esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning
###### Abstract  
Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points--namely, the linear chirp--due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used.  
acoustic diffraction; acoustic signal aberration; cross-correlation aberration; ultrasonic ranging
## 3 Numerical and Experimental Results
Further, we proceed with the experimental measurement campaign employing the fabricated prototype. Figure 11 shows the simulated and measured \(S_{11}\) parameters for the RF loop with the metasurface, for both 17 MHz and 64 MHz and with and without the biological phantom load. As is evident, in all cases, the reflection coefficients are less than \(-10\) dB, proving the effectiveness of the matching networks computed in the design process. In particular, the presence of the phantom does not significantly change the reactive part of the input impedance of the system, and, consequently, the matching is preserved. Comparing the numerical and experimental results, it is possible to conclude that the effectiveness of the overall fabrication process is satisfactorily demonstrated. Figure 8: Current distribution within the metasurface elements at (**a**) 17 MHz and (**b**) 64 MHz after the homogenization analytical procedure. As is evident, at a specific operative frequency, the currents are not flowing in the spirals resonating at the other one; moreover, their amplitude results uniform within the array. Finally, the adopted fabrication technique based on PCB manufacturing ensures the required mechanical stability and electrical and thermal robustness in operative MRI scenarios, thus straightforwardly enabling a future implementation in real scanners [37]. Figure 10: Comparison between the H-field maps obtained with ideal capacitance values and with \(\pm 1\%\) tolerances evaluated over a plane 40 mm away from the metasurface and parallel to it at the 64 MHz Larmor frequency. Figure 9: Numerical transmission efficiency increases for the RF loop in presence of the metasurface: without biological load at 17 MHz (**a**) and at 64 MHz (**b**); with biological load at 17 MHz (**c**) and at 64 MHz (**d**).
# RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction
## 1 Introduction
By exclusively using test point clouds for training, we avoid the need for constructing paired datasets as training data, thereby mitigating the potential issue of significant differences between training and test data. The ability to sample any number of points from the prior distribution enables achieving point cloud upsampling at arbitrary rates. More specifically, we introduce a novel neural network for point cloud reconstruction and upsampling. The encoder employs a multi-layer graph convolutional structure [11], dynamically constructing KNN graphs at each layer to increase the network's receptive field, obtain multiscale information, and capture long-distance semantic features at higher layers. It can adaptively acquire local geometric details based on the specific shape of the point cloud. The decoder employs a module based on offset attention [12] for point cloud reconstruction. Offset attention using the Laplacian operator sharpens attention weights compared to typical self-attention mechanisms. This allows for improved preservation of point cloud details and reduces the impact of noise. The prior distribution can be a unit square, a unit sphere, or something else. We conducted extensive experiments with various prior distributions. The results indicate that despite the absence of ground truth (GT), we achieved comparable outcomes to supervised methods. The source code of the proposed method is available at [[https://github.com/[PERSON]/RE-PU](https://github.com/YazhenHan/RE-PU)]([https://github.com/YazhenHan/RE-PU](https://github.com/[PERSON]/RE-PU)), accessed on 29 July 2024. The main contributions of this paper are as follows:  
* We propose a novel reconstruction-based point cloud upsampling framework. * We introduce a prior-based point cloud processing network, which can be utilized for both reconstruction and upsampling. * We demonstrate that the proposed method achieves comparable results to the state-of-the-art methods in terms of both visual quality and quantitative metrics.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 3 Calculation of Additional Stresses in Tunnel Undercutting
### Additional Stresses Caused by Shield Cutter Thrusts  
The tunnel shield cutter thrust will generate additional stresses on the soil layer. The cutter thrust is the support force needed to keep the excavation surface stable during shield construction. With reference to [PERSON] et al.'s study findings [24], it may be computed using the formula below:  
\[q=\frac{10.13(1-\
u)E\pi\
u_{0}(1-\xi)^{2}}{(1+\
u)(3-4\
u)Dmw}+\Delta q \tag{16}\]  
where: \(\Delta q\) is the squeezing force generated by the cutter disk cutting into the soil; \(E\) is the elastic modulus of the soil; \(\
u_{0}\) is the propulsion speed of the shield machine; \(w\) is the rotational speed of the cutter disk; \(\zeta\) is the opening rate of the cutter disk; and \(m\) is the number of cutter disk chunks.  
As seen in Figure 3, the circular surface containing the frontal thrust is integrated based on the Mindlin extra stress solution for the horizontal focused force. Let the center coordinates of the excavation surface be (0, 0, \(h\)) and the integral transformation equations are \(ds=rd\theta dr\), \(y=r\text{cos}\theta\), and \(z=h-r\text{sin}\theta\), which give the coordinates of the homogeneous force microelement at any point on the circular surface as \((0,r\text{cos}\theta,h-r\text{sin}\theta)\). The area division of the circle can be obtained:  
\[\begin{split}&\sigma_{q}=-\int\limits_{0}^{R}\int\limits_{0}^{2\pi} \frac{qx}{8\pi(1-\
u)}\Bigg{[}\frac{1-2\
u}{R_{3}^{3}}-\frac{1-2\
u}{R_{4}^{3}} --\frac{3(3-4\
u)(z+h-r\sin\theta)^{2}}{R_{4}^{5}}-\frac{3(z-h+r\sin\theta)^{2} }{R_{3}^{5}}+\\ &\frac{6(h-r\sin\theta)}{R_{4}^{5}}(h-r\sin\theta+(1-2\
u)(z+h-r \sin\theta)+\ \frac{5z(z+h-r\sin\theta)^{2}}{R_{4}^{2}})\Bigg{]}rd\theta dr \end{split} \tag{17}\]  
Among them:  
\[\begin{split}& R_{3}=\sqrt{x^{2}+(y-r\cos\theta)^{2}+(z-h+r\sin \theta)^{2}}\\ & R_{4}=\sqrt{x^{2}+(y-r\cos\theta)^{2}+(z+h-r\sin\theta)^{2}} \end{split}\]  
where \(\sigma_{q}\) is the extra stress brought on by the cutter's increased thrust; \(q\) is the cutter's increased thrust; \(R\) is the shield machine's radius; and \(h\) is the shield machine's depth.  
Figure 2: Schematic diagram of Mindlin solution for concentration force.
# Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis
## 2 Business Integrity Management and Analytics Framework
### Big Data Processing and Analysis Services Offered by BIMA Framework
#### 2.4.1 Monte Carlo Simulations  
Monte Carlo simulations (MCS) can be used in different types of simulations, such as simulating a possible bankruptcy of multiple suppliers based on the calculated probabilities to show that MCS can be used to reduce the risk of supplier bankruptcy. MCS can be used to show that the average losses would be more predictable and be used to calculate an insurance premium. Similar work at the Austrian National Bank has been conducted to estimate operational risk costs based on MCS, deriving probabilities of events and impacts of the latter based on historical data collected with banks, which was a method also proposed by the European Central Bank (ECB) staff [35].  
Monte Carlo analysis is a technique that is used in finance in order to simulate losses in financial risk management [36]. The idea of Monte Carlo simulations is to generate random numbers in order to model the stochastic behavior of an input parameter. Each different sequence of random numbers can generate another output.  
Due to the probability distribution of losses, Monte Carlo analysis can be used to estimate the average, total, and variance of the losses after a determined number of events. The analysis can be quite useful to evaluate premiums and risks for insurance services [37]. In the case of insurance, Monte Carlo analysis can be used to estimate the total, variance, and average losses given several policies sold. Monte Carlo analysis only requires a probability or frequency distribution that can be sampled from historical data or calculated based on mathematical models such as the BSM model.  
Monte Carlo simulations have an excellent history of providing reliable results for risk management. [PERSON] and [PERSON] [38] used MCS to estimate the probability distribution of supply chain losses caused by disruptions. [PERSON] and [PERSON] [30] used Monte Carlo simulations to produce accurate estimates of a firm's downside risk exposure to price/foreign exchange risk in the supply chain. [PERSON] and [PERSON] [39] used MCS for the estimation of reordering points in the embedded systems industry by using historical demand taken from an Enterprise Resource Planning system. [PERSON] and [PERSON] [40] used Monte Carlo for Radio-frequency identification (RFID) simulations for the supply chain management of the UK dental industry.
###### Abstract  
Nowadays, Augmented-Reality (AR) head-mounted displays (HMD) deliver a more immersive visualization of virtual contents, but the available means of interaction, mainly based on gesture and/or voice, are yet limited and obviously lack realism and expressivity when compared to traditional physical means. In this sense, the integration of haptics within AR may help to deliver an enriched experience, while facilitating the performance of specific actions, such as repositioning or resizing tasks, that are still dependent on the user's skills. In this direction, this paper gathers the description of a flexible architecture designed to deploy haptically enabled AR applications both for mobile and wearable visualization devices. The haptic feedback may be generated through a variety of devices (e.g., wearable, graspable, or mid-air ones), and the architecture facilitates handling the specificity of each. For this reason, within the paper, it is discussed how to generate a haptic representation of a 3D digital object depending on the application and the target device. Additionally, the paper includes an analysis of practical, relevant issues that arise when setting up a system to work with specific devices like HMD (e.g., HoloLens) and mid-air haptic devices (e.g., Ultrahaptics), such as the alignment between the real world and the virtual one. The architecture applicability is demonstrated through the implementation of two applications: (a) Form Inspector and (b) Simon Game, built for HoloLens and iOS mobile phones for visualization and for UHK for mid-air haptics delivery. These applications have been used to explore with nine users the efficiency, meaningfulness, and usefulness of mid-air haptics for form perception, object resizing, and push interaction tasks. Results show that, although mobile interaction is preferred when this option is available, haptics turn out to be more meaningful in identifying shapes when compared to what users initially expect and in contributing to the execution of resizing tasks. Moreover, this preliminary user study reveals some design issues when working with haptic AR. For example, users may be expecting a tailored interface metaphor, not necessarily inspired in natural interaction. This has been the case of our proposal of virtual pressable buttons, built mimicking real buttons by using haptics, but differently interpreted by the study participants.  
augmented reality; haptics; wearable AR; software architecture; interaction; mid-air; HMD
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 3 Sensing System
### Sensing Technologies  
An anti-drone system, to fulfill its purpose, must be equipped with a sensing system consisting of one or more sensors, including those of different technologies. There are, in fact, different types of sensors, which are characterized by the observed phenomenon, electromagnetic or acoustic, and by the spectrum band they use. For example, image sensors operate in the electromagnetic spectrum, in all visible frequencies, while a radar can operate at microwave frequencies.  
The first technological distinction to which we can refer for the classification of sensors is between active and passive sensing. The substantial difference between the two types is based on the use of energy to \"feel\" the objects present in space. For active sensors, an electromagnetic or acoustic radiation is emitted, with which it is possible to directly measure the distance of objects in space through, for example, the measurement of the delay between the radiation emitted by the sensor itself and that received by the back-scattered reflections of the objects. Radars and LiDAR sensors belong to this category. On the other hand, passive sensors receive energy from the environment and from the object to be detected, which can be used to reconstruct useful information. Most of the passive sensors used are optical and infrared cameras.  
There are many reviews on the current state-of-the art technology in this wide variety of sensors, both commercial and academic ones (for example: [31]). Instead, in the following subsections, we are focusing on the literature with drone-based-only use cases. So, it is possible to assume that all the following sensing technologies are suitable for use aboard mini and micro-drones.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### One-Dimensional Examples
#### 3.1.3 1D Melting in a Half-Scale (Phase Change)
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Reference Variables in Physical-Scale and Lattice-Scale**} \\ \hline
**Reference Variables** & **Physical-Scale** & **Lattice-Scale** \\ \hline \(m_{o}\) & 21.59 kg & \(1\times 10^{6}\) mu \\ \hline \(x_{o}\) & 0.2 m & 100 lu \\ \hline \(t_{o}\) & 4.6267 \(\times\) 10\({}^{2}\) s & \(6\times 10^{4}\) ts \\ \hline \(\Theta_{o}\) & 1023 K & 1 * tu \\ \hline \multicolumn{3}{c}{Converting properties between scales} \\ \hline Property & Physical-scale & Dimensionless-scale & Lattice-scale \\ \hline Length (\(L\)) & 0.2 m & 1 & 100 * lu \\ \hline Latent heat (\(h_{f_{S}}\)) & \(3.869\times 10^{5}\) 1/kg & 2.0705 \(\times\) 10\({}^{12}\) & 5.7514 \(\times\) 10\({}^{6}\) lu\({}^{2}\).ts\({}^{-2}\) \\ \hline Melting temperature (\(T_{x\to l}\)) & 933 K & 0.912 & 0.912 tu \\ \hline Subjected temperature (\(T_{x\to o}\)) & 1023 K & 1 & 1 tu \\ \hline Density (\(\rho\)) & 2698.9 kg/m\({}^{3}\) & 1\({}^{*}\) & 1 * mu.lu\({}^{-3}\) \\ \hline Specific heat (\(C_{p}\)) & 900 J/kg.K & 4.9272 \(\times\) 10\({}^{12}\) & 1.3687 \(\times\) 10\({}^{7}\) lu\({}^{2}\).ts\({}^{-2}\).tu\({}^{-1}\) \\ \hline Thermal conductivity (\(k\)) & 210 W/m.K & 4.9272\(\times\)10\({}^{12}\) & 2.2811\(\times\)10\({}^{6}\) mu.lu.ts\({}^{-3}\).tu\({}^{-1}\) \\ \hline Thermal diffusivity (\(a\)) & 8.6455 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s & 1 * & 0.1667 lu\({}^{2}\).ts\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Converting data for phase change problem. The values with * represent assumed values. Figure 8: Temperature distribution along the slab for dimensionless time \(t^{*}=1\) (i.e., for 4.6267 \(\times\) 10\({}^{2}\) s); (**a**) solution at the dimensionless-scale, and (**b**) solution at the physical-scale. The analytical solutions are achieved by using Equation (81)) at the dimensionless-scale and physical-scale. The numerical solution is achieved by using the LBM at the dimensionless-scale.
## 2 Materials and Methods
### Data Adjustment Techniques
#### 2.4.2 Data Adjustment via Artificial Neural Network  
Artificial neural networks are computational models inspired by the structure and function of biological neural networks in the human brain. They are a type of machine learning that works well for tasks like recognizing patterns, figuring out what to perform next, and making difficult decisions. ANNs are composed of interconnected nodes, commonly referred to as neurons or perceptrons. They are organized into layers to allow a better fit for data [26].  
To generate the ANN models, the software Matlab version 9.9 R2020b (provided by MathWorks, Natick, MA, USA) was employed. The response variable was, in the same way, the value of total reducing sugar. Thus, an ANN model was developed, where the input layer contained three neurons, denoting as input parameters the particle size of the mesquite sample, the hydrolysis time, and the sulfuric acid concentration; meanwhile, the output layer was composed of only one neuron, representing the response variable. To obtain the ANN model with the best performance, different configurations of the hidden layer were trained. To avoid an exhaustive search for the best arrangement of neurons in the hidden layers, a local search algorithm was applied [27], starting from a random configuration of one to three hidden layers. Every hidden layer was assigned a random number of neurons between one and ten.  
To avoid overfitting the neural network models, 70% of the data were randomly chosen, 15% were used to check the model, and the other 15% were used to test it.  
For ANN training, the hyperbolic tangent sigmoid transfer function was used in the hidden layers due to its ability to handle the non-linearity present in obtaining total  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & \begin{tabular}{c} **Particle Size** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **Acid (N)** \\ **(B)** \\ \end{tabular} & \
\begin{tabular}{c} **Time (min)** \\ **(C)** \\ \end{tabular} &
\begin{tabular}{c} **TRS (g/L)** \\ **Experimental** \\ \end{tabular} \\ \hline
1 & 30 (0) & 0.25 (0) & 20 (0) & 9.75 \\
2 & 16 (\(-\)1) & 0 (\(-\)1) & 20 (0) & 1.79 \\
3 & 50 (+1) & 0 (\(-\)1) & 20 (0) & 2.7  
reducing sugar. The linear transfer function was applied to the output layer. Furthermore, the learning rate utilized for training was 0.01, and the number of epochs was configured at 1000. This process used the backpropagation algorithm because it uses gradient descent, which facilitates the efficient training of neural networks [28].
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 3 Results
### RED Reactor Characterization  
The RED reactor developed in this study was characterized using a unit cell, with a low-concentration solution of 3 g/L NaCl and a high-concentration solution of 35 g/L NaCl, simulating river water and seawater, respectively. Additionally, an ERS composed of 0.1 M K\({}_{4}\)[Fe(CN)\({}_{6}\)]:3H\({}_{2}\)O, 0.1 M K\({}_{3}\)[Fe(CN)\({}_{6}\)], and 0.3 M NaCl was utilized, with a total volume of 80 mL. For the NaCl solutions, a control volume of 500 mL was employed. The RED reactor's performance was evaluated at different flow rates of 20, 30, 40, 50, and 60 mL/min to assess the influence of hydrodynamic conditions on energy generation.  
Figure 6: Velocity profile simulations of geometry 2 at different initial flow rates: (**a**) 10 mL/min, (**b**) 20 mL/min, (**c**) 30 mL/min, (**d**) 40 mL/min, (**e**) 50 mL/min, (**f**) 60 mL/min, (**g**) 70 mL/min, and (**h**) 80 mL/min.  
During the characterization, the open-circuit potential (OCP) and electric current were measured across a series of variable external resistances, ranging from 0 to 900 ohms, to determine the electrical behavior of the reactor. From these measurements, the power density was calculated to evaluate the reactor's capacity to generate energy under different operating conditions. This characterization aimed to determine the optimal operating conditions for energy production.  
The results summarized in Table 3 provide a comparison between the experimental measurements and the theoretical predictions. The observed data indicate that the experimental results are consistent with the theoretical model, demonstrating the functionality of the developed RED reactor. Additionally, Figure 7 shows the effect of varying flow rates on the reactor's energy generation. As the flow rate increased, the power density improved, enhancing energy output due to better ionic exchange across the membranes. The highest energy generation was achieved at a flow rate of 60 mL/min, consistent with CFD simulations, which demonstrated that increasing flow velocity is an alternative to enhance energy production. Greater energy production could contribute to improved efficiency in the desalination process.