esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimized Joint Signal Processing Schemes
### Sparsity Reconstruction-Based Improved Parameter Estimation
#### 3.1.2 Information Compensation  
According to the ambiguity function theory of integrated signal in [42], it is known that communication information will affect the echo matching filtering effect, thus reducing the radar detection performance. Based on this, it is necessary to compensate for the signal in (13) in the frequency domain, and the signal compensated by \(\mathbf{s}_{p}^{-1}\) can be rewritten as  
\[\mathbf{\hat{y}}_{p,g}=N_{r}\sum_{q=1}^{Q}\beta_{q}e^{j2\pi\tau_{d}^{d}pT_{r}}e^{- j2\pi(g-1)d_{t}\sin\theta_{t}/\lambda}\mathbf{\psi}_{g}\mathbf{\tau}_{q}+\mathbf{W}_{p,g} \tag{14}\]  
By stacking all OFDM signals into a matrix, the following formula can be given as  
\[\begin{split}\mathbf{Y}_{g}=& N_{r}e^{-j2\pi(g-1)d_{s} \sin\theta_{t}/\lambda}\mathbf{\psi}_{g}\\ &\times\left[\sum_{q=1}^{Q}\beta_{q}e^{j2\pi\tau_{d}^{d}T_{r}}\bm {\tau}_{q},\sum_{q=1}^{Q}\beta_{q}e^{j2\pi\tau_{d}^{d}2T_{r}}\mathbf{\tau}_{q}, \cdots,\sum_{q=1}^{Q}\beta_{q}e^{j2\pi\tau_{d}^{d}pT_{r}}\mathbf{\tau}_{q}\right] \\ &+[\mathbf{W}_{1,g},\mathbf{W}_{2,g},\cdots,\mathbf{W}_{p,g}]\\ =& N_{r}e^{-j2\pi(g-1)d_{t}\sin\theta_{t}/\lambda} \mathbf{\psi}_{g}\sum_{q=1}^{Q}\beta_{q}\mathbf{\tau}_{q}\otimes\mathbf{D}_{q}^{\text{T}}+ \mathbf{W}_{g}\end{split} \tag{15}\]  
where \(\mathbf{W}_{g}=\big{[}\mathbf{W}_{1,g},\mathbf{W}_{2,g},\cdots,\mathbf{W}_{p,g}\big{]}\), \(\otimes\) denotes the Kronecker product of a matrix, and \(\mathbf{D}_{q}\) is the Doppler frequency shift vector of the \(q\)th target, which can be written as  
\[\mathbf{D}_{q}=[e^{j2\pi\tau_{d}^{d}T_{r}},e^{j2\pi\tau_{d}^{d}2T_{r}},\cdots,e^{j 2\pi\tau_{d}^{d}pT_{r}}]^{\text{T}} \tag{16}\]  
Next, each channel is compensated for phase difference information, and (15) can be rewritten as  
\[\mathbf{Y}=\sum_{g=1}^{N_{t}}\mathbf{Y}_{g}e^{j2\pi(g-1)d_{t}\sin\theta_{t}/\lambda}= N_{r}\sum_{q=1}^{Q}\beta_{q}\mathbf{\tau}_{q}\otimes\mathbf{D}_{q}^{\text{T}}+\mathbf{ \tilde{W}} \tag{17}\]
## 3 Results and Discussion
### Design Evaluation  
Inconsistencies between the Fairwheel test method [24] and the simulation exist. [PERSON] used a 222.5 N preload before increasing to 890 N, reporting deflection between these two values. The simulated deflection, however, included preload deflection. The simulation used fixed constraints on the square taper faces, whereas in the actual test this was connected to a BB and therefore torsion and bending of this component were not accounted for in the simulated results, whilst approximately 70% of total deflection measured at the pedal came from axle twist [28]. Finally, the Fairwheel test deflection was measured with force applied to the pedal spindle, thus including deformation of the pedal spindle in its measurement. The results are shown in Figure 12a, with a resultant displacement of 2.258 mm.  
Figure 11: Design for Additive Manufacture (DfAM) Diagram.  
Figure 12: (**a**) Static Test Using Fairwheel Bikes Method (Resultant Displacement); (**b**) Static Test Using Fatigue Parameters (von Mises Stress).  
The fatigue test was replicated identically in SolidWorks as per BS EN ISO 4210. While this also used fixed constraints on the square taper faces, this would only increase the stress levels and therefore produce a worst-case. Accounting for this, no regions of the results (see Figure 12b) were significantly beyond the yield stress value.
# Human Pose Detection for Robotic-Assisted and Rehabilitation Environments
###### Abstract  
Assistance and rehabilitation robotic platforms must have precise sensory systems for human-robot interaction. Therefore, human pose estimation is a current topic of research, especially for the safety of human-robot collaboration and the evaluation of human biomarkers. Within this field of research, the evaluation of the low-cost marker-less human pose estimators of OpenPose and Detectron 2 has received much attention for their diversity of applications, such as surveillance, sports, videogames, and assessment in human motor rehabilitation. This work aimed to evaluate and compare the angles in the elbow and shoulder joints estimated by OpenPose and Detectron 2 during four typical upper-limb rehabilitation exercises: elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A setup of two Kinect 2 RGBD cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises. Finally, we provided a numerical comparison (RMSE and MAE) among the angle measurements obtained with OpenPose, Detectron 2, and the ground truth. The results showed how OpenPose outperforms Detectron 2 in these types of applications.  
human-robot interaction; human pose estimation; robotic rehabilitation +
Footnote †: journal: Applied Sciences
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 3 Hybrid Methodology EMD-VMD
### Flowchart for Hybrid Methodology  
To further illustrate the hybrid EMD-VMD methodology, Figure 9 provides a visual representation of the process. This flowchart outlines the key steps involved in the methodology, from the initial signal input to the final reconstruction of the signal using the combined IMFs and modes.  
The flowchart in Figure 9 provides a step-by-step visual guide to the hybrid methodology, illustrating how the EMD and VMD techniques are integrated to enhance signal decomposition. This visual representation helps in understanding the sequential flow and decision points within the methodology, making it easier to grasp the overall process and its components.  
Figure 9: Flowchart of the hybrid EMD-VMD methodology.  
The hybrid EMD-VMD methodology offers a robust and efficient approach for the decomposition of complex signals. By combining the strengths of EMD and VMD, this method addresses the limitations of traditional techniques, providing a more accurate and computationally efficient solution for estimating vibratory components. The detailed algorithm and flowchart further illustrate the implementation and benefits of this approach, highlighting its potential applications in various fields of signal analysis.
# Real-Time Communication Aid System for Korean Dysarthrie Speech
## 2 Related Works
### Aac: Vivoca  
VIVOCA is one of the representative AAC systems developed for individuals with severe dysarthria. It works by receiving simple spoken commands from the user, which are then reassembled into messages and output as speech. The traditional input methods used in VOCAs, i.e., switches or keyboards, have been found to be cumbersome and tiring, which hinders natural communication. To address this problem, VIVOCA proposes using speech input instead of switches or keyboards. The system allows the user to read words from a pre-configured list, and it recognizes the spoken word to generate possible following words. By repeating this process, the user's intent is gradually refined, and the final sentence that the user intends to convey is generated, which is then output as speech using TTS. The model for dysarthric speech recognition in VIVOCA is based on a statistical model, the Hidden Markov Model, and the results of validating the word reassembly process showed high recognition accuracy. Therefore, VIVOCA can be highly useful for individuals with severe dysarthria in performing basic communication tasks. However, a limitation of VIVOCA is that users cannot manipulate their speech with fine control, which means it can only be used in specific situations. Subsequently, the system operates based on specific words or phrases, making it insufficient for processing all of the user's speech in real-time.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 2 Mission and Payload Operations
### Research Campaigns
In addition, the movement of turquoise patterns between Kongsfjorden and Krossfjorden can indicate Total Suspended Matter (TSM) from glacial run-off. There should be little CDOM present in the waters around Svalbard as there is no topsoil and not many plants. However, some chlorophyll-a present in the water might contribute to a more greenish color as blue and red light would be absorbed. The hyperspectral data from HYPSO-1 can help establish the content of the water by investigating the spectral signatures. Figure 4: RGB render of HYPSO-1 captures over Svalbard, outside of Ny-�lesund and Kongsfjorden from July 2022. The bands used for the RGB composite representation are 602.5 nm, 565 nm and 498.4 nm, respectively.
## 2 Architecture and Operational Analysis
### Queue-Length Models
#### 2.3.1 Model 1: Queue Length of Vehicles Delayed from Previous Cycle  
From the moment when vehicles from all directions at the upstream intersection merge into the through phase, exiting at the downstream intersection after the green ends, the vehicle queuing begins. The length of the queue is determined by the number of vehicles that have not yet exited and can be determined by the arrival time of the last vehicle at the end of the queue. The specific details are shown in Figure 3.  
The queue of vehicles from the previous cycle begins to form at the downstream intersection when the green from the previous cycle ends. This queue continues until the last vehicle arrives at the end of the queue. During this process, arriving vehicles create a queue wave with a propagation speed of \(V_{pw}\). The expression for the queue length of vehicles delayed from the previous cycle is represented as \(L_{p,k-1}^{det}\), which is given by  
\[L_{p,k-1}^{det}=\frac{\left[L_{g}+\left(g_{l_{u},slr}-g_{l_{u},s}-T_{os,k-1} \right)\times V_{g}\right]\times V_{pw}}{V_{g}+V_{pw}} \tag{3}\]  
where \(L_{g}=\) distance between the stop line at the upstream intersection's entrance lane and the stop line at the downstream intersection's entrance lane, \(V_{g}=\) speed at which vehicles can travel continuously without stopping, \(g_{l_{u},s}=\) duration of the green phase for the through movement at the downstream intersection, \(g_{l_{u},slr}=\) durations of the green phases for the through, left-turn, and right-turn movements at the upstream intersection, and \(T_{os,k-1}=\) remaining offset value for cycle \(k-1\).
## 3 Numerical Examples  
### Media Randomness  
#### 3.1.1 Calculation Model  
The saturated sedimentary valley model is shown in Figure 2, with a depth of 200 m, a width of 400 m. The width of the cover layer is 200 m, and the thickness is 26.8 m. The incident angles are \(0^{\circ}\) and \(30^{\circ}\). The excitation is incident at an angle \(\theta\) from the infinity of the bedrock half-space. It is necessary to consider the influence of the randomness of parameters such as the porosity and permeability coefficient on the amplification effect of valley ground motion.
## 2 Material and Methods
### Data Elaboration and Interpretation  
After obtaining a homogeneous database, the concentrations of major ions were used to assess the hydrochemical facies. Distributions of chemical-physical parameters, metals, and compounds of environmental interest were also analyzed and discussed in relation to the aquifer types. The aquifer type was chosen as discriminating character to assess any difference in groundwater, since it was one of the most comprehensive and homogeneous characteristics provided by all regional datasets. Data were also divided according to the sampling season (spring-summer and autumn-winter considered from now on as \"warm season\" and \"cold season\", respectively) to assess changes over the year. Analyses were carried out on 3671 samples distributed as follows:  
* 2243 in phreatic aquifers: 1220 for warm season, and 1023 for cold season;
* 1149 in confined aquifers: 597 for warm season, and 552 for cold season;
* 279 in semi-confined aquifers, equally divided between the two periods.  
Considering the difficulty to distinguish between confined and semi-confined aquifers in the complex multilayer hydrogeological setting of the Po Plain (Figure 2), we decided to treat the data from confined and semi-confined aquifers together both for the statistical analysis and the map production.  
In order to test for statistically significant differences between warm and cold seasons sub-populations and between different aquifer type, the Kruskal-Wallis nonparametric test was applied to our database [54; 55].  
The samples were classified according to the chemical and physical parameters (electrical conductivity-EC; water hardness; temperature). Boxplots for all components were created to assess the effects of aquifer type. Hydrochemical facies were assessed based on the concentrations of main dissolved ions using the Langelier-Ludwig diagram [56]. The ratios Na\({}^{+}\)/K\({}^{+}\), Ca2\({}^{+}\)/Mg2\({}^{+}\), SO4\({}^{2-}\)/Cl\({}^{-}\) were also calculated to distinguish the dominant species. The distributions of the associated ion pairs Ca2\({}^{+}\)-HCO3\({}^{-}\), Na\({}^{+}\)-Cl\({}^{-}\) Mg2\({}^{+}\)-SO4\({}^{2-}\), K\({}^{+}\)-NO3\({}^{-}\) were defined to assess the origin of the main ions and the lithological effect on groundwater quality.  
In order to investigate distributions of metals and compounds of environmental interest, three criteria of selection were applied:  
* The analyses were carried out by at least four regions (Table S1 in Supplementary Material);
* The data population had at least 25% of the values > D.L. (Table S2 in Supplementary Material);
* Presence of regulatory limits for the analyte that impose a maximum allowable concentration. Based on these criteria, the selected elements were: NO3\({}^{-}\), NH4\({}^{+}\), As, Fe, and Mn. Elaboration of all spatial distribution maps of groundwater parameters and components were carried out by QGIS software after the merge and conversion into a uniform coordinate system (WGS84/UTM zone 32N, EPSG: 32632).  
Figure 2: Boxplot of the geochemical parameters grouped according to aquifer type.
## 3 Results: Prediction of Wear Characteristics in a Mining Pump
### Wear Predictions for the Low-Flow Condition
#### 3.3.2 Wear Rates of the Flow-Passing Components  
To quantify the wear of the flow-passing components, the average wear rates of the first- and second-stage impellers and guide vanes were calculated using area-mass weighted averages. Subsequently, the effects of particle size on these wear rates were analyzed, as shown in Figure 6.  
In Figure 6, it is shown that wear intensity decreased with increasing particle size at the first-stage guide vane, second-stage impeller, and second-stage guide vane (i.e., all flow-passing parts except the first-stage impeller). Although the wear intensity trends were quantitatively different, in general, the wear was negatively correlated with particle size at the aforementioned flow-passing components. Moreover, the greatest wear rate was observed at the first-stage guide vane (13 \(\times\) 10\({}^{-8}\) kg/s\(\cdot\)m\({}^{2}\)) for a particle size of 1 mm, whereas the lowest was observed at the second-stage guide vane (4.8 \(\times\) 10\({}^{-8}\) kg/s\(\cdot\)m\({}^{2}\)) for a particle size of 5 mm.  
The wear rate of the first-stage impeller increased with increasing particle size because the force of the impact between the LE and solid particles increased when particle mass (size) was increased with flow velocity maintained at a constant value. At other flow-passing components, an increase in particle size resulted in a reduction in the work done by the impeller on the particles for the same energy, which then reduced the increase in radial velocity. Furthermore, for an unchanged volume fraction, an increase in particle size reduces the number of particles. Consequently, both of these effects reduce wear.
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
###### Abstract
To deal with this issue, the multi-band OCT-based precoding technique was proposed [17]. In addition, a block precoding (BL) scheme, in which the data-carrying SCs are divided into several groups and the number of data-carrying SCs in each group is an integer power of 2, was proposed in [18; 19]. However, the difference in equalized SNRs is relatively large among these groups. Compared to the uniform modulation scheme, these methods need to adopt different modulation formats and probabilistic distributions according to the SNRs of groups. In this work, to further reduce the implementation complexity of the precoding-enabled PS-DMT transceiver, a fast WHT-based partial data-carrying SC precoding (PDSP) technique is proposed and experimentally verified in the optical IM-DD transmission system. The proposed PDSP technique can realize SNR equalization and provide a significant reduction in implementation complexity. We also compare the proposed technique with the FDSP and digital pre-equalization techniques. The rest of this paper is structured as follows. The operation principle of the probabilistically shaping technique, fast WHT-based partial data-carrying SCs pre-coding (PDSP) scheme, and its computational complexity is described in Section 2. The experimental setup and verification are presented in Sections 3 and 4, respectively. The corresponding conclusion is finally summarized in Section 5.
###### Abstract  
In order to study the instability characteristics of interlayer rock strata (IRS) in shallow buried close-distance coal seams under insufficient mining areas, based on the background of interval mining under goat in Nanliang Coal Mine, this paper studies the instability characteristics of interlayer strata in interval mining under goat by means of similar simulation, numerical simulation, and field measurement. The results indicated that the first weighting interval of the main roof during mining in the lower coal seam was 49 m, while small and large periodic weightings with intervals of 10-14 m and 15-19 m were identified. During periodic weighting, the support resistance ranged from 6813 to 10,935 kN, with a dynamic load factor of 1.07-1.74, and the peak abutment pressure in front of the working face was 5.85-9.85 MPa. The mining under the interval coal pillar (ICP) was the'stress increase zone', and the mining under the temporary coal pillars (TCPs) and the interval goat was the'stress release zone'. During the working face mining out of the ICP, the support resistance reached 10,934 kN, the dynamic load factor reached 1.74, and the abutment pressure (AP) reached 9.85 MPa, which was 60% higher than the AP mining under the \"stress release zone\". Analysis suggests that the cutting instability of the IRS was the root cause of the increased AP in the working face of the lower coal seam. A numerical simulation was performed to verify the instability characteristics of the IRS in the interval goat. The relationship between support strength and roof subsidence during the period of the working face leaving the coal pillar was established. A dynamic pressure prevention method involving pre-splitting and pressure relief of the ICP was proposed and yields superior field application performance. The findings of the study provide a reference for rock strata control during mining under the subcritical mining area in shallow and closely spaced coal seams.  
shallow coal seam; interval goat; interlayer rock strata; structural instability; pressure relief
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 6 Conclusions and Future Directions  
This paper presented solutions to improve the performance of the IRS-based NOMA system. An optimized approach is obtained to find the optimal number of reflective elements in the IRS-NOMA system. In addition, a new multi-IRS approach is proposed to further improve its performance. Simulation results show that the combination of IRS and NOMA offers very promising performance trends in terms of EE and SE, as well as the trade-off between them. In addition, adopting the idea of multiple IRSs proved its effectiveness in boosting the user's experience, and in improving the propagation environment. Utilizing multiple IRSs could also act as a cost-effective solution, as it reduces the required number of serving BSs in a given geographical coverage area. In future works, it is necessary to consider the case where the IRS plane is not at a fixed place, but rather, it could be moving in favor of a better reception experience. It is also interesting to examine the combination of IRS and techniques such as cognitive IRS, massive-MIMO (mMIMO), and cell-free massive MIMO system, especially in terms of the SE gains that these combinations could offer.  
Methodology, Z.Q.A.-A., L.F. and R.S.A.; Software, Z.Q.A.-A., L.F. and R.S.A.; Formal analysis, R.S.A.; Investigation, Z.Q.A.-A., L.F. and R.S.A.; Resources, Z.Q.A.-A. and R.S.A.; Data curation, L.F.; Writing--original draft, Z.Q.A.-A. and L.F.; Writing--review & editing, R.S.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Wind Tunnel Test on Windblown Sand Two-Phase Flow Characteristics in Arid Desert Regions
## 3 Experimental Results and Discussion
### Energy Distribution of Sand Particles in the Windblown Sand Flow Field
Figure 14 shows that the relationship between kinetic energy and height can be expressed as the exponential function (\(E_{k}=m\exp(-nz)\), where \(m\) and \(n\) are regression coefficients obtained by the least-square method). Table 3 indicates that the correlation between the kinetic energy and height is reasonably good. The correlation coefficient \(R^{2}\) is over 0.96 in any situation; the values of coefficients \(m\) and \(n\) at different conditions indicate that the decay curve of the kinetic energy is affected by the wind speed and the particle size at the same time. The coefficient \(m\) reflects the erosion ability of the sand particles on the surface to a certain extent. The coefficient \(m\) increases with the increase in the wind speed. At the same indicated wind speed, the coefficient \(m\) is the largest for mixed sand, followed by fine sand, and the smallest for coarse sand. The coefficient \(n\) represents the relative decay rate of the kinetic energy at different heights above ground level. The larger \(n\) is, the faster the kinetic energy decreases with height. At the same wind speed, the value of coefficient \(n\) is the largest for coarse sand, followed by fine sand, and the smallest for mixed sand. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Particle Size** & **Wind Speed (m/s)** & \(m\) & \(n\) & \(R^{2}\) \\ \hline \multirow{3}{*}{Coarse sand} & 14.74 & 0.03 & 11.19 & 0.96 \\  & 16.68 & 0.15 & 11.20 & 0.99 \\  & 18.61 & 0.42 & 10.80 & 0.99 \\ \hline \multirow{3}{*}{Fine sand} & 14.74 & 0.16 & 10.48 & 0.99 \\  & 16.68 & 0.38 & 9.31 & 0.98 \\  & 18.61 & 0.81 & 10.22 & 0.99 \\ \hline \multirow{3}{*}{Mixed sand} & 14.74 & 0.20 & 9.87 & 0.99 \\  & 16.68 & 0.46 & 9.04 & 0.98 \\ \cline{1-1}  & 18.61 & 0.95 & 8.73 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The correlation between the kinetic energy of sand particles and height. Figure 14: The energy distribution: (**a**) At the indicated wind speed of 14.74 m/s; (**b**) At the indicated wind speed of 16.68 m/s; (**c**) At the indicated wind speed of 18.61 m/s.