esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Ground Radar Processing  
The radar analysis was performed using the Python Atmospheric Radiation Measurement (ARM) Radar toolkit (Py-ART; [46]), which is an open-source Python module containing a collection of weather radar algorithms and utilities for data processing and visualization. The first step was to perform a quality control of the data that included de-aliasing of the radial velocity field, removal of non-meteorological targets (e.g., insects) via thresholds on various polarimetric variables [47], and correction for attenuation, clutter, and second-trip contamination. Cartesian gridding of the data was performed at 225 \(\times\) 225 horizontal domain (corresponding to 3.5 km grid spacing) and 40 vertical levels (corresponding to 500 m). The rainfall estimation is based on the algorithm developed by [PERSON] et al. [48], referred to as CSU-HIDRO, which is coded in a Python package named CSU_RadarTools [49].  
A single-Doppler analysis algorithm was applied to the radar data prior to the Cartesian gridding, utilizing 2 DVAR approach of [PERSON] et al. [50], which is an extension of the Velocity Azimuth Display (VAD) technique [51]. More specifically, after basic quality control, a background wind field is created using the traditional VAD analysis of radar data. Then, the raw radial velocity data is used to apply an optimal correction to the background winds, based on their error covariances. This method retrieves high-quality low-level 2-D Doppler vector winds on the conical radar scan surface, which are comparable to scatterers' performance (\(\sim\)0 bias, \(\sim\)2 m s\({}^{-1}\) speed and \(\sim\)20\({}^{\circ}\) direction errors, 0-32 m s\({}^{-1}\) valid range; [52]), but have much finer resolution in space and time with no degradation in the presence of precipitation. The output velocity vector field obtained by our single-Doppler analysis has a spatial resolution of 3 km. For the purpose of this study, Doppler winds at only the lowest elevation sweep (0.5\({}^{\circ}\)) were used to identify near-surface wind features.
# Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
###### Abstract  
As deep space exploration tasks become increasingly complex, the mobility and adaptability of traditional wheeled or tracked probe robots with high functional density are constrained in harsh, dangerous, or unknown environments. A practical solution to these challenges is designing a probe robot for preliminary exploration in unknown areas, which is characterized by robust adaptability, simple structure, light weight, and minimal volume. Compared to the traditional deep space probe robot, the spherical robot with a geometric, symmetrical structure shows better adaptability to the complex ground environment. Considering the uncertain detection environment, the spherical robot should brake rapidly after jumping to avoid reentering obstacles. Moreover, since it is equipped with optical modules for deep space exploration missions, the spherical robot must maintain motion stability during the rolling process to ensure the quality of photos and videos captured. However, due to the nonlinear coupling and parameter uncertainty of the spherical robot, it is tedious to adjust controller parameters. Moreover, the adaptability of controllers with fixed parameters is limited. This paper proposes an adaptive proportion-integration-differentiation (PID) control method based on reinforcement learning for the multi-motion mode spherical probe robot (MMSPR) with rolling and jumping. This method uses the soft actor-critic (SAC) algorithm to adjust the parameters of the PID controller and introduces a switching control strategy to reduce static error. As the simulation results show, this method can facilitate the MMSPR's convergence within 0.02 s regarding motion stability. In addition, in terms of braking, it enables an MMSPR with random initial speed brake within a convergence time of 0.045 s and a displacement of 0.0013 m. Compared with the PID method with fixed parameters, the braking displacement of the MMSPR is reduced by about 38%, and the convergence time is reduced by about 20%, showing better universality and adaptability.  
spherical robot; multi-motion mode; reinforcement learning; motion stability; rapidly brake
## 2 Materials and Methods
### Machine Learning Approach
#### 2.3.1 Image Preprocessing  
First, the RGB images are resized to a smaller pixel size to accelerate the computations, followed by using Gaussian blur for noise removal. Since the RGB format cannot separate the image's intensity, it has shadows and lights that make it less suitable for removing the background. So, the RGB crop leaf images are converted to another color space called HSV (Hue, Saturation, Value), which can separate the color from the intensity.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Datasets
#### 3.1.2 DNS-Challenge  
The Deep Noise Suppression (DNS) challenge [34] at Interspeech 2020 provides a large dataset. It includes over 500 h of clean audios across 2150 speakers from the Librivox ([[https://librivox.org/](https://librivox.org/)]([https://librivox.org/](https://librivox.org/)) (accessed on 20 September 2024)) dataset and over 180 h of noise audios from Audioset ([[https://research.google.com/audioset/](https://research.google.com/audioset/)]([https://research.google.com/audioset/](https://research.google.com/audioset/)) (accessed on 20 September 2024)) [41] and Freesound ([[https://freesound.org/](https://freesound.org/)]([https://freesound.org/](https://freesound.org/)) (accessed on 20 September 2024)) for training. The noise dataset contains around 150 types of noise, with each type having at least 500 clips to ensure a balanced distribution. Meanwhile, the DNS-Challenge provides a non-blind test set of 150 noisy-clean audio pairs for model performance evaluation, whose mixed SNRs are randomly distributed between 0 dB and 20 dB. The clean audios in the test set are sourced from another 20 unseen speakers in the Graz University's clean speech dataset [42], ensuring no speaker overlap between the training and test sets. Meanwhile, the noise clips in the test set are sampled from Audioset and Freesound and these are not present in the training set. Utilizing the official scripts ([[https://github.com/microsoft/DNS-Challenge](https://github.com/microsoft/DNS-Challenge)]([https://github.com/microsoft/DNS-Challenge](https://github.com/microsoft/DNS-Challenge)) (accessed on 20 September 2024)), we totally generate 3000 h of noisy-clean audio pairs as the training set, in which the mixed SNRs range from \(-5\) dB to 15 dB.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 2 Methodology and Materials
### Electromagnetic Simulation of Spoof Surface Plasma Waves on a Senawater Surface  
On the other hand, an electromagnetic simulation has been conducted to prove the feasibility of excitation of surface waves using a monopole antenna and to prove the feasibility of transmitting surface waves along the air/seawater interface.  
During the simulation, a surface electromagnetic wave was excited at 44 MHz using a 2 m tall monopole antenna as illustrated in Figure 1b. The monopole antenna is similar to a half-wave dipole antenna, with two exceptions: (a) one of the arms of the dipole antenna is partially submerged in water whilst the other arm is exposed to free space; and (b) the voltage of the excitation source is ways higher than 100 kV.  
Figure 1c shows the simulated surface electromagnetic wave in a tank of seawater. The dimension of the tank is about 20 m \(\times\) 20 m. The middle red dot in Figure 1c represents the monopole antenna as shown in Figure 1b. The simulated result has clearly shown that the circular wave fronts of the surface electromagnetic wave spread from the monopole antenna at the middle. The separation between a wave front and the next is about 3.3 m, corresponding to the wavelength of 44 MHz along the rubber/water interface. The electric field strength of the wave on the ocean surface is clearly stronger than the electric field strength below the seawater surface, suggesting that a surface electromagnetic wave was formed by the monopole antenna on the seawater surface.  
In the simulated S1,1 as shown in Figure 1d, the S1,1 was trending to a higher return loss as the frequency goes lower, suggesting that there is a power exchange between the antenna and the surrounding. Some of the energy from the antenna has either gone to medium 2 or radiated out as a space wave.
## 3 RTI Data Acquisition Methods
### Thermographic Methods  
Subsurface faults modify thermal emission patterns of nearby interior tunnel surfaces, causing abnormal variations. Visualising temperature distribution profiles (Therrometry) facilitates localisation of suspected near-surface features (Thermography) [53; 54], but recovery of specific attributes defers to higher quality UST or localised GPR imaging. Active Thermography (ACT) heats surfaces using halogen lamps [55], air guns [56] or inductive-heating elements [57] to induce exaggerated thermal responses. Abandoned testing by [58] and remarks of [59] affirm that heating element operation for RTSSI would incur impractical cost and could debond masonry, explaining its literary absence. We find use of infrared camera arrays for passive Infrared Thermography (IRT) more commonplace, owing to swifter and less costly implementation. Leading systems identify both air and water filled voids, with individual scans displayable as 2D panoramic imagery [53] or pioneering 3D mesh overlays on digital structural models rendered using TOSCA-FI [60] (Figure 3) or Augmented Reality [61] (Section 6.2). Despite recent work, Thermography still exhibits persistent limitations [54] undermining direct application to RTSSI:  
* Results are highly sensitive to ambient temperature conditions which diminishes anomaly contrast (e.g., daily and seasonal variation);
* Thermal insulation and heat-resistant coatings used for tunnel temperate regulation and fire resilience can skew results. High thermal dissipation can easily restrict penetration d < 30 mm [62];  
Figure 2: TLS for tunnel inspection. (**a**) 3D point cloud returned from a TLS metro tunnel survey [52]; (**b**) A FARO® FOCUS 350 scanning module, commonly deployed for infrastructure surveys; (**c**) TMMS rail-trolley transports a Z+F9012 to capture a TLS point cloud of a Zhengzhou metro tunnel.  
* Subsurface water content variation (e.g., increased permeation following rainfall or snow) can mask or exaggerate thermal profiles of faults;
* Enclosed, curved tunnel geometry restricts available viewing angles and confine results to 2D, even in a 3D mesh overlay, making inference of feature depth and physical form very challenging even for experienced operatives.
## 3 Model of Image Motion
### Analysis of In-Orbit Disturbances
#### 3.2.2 The Disturbances of a Pointing Platform  
The pointing platform, which is driven by a permanent magnet synchronous motor in orbit, is subject to various sources of disturbances, including the following:  
1. Transition from a gravity environment to microgravity environment: The transition from a gravity environment on the ground to a microgravity environment in space involves a release of gravity. This can cause structural deformations as the structure adapts to the absence of gravity, resulting in changes in the friction forces between the motor shaft and the structure. These changes in friction forces can impact control precision.
2. Temperature variations: In a space environment, there are significant temperature variations. These variations can cause changes in the magnetic materials and coil parameters inside the motor, thereby affecting the motor's output characteristics and control performance.
3. Radiation and electromagnetic interference: The presence of radiation and electromagnetic interference around the spacecraft can interfere with the control circuits and sensors of the motor, leading to fluctuations in torque and speed.  
To overcome these disturbances, the motor design and material selection can be optimized to enhance the motor's resistance to magnetic field interference and temperature adaptability. Additionally, it is essential to design more robust control algorithms to improve the precision of pointing platform tracking control, which will be introduced in the next subsection.  
In general, the disturbances of a spacecraft affect the accuracy of satellite attitude and orbit parameters \(h\), \(\varphi\), \(\theta\), and \(\psi\), which will reduce the accuracy of IMC calculations and reduce imaging quality. At the same time, the disturbances of a pointing platform will affect the pointing accuracy of said pointing platform, i.e., \(A_{t}\) and \(E_{t}\), which will reduce the compensation accuracy and affect the imaging accuracy.
## 3 Results
### MMS Reference Trajectory and RKTLIB Results
The “combination” indicates the set of considered solutions related to possible configurations with n satellites (with n = 4, 5, 6) for the Galileo (gal), GPS (gps), and GLONASS (glo). The “n. of combinations” is equal to the number of compared solutions. should be considered as the most reliable Galileo information. Another reason could be related to a better overall performance of the Galileo constellation. Indeed, the second row in Table 3 (best_gal_5) indicates a prevalence of the \"Instantaneous\" ambiguity fixing method, the most conservative among the ones proposed by RTKLIB (appendix E.7 [41]). The whole path length MMS solution was 4803 m. Since the distance between the survey area and the nearest reference station (named \"Trieste\" and belonging to the \"Antonio Marussi\" network, managed by \"Regione Friuli Venezia Giulia\" [54]) was in the range of 5-6 km, a network solution was not required. In particular, the \"IN-Fusion Single-Base Station Processing\" mode was chosen, processing both L1 and L2 frequencies and reaching centimetric rms both in planimetric and altimetric positioning.
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
###### Abstract
Sustainable development is an inherently complex process due to the complex interlinking of information domains that require consideration. The usage of geographic datasets and information systems for planning and decision support permeates across the entire spectrum of sustainable natural resource management. The fusion of web mapping services, spatial visualization, and analysis have considerable potential to support the monitoring and evaluation of sustainable development goals. The South Africa National Space Agency (SANSA) in partnership with New Partnership for Africa's Development-African Union Development Agency (NEPAD-AUDA), the implementing arm of the African Union, has developed a live prototype internet-based geoportal to facilitate data collection, management, integration, analysis, and visualization of Agenda 2063 development indicators. The geoportal is aimed at supporting the planning, implementation, and monitoring of Agenda 2063 goals at continental, regional, and national levels. In this paper, we aim to describe the implementation, structural design, and the functionality of the pilot Agenda 2063 geoportal. Based on the monitoring and evaluation requirements from NEPAD-AUDA, we specifically aim to demonstrate the ability of the geoportal to uploading, accessing, visualizing, storing, analysing, and comparing development information related to the wealth creation, share prosperity, and transformative capacity impact indicators. Selected wealth creation indicators encompassed manufacturing value addition, access to electricity, and improved water sources. Shared prosperity indicators included incidences of tuberculosis, the prevalence of Human immunodeficiency virus (HIV), unemployment with intermediate education, the mortality rate for children under five, and incidences of malaria and gross national income (GNI) per capita growth. Lastly, tax revenue was used as a transformative capacities indicator. Typical users for the geoportal include planning officers from African Union member states, regional economic communities, international development institutions, development aid partners, and funders, NEPAD-AUDA, African Union Commission (AUC), and the public.
## 3 Results and Discussion
### Transmission Loss
Panels GW270, GW300, and GW448 have poor absorption below this range (\(\alpha\approx 0.3\)). However, an advantage here is an increased absorption between 100-400 Hz. A higher absorption coefficient was observed in the frequency range, with a decrease in transmission loss. The absorption properties of the studied fluted PVC panels are strictly connected with their resonance. In studied cases, this dip could correspond to the resonance frequency associated with the space between the ribs and wave propagation in the panel's plane. This frequency is independent of panel size, and although it can be calculated reasonably accurately, the magnitude of the corresponding dip cannot be calculated at this research stage.
## 2 Performance Metrics
### Power Consumption  
Low power consumption is highly desirable for mobile display devices, such as notebook computers and smartphones, since the operation time is determined by the battery. Figure 6 shows the measured power consumption of the 15.6\" mLCD and OLED display panels, where the luminance level was controlled at 200 nits. When the pixels were turned off, the power consumption mainly came from the power source circuitry and peripheral circuits for driving the pixels. The power consumption of the mLCD and OLED panels at APL = 0% and APL = 100% was about [1.10 W, 2.53 W] and [3.45 W, 8.83 W], respectively. This means that the mLCD and the OLED display consumed 2.35 W and 6.30 W, respectively, at APL = 100% without considering the peripheral circuits. The mLCD showed a much lower power consumption than the OLED display, which helped to increase the operating time of the display devices.  
Figure 5: Measured (**a**) white spectra and (**b**) color shifts of the mLCD and the OLED panels.  
Figure 6: Power consumption as a function of the APL for the 15.6-inch mLCD and OLED display panels.
# A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
## 4 Experiments and Results
### Overview and Setup  
The set we used in the experiment was the Tobacco800 database [40; 41; 42]. The Tabacco800 dataset is a publicly accessible document image set with a realistic scope and complexity. It is a set of 1291 official documents and is the most frequently used image database for logotype recognition and detection; the resolutions of the documents vary significantly from 150 to 300 DPI, and the dimensions of images range from 1200 by 1600 to 2500 by 3200 pixels. This set is a good representation of official documents for pattern matching and has been extensively used in the literature [9; 23; 43]. There is also ground-truth information about this set, annotated by humans, publicly available on the web ([[http://www.umiacs.umd.edu/~zhuyg/tobacco800.html](http://www.umiacs.umd.edu/~zhuyg/tobacco800.html)]([http://www.umiacs.umd.edu/~zhuyg/tobacco800.html](http://www.umiacs.umd.edu/~zhuyg/tobacco800.html)) (accessed on 12 April 2024)).  
The first step of the algorithm, noise estimation, was performed on the Tobacco800 set. Figure 7 shows a graph with the estimated standard deviation of the noise for each image of the Tobacco800 set. The threshold, that is the median of the outlier values in this set, was 0.02. We consider this test set a good representative of real-world scans of documents, and we expect that this threshold can be extrapolated to other sets of real documents as well. As a result, the set was divided in two groups: 25 images where a noise-reduction step was applied (those with a \(\sigma_{n}\) greater than 0.02), and the rest of the 1267 images, who had no noise reduction (those with a \(\sigma_{n}\) smaller than 0.02). After this step, the regions of interest were extracted and the modified extended feature rectangle (FR) method was applied to each of them, giving as a result a set of logo candidates, regardless of the rotation or scaling, which would not affect the accuracy. The effectiveness of our heuristics is explained next.
# The Influence of Track Irregularity in Front of the Turnout on the Dynamic Performance of Vehicles
## 2 Numerical Approach
### Wheel-Rail Contact Model  
The vehicle subsystem and the turnout subsystem are connected by the wheel-rail contact model. The wheel-rail contact includes the identification of the contact patch and the calculation of the normal and tangential contact forces. In this work, a STRIPES-based method [31, 32] is used to calculate the number and location of the contact patches.  
The semi-Hertz contact theory [33] is adopted to calculate the normal force by calculating the normal wheel-rail equivalent penetration (Figure 5). The tangential contact force adopts the simplified Kalker theory, and the FASTSIM algorithm is used to calculate the nonlinear creep force [34]. The stresses in the semi-Hertzian method are:  
\[\left\{\begin{array}{ll}\sigma_{zi}(x,y_{i})=&\frac{4E(1+\lambda)}{3\pi n^{3 }\left(1-\
u^{2}\right)}\frac{h_{i}}{a_{i}}\left(1-\frac{x^{2}}{a_{i}^{2}} \right)k_{i}^{2}\\ \sigma_{xi}(x,y_{i})=&\frac{3}{8}GC_{11,i}\
u_{xi}\left(\frac{a_{i}-x}{a_{i}} \right)k_{i}\\ \sigma_{yi}(x,y_{i})=&\left(\frac{3}{8}GC_{22,i}\
u_{yi}+\frac{2}{\pi}\sqrt{ \frac{n}{m}}GG_{23,i}\varphi_{i}(a_{i}+x)\right)\times\left(\frac{a_{i}-x}{a_ {i}}\right)k_{i}\\ k_{i}=\frac{a_{i}}{B}\\ \lambda=\frac{A}{B}\end{array}\right. \tag{2}\]  
where \(\sigma_{zi}\) are the normal contact stress; \(\sigma_{xi}\) and \(\sigma_{yi}\) are the tangential contact stresses; \(E\) is the Young's modulus; \(h_{i}\) is the interpenetrations of the \(i\)th strip; \(m\) and \(n\) are Hertz parameters that depend on two relative curvature constants A and B; \(v\) is the [PERSON]'s ratio; \(G\) is the elastic shear modulus; \(C_{11,i}\), \(C_{22,i}\), and \(C_{23,i}\) are [PERSON]'s creep coefficients of the \(i\)th strip; \(v_{xi}\), \(v_{yi}\), and \(\varphi_{i}\) are the longitudinal, lateral, and spin creepage, respectively; \(a_{i}\) is the half-length of each strip; \(a\) is the semi-axes of the elliptical contact patch in the \(x\)-direction.  
Figure 4: Critical rail sections and interpolation simulation of turnout: the critical rail sections in (**a**) the switch panel; (**b**) the crossing panel; (**c**) the stock rail and switch rail model.  
The detailed derivation process of the normal and tangential contacts in the semi-Hertzian method can be found in Reference [35]. The calculated wheel-rail contact force can be laterally and vertically distributed in the vehicle-turnout system through the wheel-rail contact angle, thereby reflecting the coupling interaction between the vehicle and turnout [36].