}\omega_{y}+T_{z}\omega_{z} =0 \tag{1c}\]  
where \(\tau_{1}\) and \(\tau_{2}\) depend on the number of teeth ratio.  
The readers will readily recognize (1a) as the Willis' equation. In fact, setting \(z\) and \(x\) as the subscripts denoting the meshing gears and \(y\) the gear carrier, then  
\[\frac{\omega_{z}-\omega_{y}}{\omega_{x}-\omega_{y}}=N_{x,z}=\pm\frac{\text{ No. of Teeth of gear $x$}}{\text{No. of Teeth of gear $z$}} \tag{2}\]  
The comparison of (1a) with (2), yields \(\tau_{1}=-N_{x,z}\) and \(\tau_{2}=N_{x,z}-1\). Obviously, other subscript combinations are possible, with corresponding different analytical expressions of \(\tau_{1}\) and \(\tau_{2}\). The choice \(x\), \(y\) and \(z\) for the initial indistinct labeling all the moving bodies composing the PGU is justified by the advantage of condensing all possible cases for mechanical efficiency computation in two formulas only. In other words, what is lost in apparent initial abstraction is later gained in the results' generality.  
After some algebraic manipulation of (1) one obtains the following:  
\[\frac{P_{x}}{P_{y}} =-\frac{(1+\tau_{2})+\tau_{1}\frac{\omega_{x}}{\omega_{y}}}{(1+ \tau_{1})+\tau_{2}\frac{\omega_{y}}{\omega_{x}}} \tag{3}\] \[\frac{P_{y}}{P_{z}} =-\frac{(1+\tau_{1})+\tau_{2}\frac{\omega_{y}}{\omega_{z}}}{(\tau _{1}+\tau_{2})+\frac{\omega_{z}}{\omega_{y}}} \tag{4}\]  
where \(P_{x}=T_{x}\omega_{x}\), \(P_{y}=T_{y}\omega_{y}\) and \(P_{z}=T_{z}\omega_{z}\) are the powers sustained by links \(x\), \(y\) and \(z\), respectively. The power ratios deduced by [PERSON] & [PERSON] [29] are particular cases of the previous equations.  
Under ideal conditions, in a PGU, the sign of powers is established prescribing the following: two angular speeds and one torque (kinematically driven PGU) or, alternatively, one angular speed and two torques (torque driven PGU) [30].  
In a two dof PGU we distinguish the following working modes:  
* Two driving links (namely \(x\) and \(y\), \(P_{x}>0\) and \(P_{y}>0\)) and one driving link (namely \(z\), \(P_{z}<0\));  
Figure 1: Example of planetary gear unit (PGU) with internally meshing gears.  
* Two driving links (namely \(x\) and \(y\), \(P_{x}<0\) and \(P_{y}<0\)) and one driving link (namely \(z\), \(P_{z}>0\)).  
In the current analysis, it is assumed that the presence of meshing losses does not alter the algebraic sign of actual power flows.
## 1 Introduction
Results from ANN modelling methods showed good agreement with those obtained from experiments. The authors concluded that compressive strength of geopolymer concrete was profoundly influenced by mixture proportion and curing conditions. Performance of deep neural network (DNN) and deep residual network (ResNet) approaches in predicting the compressive strength of FAGC was investigated in a study by [PERSON] et al. [20]. With high rate of recognition accuracy within a complex network, the ResNet model showed better performance than the DNN models, with two main forward and backward passes; therefore, it has been used in several advanced engineering problems [26; 27]. ResNet and DNN approaches were also employed to predict compressive strength of conventional and foamed concrete in the studies by [PERSON] et al. [28] and [PERSON] et al. [29], respectively. Against this background, current solutions to predict compressive strength of FAGP concrete have not been dealt with in depth within existing literature. Although various machine learning approaches including ANN and DNN have been separately introduced in several studies [19; 21] as numerical predictors for FAGP strength, a thorough search of relevant published literature yielded a mere presence of the ResNet approach in FAGP property prediction. The lack of studies on impacts of input parameters (e.g., mix proportion ratios, NaOH concentration, and curing conditions) on geopolymer strength indicates possible improvements for upcoming research. More comprehensive research needs to be carried out to investigate the effectiveness of various machine learning methods in predicting compressive strength of FAGP concrete, considering a wider variety of input parameters and sensitivity analysis. As such, this study aims to offer advancements to the existing literature by employing ANN, DNN, and ResNet approaches integrated with sensitivity analysis to predict the compressive strength of FAGP concrete. These models were trained through 263 pairs of input/target values obtained from experiments. Performance of FAGP strength prediction of the three proposed approaches was investigated in two phases. In the first phase, the models were trained and validated using randomly shuffled datasets. Additional training and assessment under K-fold cross validation schemes were then carried out to confirm the results obtained from the first phase. Impacts of six input parameters (including NaOH/Na\({}_{2}\)SiO\({}_{3}\) ratio, fly ash/aggregate ratio, alkali liquid/fly ash ratio, NaOH concentration, curing time, and temperature) on prediction models were investigated using sensitivity analysis. Outcomes from sensitivity analysis are expected to identify the critical input parameters in FAGP strength prediction and control them carefully during geopolymer production. Three measures including R-squared (R\({}^{2}\)), root mean square error (RMSE), and mean absolute percentage error (MAPE) were employed to evaluate the accuracy of the proposed machine learning techniques.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 2 Principle and Modeling
### Improvement of the SSA  
#### 2.3.1 Firefly Disturbance Strategy  
The firefly algorithm can implement position optimization by simulating the firefly's luminous behavior [14]. In the SSA, the discoverers tend to jump directly to the vicinity of the current extreme value at the later stage, resulting in an inadequate search range and, thus, being trapped in the local optimum, and the searching accuracy is also affected.  
To improve the shortcomings mentioned above, the iterative strategy of the firefly algorithm is introduced after the sparrow search, and the firefly disturbance strategy is applied to the algorithm. By the characteristics of the firefly's luminous attraction, we can search for individuals with better positions in the neighborhood structure, enhance the diversity of solutions, expand the search area, increase the global exploration ability, and assist to escape out of the local optimum. Meanwhile, the location of the sparrow is further updated to make the overall population search more sufficient and to increase the convergence accuracy. The mathematical modeling involved in the firefly algorithm is as follows:  
I is the relative fluorescence brightness of fireflies and is defined as Equation (6):  
\[I=I_{0}\times\exp(-\gamma r_{ij}) \tag{6}\]  
where \(I_{0}\) is the maximum fluorescence brightness, which is its fluorescence brightness at zero distance, and the individual with the better fitness value has the higher value of \(I_{0}\). \(\gamma\) is the absorption coefficient of light intensity, which reflects the fluorescence attenuation effect of firefly individuals with the increase of distance and the absorption of the propagation media. \(r_{ij}\) is the spatial distance between fireflies.  
\(\beta\) is the firefly's attractiveness and defined as Equation (7):  
\[\beta=\beta_{0}\times\exp(-\gamma r_{ij}) \tag{7}\]  
where \(\beta_{0}\) is the maximum attraction, i.e., the attraction of an individual light source.  
The firefly disturbance position update formula is as follows:  
\[x_{i}=x_{i}+\beta\times\left(x_{j}-x_{i}\right)+\alpha\cdot\left[\text{rand} (*)-1/2\right] \tag{8}\]  
where \(x_{i}\) and \(x_{j}\) are the spatial positions of sparrow i and j, respectively. \(\alpha\) is the step size control parameter and \(\text{rand}(*)\in[0,\ 1]\) is a random factor subject to a uniform distribution.
## 4 Experiments and Results
### Real-Time Test  
The purpose of this experiment was to show that the real-time complexity of the proposed OAC-DES is less than the requested velocity sampling time \(T_{sv}=3\) ms. Indeed, three program routines running consecutively one after another increased the response time of the adaptive controller. The first routine was dedicated to measuring the rotational velocity \(\omega_{a}(k-1)\), the second to ANN-learning, and the third to executing the (1+1)-DES optimization algorithm.  
The partial time complexity of each routine was measured using a Tektronix TBS1052B digital oscilloscope. Obviously, the total time complexity of the proposed controller was obtained simply by adding together the individual time complexities of all three program routines. In fact, the velocity sampling time \(T_{sv}\) was determined on the basis of the measured total time complexity, which consequently ensured there would be enough time for the successful execution of all three routines. On the other hand, the OAC-DES responds in real-time when the velocity sampling time is drawn from the interval \(T_{sv}\in[1,5]\) ms.  
The results of the real-time test are illustrated in Figure 9, where the real-time execution of the OAC-DES is presented.  
Figure 8: Online control RMSE comparison between linear PI-controller and OAC-DES.  
Figure 9: Real-time complexity estimation.  
As can be seen in the figure, ANN-learning took 560 \(\upmu\)s on average. The online learning and evaluation never took the same amount of time. More precisely, the minimum ANN-learning time was 540 \(\upmu\)s, while the maximum was 600 \(\upmu\)s. On the other hand, the execution of the (1+1)-DES using 10 generations (21 evaluations of ANN-simulation) lasted 650 \(\upmu\)s on average, where the single evaluation of the fitness function using the ANN-simulation went on for 8.3 \(\upmu\)s. More precisely, the minimal time complexity of the (1+1)-DES was 640 \(\upmu\)s, while the maximum was 690 \(\upmu\)s. As a result, ANN-learning and (1+1)-DES optimization together took 1.21 ms on average, which means that enough time was left for applying more complex nature-inspired algorithms in the future.  
In contrast, we neglected the time complexity of linear PI-controllers due to their simplicity and rapid execution on the CLA coprocessor. Furthermore, the program routine for this controller was written in the assembly language, which ensures that execution takes less than 10 \(\upmu\)s.
## 7 Discussion
### Limitations, Computational Complexity, and Optimization Strategies  
Although the proposed hybrid approach demonstrates substantial improvements in route optimization, it also presents several limitations that must be acknowledged. First, the computational cost of the GAAM-TS variant is significantly higher than that of simpler configurations, especially as the number of locations increases. This is primarily due to the Tabu Search component, which introduces a nested local search process for each selected individual in the population. The computational complexity per generation becomes approximately \(\mathcal{O}(P\cdot N^{2})\), where \(P\) is the number of individuals and \(N\) the number of locations, as each local refinement step explores a neighborhood of size \(\mathcal{O}(N^{2})\).  
This complexity makes the algorithm less practical for large-scale or real-time applications without further optimization. Additionally, the sequential nature of the implementation (particularly in LSTM-based travel time evaluations and Tabu Search loops) contributes to longer runtimes and hinders scalability.  
To address these challenges, several strategies can be considered. The fitness evaluation step, which includes multiple LSTM predictions per route, is highly parallelizable, as each individual's fitness can be computed independently. This opens the door for multi-threaded CPU execution or GPU-accelerated batch predictions. Furthermore, the Tabu Search process can be distributed across cores or nodes by assigning each individual or its neighborhood to a separate processing unit. In addition, the dynamic control of Tabu Search invocation--triggering it only when convergence stagnates--could reduce overhead without compromising solution quality.  
Despite these current limitations, the modular structure of the proposed algorithm makes it amenable to scalable parallel implementations, which are a natural direction for future development.
# A Comparative Analysis of IMUs and Optical Systems in Layup Shot Biomechanics
## 2 Materials and Methods
### Statistical Analysis  
The normality of the data was assessed using the Anderson-Darling test (\(p<0.05\)), confirming a normal distribution and justifying the use of parametric statistical tests. Of the 200 collected trials, 188 valid trials (94%) were retained for comparison, with 12 excluded due to errors in either system. Averages and standard deviations were computed at the participant level and combined for group-level interpretation. Root mean squared difference (RMSD), a measure of the systematic error between predicted and observed values [23], was used to compare joint angle and centre of mass (COM) displacement data between the Vicon and Xsens systems. RMSD was calculated at the trial level for each joint (shoulder, hip, elbow, wrist, knee, and ankle) across relevant movement planes as well as for COMx and COMy displacement during the run-up before the shot. RMSD values were averaged across trials for each participant and used for group-level statistical interpretation, with values \(\leq 5^{\circ}\) indicating excellent agreement and values above this threshold considered poor [24; 25]. Agreement between systems was further evaluated using intraclass correlation coefficients (ICC) and Bland-Altman plots, which measure reliability and assess consistency and limits of agreement [26; 27]. These measures were analysed using a paired-sample \(t\)-test (SPSS Inc., Chicago, IL, USA), which was applied to both take-off angle and jump height for between-system and between-condition comparisons. Statistical parametric mapping (SPM) was conducted using MATLAB's spm1d package [28] to assess differences in continuous biomechanical data for both system-based (Vicon vs. Xsens) and condition-based (defended vs. undefended trials within each system) comparisons. Individual trial time traces were entered into an SPM paired-sample \(t\)-test to identify significant differences across the entire movement cycle. The statistical significance of all tests was set at \(\alpha=0.05\).
## Abbreviations  
We show list of some abbreviations in the table below.  
**Abbreviation** **Definition**  
RANSAC  Random Sample Consensus  
MAGSAC++  Marginalizing Sample Consensus plus plus  
LO-RANSAC  Locally Optimized Random Sample Consensus  
GroupSAC  Group Sample Consensus  
GC-RANSAC  Graph-Cut Random Sample Consensus  
NG-RANSAC  Neural-Guided Random Sample Consensus  
BANSAC  Bayesian Network Random Consensus  
PARSAC  Parallel Sample Consensus  
CDF  Cumulative Distribution Functions  
CGM  Cluster-based Graph Mapping Block  
CL  Chamfer Loss  
MAGSAC  Marginalizing Sample Consensus  
NAPSAC  N Adjacent Points Sample Consensus  
PROSAC  Progressive Sample Consensus  
USAC  Universal Sample Consensus  
DSAC  Differentiable Random Sample Consensus  
D-RANSAC  Generalized Differentiable Random Sample Consensus  
NeFSC  Neurally Filtered Sample Consensus  
CDE  Confidence Distribution Encoder  
FAD  FPS Aggregation Dispatch Block
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 3 Design Architecture  
The proposed Variable Delayed Dual-Core Lockstep (VDCLS) technique is implemented using the CCRV32 ST processor core [29; 30; 31] utilizing the RISC-V standard \"GC\" instruction set. The CCRV32 ST integer pipeline is depicted in Figure 2. Its main features are:  
* RV32 GCX instruction set;
* Six-stage integer pipeline;
* Single-issue, in-order design;
* Gshare branch prediction;
* Single or multicore implementation;
* Up to 1.38 DMIPS/MHz/Core;
* Up to 2.10 CoreMark/MHz/Core;
* Custom instruction set extension;
* On-chip debug support.  
The adopted processor is suitable for use in application-specific integrated circuits (ASIC) and in the field-programmable gate array devices (FPGA). The latter is commonly used as a design and verification framework due to its re-programmability feature. The main idea of the implementation process was to use the already existing CCRV32 ST multi-core infrastructure including the instruction and data caches and bus interconnects. Consequently, one of the design aims, i.e., to implement the lock/split feature mentioned earlier, was easily achievable. The block diagram of the typical processor implementation is presented in Figure 3. The following subsections briefly describe the VDCLS design phases.  
Figure 1: Block diagram of a typical dual-core lockstep processor.  
Figure 3: Block diagram of a typical CCRV32 ST processor system-on-chip.  
Figure 2: Integer unit datapath of the CCRV32 ST processor core.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### LPWAN Technologies
The entire LPWAN technologies listed above are appraised in Table 2 and ranked according to their supposed integration of CR in their respective LPWAN technologies. However, the Adaptrum and Platanus technologies were omitted for a lack of detailed technical information from the proprietary developers regarding the parameters of comparison in Table 2. Ranking was conducted based on the number of ticks in Table 2, which aims at assessing the use of specific CR technologies and strategies in the different LPWAN technologies. Following Table 2, it is noted that both Nwave and Weightless adopt the most number of CR techniques and strategies in their respective technologies, which agrees with similar claims in the literature that they presently support CR in their different technologies [37].
# Initial Results from SQUID Sensor: Analysis and Modeling for the ELF/VLF Atmospheric Noise
## 1 Introduction
Direct current (dc) Superconducting QUantum Interference Device (SQUID), consisting of dual Josephson junctions connected in parallel to form a loop, is very sensitive to the magnetic flux threading the loop [14]. Moreover, the SQUID sensor can be fabricated with a size about 1 cm\({}^{2}\)[15], which is more suitable to be the antenna of a mobile receiver compared with the traditional ones. The gain of the receiver within the frequency range of interest is independent of frequency and thus it can be easily calibrated with a known-amplitude signal in the center of a magnetically shielded room (MSR). The most significant advantage of the application of the SQUID is that they allow the compact construction of three-axis receivers. The motion-related noise are mainly caused by the effect of sensor rotation in the Earth's magnetic field. As the field is uniform on the physical scale of SQUID, the Earth's field component can be removed as a dc component by summarizing the vector components [2; 16]. The first ELF reception experiment by a low-temperature (4.2 K) SQUID was reported for submarine communication by [2]. A more detailed description of the low-temperature SQUID based receiver was given in [16]. Similar low frequency mobile receivers using SQUID can be also found in [17; 18]. Existing applications of SQUID sensor are concentrated on the reduction of atmospheric noise [19; 20; 21]. However, the analysis and modeling for the wideband ELF/VLF noise are rarely reported in state-of-art of works. The types of modulations and the corresponding optimal detector are highly dependent on the characteristics of the communication channel. As a result, the SQUID based optimal receiver cannot be derived for the wideband communication using ionosphere heating. In this paper, we adopted a low-temperature SQUID receiver to observe ELF/VLF atmospheric noise during seven days. The field data are preprocessed to suppress baseline wandering and harmonics by symmetric wavelet transform and least square (LS) estimation before further analysis. Statistical and fitting experiments are performed for the processed data to investigate the wideband model of the ELF/VLF noise and initial results are derived in this paper.
## 2 Design and Simulation
Therefore, CW and USRs can be used as the bright and dark modes of the vertical electric field excitation, satisfying the prerequisite conditions required to realize the bright-dark mode of the EIT-like effect. In the designed EIT structure, the CW was directly excited by the terahertz wave along the incident electric field to generate electrical resonance, and the near-field coupling simultaneously excited the LC resonance in the USRs. The destructive interference between LSP and LC resonance resulted in a clear transparent peak in a broad absorption background at 1.01 THz, showing an electromagnetically induced transparency-like window. In addition, the left and right transmission troughs were located at 0.95 THz and 1.05 THz, respectively. Moreover, the asymmetric structure was simulated and analyzed, while a transparent window could also be found, similar to the symmetric structure. The window was located at 1.02 THz, and the left and right transmission troughs were at 0.98 THz and 1.05 THz, respectively, see Figure 2b. Based on the optimized parameters of metamaterial structure, the electromagnetic field of the symmetric structure metamaterial was simulated and analyzed, as shown in Figure 3. The electromagnetic field distribution (Figure 3a,b) and surface current distribution map (Figure 3c) of the three characteristic frequencies (0.95 THz, 1.01 THz, and 1.05 THz) were used to analyze the coupling of bright and dark modes. It was found that its coupling in the electromagnetic field was very weak at the transmission troughs of 0.95 THz and 1.05 THz. In addition, in the surface current distribution shown in Figure 3c, it can be noted that the surface current directions of CW and USRs at low-frequency trough are opposite, while the currents at high-frequency trough are in the same direction. By observing the transparent window at a frequency of 1.01 THz, it can be found that the bright mode is significantly suppressed while the dark mode is excited. This is because the bright mode  
Figure 2: The transmission properties of metamaterial structure based on asymmetric structures. (**a**) Asymmetric structure distribution of upper and lower USRs with different distances from the CW; (**b**) The transmission spectra of the asymmetric EIT structure shows a transparent window similar as the symmetric structure at 1.02 THz. CW generates an asymmetrical distribution of electric field on both sides of USR after being radiated by the incident wave, and the x component of the electric field excites the LC resonance of USR, so that part of the energy of the bright mode is transferred to the dark mode. This is a typical phenomenon of interference between bright and dark resonators, which fully indicates that the structure we propose can achieve EIT-like effects. In addition, Figure 3d-f shows the electromagnetic field distribution at three frequencies corresponding to the asymmetric structure.