esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 6 Discussion
So, a question arises about the efficiency of the system in more sparse rural environments. Another limitation is the hardware used to run and train the models of the system as the main hardware used was an office machine which can be said to be a rather weak processing unit for calculating the kind of operations associated with neural networks. However, this limitation can be said to be a strength of the approach as it shows very strong results despite the weak processing power. In terms of results, our traffic prediction model tends to approximate the long-term trends in data (e.g., in the span of 60 time steps) but lacks point-by-point prediction. To address this issue, more experiments in large datasets and employing larger Transformer-TCN models are to be conducted. The latter entails more computational resources. One more limitation is the training of the user allocation module on synthetic data, which may not capture the entirety of real-world complexities.
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 3 Central DRT
### Memory Footprint at Partial Transforms  
The proposal of central DRT arises from the finding that if we prune the computations of the \(\mathfrak{R}f\{s,d\}\) values outside this central band, as a collateral effect, half of the values at partial transforms do not need to be evaluated. Remember that the discarded output values represent less than 15% of Radon summations on input data; this is because the zones to be discarded basically operate on the zones padded with zeroes. So, for certain applications, they can safely be ignored.  
Pruning techniques have a very reduced effect in fast Fourier transform [27]: from \(O(N\log_{2}N)\) to \(O(N\log_{2}K)\), when only \(K<N\) coefficients are required. This is, to achieve the same reduction by half of computations, on a 256 1D FFT, the output size should be limited to just 16 coefficients. This reduction by half of computations, when halving required coefficients in an already linearithmic transform, was unexpected.  
Figure 10 shows the memory patterns of data required for the computation of a quadrant of central DRT for an image of size \(16\times 16\), that is, \(N=16,n=4\). The rectangles represent, visually, if a value at partial stages \(\widetilde{f}^{\,m}(\underbrace{s\mid v\mid}_{horizontal}\mid\underbrace{d}_{ vertical})\), as defined by Equation (4), is needed to compute the central DRT; in that case, it is shown in gray and, otherwise, in black.  
This depiction reveals that, at each partial stage, only \(N\) adjacent values must be computed per column.  
The other finding is that the pattern, observed here, for \(N=16\) (that sort of sawtooth on the first horizontal half of each stage) follows a formula that will be described later.  
Figure 9: A depiction of the whole set of discrete lines covering the displacements and slopes, as computed by central DRT.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design  
Prior work indicates that haptic information transmission can be optimized through parameter adjustments including speed, direction, intensity, and positioning [92]. Dynamic haptic signals have shown multiple advantages over static feedback: they effectively guide attention and convey hazard information while improving processing speed and accuracy [93; 94], provide more natural and intuitive interactions [95; 96], and enable the robust recognition of continuous motion and direction [97]. For visual feedback, research confirms that many visually impaired individuals retain sufficient light perception capabilities to discern positional and brightness changes [27; 28].  
Building on these findings, Study 2 maintained the same feedback implementation as Study 1 (as shown in Figure 8) while introducing additional parameters to examine both static and dynamic patterns, as well as directional cues for indicating the vehicle approach. This enhanced design allows for a systematic comparison of how different signal patterns aid safe crossing decisions across both feedback channels.
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 3 Results and Discussion
### Adsorption Kinetics Study  
The adsorption kinetics study was conducted to explore the relationship between the adsorption amount \(q_{t}\) and \(t\). As seen in Figure 3, the adsorption capacity of the fly ash-based and slag-based geomaterials on Cs\({}^{+}\) increased significantly within 24 h. In order to understand the adsorption kinetics in more detail, the mechanism of the adsorption process was studied by fitting the pseudo-first-order reactions and pseudo-secondary reactions to the experimental data of this study. In order to investigate the consistency between the model and the experimental results under the optimized experimental conditions, the pseudo-first-order and pseudo-secondary results were used to apply the linear graphs of \(\ln(q_{e}-q_{t})-t\) and \(t/q_{t}-t\) to the Cs adsorption kinetic model, as shown in Figures 6 and 7, respectively. Table 3 shows the linear constants (\(R^{2}\)) and the other parameters of the two kinetic models of Cs\({}^{+}\) adsorption to geomaterials calculated from Figures 6 and 7. For the cesium adsorption kinetics of the slag-based geomaterials, the pseudo-first-order model has a higher \(R^{2}\) value than that of the pseudo-second-order model, indicating that the adsorption process was mainly physical adsorption. On the other hand, the Cs adsorption kinetics of the fly ash-based geomaterials is in good agreement with the pseudo-secondary model, which indicates that chemical adsorption was more dominant. This can be explained by the fact that the fixation mechanism of Cs in alkali-activated fly ash is controlled by the cation exchange mechanism [42].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **BET Surface Area** & **Pore Volume** & **Pore Size** \\  & **[m\({}^{2}\)**-g\({}^{-1}\)]** & **[cm\({}^{3}\)**-g\({}^{-1}\)]** & **[nm]** \\ \hline Portland Cement & 0.9582 & 0.002652 & 11.07 \\ Slag & 1.388 & 0.004432 & 12.77 \\ Fly Ash & 1.869 & 0.006359 & 13.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The BET surface area, pore size, and pore volume of Portland cement, fly ash, and slag.  
Figure 6: Pseudo-first-order linear kinetic model of Cs adsorption on the fly ash-based geomaterials and slag-based geomaterials.
## 3 Test Results and Analysis
### Mechanical and Deformation Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Schist**} & \multicolumn{5}{c}{**Sandstone**} \\ \hline
**Sample No.** & **Steps** & \(F_{\text{\small{M}}}\)**\(\mu\)/kN** & \(F_{\text{\small{M}}}\)**\(\mu\)/kN** & **Sample No.** & **Steps** & \(F_{\text{\small{M}}}\)**\(\mu\)/kN** & \(F_{\text{\small{M}}}\)**\(\mu\)/kN** \\ \hline SC-BI-5 & 9 & & 191.58 & SA-BI-5 & 6 & & 223.20 \\ SC-BI-10 & 10 & 107.00 & 211.28 & SA-BI-10 & 7 & 191.90 & 257.43 \\ SC-BI-15 & 23 & & 472.73 & SA-BI-15 & 7 & & 265.68 \\ SC-BP-5 & 5 & & 88.05 & SA-BP-5 & 7 & & 145.50 \\ SC-BP-10 & 9 & 78.72 & 157.28 & SA-BP-10 & 8 & 108.93 & 168.08 \\ SC-BP-15 & 13 & & 230.83 & SA-BP-15 & 9 & & 187.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Testing results of the biaxial step-loading test. increase, the energy absorbed by the rock was utilized to create new cracks with relatively little deformation. The cracks gradually extended and coalesced with each other, and finally, the overall failure of the sample occurred with sudden changes in the axial strain at this time. From the enlarged local details, upon applying load to the sample, there was a rapid increase in axial strain. As the stress was maintained, the axial strain continued to increase slightly. Finally, just before the sample failed instantaneously, there was a sharp increase in axial strain.
## 3 Results
### Influence of Drilling Parameters on Bit Force
#### 3.2.1 Influence of Drilling Speed on Bit Force  
The effects of different drilling speeds on thrust and torque under the same rotational speed are shown in Figure 5. The rotation speeds are fixed at 50 rpm, 100 rpm, 200 rpm, and 300 rpm, respectively. Change the drilling speed at the same rotation speed, and the drilling speeds are 0.5 mm/s, 1.0 mm/s, 1.5 mm/s, and 2.0 mm/s, respectively. The rotation speed in Figure 5a is 50 rpm, the rotation speed in Figure 5b is 100 rpm, the rotation speed in Figure 5c is 200 rpm, and the rotation speed in Figure 5d is 300 rpm. Test parameter values are determined based on experimental test results. It can be seen from the figure that at any fixed rotation speed, the thrust force and torque curves have obvious levels of change. As the drilling speed increases, the thrust force and torque also increase. During the drilling process, due to the continuous drilling speed and rotation speed, the drill bit came into contact with the rock within 0.5 s, resulting in a large impact thrust and torque, which was in an unstable state at this stage. After about 0.5 s, the thrust force and torque fluctuate steadily back and forth within a certain fixed range, indicating that this stage is in a stable state. The fluctuations in the drilling weight and torque are oscillations caused by the continuous viscosity-slip-viscosity cycle between the drill bit and the rock.  
For each drilling speed, the average values of thrust force and torque during their stable phases were computed, resulting in the relationship curves between thrust force, torque, and drilling speed at various rotational speeds, as illustrated in Figure 6. Under each fixed rotational speed, both thrust force and torque exhibit a linear increase with the augmentation of drilling speed. Additionally, as rotational speed decreases, the slope of the curves depicting the relationship between thrust force, torque, and drilling speed becomes steeper. This is the outcome of the drill bit's vertical motion along the axis of the drill rod, its rotational motion around the drill rod axis, and the accumulation of energy resulting from the axial and lateral sliding frictional resistance between the drill rod wall and the drill bit. Hence, we can formulate the relationship expressions between thrust force, torque, and drilling speed as follows:  
\[\left\{\begin{array}{l}F=\xi_{1}v+F_{0}\\ T=\xi_{2}v+T_{0}\end{array}\right. \tag{11}\]  
where \(F\) is the thrust force, \(T\) is the torque, \(v\) is the drilling speed, \(\xi_{1}\) is the slope of the relationship between the weight on bit and the drilling speed, \(\xi_{2}\) is the slope of the relationship between the torque and the drilling speed, \(F_{0}\) is the thrust force constant, and \(T_{0}\) is the torque constant.  
Figure 5: Cont.  
Figure 5: Curvature and torque curve at different drilling speeds during drilling. (**a**) Rotation speed is 50 rpm (**b**) Rotation speed is 100 rpm (**c**) Rotation speed is 200 rpm (**d**) Rotation speed is 300 rpm.  
Figure 6: Curve of thrust force, torque and drilling speed at different rotational speeds.
## 6 Extension with Side-Lobes/Back-Lobes as Well as the Path Loss Effect
### Physical Channel Model
Note that we choose \(R_{i}(MS)\) instead of \(R_{i}(SS)\) because \(R_{i}(MS)\gg R_{i}(SS)\). Besides, the number of nodes in the two sectors is at most \(2D-2\). Thus, there are at most \(2D-2+\frac{4\pi D}{\theta_{m}}. (\frac{g_{s}}{g_{m}})^{\frac{2}{\alpha}}\) nodes in the interference region. From Lemma 1, the conflict graph can be colored by \(2D+\frac{2\pi D}{\theta_{m}}\cdot(\frac{g_{s}}{g_{m}})^{\frac{2}{\alpha}}-1\) colors. As shown in Theorem 8, the upper bound on the number of channels is \(2D+\frac{2\pi D}{\theta_{m}}\cdot(\frac{g_{s}}{g_{m}})^{\frac{2}{\alpha}}-1\). When the beamwidth \(\theta_{m}\) of a directional antenna is narrow, the term \(\frac{2\pi D}{\theta_{m}}\cdot(\frac{g_{s}}{g_{m}})^{\frac{2}{\alpha}}\) is so small that we can often ignore the effect of the side-/back-lobes.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 4 Simulation Results and Discussion
### Determining the Best Ratio between the Number of Unknown Nodes and the Number of Anchors  
It is reasonable to state that the completion of a high dimensional matrix improves the localization accuracy. This is due to the diversity of the information introduced by each sensor node. However, from a finite dimension, the algorithm converges and the use of additional data increases the complexity and the execution time. To ensure the best localization accuracy and decrease the execution time of the algorithm, we set up this ratio. Let  
\[\vartheta=\frac{Nb_{Un}}{Nb_{An}}, \tag{35}\]  
where \(Nb_{Un}\) is the number of unknown nodes and \(Nb_{An}\) is the number of anchors. To determine the best ratio \(\vartheta\) in terms of localization accuracy, simulations have been done. Using 35 unknown nodes, the number of anchors varies from 5 to 20. The localization error applying Adam respect to the number of anchors is shown in (Figure 2). When the number of anchors reaches 10, the localization error becomes almost stable. Figure 3 illustrates the localization error in meters depending on the number of unknown nodes using 10 anchor nodes. It can be observed that the localization error is almost the same from 35 unknown nodes. The trade-off between accuracy and complexity leads us to use a ratio \(\vartheta\) equal to 3.5.  
We mention that simulations have been done for each optimization algorithm and for each value of sigma shadowing. The ratio guaranteeing the best performance is always 3.5. Thus, to simplify presentation and without loss of generality, we present only simulation results corresponding to Adam when working with sigma shadowing equal to 2.  
Figure 2: The localization error using 35 unknown nodes with the varying number of anchors in 400 m\({}^{2}\) applying Adam for sigma shadowing = 2.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 3 Proposed Effective PSCCH Searching  
This section presents the proposed algorithm for the received signal processing. Firstly, the general receiver processing is introduced, and then, the commonly used Blind algorithm is discussed. After that, a brief study on the correlation of the received signal with the DMRS is presented. Based on this study, a new algorithm is proposed and finally a theoretical runtime analysis is performed.
# Mesh-Free MLS-Based Error-Recovery Technique for Finite Element Incompressible Elastic Computations
###### Abstract  
The finite element error and adaptive analysis are implemented in finite element procedures to increase the reliability of numerical analyses. In this paper, the mesh-free error-recovery technique based on moving least squares (MLS) interpolation is applied to recover the errors in the stresses and displacements of incompressible elastic finite element solutions and errors are estimated in energy norms. The effects of element types (triangular and quadrilateral elements) and the formation of patches (mesh-free patch, mesh-dependent element-based patch, and mesh-dependent node-based patch) for error recovery in MLS and conventional least-square interpolation-error quantification are also assessed in this study. Numerical examples of incompressible elasticity, including a problem with singularity, are studied to display the effectiveness and applicability of the mesh-free MLS interpolation-error recovery technique. The mixed formulation (displacement and pressure) is adopted for a finite element analysis of the incompressible elastic problem. The rate of convergence, the effectivity of the error estimation, and modified meshes for desired accuracy are used to assess the effectiveness of the error estimators. The error-convergence rates are computed in the original FEM solution, in the post-processed solution using mesh-free MLS-based displacement, stress recovery, mesh-dependent patch-based least-square-based displacement, and stress recovery (ZZ) as (0.9777, 2.2501, 2.0012, 1.6710 and 1.5436), and (0.9736, 2.0869, 1.6931, 1.8806 and 1.4973), respectively, for four-node quadrilateral, and six-node triangular meshes. It is concluded that displacement-based recovery was more effective in the finite element incompressible elastic analysis than stress-based recovery using mesh-free and mesh-dependent patches.  
error estimation; effectivity; meshfree recovery technique; moving least square interpolation; incompressible elasticity
## 3 Results
### Effects of the Local Gas Flow on the Keyhole Length
The flow rate of 5.0 L/min had a maximum amplitude for the frequency of 70 Hz. The depicted weld seam in Figure 9 had periodic melt accumulations for this flow rate. Since these have not yet formed into complete humps, this process behavior was assigned to the \"pre-humping\" regime. Counting of the melt accumulation resulted in an average frequency of 72 Hz for the three welded specimens. Thus, there is a distinct correlation between the  
Figure 15: Width-to-length ratio of the keyhole at different gas flow rates and laser spot sizes for the welding speed 12 m/min. keyhole oscillation in the welding direction and the occurrence of melt accumulations. No melt accumulations were discernible for the lower flow rates. Using the larger laser spot size of 600 \(\upmu\)m the flow rate of 1.5 L/min reduced the frequency of the maximum amplitude by 21% from 920 Hz to 760 Hz. The increase of the flow rate to 5.0 L/min enhanced the oscillation amplitude, as was the case for the smaller laser spot size. In addition, the frequency of the maximum amplitude was reduced to 540 Hz. The calculated oscillation frequency did not indicate any melt accumulations, which are also not visible in Figure 9.  
In general, the flow rate of 1.5 L/min reduced the oscillation frequencies of the keyhole for both laser spot sizes. Increasing the gas flow to 5.0 L/min influenced the oscillation of the keyhole length. The oscillation amplitudes were increased and the frequencies of the maximum amplitude were reduced. An inverse correlation between the oscillation frequency and amplitude was evident. The amplitudes were increased with a decreasing frequency and vice versa. This has already been investigated by means of calculations in [46]. The keyhole oscillation is influenced by the changed Laplace pressure due to the shielding of oxygen using a gas flow. Furthermore, an excessive flow rate (in this case 5.0 L/min) increased the oscillation amplitude due to a higher momentum transfer to metal vapor, melt pool, and keyhole rear wall. However, the oscillation frequency does not seem to be the cause of the significant spatter reduction. The oscillation frequency was still distinct, but the spatter quantity was significantly reduced for a flow rate of 1.5 L/min. Further considerations are required to analyze the occurring effect mechanisms using a local gas flow. Figure 16: Oscillation of the keyhole length for different laser spot sizes and gas flow rates at the welding speed of 12 m/min.
## 4 Application Results
### History of Patients' Analyses  
As shown in Figure 5, our system provides a table with analysis outcomes over time for each patient. For each of these analyses, the clinician can access detailed information, diagnoses and suggestions of the system and commentaries previously added to these results. The \"Read analysis from PDF...\" button allows adding a new analysis from pdf document, the \"Start Analysis\" button brings a detailed view of results and suggestions of the system, the \"See Analysis Results\" button brings a detailed view of results plus comments previously added, while the \"Comparative Analysis\" button shows development graphics.
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results
### The Experimental Results
#### 5.2.3 Results on Various-Scale Datasets  
We conducted an experiment with various sizes of datasets to evaluate the stability of our approach. We randomly selected 1000, 2000, and 3000 entities from the original KGs to construct three sub-KGs, noted as KG_1K, KG_2K, and KG_3K, in order to evaluate DomainEA and observe the trend of the change in metrics. The experimental results are depicted in Table 9. We can observe the trends of variation for all the metrics. Firstly, Hits@1 exhibits fluctuations with the increasing size of the dataset. From KG_1K to KG_2K, Hits@1 decreases by 0.35 percentage points. However, Hits@1 and MRR achieve significant improvement on KG_3K, reaching 60.48% and 75.22%. Secondly, in relation to Hits@10 and Hits@50, there is a positive correlation between the expansion of the dataset size and the improvement in these metrics. For instance, Hits@10 rises from 95.37% to 95.98%, and Hits@50 escalates from 97.48% to 98.16%. In summary, as the size of the dataset changes, the fluctuations of all the evaluation metrics are small. It implies that the various sizes of datasets have a subtle influence on the evaluation metrics and our approach has favorable stability.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 2 Theoretical Analyses
### Mathematical Model of the Temperature Field  
The temperature of the wafer during the diamond wire saw cutting process redistributes with the movement of thermal load, and the cutting temperature field is related to the loading time of the thermal load; therefore, it belongs to non-steady state heat transfer.  
During the sawing process, the position of the heat source changes. According to Fourier heat transfer law and energy conservation law, the differential equation for the three-dimensional transient temperature field of heat transfer without an internal heat source during the sawing workpiece process is established as:  
\[\frac{\partial}{\partial x}\bigg{(}k_{x}\frac{\partial T}{\partial x}\bigg{)} +\frac{\partial}{\partial y}\bigg{(}k_{y}\frac{\partial T}{\partial y}\bigg{)} +\frac{\partial}{\partial z}\bigg{(}k_{z}\frac{\partial T}{\partial z}\bigg{)} =c_{T}\rho\frac{\partial T}{\partial t} \tag{4}\]  
where \(k_{\text{x}}\), \(k_{y}\), and \(k_{\text{z}}\) are the thermal conductivity coefficients of sapphire crystal along the \(x\), \(y\), and \(z\) directions, respectively. \(c_{T}\), \(\rho\), and \(T\) are the specific heat capacity, density, and temperature of the crystal, respectively. The left side of Equation (4) represents the heat transferred into the crystal from the \(x\), \(y\), and \(z\) directions, while the right side represents the heat required to raise the temperature of the crystal.  
In order to solve the equation, the following three kinds of boundary conditions are specified in the computational domain. The boundary conditions during the heat transfer process of wire saw cutting sapphire crystal include \(\Gamma_{1}\), \(\Gamma_{2}\), and \(\Gamma_{3}\).  
The prescribed temperature on the boundary of \(\Gamma_{1}\)  
\[T(x,y,z,t)=\overline{T}(t) \tag{5}\]  
The heat flux density boundary conditions \(\Gamma_{2}\) can be expressed as:  
\[k_{x}\frac{\partial T}{\partial x}n_{x}+k_{y}\frac{\partial T}{\partial y}n_{ y}+k_{z}\frac{\partial T}{\partial z}n_{z}=q_{\text{v}} \tag{6}\]  
where \(n_{\text{x}}\), \(n_{\text{y}}\), and \(n_{z}\) are the direction cosine of the normal outside the boundary.  
The convective heat transfer coefficient boundary conditions for \(\Gamma_{3}\) can be expressed as:  
\[k_{x}\frac{\partial T}{\partial x}n_{x}+k_{y}\frac{\partial T}{\partial y}n_{ y}+k_{z}\frac{\partial T}{\partial z}n_{z}=h(T_{\infty}-T) \tag{7}\]  
where \(h\) is the convective heat transfer coefficient, and \(T_{\infty}\) is the ambient temperature.  
The boundary conditions should meet the following equation.  
\[\Gamma_{1}+\Gamma_{2}+\Gamma_{3}=\Gamma \tag{8}\]  
Figure 1: Schematic diagram of diamond wire saw cutting sapphire crystal wafers.  
The initial condition for the temperature is set to be at an ambient temperature for the entire workpiece.