esa-sceva/satcom-chunk-collection huggingface dataset:
# Stabilization of an Unconventional Large Airship When Hovering
## 5 Numerical Results
### System Analysis in the Presence of Wind  
In this section, we consider two scenarios of the airship expecting acceleration due to a gust of wind pre-detected by a LIDAR. In one case, the wind is regular, and in the other, the wind is turbulent (sinusoidal). We will apply the combined feedforward/feedback control developed in order to minimize the drift of the airship under the effect of this gust of wind and thus be able to save a lot of energy necessary for the displacement of the airship due to the great inertia of the latter. This also helps to minimize the possible sway of a load suspended by the airship.  
The wind is supposed to arrive along the X axis and produce an average force of \(10^{4}\) N (see Figure 12).  
Figure 11: Swing angles of the propellers.  
Figure 12: Average and simulated wind forces.  
It can be seen in Figure 13 that the airship is subject to a large drift with respect to its equilibrium position if it is subjected solely to feedback control (red dash). However, with the application of the feedforward/feedback control previously developed, the reduction of this drift is well demonstrated, especially in the case of the use of the analytical algorithm of transformation of the controls to the actuators (blue dash).  
To make the experiment more realistic and to demonstrate the robustness of our algorithm, we have developed our control on the basis of a constant wind step with an intensity of \(10^{4}\) N, whereas, for the force applied to the airship, we allowed a sinusoidal variation of the wind intensity (see Figure 12). In spite of the relatively large difference between this applied force and the force on which the feedforward/feedback control was based, we note (in Figure 14) that our feedback control is efficient.  
Figure 14: Drift facing turbulent wind.  
Figure 13: Drift facing constant wind.  
The response of the actuators to the disturbance due to the wind is just as effective as in the previous scenario when the feedforward control is applied, in particular, when the analytical algorithm is used. The airship stabilizes quickly after having undergone low amplitude oscillations. This drift is even less when using the analytical control allocation algorithm.  
We must emphasize that the established control is not considered to be robust, and can suffer from modeling errors. We tested its robustness through the modification of the terms of the mass matrix which were increased by 50%. The results are presented in Figure 15. It is noted that the control no longer manages to stabilize the flying machine with such deviations in the modeling. Concretely, this control remains valid as long as the errors in the mass matrix do not exceed 30%.
## 3 Literature Classification and Analysis
### Dual Quaternion Algebra  
#### 3.3.1 DQ Robotics  
DQ Robotics is a computational library that uses dual quaternion algebra for robot modeling and control. Dual quaternion algebra has gained significant traction in robotics because of its capability to represent geometric transformations in a very compact and efficient way, which makes it into a very useful tool for both modeling and control applications. The dual quaternions can represent mathematical objects such as points, lines, planes, and rigid transformations easily. They emphasize that while dual quaternions enjoy some advantages, such as reduced computational complexity compared to homogeneous transformation matrices, their use has been rather limited due to a lack of efficient and user-friendly computational tools. Few existing libraries focus on quaternion algebra, let alone dual quaternion algebra. In this sense, the gap between theory and implementation must be bridged [33]. To fill this gap, the DQ Robotics library is proposed. It is suitable for self-study and education but at the same time computationally efficient for practical applications. DQ Robotics is implemented in Python (PyPI 23.4.0a61), MATLAB R2020b, and C++ 20, all sharing a common programming style that makes it easy to switch between languages. The library has been designed to resemble mathematical notation as much as possible to let the user to translate their thoughts directly into code [34]. The tutorial starts with an introduction to dual quaternion notation and basic operations, followed by robot modeling and control, and ends with a complete example of two robots collaborating on a task while avoiding obstacles. This tutorial will help provide practical information on how to use the library; code snippets are mainly in MATLAB but can also be applied in Python and C++ [35]. The object-oriented design of the library architecture is explained with simplified UML diagrams. The library provides a broad set of mathematical operations and methods for manipulating dual quaternions that makes it versatile for various robotic applications. It also supports the modeling of different types of robots, such as serial manipulators and mobile bases, and allows for the integration of these components into more complex robotic systems, such as mobile manipulators and bimanual systems. The library provides tools for both kinematic modeling and control, enabling users to compute forward kinematics and Jacobians and implement various motion controllers based on dual quaternion representations. The compatibility of the library with ROS (Robot Operating System) and V-REP--a versatile robot simulation framework--demonstrates the utility of the library in practical robotic scenarios, is presented. The performance of DQ Robotics through comparative benchmarks, showing its efficiency in dual quaternion computations compared to MATLAB and native Python implementations, is highlighted [36]. This will increase the acceptance and use of DQ-algebra, thereby significantly advancing robotic technologies and methodologies.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 1 Introduction
### Contributions.  
We make the following contributions in this work:  
**(i)**: We present an efficient unsupervised rPPG pipeline that can estimate heart-rate from RGB webcams. This method has the advantage that it does not require any rPPG specific training and it can perform its analysis with real-time speeds.
**(ii)**: Our method is able to time individual heart beats in the estimated pulse signal to compute heart rate variability. This body of work has received little attention, and we set the first benchmarks on multiple public datasets.
**(iii)**: We perform an in-depth HR and HRV evaluation on an exhaustive collection of 13 public and self-recorded datasets exploring a varied range of unique facets. We also demonstrate state-of-the-art level performance on six public datasets.  
Figure 1: An overview of the proposed heart rate and heart rate variability estimation pipeline (left to right). The face in captured webcam images are detected and modelled to track the skin pixels in region of interest. A single 1-D signal is extracted from the spatially averaged values of these pixels over time. In parallel, 3-D head movements are tracked and used to suppress motion noise. An FFT based wide and narrow band filtering process produces a clean pulse waveform from which peaks are detected. The inter-beat intervals obtained from these peaks are then filtered and used to compute heart rate and heart rate variability. The full analysis can be performed in real time on a CPU.  
**(iv)**: We introduce a new publicly available high frame-rate dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms under various subject conditions for HR and HRV analysis.
**(v)**: Lastly, we tackle the problem of noisy ground truth signals and the peak detector bias by releasing a collection of hand-cleaned heart-beat peaks for existing public datasets.
## 2 Principle
### RBFNN Nonlinear Equalization
The number of neurons is determined through parameter tuning, and a value that yields favorable training performance is selected. As shown in Figure 4, the training and testing results of the proposed RBFNN indicate fast convergence, achieving a low mean squared error within only seven iterations. Furthermore, the predicted values closely match the true values, demonstrating the effectiveness of the network's convergence. Figure 3: The construction of the RBFNN.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Preprocessing the Wi-Fi Data
#### 3.2.2 Unified Timestamp Format  
The timestamp is the key data required for obtaining traffic information at intersections. In practice, a large number of acquisition devices are used on-site, and the batches of the devices are inconsistent, which will lead to inconsistent timestamp formats. Therefore, the timestamp format is set to yyyy/MM/dd hh:mmss to ensure efficiency and accuracy in subsequent data processing.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 3 Data and Methods
### Method Used for the Simulation Study
#### 3.2.3 Evolution of Synthetic Surface Reflectances  
The evolution of synthetic surface reflectances is simulated over a sample vegetated site, whose BRDF parameters are assumed invariant with time. The rationale was to simulate temporal anomalies induced solely by variations of sun-viewing conditions, driven by the orbital drift. Yet, in order to approximate realistic temporal evolution of surface reflectances, a homogeneous, densely vegetated evergreen forest site was chosen as the region of interest. This site, located in the Amazon rainforest at \(-8.52^{\circ}\) latitude and \(-53.25^{\circ}\) longitude, shows spatial homogeneity at kilometric scale, stable phenology with minor seasonal fluctuations of the BRDF parameters and lack of major land cover changes in recent years. Both the spatial homogeneity, in a radius of 5 km around the site and the temporal stability for the considered time frame, were qualitatively verified via Google Earth, using the time slider and image browser provided as part of the tool.  
The BRDF parameters used for the simulation were derived from MODIS BRDF Albedo daily product Version 6 at 500 m resolution (MCD43A1.006) [32]. The product was accessed through the Google Earth Engine (GEE) platform [33]. The MODIS Albedo parameters for Bands 3, 1, 2 and 6 were used to simulate the BRDF in the overlapping Prob-V spectral bands, particularly for bands Blue, Red, NIR and SWIR. A 5-year average (2015-2020) of MODIS BRDF parameters in a region of \(5\times 5\) km centered on the region of interest was used to derive the parameters for the simulation, which are reported in Table 1. The rationale of the multi-annual and spatial averaging was to smooth out residual random uncertainties in the retrieval of the BRDF parameters in the original MODIS daily products at 500 m spatial resolution.  
The simulation considers the viewing angles at the center of each camera and focuses only in the principal plane, i.e., the plane containing the sun, the surface normal and the observer, where we have strongest BRDF variations. This condition provides a worst-case scenario to magnify the impact of the orbital drift. The evolution of surface reflectances for a given spectral band and a given camera is computed using Equation (4) and the BRDF parameters of Table 1.
#### Research Questions  
This study aims to systematically review the current use of VR exergames targeted at the elderly population, focusing on how these games enhance physical health, including balance, flexibility, and muscle strength, as well as cognitive functions such as memory and attention. Additionally, this study evaluates the effectiveness of these games in practical applications. Through these analyses, the research seeks not only to provide theoretical support for the design and application of future VR exergames but also to explore ways to effectively integrate them into community care and home-based rehabilitation, promoting the holistic well-being of older adults and advancing the goal of healthy aging.  
RQ1: What types of VR exergames are currently used to improve the physical and cognitive health of older adults?  
RQ2: How effective are VR exergames in enhancing the physical and cognitive well-being of older adults?
## 4 Simulation Results
### Case Study 2: Time-Varying Demand  
In this case study, the focus is on the performance of the proposed method under time-varying loading conditions. The variable load demands of one day, shown in Figure 6a, are taken into consideration. The scheduling cycle is set to one hour. The initial values can be obtained by Equation (13). Then, the proposed method is used to calculate the optimal solution. From the results shown in Figure 6b-d, it can be observed that the system can automatically converge to new solutions at each scheduling cycle. Both the estimated electrical and heat power incremental cost are modified following the new demand, the estimated electrical and heat power mismatches also converge to zeros meeting the supply-demand balance, and all the electrical and heat power outputs are within their corresponding feasible range. Therefore, this case study verifies that the proposed method can automatically adjust to meet the time-varying loading conditions.  
Figure 5: Case study 1, comparison with centralized scheme: (**a**) estimated electrical and heat incremental cost; (**b**) estimated electrical and heat power mismatch; and (**c**) electrical and heat power outputs.  
Figure 6: \(Cont.\)
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 5 Discussion  
The MLP model displayed better accuracies than other ML models; nonetheless, it required more time to complete its training process. However, it should be noted that its time performance was in the same order of that of the other models when doing inferences as tested during our experimentation. This is important for a real application in the industry because it is possible to adjust the transceiver's parameters and location inside the workshop with the aim to increase the reliability of the information sent by the ANDON towers to the coordinator node. Additionally, reliable metrics of LQE could be used to evaluate the configuration of a sensor node that works in a real environment. We considered that a PRR less than 0.89 is not acceptable for this application, and we selected LQE classes between 0.89 and 1. If LQE was equal to 1, then the alarm signal sent by the ANDON tower reached higher reliability levels. We used a novel dataset comprised by different input data types, collected from the transceiver setting option and experimental measurements. However, some input features to MLP model were restricted to the parameters allowed  
Figure 14: Comparison of training and estimation time for the MLP model against other machine learning models.  
Figure 15: Comparison of the accuracy achieved for the test data by the MLP model against other machine learning models.  
by the transceiver and physical constraints of our setup. As a future work, we suggest collecting more data from larger distances and payload measurements.  
The alarm signal used in conjunction with LQE is a novel application for wireless ANDON towers, with the potential to ameliorate problems faced by factories located in developing countries through the incorporation of new technology during transition into the Industry 4.0 paradigm.
## 4 Evaluation Experiment: Shadow and Caustics
### Preliminary Experiment
#### 4.1.2 Results and Discussion  
The results of the experiment are shown in Figure 4, in which (A) and (B) show the results for the glass sphere and diamond, respectively. Each comparison result was subjected to a \(\chi^{2}\) goodness-of-fit test using a \(p\)-value (\(\alpha(0.05)=0.017,\alpha(0.01)=0.003\)) corrected using the Bonferroni method.  
The results revealed that \"shadow + caustics\" had a significantly higher response rate than \"no shadow\" for all questions, regardless of the floor pattern and the shape of the transparent object. For the checker pattern-and-diamond combination, \"caustics\" had the highest response rate for questions 1 and 2. Additionally, the proposed method had a higher response rate for most of the questions, although there was no significant difference between \"shadow + caustics\" and \"caustics.\"  
The experimental results suggest that even pseudo-optical phenomena produced as illusions using the proposed method can be perceived as the shadow and caustics of transparent objects. However, the results also suggest that the magnitude of the effect produced by our method might be dependent on the floor pattern and object shape. Specifically, the effect of the proposed method was greater for wood grains with a uniform pattern and less for checker patterns with a complex pattern. This might be attributable to the fact that the shadow inducer becomes conspicuous due to the misalignment of high-frequency patterns. In terms of virtual transparent object selection, the effect obtained by the glass sphere was greater than that obtained by the diamond. This is consistent with some participants' comments, in which they stated that they could not perceive the shadow cast by the real diamond. Therefore, the effect of the proposed method might have been diminished in the environment comprising a diamond shape and checkered pattern.
# An Assessment of the Impact of Gypsum Deposit Development on Changes in the Radiation Environment
## 2 Materials and Methods
### Analytical Procedures
The calibration and quality control of gamma-spectrometric measurements were carried out by using measurements of volumetric activity--Marinelli vessels of different densities (1 L) (RITVERTS, Russia-Germany). Each reference source of radionuclides had a calibration certificate. The sample acquisition time was from 7000 to 10,000 s.  
In addition to determining the activity concentrations of radionuclides, pH and the content of carbonates, organic matter and water-soluble salts were also determined in the bottom sediments, according to the method described in [29] (see Supplementary Materials, Texts S2 and S3). The granulometric composition of the bottom sediments was determined by using an AS 200 Control vibrating sieve machine (Retsch GmbH Germany, Genprice, San Jose, CA, USA) with a set of sieves from 45 \(\upmu\)m to 2 mm. Area Measurements of Ambient Dose Equivalent Rate (ADER) of Gamma Radiation and Measurements of Radon Flux Density (RFD) in Soil Air  
In August 2024, an expedition was conducted to comprehensively study the radiation background of the area in the immediate vicinity of the gypsum deposit (Figure 2b). The study included the following experiments. We conducted a gamma survey of the mining allotment area, as well as adjacent areas at a distance of up to 300 m. To determine the ADER [30], a dosimeter-radiometer (MKS-AT1117M) with a BOI information processing unit and a BDKG-11 detection unit (NPUP \"ATOMTEKH\", Minsk, Belarus); ambient dose equivalent rate measurement ranges from 0.01 to 100 \(\upmu\)Sv h\({}^{-1}\); limits of permissible basic relative error \(\pm\)20%) was used. The gamma dose rate measurements in air were performed at a height of 0.1 m above the ground [31; 32]. A lightweight tripod made of aluminum was used to support the dosimeter during the measurements. Due to the low weight of such tripods, their effect on gamma radiation is considered to be insignificant, and the measurement results can be directly used to calculate the effective dose for mining workers [33; 34]. A total of 229 ADER measurements were performed (Table S5). Determination of RFD on the North-South profile. The ADER often correlates with another exposure source, radon. ADER data were used to predict the radon flux from soil [35; 36; 37; 32]. For field measurements of RFD, we used the Measuring Complex for Monitoring Radon, Thoron and Their Daughter Products \"Alfarad Plus\"--AR (manufacturer: Manufacturing Company \"NTM-Zashita\", Russia) with an autonomous air blower, filter-driers and a collection chamber. The measurement range of radon flux density from the soil surface is from 20 to 1000 mBq (m\({}^{2}\)s)\({}^{-1}\). The limit of permissible relative error in measuring the \({}^{222}\)Rn flux density from the soil surface is no more than \(\pm\)30%. A total of 14 radon flux density measurements in soil air were performed.
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 3 Experiment
### Experimental Dataset Creation and Analysis
Given the diminutive scale of the small targets, the resultant detections were cropped to showcase only the localized areas encompassing the targets. Visible light images convey more information about the target under favorable lighting conditions. However, there are still many things that could be improved by relying on visible images for small target detection. Figure 12c shows the case of using only visible light images as input to the network. Visible light images possess non-negligible advantages, but the fifth and sixth images show missed and wrong detection. So, visible light images have non-negligible drawbacks in poor lighting conditions, such as at night. Figure 12d shows the use of SLBAF-Net with both visible and infrared images as inputs to the network. SLBAF-Net effectively improves the network's performance in detecting small targets at night. However, as shown in the first figure, the target detection accuracy is unsatisfactory. The third result plot in Figure 12d shows that the confidence level of correctly detecting the target as a key is 0.34 and 0.55, respectively, which is a decrease of 0.47 and an increase of  
Figure 12: Sample diagram of detection results: (**a**) original infrared image; (**b**) original visible light image; (**c**) YOLOv5—input visible light image; (**d**) SLBAF-Net—input visible light image and infrared image; (**e**) YOLOv5—input visible light image and infrared image; (**f**) proposed—input visible light image and infrared image. 0.19, respectively, when compared to the third result plot in Figure 12c. Figure 12e shows the use of YOLOv5m with both visible and infrared images as inputs to the network. The first and fourth images both show missed detection. The third image shows the phenomenon of wrong detection. Moreover, the detection accuracy is less satisfactory than SLBAF-Net. Figure 12f shows the result images using the method proposed. Based on reducing wrong and missed detection, it effectively improves the accuracy of network detection of small targets. The third result plot in Figure 12f shows that the confidence level of correctly detecting the target as a key is 0.68 and 0.89, respectively, which are increased by 0.34 compared to the third result plot in Figure 12d. Further, it improves the detection accuracy of small targets at night.