esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 3 Preliminaries
### Communication Model  
Considering a WSN that consists of \(N\) nodes, \(\{u_{1},u_{2},\ldots,u_{N}\}\). Suppose only one wireless channel is available for communication. When the nodes turn on their radios simultaneously, they can transmit information through the wireless channel. Denote the communication range of each node as \(d_{c}\), and two nodes are called _neighbors_ if their distance is no larger than \(d_{c}\) (denote the distance of nodes \(u_{i},u_{j}\) as \(d(u_{i},u_{j})\)).  
In real networks, whether one node can communicate with a neighboring node successfully is dependent on many factors, such as environment noises, the sending power energy, the path-loss exponent during transmission, beaconing, and handshaking. The signal-to-interference-plus-noise ratio (SINR) model is a realistic model that captures the collision among multiple transmissions [34]. It is also shown that the SINR model can be converted to the communication graph model that two nodes are neighbors if their distance is within the communication range. Therefore, we simplify the process and assume that two neighboring nodes can communicate if their distance is within \(d_{c}\) and they both have turned on the radio.  
In the practical networks, multiple nodes may turn on the radio simultaneously and they could cause communication collisions on the channel. For example, node \(u_{1}\) has two neighbors \(u_{2},u_{3}\) and they all turn on the radio simultaneously. Suppose both \(u_{2},u_{3}\) send a message to \(u_{1}\); then, \(u_{1}\) cannot decode the composited message correctly. Therefore, we say _communication collision_ happens and node \(u_{1}\) cannot find its neighbors.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 2 Related Work on Point Cloud Attribute Compression
### Overview of Our Proposed Framework  
The pipeline of the proposed texture-guided graph transform optimization framework for point cloud attribute compression is depicted in Figure 1. Before the attribute compression, the geometry information is already coded and available as side information for point cloud attribute compression. Then, at the beginning of the attribute compression framework, the geometry information is organized based on the Hilbert curve in 3D space to preserve attribute spatial correlation. Subsequently, the reordered point cloud is divided into local clusters with uniform point counts, ensuring balanced graph sizes for subsequent processing. Intra-cluster attribute analysis is then conducted to extract texture complexity priors from local regions, which guide the optimization of the graph. Inter-cluster attribute prediction further reduces spatial redundancy by leveraging attributes from previously decoded clusters. During the graph optimization process, point attribute residuals are treated as graph signals, and the graph topology and adjacency matrix are determined by our optimization-based graph construction scheme. This scheme considers transform decorrelation, graph sparsity, distortion, and bitrate terms to optimize the graph transform for each cluster, integrating both geometry structure and texture priors. Compressed attribute residuals are obtained using the optimized graph transform, resulting in transformed point cloud attribute coefficients. Finally, these coefficients are quantized, entropy coded, and assembled into the total bitstream, thereby completing the compression process.
### Green's Function Database Generated Based on the Reciprocity Theorem  
The general idea of global grid search and waveform inversion-based location (GSWI location) is to calculate the Green's function from each candidate grid point to each sensor, and then the synthetic waveform can be obtained through the product of Green's function, source-time function, and point source moment tensor. However, such a large number of wavefield modeling in a 3D space is impossible for existing parallel computing. An accurate and wise choice is to use the reciprocity theorem in elastic dynamics [40] for reducing the computation of Green's functions. According to the time-space reciprocity of point source Green's function between source point and sensor point, it only needs to set the sensor locations as virtual sources for wavefield modeling. In this way, the Green's function of each point in the whole space is generated and stored as a database. The reciprocity theorem can significantly reduce the computation of waveform modeling with tens of thousands of candidate source grid points, and it will be easy to recalculate the Green's function database for any updated velocity model.  
Taking advantage of the reciprocity theorem, many researchers have carried out a series of studies on GSWI location. [PERSON] [41] investigated the radiation mode of teleseismic body waves by wavefield modeling combining the reciprocity theorem and flat layer velocity model. [PERSON] and [PERSON] [42] generated a [PERSON]'s function database using the reciprocity theorem and found that the 3D velocity model can more accurately invert finite fault sources than the 1D velocity model. [PERSON] and [PERSON] [43, 44] used the reciprocity theorem to simulate the response of 300 earthquakes to Southern California faults along with site and complicated wavefield propagation path effects in complex heterogeneous media. In some other studies, focal mechanisms, source location, and velocity model are jointly studied by utilizing the reciprocity theorem. [PERSON] et al. [45] and [PERSON] and [PERSON] [46] carried out lowpass filtered waveform inversion to focal mechanism solutions of Southern California and Lushan earthquakes, respectively, in which the 3D Green's function database is generated by the reciprocity theorem. By utilizing the reciprocity theorem and grid search algorithm, [PERSON] et al. [24] and [PERSON] and [PERSON] [25] conducted high-resolution waveform inversion on focal mechanism solutions and locations of earthquakes in Papua New Guinea, the Solomon Islands, and Australia. The results show that the waveform inversion results based on 3D velocity models are improved compared with those obtained by 1D velocity model. The above source parameter studies almost focus on natural earthquakes at regional scales, and there are few applications in broadband waveform modeling and source parameter inversion in a local scale (e.g., in the mine region) with more complex tectonic settings. With the improvement of computation capability and quality of broadband seismic records, it has become an inevitable trend to comprehensively carry out the 3D GSWI location study in a mine.
###### Abstract  
With the gradual increase in the mining depth of coal resources, the destruction of the rock structure of the inter-layered rock of the near coal seam under the influence of mining has led to the frequent occurrence of water-inrush disasters in mines, which seriously affects the safety of mine production and the safety of the people in the underground. Therefore, it is important to study the mechanism of the water inrush of the rock between the coal seams under the influence of mining to control the occurrence of water inrush disasters and protect the loss of groundwater resources. This paper takes the Hanjiawan coal mine with typical stratigraphic characteristics as the background for research and studies the structural characteristics of interlayer rock breakage and the solid-liquid coupling inrush water disaster mechanism during the mining of \(2^{-2}\) and \(3^{-1}\) coals. The study shows that according to the damage degree and destruction depth of the inter-layered rock caused by the mining of the upper and lower coal seams, combined with the slip line theory and the \"three bands\" collapse theory, the inter-layered rock is classified into a completely fractured inter-layer, a fractured-broken stacked inter-layer, and a fractured-broken-fractured combined inter-layered rock using \(L\leq h_{m}+H_{\text{L}}{}^{2}\), \(L>h_{m}+H_{\text{L}}{}^{2}\), and \(L\geq h_{m}+H_{\text{L}}{}^{2}\) as the discriminating criteria. Combined with the structural classification of inter-layer rock and the discriminating criteria, we used similar simulation experiments and on-site research to analyze the evolution law and distribution characteristics of four types of inter-layer rock water-inrush fractures in different mines and put forward the classification of inter-layer rock water-inrush channels based on the width, length, and penetration of the fractures. Based on the characteristics of the water-inrush channel of inter-layer rock, we constructed the network-boundary inrush water calculation model of inter-layered rock and network-attach-boundary inrush water calculation model, solved the water movement of the water-inrush channel in the model by transforming the flat flow state, fracture to flow state, and pore-fracture flow state, and finally revealed the mechanism of the disaster by which water-inrush of inter-layer rocked was induced. Finally, we revealed its mechanism of inducing the inter-layer rock inrush water disaster. Our research enriches the theory and research ideas of the water-inrush disaster, provides theoretical support and a basis for the control of water-inrush disasters in similar conditions, and ensures the safe production of mines.  
Close distance coal seam; inter-layered rock; structural classification; inrush water disaster mechanism; flow-solid coupling model +
Footnote †: journal: Applied Sciences
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 5 Simulation Results
### SINR-Threshold-Based Packet Duplication
In addition to the original scenario explained above that we extensively used to evaluate even more methods (5.2 and 5.3) to determine the activation and de-activation of packet duplication, we present a few more scenarios using the Delta SINR method. Our original scenario consisted of a single building that acts as an obstacle to our dual stack UE, which moved from point A to point B. Now, we add three more scenarios to evaluate and analyze the results in Table 2, which are very similar to the original scenario to help support our proposed A-FPD method. In the second scenario, the dimensions of the building are changed, and as a result, the attenuation from the two BSs is reduced to a certain extent. The third scenario has two buildings placed very close to each other, and finally the fourth scenario has two buildings fairly distant from each other. All these four scenarios, including the original, are for the SINR difference of 10 dB between the two BSs. The movement of the UE is the same to better understand the impact of the environmental changes on the packet-level UE performance. Another criterion is added to validate the uniformity of all four scenarios where we consider a hard handover instead of dual connectivity. This is where the UE will always hand off to the stronger signal BS and will have single connectivity at all times. The respective corrupt packet percentages are shown as compared to a standard adaptive fractional packet duplication approach. This is shown in Table 2 where the first row is for the A-FPD approach with a SINR difference of 10 dB, and the second row is the single connectivity hard handover approach. In all cases, the Delta SINR method produced a lower corrupt packet percentage, so the average values over the four scenarios certainly show lower percentage of corrupt packets. Furthermore, this was only when duplicating packets when the SINR values were close (with 10 dB) for both links, duplicating 35.38% as shown in Figure 12.
## 2 Materials and Methods
### Antioxidant Activity Evaluation  
The scavenging activity of ZnO-NPs was carried out using DPPH assay following methods adopted from [PERSON] et al. [48], however, with minor modification. The DPPH solution was prepared by dissolving 6.4 mg of DPPH in 25 mL of methanol to obtain 0.016 mM concentration of DPPH. The solution was placed in the dark for 30 min. After incubation, 1 mL of DPPH solution was added to ZnO solutions, which were prepared by dissolving different concentrations of ZnO-NPs (50, 25, 12.5, 6.25, 3.13, and 1.56 ug) in 1 mL of DMSO. The blank contained only DMSO and DPPH solution. The absorbance was recorded at 520 nm against 0.016 mM DPPH as a control, and the study was carried out three times. The inhibition of ZnO-NPs was estimated using the following equation:  
\[\%\ \text{Inhibition}=(\text{Ac}-\text{As})/\text{Ac}\times 100\% \tag{1}\]  
(Ac) represent the absorbance of the control, while (As) is the absorbance of the sample.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 3 Theoretical Model
### Phase Error Model : Multipath, Phase Standard Deviation and Radiation Pattern  
While the previous section focuses on geometrical localization error, we will now incorporate the impact of real-scenario error sources, e.g., antenna radiation pattern and multipath. The following derivation is based on the work of [20].
## 2 Numerical and Experimental Methodologies
### Experimental Methodologies for Velocity and Aerodynamic Noise Measurements  
Figure 5a shows the experimental equipment for the velocity measurements using a single hot-wire probe of type I. The hot-wire anemometry is a technique used for measuring the fluid velocity. The hot-wire anemometer consists of two prongs with a wire stretched between them. For velocity measurements, the resistance measured with the singe hot-wire is converted into a voltage across an anemometer, and it is then saved in a computer as a digital file through a computer DAQ board. Single hot-wire calibration was performed, and its accuracy for several measurement values was within 5%. The hot-wire probe is located 5 mm away in the upstream at the front side of the hub and 3 mm away in the downstream at the back side. The data were measured at 1000 sampling rates per second for 5 s from the radius of \(\rm{r_{h}}\) to \(\rm{r_{t}}\), and then averaged to obtain the velocity. Figure 5b illustrates that the velocity in the simulation was obtained by averaging all the data of a radial band in the circumferential direction, from the hub outside to the rig with radial bands of 50. The aerodynamic noise was measured at an anechoic room in Korea Advanced Institute of Science and Technology (KAIST). The FFT analysis of the measured data was performed on the temporal signals obtained with 5000 samples during approximately 174 revolutions, which induced spectral resolutions of 1 Hz [28].  
Figure 5: Experimental equipment for velocity measurements (a) and numerical velocity-averaged method with radial bands of 50 (b).
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Al-Based Approaches to Gait Analysis
#### 1.4.1 Previous Gait Studies Using Artificial Neural Networks  
The use of artificial neural networks for gait analysis and clinical decision support in this area was implemented relatively late, with the spread of personal computers and the rise in popularity of clinical gait analysis. It introduced the possibility of classifying gaits into normal and pathological categories directly based on gait examples rather than on a pre-built gait model. Positive results in supportive gait classification were obtained by [PERSON] and [PERSON] using artificial neural networks [34], and this method was soon successfully applied in athletes [35] and animals [36]. The classification of gait patterns, as well as their ageing-related changes, was demonstrated by [PERSON] and [PERSON]. Their method achieved 100% classification accuracy on a selected group of patterns and enabled the identification of risk factors and measurement errors [37; 38], while only a decade earlier an accuracy of 75-80% was considered acceptable. This also led to the first papers on the qualitative (rather than just quantitative) assessment of gait using artificial neural networks, based mainly on self-organizing networks (Kohonen networks). These compared spatial-temporal gait patterns, recorded in the form of curves, and related them to the correct gait cycle extracted by the network (not explicitly stated). Artificial neural networks are also being adapted to solve classification tasks related to gait analysis in patients with hemiparesis or who are recovering from fractures. Research is underway on networks adapted for personalized therapy (patient-tailored therapy) in combination with rehabilitation robots, and for predicting gait patterns and therapy outcomes [39; 40].  
Some have argued that the artificial neural networks used for gait analysis, particularly in athletes, remain underutilized, and that their true value remains unknown. The absence of expert systems dedicated to the analysis of movements in athletes is evidence of this.  
Collaboration between representatives of technical sciences and therapists is all the more important due to the pervasiveness of sensory networks and multi-agent systems in clinical practice approaches. More and more solutions of this type are emerging, providing artificial neural networks with additional data for analysis and prompting other solutions from the field of computational intelligence. Artificial neural networks are increasingly supported by the support vector machine (SVM) method. Increasingly, deep neural networks with up to 10 layers are also being used for similar analyses, enabling much more complex feature decomposition and the discovery of hidden correlations.
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
###### Abstract  
Three-dimensional printing quality is critically affected by the transmission condition of 3D printers. A low-cost technique based on the echo state network (ESN) is proposed for transmission condition monitoring of 3D printers. A low-cost attitude sensor installed on a 3D printer was first employed to collect transmission condition monitoring data. To solve the high-dimensional problem of attitude data, feature extraction approaches were subsequently performed. Based on the extracted features, the ESN was finally employed to monitor transmission faults of the 3D printer. Experimental results showed that the fault recognition accuracy of the 3D printer was obtained at 97.17% using the proposed approach. In addition, support vector machine (SVM), locality preserving projection support vector machine (LPPSVM), and principal component analysis support vector machine (PCASVM) were also used for comparison. The contrast results showed that the recognition accuracies of our method were higher and more stable than that of SVM, LPPSVM, and PCASVM when collecting raw data via the low-cost attitude sensor.  
e 20 July 2019; Published: 29 July 2019
## 2 Materials and Methods
### Experimental Procedures
#### 2.1.4 Quantification Methods  
To quantify the signal amplification based on the magnetic dipole-dipole interaction and accumulation, both grey scale and color images were investigated. The recorded images were analyzed using an in-house Matlab (version R14, The Mathworks Inc., Natick, MA, USA) code and ImageJ software (version 1.51e, U.S. National Institutes of Health, Bethesda, MD, USA) [23].  
To calculate the pixel area, color images were first converted to grey scale, and by applying dynamic threshold, the background and magnetic particles were assigned to white or black colors. The number of pixels for the magnetic particles in the overall image was counted both for before applying the amplification and after applying the amplification. Having a periodic pattern on the chip (alternating PDMS pattern) enabled differential measurements, defined as the difference between the pixel area of the printed lines and the non-printed lines. Differential measurements provided information about specific and non-specific interactions occurring on the surface.  
\[\Delta\text{PN}=\text{PN}_{P}-\text{PN}_{C}, \tag{1}\]  
where \(\text{PN}_{P}\) is the pixel number of printed area, \(\text{PN}_{C}\) is the pixel number of the non-printed/control area, and \(\Delta\text{PN}\) is the difference.  
The contrast-to-noise ratio (\(CNR\)) is widely used in medical image processing to detect differences between two regions in a recorded image, and is defined as [24]  
\[CNR=\frac{\left|\mu_{\text{object}}-\mu_{\text{background}}\right|}{\sigma_{ \text{background}}}, \tag{2}\]  
where \(\sigma_{\text{background}}\) is the standard deviation of pixel values in the background, \(\mu_{\text{object}}\) is the mean pixel value of the object, and \(\mu_{\text{background}}\) is the mean pixel value of the background. Accumulation of iron nano particles around the base beads changed the color intensities, and \(CNR\) was calculated to quantify these variations in the color images. Our experiments revealed that the intensity of green color change was higher than changes in the intensity of red and blue colors (Figure 3); for that reason, the green color values in each pixel was used to calculate the \(CNR\) for before and after signal amplification for various concentrations.  
After the optimization of experimental parameters, all measurements were performed six times to ensure reproducibility and repeatability. From a recorded image, measurements are performed on four different areas approximately defining quadrants of the center point.  
Figure 3: \(Cont.\)
## 1 Introduction
Besides the limit analysis, the FEM and the DEM mentioned above, other methods like the two-step procedure [23] have also been employed in analyses of the seismic capacity of masonry structures in general. Though traditional DEM uses rigid blocks, new computational approaches also include deformation in the blocks, e.g., UDEC, 3 DEC, ABAQUS etc. The combined finite-discrete element method (FDEM) developed by [PERSON] [24; 25; 26; 27] is an extension of the FEM and the DEM. With it, structures are discretised into numbers of discrete elements. Within each discrete element, a finite element mesh is incorporated. Consequently, more accurate predictions on the contact forces between elements and the deformation of structures can be achieved. Further details on the FDEM can be referred to the monographs of [PERSON] and his co-workers [28; 29; 30]. The FDEM has been employed to simulate the failure of brittle or quasi-brittle materials, such as glass impact fracture [31; 32], shell fracture [33], slope instability [34], rock compression fracture [35] etc. Applications of the FDEM on structural engineering were comprehensively summarised by [PERSON] et al. [36]. Regarding the FDEM applications on masonry, [PERSON] et al. [37; 38; 39; 40] analysed the static and dynamic behaviour of both dry-stone and mortared masonry structures subjected to different loads. [PERSON] et al. [41] examined the collapse mechanism of stone arches due to seismic excitations. Recently, the FDEM has been employed to study the collapse of masonry arch structures subjected to base impulses [42] and support spreading [43], as well as the failure of masonry walls under uneven support settlements [44]. The seismic capacity of masonry arches is worthy of investigating with the FDEM, and a good attempt has been made in this paper to enrich the FDEM applications in structural engineering. Herein, the terminology'seismic capacity' corresponds to the first-order seismic capacity of masonry arch structures under the combination of gravity and constant horizontal acceleration. Under such loading, the failure of masonry arch structures is a dynamic process. Therefore, the FDEM is an appropriate simulation tool, and the 2D FDEM program 'Y' [45] is employed in this study. The layout for the rest of the paper is as follows. Fundamentals on the FDEM modelling are addressed in Section 2. Numerical examples, including a variety of arch structures subjected to both gravitational and constant horizontal accelerations, are presented in Section 3. A parametric investigation regarding geometric parameters and friction coefficient are conducted in Section 4. Finally, the conclusions are reached in Section 5.