esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
###### Abstract  
Time-of-flight (ToF) measurement technology based on the amplitude-modulated continuous-wave (AMCW) model has emerged as a state-of-the-art distance-measurement method for various engineering applications. However, many of the ToF cameras employing the AMCW process phase demodulation sequentially, which requires time latency for a single distance measurement. This can result in significant distance errors, especially in non-static environments (e.g., robots and vehicles) such as those containing objects moving relatively to the sensors. To reduce the measurement time required for a distance measurement, this paper proposes a novel, parallel-phase demodulation method. The proposed method processes phase demodulation of signal in parallel rather than sequentially. Based on the parallel phase demodulation, \(2\pi\) ambiguity problem is also solved in this work by adopting dual frequency modulation to increase the maximum range while maintaining the accuracy. The performance of proposed method was verified through distance measurements under various conditions. The improved distance measurement accuracy was demonstrated throughout an extended measurement range (1-10 m).  
time-of-flight; AMCW; parallel phase demodulation; \(2\pi\) ambiguity +
Footnote †: journal: Applied Sciences
# Frost Resistance of Coal Gangue Aggregate Concrete Modified by Steel Fiber and Slag Powder
## 3 Results and Discussion
### Effect of Steel Fiber on Frost Resistance
#### 3.1.4 Rdme  
The effect of steel fiber content on the RDME of CGAC is shown in Figure 9. It can be seen that the RDME of CGAC without steel fibers decreased sharply when F-T cycles increased, while the RDME of fiber-reinforced CGAC increased greatly with the increase of steel fiber content. The RDME of CGAC with 1 vol.% HESF fluctuated greatly, and it reached a maximum of 103.4% after 400 F-T cycles, while that of the CGAC with 1.5 vol.% and 2 vol.% HESF content was relatively stable, fluctuating between 100.0% and 101.9%. Although the performance of the concrete with 0.5 vol.% steel fiber content was better than that of the reference group, the reinforcing effect of steel fiber with this dosage was not as obvious as that of the other three. This may be due to the incorporated steel fiber changing the speed of ultrasound during its transmission in CGAC, causing the increase of the dynamic elastic modulus of concrete [43].  
For different types of steel fibers, it can be seen from Figure 9 that the RDME of the three kinds of steel-fiber-reinforced concrete with 1 vol.% steel fiber are very close after 500 F-T cycles, and it is higher than that of the ordinary CGAC. This indicates that the bond force, including chemical bond action and the mechanical interlocking of the steel fiber and concrete, can partially bear the internal stress of concrete caused by frost heaving and delay the development of microcracks in CGAC after F-T cycles.
## 4 Performance Evaluation and Result Analysis
### Recovery Time Analysis  
A reconstruction time comparison of the proposed algorithm with the different OMP-based reconstruction algorithms is shown in Table 6. The results show that the proposed algorithm consistently outperformed the other three algorithms in terms of execution time across all the configurations. In Configuration \((8,32,64)\), the proposed algorithm achieved an execution time of 0.3261, which was lower than the execution times of OMP (0.7956), CoSaMP (0.8071), and StOMP (0.3439). Similarly, in Configuration \((16,64,128)\), the proposed algorithm (0.3527) demonstrated a lower reconstruction time compared to OMP (0.9434), CoSaMP (0.7652), and StOMP (0.3694). This trend continued in Configurations \((32,128,256)\) and \((64,256,512)\), where the proposed algorithm consistently exhibited the lowest reconstruction time among the four algorithms, followed by the StOMP as the second best, while the OMP took the longest time. In terms of percentage improvements, the proposed algorithm achieved a maximum reduction of 70% in reconstruction time for the single column selection algorithm (OMP), while the maximum improvements over the multiple column selection algorithms were 59% and 13% for CoSaMP and STOMP, respectively. The results also show that the reconstruction time increased with the sensing matrix size.
## 2 Materials and Design Methods  
### Materials  
#### 2.1.1 Aggregate Material Properties  
Granite-gneiss manufactured sand (MS) and alluvial marine siliceous natural sand (NS) were used as fine aggregate in this study. Additionally, the two types of coarse aggregates were dolomite and granite. Figures 1 and 2 respectively show the detailed particle size distributions of the fine and coarse aggregates obtained by sieving with corresponding the American Society for Testing and Materials (ASTM) C33 grading limits [29]. The gradation curves show that all aggregates comply with the standard grading requirements. The coarse aggregate is an ASTM C33 size number 57. In addition, a series of tests was conducted for the coarse aggregates to evaluate the basic physical properties. The results tested in accordance to ASTM C127 and C535 [30; 31] are listed in Table 1. Dolomite was shown to exhibit a higher specific gravity (SG) and toughness than granite, while similar absorption values were obtained from both types of coarse aggregates.
## 2 Materials and Methods
### TGC Computation
#### 2.7.3 Extraction of Oscillations for Phase and Amplitude
This raises concern about the correctness of the analysis in the papers using narrow bands for amplitude. At the same time, as explained in [24], in the eegfilt function, the transition bands scale with increasing frequency. This results in a higher total bandwidth of the filter than intended and possibly enables such a filter to extract the modulated signal. Overall, the question of minimally sufficient bandwidth for amplitude for different filter types needs further clarification. In addition to using fixed bandwidths, there is an adaptive approach aimed at obtaining the best frequency resolution while satisfying the above-mentioned side-peak condition. Thus, in [21], the bandwidth for amplitude was set to 2 \(\mathrm{f_{p}}\) + 4 Hz, where \(\mathrm{f_{p}}\) is the phase band-center frequency, while in [28], it was set to 2 \(\mathrm{f_{p}}\). A similar but distinct approach is to set the bandwidth for amplitude to twice the maximum considered phase frequency, as is set by default in the package pactools (in the Python programming language) described in [49]. In this study, we used as a starting point ('basic' method below) the bandwidths of 4 and 20 Hz for phase and amplitude, considering that they satisfy the side-peak condition for TGC and were used in the scripts deposited by [PERSON] et al., producing strong PAC in the benchmark LFP data. We also tested the adaptive bandwidth approach from [21] (method 'Butterworth adaptive' below). For band center frequencies, we adopted the settings from [21], calculating the comodulograms in the 3-9 Hz range for phase and the 20-70 Hz range for amplitude, and then selecting the frequencies of maximal coupling within the window of 4-8 Hz for phase and 30-60 Hz for amplitude. The initial calculation of the coupling over the larger windows allowed us to check whether the maximum in our window was indeed a local maximum of PAC or lied on a slope from a peak located outside this window. In this latter case, the comodulogram can be said to be dominated by the coupling of other rhythms that obscure the TGC.
## 2 Organic-Inorganic Hybrid Nanofibers  
Nanofibers refer to nanostructures characterized by a distinctive flower-like morphology at the nanometer scale. These structures have undergone extensive research owing to their promising applications in diverse fields, including catalysis, energy storage and conversion, sensors, and biomedical imaging. The notable characteristics of nanoflowers, such as their high surface area and unique morphology, contribute to enhanced catalytic activity, superior optical properties, and increased sensitivity. Moreover, the flower-like structure offers a larger contact area for biomolecules to bind, making nanoflowers particularly suitable for applications in biosensors and bioimaging. Based on differences in composition, nanoflowers can be classified into several types, including inorganic nanoflowers [27; 28; 29; 30], organic nanoflowers [31; 32; 33], and hybrid nanoflowers (consisting of both organic and inorganic components) [28]. In 2012, [PERSON] et al. [34] first discovered the  
Figure 1: A flowchart for the generalized classification of HNFs, MOFs, and CPs as nanocarriers for enzyme immobilization.  
formation of organic-inorganic hybrid nanoflowers by co-incubating proteins, copper ions, and phosphates. During the synthesis process, inorganic components precipitate, grow, and aggregate around the enzyme, eventually forming flower-shaped particles with a size of approximately 100-500 nm. This novel nanomaterial is referred to as organic-inorganic hybrid nanoflowers (HNFs) [35].  
Subsequent research has revealed that HNFs constitute a versatile strategy for the immobilization of large biomolecules. Antibodies [36; 37; 38; 39; 40], proteins [41], DNA [42; 43; 44; 45; 46], amino acids, and other biomolecules can serve as organic components of HNFs. In principle, any biomolecule with metal-binding sites can form complexes with metal ions through coordination [47]. HNFs integrate the advantages of both organic and inorganic materials, making them an ideal carrier for enzyme immobilization. The formation of HNFs with enzymes as organic components generally involves three steps: coordination, precipitation, and self-assembly [2; 35; 48]. In the initial stage, metal ions react with phosphate ions to form primary crystals of metal phosphates. Functional groups on proteins (amide, carboxyl, or hydroxyl groups) coordinate with metal ions, providing a site for the nucleation of proteins and primary crystals. In the second stage, metal phosphate crystals begin to grow at the metal ion-binding sites and continue to grow through the continuous binding of protein nanoflower petals and primary crystals. In the final stage, the formation of nanoflowers is completed through anisotropic growth. In recent years, HNFs have garnered widespread attention due to their comprehensive functionality, combining organic and inorganic materials, as well as their environmentally friendly and straightforward preparation steps.
###### Abstract  
We propose and demonstrate a Mach-Zehnder Interferometer (MZI)-based optical neural network (ONN) to classify and regenerate a four-level pulse-amplitude modulation (PAM4) signal with high inter-symbol interference (ISI) generated experimentally by a silicon microring modulator (SiMRM). The proposed ONN has a multiple MZI configuration achieving a transmission matrix that resembles a fully connected (FC) layer in a neural network. The PAM4 signals at data rates from 160 Gbit/s to 240 Gbit/s (i.e., 80 GBaud to 120 GBaud) were experimentally generated by a SiMRM. As the SiMRM has a limited 3-dB modulation bandwidth of \(\sim\)67 GHz, the generated PAM4 optical signal suffers from severe ISI. The results show that soft-decision (SD) forward-error-correction (FEC) requirement (i.e., bit error rate, BER \(<\) 2.4 \(\times\) 10\({}^{-2}\)) can be achieved at 200 Gbit/s transmission, and the proposed ONN has nearly the same performance as an artificial neural network (ANN) implemented using traditional computer simulation.  
silicon photonics (SiPh); silicon-on-insulator (SOI); pulse amplitude modulation (PAM); silicon microring modulator (SiMRM); optical neural network (ONN)
## 5 Disturbance Rejection and Tracking Reference
### Disturbance Rejection  
The following result addresses the problem of rejecting disturbances.  
**Lemma 6**.: _Let us cosider system (1) with \(\tau<\frac{1}{a}-\frac{1}{b_{1}}+\sqrt{\frac{1}{a^{2}}+\frac{1}{b_{1}^{2}}}\); the predictor scheme given by Figure 2, \(C(s)\) described by (22), the input reference \(R(s)=0\) and \(H(s)=\frac{\beta}{s}\) a step-type disturbance. Then, \(\lim_{t\to\infty}y(t)=0\)._  
Proof.: The transfer function in Figure 2\(Y(s)/H(s)\) is  
\[\frac{Y(s)}{H(s)}=\frac{G(s)\mathrm{e}^{-\tau\mathrm{s}}(G_{u}(s)(G_{stb}(s)(s _{2}+G_{s}(s))+g_{1})\mathrm{e}^{-\tau\mathrm{s}}-G_{stb}(s)G_{s}(s)+1)}{(1-G( s))(G_{u}(s)\mathrm{e}^{-\tau\mathrm{s}}(G_{stb}(s)g_{2}+g_{1})+1)}, \tag{25}\]  
By applying the final value theorem to (25), one obtains  
\[\lim_{t\to\infty}y(t)=\lim_{s\to 0}sY(s)=0 \tag{26}\]  
This shows that the scheme shown in Figure 2 is able to reject step-type disturbances.  
Note that the design of the controller \(C(s)\) involved in the control strategy developed in Theorem 3 is carried out by using the delay-free system \(G(s)\). However, in the proposals of Lemma 4 and Corollary 2, the controller \(C(s)\) must be carried out considering the delayed system \(G(s)\mathrm{e}^{-\tau\mathrm{s}}\). Therefore, for the latter case, the parameters of the PID controller corresponding to \(C(s)\) can be calculated using results related to Lemma 1. Furthermore, reference tracking and disturbance rejection issues, both of the step type, can be proven for the control strategies proposed in Lemma 4 and Corollary 2 by using a similar development as that presented in the proofs of Lemmas 5 and 6.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 3 Morphological Semantics Memory Model for Visual Stimuli  
This section describes the proposed model with three concepts such as sensory memory, working memory, and long-term memory. This model provides a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn. This paper proposes a novel memory model to reflect remembrance of morphological semantics of visual input stimuli by combining the concepts of traditional cognitive and psychological memory models [11; 12] and a neuron activation simulation model, that is, HTM [13]. The proposed model employs SDR for modeling interaction of neocortex in human brain which takes in charge of information processing and memory.  
The general flow of the proposed memory model is illustrated in Figure 8. The proposed memory model is composed of three conceptual parts such as sensory memory, working memory, and long-term memory. First, the sensory memory part senses visual stimuli and extracts feature information from the sensed visual stimuli. Then, the working memory part converts the extracted feature information into SDR, known as a data representation pattern processed by a biological brain [5; 6; 13]. The working memory can also store input SDR as a new memory pattern, which corresponds to the consolidation process in cognitive psychology. And the working memory can also update the stored memory patterns through comparison operation with the previously stored memory patterns, which corresponds to the reconsolidation process in cognitive psychology. Finally, the long-term memory part defines what memories are stored and recalled, and it serves as the permanent storage of memories.  
Figure 7: Concept of the proposed morphological semantic preservation scheme based on the idea of Cortical.IO. The regions of semantic similarities among SDRs of images ‘0’, ‘5’, and ‘6’ are indicated with circles.
## 3 Influence of Face Advancing Rate on Roof Caving Interval  
### Establishment and Design of Numerical Model  
(1) Numerical Model Construction  
This numerical simulation investigates roof caving characteristics under different mining advance rates using 3 DEC 7.0 software. 3 DEC 7.0, developed by Itasca Consulting Group, is a three-dimensional discrete element method (DEM)-based numerical modeling software specifically designed for analyzing discontinuous media (e.g., jointed rock masses, fractured structures).  
The numerical model was established based on geological conditions and field measurements from Panel 12308 of the Hongqingliang Coal Mine. The model dimensions were designed as follows, according to actual coal seam distribution: 300 m in the x-direction, 3 m in the y-direction, and 134 m in the z-direction. Horizontal constraints were applied to lateral boundaries, fixed constraints to the bottom boundary, and free boundary condi  
Figure 3: Schematic diagram of advance rates.  
tions to the top surface. A uniformly distributed load of 8.0 MPa was applied to simulate overburden pressure. The computational model of Panel 12308 is shown in Figure 4.  
(2) Constitutive Models and Mechanical Parameters of Coal-Rock Mass  
The selection of constitutive models in 3 DEC critically determines the accuracy of rock mass behavior characterization. Built-in models include Mohr-Coulomb, strain-softening, strain-hardening, and creep models. Given that weakly cemented sandstone dominates the study area with shear-dominated progressive failure mechanisms, the Mohr-Coulomb model was adopted due to its strong correlation with the linear \"shear strength-normal stress\" relationship.  
The mechanical parameters of coal/rock masses were determined through uniaxial compression tests, tensile strength tests, and shear strength tests (Table 1). The joint mechanical parameters are listed in Table 2.  
(3) Numerical Simulation Scheme  
Considering actual advancing rate variations at Panel 12308, the simulation was designed with a total mining distance of 240 m using the full-seam mining method. A 30 m boundary coal pillar was reserved for the 3-1 coal seam. Four advancing rate scenarios (4 m/d, 6 m/d, 8 m/d, and 10 m/d) were simulated, with 5000 equilibrium steps required after each excavation increment. Pressure monitoring lines were installed above the roof to observe abutment pressure evolution during mining.
## 4 Joint Routing and Resource Management with Conjecture Based Multi-Agent Q-Learning
### Equal Reward Time-Slots Based Conjectural Multi-Agent Q-Learning
3:For each \(SU_{i}\)Do
4:For each \(s_{i}\in S_{i}\), \(a_{i}\in A_{i}\)Do
5: Initialize transmission strategy \(\zeta_{i}^{t}(s_{i},a_{i})\), conjecture belief \(\tilde{\varphi}_{i}^{t}(s_{i},\mathbf{a}_{-i})\),  Q-value \(Q_{i}^{t}(s_{i},a_{i})\), and replay memory \(D=\big{\{}\tilde{\varphi}_{i}^{t}(s_{i},\mathbf{a}_{-i})\), \(\zeta_{i}^{t}(s_{i},a_{i})\big{\}}\). 6:EndFor
7:EndFor
8:RepeatedLearning:
9:For each \(SU_{i}\)Do
10:For\(\textit{episode}=1,\ M\)do
11:Initialize state \(s_{i}^{1}\). 12:Loop
13: Select action \(a_{i}^{t}\) according to the strategy \(\zeta_{i}^{t}(s_{i},a_{i})\). 14: Execute action \(a_{i}^{t}\), and obtain strategies of previous nodes \(\zeta_{k}(s_{k},\ a_{k})\) and the SINR indicator \(\rho_{i}\). 15: Observe reward \(R_{i}^{t}(s_{i},a_{i},\mathbf{a}_{-i})\) and state \(s_{i}^{t+1}\) according to (14) and (16). 16: Update \(Q_{i}^{t+1}(s_{i},a_{i})\) based on \(\tilde{\varphi}_{i}^{t}(s_{i},\mathbf{a}_{-i})\) according to (26). 17: Update the strategy \(\big{\{}\zeta_{i}^{t+1}(s_{i},a_{i})\big{\}}_{a_{i}\in A_{i}}\) according to (19). 18: Sample experience \(\tilde{\varphi}_{i}^{t}(s_{i},\mathbf{a}_{-i})\) and \(\zeta_{i}^{t}(s_{i},a_{i})\) from \(D\). 19: Update the conjecture belief \(\tilde{\varphi}_{i}^{t+1}(s_{i},\mathbf{a}_{-i})\) according to (28). 20: Store \(\tilde{\varphi}_{i}^{t+1}(s_{i},\mathbf{a}_{-i})\) and \(\zeta_{i}^{t+1}(s_{i},a_{i})\) in \(D\). 21:\(s_{i}=s_{i}^{t+1}\)
22:\(t=t+1\)
23:Until \(s_{i}\) is the terminal state
24:EndFor
25:EndFor
```  
**Algorithm 1** Equal Reward Time-Slots Based Conjectural Multi-Agent Q-Learning
## 3 Numerical Studies
The evolution of the Gelman-Rubin factor for estimation parameters is illustrated in Figure 6. Evolutions of the samples converge rapidly as shown in Figure 7. Six thousand samples generated after convergence are used to construct the marginal posterior probability distributions for the structural model estimation parameters as illustrated in Figure 8. \begin{table}
\begin{tabular}{c c} \hline \hline
**Mass (kg)** & \\ \hline \(m_{1}\)\(\sim\)\(m_{4}\) & 6000 \\ \(m_{5}\)\(\sim\)\(m_{8}\) & 4200 \\ Stiffness (kN/m) & \\ \(k_{1}\)\(\sim\)\(k_{4}\) & 5000 \\ \(k_{5}\)\(\sim\)\(k_{8}\) & 4000 \\ \(k_{9}\)\(\sim\)\(k_{10}\) & 3000 \\ Stiffness (kN/m) & \\ First mode & 1.321 \\ Second mode & 0.505 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structure properties of the 10-DOF system. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \multicolumn{3}{c}{**S.D.**} & \multicolumn{3}{c}{**R.E**} \\ \cline{2-7}  & **Case1** & **Case2** & **Case3** & **Case1** & **Case2** & **Case3** \\ \hline \(k_{1}\) (kN/m) & 0 & 8.81 & 25.5 & 0\% & 0.16\% & 0.30\% \\ \(k_{2}\) (kN/m) & 0 & 4.39 & 9.55 & 0\% & 0.16\% & 0.35\% \\ \(k_{3}\) (kN/m) & 0 & 3.6 & 8.72 & 0\% & 0.13\% & 0.32\% \\ \(k_{4}\) (kN/m) & 0 & 4.27 & 6.56 & 0\% & 0.16\% & 0.24\% \\ \(k_{5}\) (kN/m) & 0 & 7.49 & 1.51 & 0\% & 0.28\% & 0.42\% \\ \(k_{6}\) (kN/m) & 0 & 4.56 & 7.3 & 0\% & 0.17\% & 0.28\% \\ \(k_{7}\) (kN/m) & 0 & 5.44 & 8.81 & 0\% & 0.20\% & 0.32\% \\ \(k_{8}\) (kN/m) & 0 & 6.37 & 10.3 & 0\% & 0.23\% & 0.38\% \\ \(\zeta_{1}\) & 0 & \(1.64\times 10^{-4}\) & \(2.63\times 10^{-4}\) & 0\% & 1.64\% & 2.63\% \\ \(\zeta_{2}\) & 0 & \(5.8\times 10^{-5}\) & \(1.09\times 10^{-4}\) & 0\% & 0.49\% & 0.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the posterior parameter distribution with the relative error (R.E.) between the posterior mean and the true value, including the standard deviation (S.D.) for each scenario. Table 5 presents the final posterior moments of the generated samples after the convergence. The most likely parameter values of the structural model are listed for comparison, identified using  
Figure 8: Marginal posterior probability distributions of the six estimation parameters (\(k_{1}\), \(k_{6}\), \(k_{9}\), \(m_{1}\), \(m_{6}\), \(\zeta_{1}\)). Figure 6: Evolution of the scale reduction factor for the estimation parameters. Figure 7: Generated samples for the six estimation parameters (\(k_{1}\), \(k_{6}\), \(k_{9}\), \(m_{1}\), \(m_{6}\), \(\zeta_{1}\)). the GA, PSO, BB-BC, and SCE-UA global optimization algorithms after the same evaluations (50,000) for the sake of comparison. Clearly, when compared with these methods, the SCEM-UA algorithm shows a slightly better global optimization capability and can assess the uncertainties associated with the posterior distribution of the parameter estimates simultaneously within a single optimization run, which is an additional advantage of the SCEM-UA algorithm.
## 4 Discussion
We currently limit this study to daily precipitation. Since IMERG-like products have data on a half-hourly scale, future works could look at several other extreme precipitation indices on a sub-daily scale. However, comparing the performance of SPEs on a sub-daily scale is challenging due to the lack of high-temporal GP station observations. This study has some limitations. First, a point-to-pixel method implemented for evaluating IMERG against GP may lead to biases due to spatial mismatch between point measurements of rain gauges and area-averaged rainfall of each grid cell [109]. Similarly, most IMERG evaluation pixels encompass only one rain gauge. As a result, they may cover inhomogeneous terrain, resulting in the partial representation of the precipitation pixel value, which fails to provide better results in a complex landscape, such as Nepal. Another notable scarcity of enough rainfall networks for the higher altitude networks makes analysis complex and problematic, though general trends are exhibited. Since this study uses only 25 key GP stations, the appraisal of the performance of IMERG datasets at each grid point has higher uncertainty. However, due to the lack of high-quality GP stations, this study focused on spatial variation of several precipitation indices across the country rather than the performance evaluation at different locations, climates, and topography. With the availability of long-term automatic weather station data, a detailed investigation of the performances of IMERG datasets could be conducted even on a sub-daily scale [110].