esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
###### Abstract  
Mining dump trucks play an important role in engineering construction and resource extraction. Current research mainly focuses on the dynamic modeling and reliability analysis of the vehicle frame, suspension and overall model. However, with the development of electric drive, the wheel hub system has become an important component in mining truck equipment. This paper investigates the multi-body modeling of a mining truck's hub drive reduction system in order to analyze its output characteristics including the stability of the angular velocity of its planetary carriers and the fluctuations in its meshing forces. A bench experiment was also conducted to verify the accuracy and stability of the proposed modeling. And the simulation results revealed that the fluctuations in the angular velocity of the planetary carriers were primarily influenced by the excitation from the hub motor's input and the meshing forces between the gears of the reducers, which were mainly determined by the contact stiffness, damping, and clearance value during gear contact.  
Keywords:multi-body modeling; hub drive reduction system; wheel hub system; dynamic analysis +
Footnote †: journal: Applied sciences
## 3 Simulations and Results
### Synthetic DSD Field
Simulation results suggest that \(N_{0}\) for voxels with nonzero specific attenuations is between \(6.75\times 10^{2}\) and \(5.46\times 10^{4}\) mm\({}^{-1}\).m\({}^{-3}\) when \(\Lambda_{2}\) is 3.5 mm\({}^{-1}\). With \(N_{0}\) and \(\Lambda\) ready, the specific attenuation for any elevation angle and for either polarizations can then be calculated. Figure 2b shows the true field of \(a_{V}(40)\) for comparison, in which the specific attenuation for voxel A is 2.99 dB/km, 7.4% less than \(a_{H}(40)\).
## Nomenclature  
\begin{tabular}{l l} \(A\cos(\varphi)\) & Effective cross-sectional area of each electromagnetic pole. \\ \(a\), \(b\) & Steady-state vibration amplitudes of the twelve-pole system in \(X\) and \(Y\) \\  & directions, respectively. \\ \(d\) & Dimensionless derivative control gain. \\ \(c_{0}\) & Nominal air gap size. \\ \(e\) & Disc eccentricity of the twelve-pole system. \\ \(f\) & Dimensionless disc eccentricity of the twelve-pole system. \\ \(I_{0}\) & Permanentized electrical current. \\ \(I_{j}\), \(j=1,2,\ldots,6\) & Control current in the \(j\)th electromagnetic pole. \\ \(k_{1}\) & Proportional control gain. \\ \(k_{2}\) & Derivative control gain. \\ \(m\) & The rotor mass. \\ \(N\) & Number of turns of each coil of the twelve poles system. \\ \(p\) & Dimensions proportional control gain. \\ \(x\), \(\dot{x}\), \(\ddot{x}\) & Displacement, velocity, and acceleration of the twelve-pole system in \\  & \(X\) direction. \\ \(y\), \(\dot{y}\), \(\ddot{y}\) & Displacement, velocity, and acceleration of the twelve-pole system in \\  & \(Y\) direction \\ \(\alpha\) & The angle between every successive two poles. \\ \(\delta_{j}\), \(j=1,2,\ldots,6\) & Instantaneous air gap of the \(j\)th electromagnetic pole. \\ \(\epsilon\) & Small perturbation parameter. \\ \(\mu\) & Dimensionless damping coefficient of the twelve-pole system in \(X\) and \\  & \(Y\) directions. \\ \(\mu_{0}\) & Free space permeability. \\ \(\sigma\) & Detuning parameter, where \(\Omega=\omega+\sigma\). \\ \(\varphi_{1}\), \(\varphi_{2}\) & Steady-state phase angles of the twelve-pole systemin \(X\) and \(Y\) directions, \\  & respectively. \\ \(\psi\) & Angular speed of the twelve-pole rotor system. \\ \(\omega\) & Dimensionsless natural frequency of the twelve-pole system in \(X\) and \\  & \(Y\) directions. \\ \(\Omega\) & Dimensionsless angular speed of the twelve-pole rotor system. \\ \end{tabular}
## 5 Experimental Results  
### RFI emitter Geolocation Scenario  
In this scenario, the CAF method was applied to achieve peak of RFI emitter estimation and cross-correlation of TDOA and FDOA measurements based on extracted data from the Orolia Simulator. Figure 11 is illustrated the RMSEs of the position for the RFI emitter using measurements of TDOA and FDOA individually and hybrid, as well as, Figure 12 is illustrated the peak CAF and cross-correlation of TDOA and FDOA, which denoted the estimation position of the RFI emitter.
## 4 Conclusions and Future Directions  
By means of this application, it has been confirmed that neural networks offer low-cost algorithmic solutions and facilitate analysis, as it is not necessary to consider various statistical assumptions: Matrix homogeneity, normality, incorrect processing of data, and so on. Besides the advantage of the capacity of these models to modify the connection weights automatically, they are fault-tolerant systems. Additionally, the possibility of including all the information (variables) available in the model estimation and the speed with which adjustments can be obtained must also be emphasized. From the analysis carried out, it has been verified that the Multilayer Perceptron is useful for the classification of  
Figure 7: Density function for determining tax fraud probability. Source: Our own estimations.  
fraudulent and non-fraudulent taxpayers, and, also of use to ascertain each taxpayer's probability of evading tax. Furthermore, the 84.3% efficacy of the model selected is higher than that of other models. The sensibility analysis, conducted with the ROC curve, demonstrates the high capacity of the selected model in the matter of discriminating between fraudulent and non-fraudulent taxpayers. Thus, it can be concluded that the Multilayer Perceptron network is well-equipped to classify taxpayers in a very efficient manner.  
Finally, the results obtained in this study present a wide range of possibilities to the improve tax fraud detection, through the use of the kind of predictive tools dealt with in this paper to find fraud patterns which could be described a priori, through sensitivity analysis. In the future, it would be of great interest to realize applications of this methodology in other taxes.  
Conceptualization, C.P.L.; Methodology, M.J.D.R. and S.d.L.S.; Software, C.P.L.; Validation, M.J.D.R. and S.d.L.S. Formal Analysis, C.P.L.; Data Curation, C.P.L.; Writing-Original Draft Preparation, C.P.L.; Writing-Review & Editing, M.J.D.R. and S.d.L.S.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Comparison of Different Surrogate Models and Motor Optimization Design
### Comparison of Electromagnetic Performance before and after Motor Optimization  
The no-load back EMF, radial air gap flux density curve and harmonic distribution of the motor before and after optimization are shown in Figure 17. It can be seen that the optimized no-load back EMF was higher, the curve was more sinusoidal, and the harmonic content was less. Similarly, the content of higher harmonics in the optimized radial air gap flux density was also reduced.  
Figure 18 shows the comparison of load output torque and maximum output torque before and after motor optimization. From the diagram, it can be seen that after the motor reached the rated torque before and after optimization, the torque ripple after optimization was only 0.68%, which was 51.43% lower than that before optimization. In addition, the maximum output torque after optimization was 9.46 kNm, which was slightly lower than that before optimization, but the overload multiple was still far more than four times.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Initial Parameters** & **Optimized Parameters** \\ \hline \(Wt\) (mm) & 0.83 & 0.842 \\ \(B\)s0 (mm) & 3.2 & 3.5 \\ \(\alpha_{p}\) & 0.7 & 0.78 \\ \(Tp\) (mm) & 7 & 6 \\ \(\delta\) (mm) & 1.5 & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of parameters before and after motor optimization.  
Figure 17: The no-load back EMF and air gap radial flux density curves and corresponding harmonic distribution before and after motor optimization. (**a**) Curve of no-load back EMF. (**b**) Harmonic distribution of no-load back EMF. (**c**) Curve of radial air gap flux density. (**d**) Harmonic distribution of radial air-gap flux density.  
The summary and comparison of the results before optimization, optimization results and finite element calculation results of the motor are shown in Table 5. Through the comparison of various electromagnetic properties, it can be known that the accuracy of the surrogate model of LHS-Kriging fitting was good, and the error between the model prediction value and the finite element calculation result was within the acceptable range. In addition, after optimization design, the efficiency of the motor was slightly improved, the amount of the permanent magnet was reduced by 8.2%, and the torque ripple was reduced by 51.43%.
# Effects of Wearing a Swimming Cap and Goggles on Phoria and Fusional Vergence
###### Abstract  
This study evaluated changes in phoria and negative (NFV) and positive frusional vergence (PFV) when wearing a cap and/or goggles. Thirty-five healthy university students aged 20-25 years participated in the study. All participants were examined for phoria, NFV, and PFV at distance and near. A rotary prism was used before and after wearing a cap, goggles, and both a cap and goggles. The frequency of overall average change in phoria, NFV, and PFV after wearing swimming equipment compared with non-wearing ranged from one-third to four-fifths. The order of frequency in change was wearing of goggles and a cap, goggles alone, and a cap alone. The frequency of a prism prescription, determined by the relationship between phoria and vergence, was similar or higher for wearing than for non-wearing and higher at near than at distance. The effect on change in phoria and frusional vergence was greater when wearing both a cap and goggles than when wearing either alone. These findings demonstrate that wearing both swimming cap and goggles that contact or attach around the eyes influence phoria and frusional vergence by inducing pressure or pulling. These changes may contribute to the discomfort associated with the wearing of swimming equipment.  
phoria; frusional vergence; swimming goggles; swimming cap +
Footnote †: journal: Medical  
Thus far, as mentioned above, studies of eye-related protective equipment have been limited to changes in ocular biometric parameters and ocular physiology, including the visual field, visual response, and intraocular pressure. Furthermore, despite the fact that goggles used for swimming, which is one of the most popular sports and health-related leisure activities [19; 20], are eye protective and fit closely to the eyes. When evaluating non-strabismic binocular vision, it is essential to analyze the relationship between phoria and fusional vergence to maintain the zone of clear single binocular vision [21; 22]. Phoria and fusional vergence are closely related to the extraocular muscle action required for binocular vision [23; 24; 25]. However, until now, there have been no studies on changes in phoria and fusional vergence induced by wearing of a cap and/or goggles while swimming.  
The purpose of this study was to evaluate the changes in phoria and fusional vergence at distance (6 m) and near (40 cm) before and after wearing a cap and/or goggles. The study focused on horizontal phoria, negative (NPV) and positive fusional vergence (PFV), and prism prescription according to [PERSON]'s criterion.
## 1 Introduction
However, the TV regularization constraint method is exceptionally sensitive to smoothing variables due to the convergence of the inversion as well as the nonlinearity caused by the data fitting term and the total variation regularization term [25]. To address noise suppression, amplitude preservation, and resolution improvement, this paper presents the development of an LSRTM approach based on modified total variation (MTV) regularization [26]. The objective function is then constrained by MTV regularization to ensure the precision and convergence of the LSRTM, which delivers a refined edge with reconstruction details. Finally, the proposed method is assessed with theoretical synthetic data and laboratory data to confirm the efficacy and applicability of our algorithm.
## 2 Primary Theory
### Multiscale Fuzzy Entropy  
Multiscale Fuzzy Entropy (MFE) is defined to measure the complexity and irregularity of time series based on multiple scale factors. A brief description of MFE is as follows [32]:  
1. Assume that a time series is denoted as \(X=\{x(i):1\leq i\leq N\}\), where \(N\) is the length of time series. Coarse-grained time series \(\{y(\tau)\}\) is constructed as \(\{y_{1}(\tau),y_{2}(\tau),\cdots,y_{N/\tau}(\tau)\}\), where \(\tau\) is a positive integer. \(y_{j}(\tau)\) is computed based on Equation (10): \[y_{j}(\tau)=\frac{1}{\tau}\sum_{i=(j-1)\tau+1}^{j\tau}x(i),\;1\leq j\leq\frac {N}{\tau}.\] (10)  
For \(\tau=1\), the time series \(\{y(1)\}\) is an original time series. The length of each coarse-grained time series equals the length of the original time series divided by scale factor \(\tau\). The coarse-grained procedure is shown in Figure 1.  
2. The FE of each coarse-grained time series can be computed according to Equations (1)-(9) and MFE is expressed by Equation (11) as a function of scale factor \(\tau\). This procedure is called MFE analysis. \[MFE(X,\tau,m,n,r)=FE(y(\tau),m,n,r)\] (11) Here, parameter \(r=0.1\sim 0.25 SD\), \(SD\) is the standard deviation of the original time series, and it is calculated by \(SD=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(x(i)-\overline{x}\right)^{2}}\). Here, \(\overline{x}=\frac{1}{N}\sum\limits_{i=1}^{N}x(i)\).
## High Quality Geographic Services and Bandwidth Limitations  
**[PERSON] * and [PERSON]**  
GIScience-GIVA, Department of Geography, University of Zurich, Winterthurerstrasse 190, Zurich 8057, Switzerland; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +41-44-6344550; Fax: +41-44-6355848.  
_Received: 18 August 2011; in revised form: 14 November 2011 / Accepted: 7 December 2011 / Published: 20 December 2011_  
**Abstract:** In this paper we provide a critical overview of the state of the art in human-centric intelligent data management approaches for geographic visualizations when we are faced with bandwidth limitations. These limitations often force us to rethink how we design displays for geographic visualizations. We need ways to reduce the amount of data to be visualized and transmitted. This is partly because modern instruments effortlessly produce large volumes of data and Web 2.0 further allows bottom-up creation of rich and diverse content. Therefore, the amount of information we have today for creating useful and usable cartographic products is higher than ever before. However, how much of it can we really use online? To answer this question, we first calculate the bandwidth needs for geographic data sets in terms of waiting times. The calculations are based on various data volumes estimated by scholars for different scenarios. Documenting the waiting times clearly demonstrates the magnitude of the problem. Following this, we summarize the current hardware and software solutions, then the current human-centric design approaches trying to address the constraints such as various screen sizes and information overload. We also discuss a limited set of social issues touching upon the digital divide and its implications. We hope that our systematic documentation and critical review will help researchers and practitioners in the field to better understand the current state of the art.  
**Keywords:** online maps; virtual globes; high resolution data; bandwidth limitations; level of detail; cartographic design
# NESO-Based Path Following Control for
## 2 Preliminaries and Problem Formulation
### Problem Formulation
#### 2.2.2 LOS Guidance Law for Underactuated Hovercrafts  
The Serret-Frenet (SF) reference frame, composed of tangent and normal vectors of the path reference point, is introduced to describe line-of-sight (LOS) of the path following problems. The geometric schematic diagram of LOS guidance law of an underactuated hovercraft is illustrated in Figure 1b, where the \(\gamma(\alpha)=\text{atan}2\Big{(}y^{\prime}_{d}(\alpha),x^{\prime}_{d}( \alpha)\Big{)}\) such that \(x^{\prime}_{d}(\alpha)=\partial x_{d}/\partial\alpha\), \(y^{\prime}_{d}(\alpha)=\partial y_{d}/\partial\alpha\), is used to rotate the NED frame to the SF frame. \(\mathbf{P}_{d}^{n}\) represents the reference position. For any reference path point \(\mathbf{P}_{d}^{n}\), it can be parameterized as \(\mathbf{P}_{d}^{n}=(x_{d}(\alpha),y_{d}(\alpha))\) by the independent path continuous scalar variable \(\alpha\).  
Then, for an underactuated hovercraft located at \(\mathbf{P}^{n}=(x,y)\), by rotating the inertial NED frame to the SF frame, the along-tracking error \(x_{e}\) and cross-tracking error \(y_{e}\) between \(\mathbf{P}^{n}\) and \(\mathbf{P}_{d}^{n}\) in the SF frame can be obtained as  
\[\left[\begin{array}{c}x_{e}\\ y_{e}\end{array}\right]=\underbrace{\left[\begin{  
in which \(\gamma_{\Delta}>0\) is the convergence rate, and \(\Delta_{\min}\) and \(\Delta_{\max}\) represent the upper and lower bounds of \(\Delta_{t}\), respectively.  
The **control objective** is designing a disturbances rejection safe control strategy with SLC to achieve better control performance for underactuated hovercrafts that suffer from the nonlinear time-varying SLC and unknown nonlinear time-varying uncertainties.  
To realize the above control objective, some assumptions need to be made as follows.  
**Assumption 1**.: _The positions and velocities of underactuated hovercrafts are available._  
**Assumption 2**.: _The total uncertainties functions \(\mathbf{d}=\big{[}f_{u}(u,v,r,\tau_{uu}),f_{v}(u,v,r,\tau_{uv}),f_{r}(u,v,r,\tau_{ uv})\big{]}^{T}\) are bounded and differentiable such that \(\|\mathbf{d}\|\leq\|\mathbf{B}_{1}\|=\|[\mathcal{B}_{11},\mathcal{B}_{12},\mathcal{B} _{13}]^{T}\|\) and \(\|\hat{\mathbf{d}}(t)\|\leq\|\mathbf{B}_{2}\|=\|[\mathcal{B}_{21},\mathcal{B}_{22}, \mathcal{B}_{23}]^{T}\|\), where \(\mathcal{B}_{ij}(i=1,2;j=1,2,3)\) are some positive constants._
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 3 Materials and Methods
### Hardware Design
At the core of this module is the ESP32-D0 WDQ6 chip. The chip embedded is designed to be scalable and adaptive. Two CPU cores can be individually controlled, and the CPU  
Figure 7: CTD block diagram. clock frequency is adjustable from 80 MHz to 240 MHz. The chip also has a low-power co-processor that can be used instead of the CPU to save power while performing tasks that do not require much computing power, such as monitoring peripherals. ESP32 integrates a rich set of peripherals, including those needed for our project: UART and I2C. The electrical characteristics are suitable for use in a harsh environment such as the marine one [32]. The ESP32-WROOM-32 implements the \"ESP-BLE-MESH\" according to the latest Bluetooth(r) Mesh Profile v1.0.1. As can be read in the certificate \"ESP BLE Mesh is Espressif's implementation of the SIG Bluetooth Mesh. It supports all SIG Bluetooth Mesh Features, Server Models and Client Models. It is a portable and scalable embedded software solution that operates with Espressif's Bluetooth ICs family\" [33]. Built on top of the Zephyr Bluetooth Mesh stack, the ESP-BLE-MESH implementation supports device provisioning and node control. It also supports such node features as proxy, relay, low-power and friend. To bring the NB-IoT and the GNSS connectivity to the main module, a compatible shield is adopted. It is based on the BG95-M3 Quectel modem [34]. It is an embedded IoT (LTE Cat M1, LTE Cat NB2 and EGPRS) wireless communication module with ultra-low power consumption. It provides data connectivity on LTE-TDD/LTE-FDD/GPRS/EDGE networks and supports half-duplex operation in LTE networks. It also provides GNSS functionality to meet customers' specific application demands. Two U.FL connectors are used for internal or external Antennas (LTE and GNSS). The shield supports a nano-SIM card to be inserted into the Hinged Lid SIM Card Holder type located at the top of the PCB. All SIM card traces are ESD-protected. The network indication LED, located in the center of the edge of the board lets us observe the modem network status. TPS63020 Buck-Boost Converter is used to power the modem, so the shield is fully functional even with input voltage dropping down to 3.3 V. DC/DC. The output voltage is also used to power LDO, which provides 3.3 V. DC for the level translators, Open-Drain (OD) logic IC and active GNSS antenna to be connected. The shield provides 3.3 V. DC power for the active GNSS antenna. The extended operating temperature range of \(-40\)\({}^{\circ}\)C to \(+85\)\({}^{\circ}\)C makes it suitable for harsh environments.
# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 4 Discussion
### Evaluation Using Hydrological Modelling
In glaciated Alpine catchments, a comparable contribution to overall model uncertainty can be explained by the chosen modelling approach and parameter estimation for evapotranspiration and snow/ice accumulation/melt in addition to precipitation [6, 7]. The absence of a glacier melt component in the SWAT model may limit the results of GPP performance assessment based on generated runoff in glacierized catchments. For more reliable long-term evaluations of GPPs in such contexts, a distributed hydrological model with an explicit glacier melt module is recommended. In addition, evidence from a study of 273 Austrian catchments (1976-2006) [5] indicates that key model parameters related to snow and soil moisture (e.g., snow correction factor, degree-day factor, soil moisture capacity, and runoff non-linearity) exhibited significant trends over time. These changes were linked to increasing air temperatures, elevated evapotranspiration, and drying catchments, highlighting that the assumption of time-invariant parameters can impede long-term runoff simulations. Therefore, for a more robust evaluation of GPP performance in a changing environment, incorporating time-varying parameters for snow and soil moisture processes can be recommended.