esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Localization
#### 3.1.2 Acoustic-Based Localization  
There are two primary strategies for acoustic-based UAV positioning: AOA and TDOA [97]. The AOA method has been introduced in the previous section, and in this section, we focus on the TDOA method. The TDOA-based sound source localization method is implemented using a microphone array to find the three-dimensional position of the sound source. The TDOA source estimation involves a two-step procedure. First, the time delay between pairs of microphones in the array is estimated using the generalized cross-correlation function. This function measures the time difference between the arrival of a sound wave at different microphones in the array. Once the time delays have been estimated, they can be used as input to an equation that relates the time delays to the source location. Inverting this equation yields an estimate of the source location. The accuracy of the estimation depends on the quantity and distribution of microphones in the array, as well as the characteristics of the sound wave being detected [98, 99]. The time complexity of modern TDOA-SSL methods is generally low for three-dimensional estimation of the source position, but multipath propagation and environmental noise can affect its positioning process. To address these challenges, some researchers have proposed using machine learning methods to limit these effects [100], while others have used bionic methods to improve accuracy [101].  
Acoustic-based UAV localization has a maximum detection range of about 100 m under the condition that the SNR is lower than \(-5\) dB [100]. However, for far-field conditions, the acoustic localization effect may not meet expectations. In such cases, RF-based localization can complement this issue. Therefore, the use of multiple technologies to compose an anti-UAV system is necessary to achieve effective UAV detection and tracking capabilities.  
Figure 2: The positioning model of AOA involves projecting the position of the UAV onto the ground using the AOA of the LOS (line of sight) path \(\beta_{i}\) selected by the \(i\)-th receiver. The height \(h_{i}\) of the UAV can then be calculated using the pitch angle \(\alpha_{i}\) of each receiver and the projected position _pos_ of the UAV on the ground. By averaging the height calculated from multiple receivers, the overall height of the UAV \(h\) can be obtained. Finally, the UAV’s space position _posd_ can be determined by combining the height \(h\) and the projected position _pos_ on the ground.
# A Novel LOS Decision Technique Reflecting 3D DTED for Modeling an Electronic Warfare Environment
## 3 LOS Decision Technique
### Proposed Method: Sign Counting
The \((x,y)\) points that result in the \(\max(D(x,y))\) are the most blocking points that result in NLOS. A summary of this algorithm is shown in Figure 6.  
If the DTED data is compared only in the extracted area by applying this sign-counting algorithm to determine whether it is LOS or not, the LOS can be determined in a much faster way than comparing the whole area.
# Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset
###### Abstract  
In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerabilitation.  
remote therapy; markerless; pose detection; motion capture; physical rehabilitation; OpenPose; telerehabilitation; telemedicine +
Footnote †: journal: Applied Sciences
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 6 Simulation Studies
### Analyses of KF RAIM
It is on the order of 0.02 m. That said, the contributions of all epoch measurements, including those before \(N\), to the mean position error bound must be taken into account for PL calculation. Next, \(\sqrt{\Gamma^{\prime}}\) defined before Equation (37) is plotted in Figure 11 from the saved lookup table against \(\sqrt{\lambda^{\max}_{\mathbf{\bar{\Theta}}}}/\lambda^{\min}_{\mathbf{\bar{ \Theta}}}\) with DOF as a parameter. \(\sqrt{\Gamma^{\prime}}\) for \(P^{alloc}_{MD}\) of both vertical and horizontal positions and dual fault modes is shown. For a given DOF, it levels off at a moderate eigenvalue ratio. Although the figure is plotted up to a ratio of 5000, the trend continues even afterwards. Similar patterns are also seen for the single fault mode. Thus, for a given DOF (or threshold) and \(\lambda^{\max}_{\mathbf{\bar{\Theta}}}\), \(\Gamma\) calculated from \(\Gamma^{\prime}\) using Equation (39) would not change significantly with large ratios. Therefore, a large \(\sqrt{\lambda^{\max}_{\mathbf{\bar{\Theta}}}}/\lambda^{\min}_{\mathbf{\bar{ \Theta}}}\) does not imply that the PL would increase to a great extent. Figure 9: Important parameters of KF RAIM. Figure 10: Mean VPE bounds corresponding to \(\alpha_{1}\) and \(\alpha_{2}\) after an initial transient period of 15 s. It should be noted that the sudden change around 350 s is caused by the change in FMS, not in \(\Gamma\). In what follows, fault detection performance with three different navigation filters is discussed.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies
### Impact of Processing on Vitamin D Levels
#### 6.1.1 Pasteurization  
Several studies have demonstrated that food processing, especially at high temperatures, alters the Vitamin D content of foods. Pasteurization, a common heat treatment used to extend the shelf life of dairy products, has been shown to cause a significant reduction in Vitamin D levels. [PERSON] et al. investigated the effects of pasteurization on Vitamin D compounds in donor breast milk. They found significant reductions in Vitamin D2 and D3 concentration, with losses ranging from 10% to 20% after pasteurization. This study highlights the sensitivity of Vitamin D compounds to the pasteurization process [103]. Similarly, another study by [PERSON] et al. observed a significant reduction in Vitamin D (56%) following high-temperature, short-time (HTST) pasteurization and ultra-high-temperature (UHT) processing [104]. As reported by [PERSON] et al. (2017), Vitamin D3 has poor thermal stability, which results in a significant reduction (\(p\leq 0.05\)) of about 50% in Vitamin D3 in pasteurized milk compared with raw milk [105]. The heat sensitivity of Vitamin D is a key factor contributing to its degradation during pasteurization. Moreover, Vitamin D is susceptible to thermal degradation, especially at elevated temperatures and  
Figure 2: The impact of various processing methods on Vitamin D3 levels in foods and fortification approaches, including direct addition, emulsification, encapsulation, UV irradiation, and microbial fermentation, which are crucial in enhancing the stability and bioavailability of Vitamin D3, thus improving overall nutritional quality.  
prolonged heating times. Research by [PERSON] et al. [106] elucidated the kinetics of Vitamin D degradation during heat treatment, showing that the degradation rate increases with rising temperatures. [PERSON] et al. have highlighted that the stability of Vitamin D in food matrices such as oil and mushrooms significantly decreases when subjected to higher cooking temperatures. Their research found that lower temperatures tend to preserve Vitamin D levels better than high temperatures, which can significantly accelerate the degradation process.
## 4 Experimental Design and Result Analysis
### Experimental Results and Analysis
#### 4.3.3 Model Generalization Verification  
To confirm the generalization prowess of the algorithm model researched in this paper, generalization experiments were conducted using the TWHD [23] (Two Wheeler Helmet Dataset) and EBHD [24] (Electric Bicycle Helmet Detection) datasets. The TWHD dataset was derived from the open-source OSF dataset, the bike helmet dataset, and images collected from the internet, totaling 5522 images. Specifically, 4710 images were randomly picked from the OSF dataset, and the leftover 738 images came from the bike helmet dataset and internet collections. The EBHD dataset was sourced from Alibaba Cloud's open data platform, totaling 629 images. Both datasets feature complex backgrounds and diverse environments, making them suitable for validating the effectiveness and generalization ability of the algorithm studied in this paper.  
The experimental findings are displayed in Table 5. In the TWHD and EBHD datasets, the performance of the model presented in this paper, in terms of metrics such as P, R, [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, and Params, is generally better than the original YOLOv8n model. This validates the generalization and versatility of the algorithm studied in the present research.  
Figure 8: Comparison of Improvement Effects.
# Service Interface Translation. An Interoperability Approach
## 4 Performance Evaluation
### Testbed Implementation  
Experimentation was conducted through a testbed. Davis Vantage Pro2 was the weather station device selected for the tests. The weather station was connected to a Raspberry Pi (Raspberry Pi 4 Model B) acting as a service provider in the Arrowhead local cloud. The connection between the Davis Vantage Pro2 and the Raspberry Pi was established via serial communication. The service consumer was executed in another Raspberry Pi. Finally, the local cloud mandatory core systems and the interface generator system operated on a laptop. The specifications of devices used in the testbed are shown in Table 3. All systems were connected via a local WLAN network.  
The application systems (provider and consumer) were implemented in Java Spring (available: [[https://github.com/CristinaPaniagua/WeatherStation_Scenario](https://github.com/CristinaPaniagua/WeatherStation_Scenario)]([https://github.com/[PERSON]/WeatherStation_Scenario](https://github.com/CristinaPaniagua/WeatherStation_Scenario)), accessed on 6 December 2021 ) following the Arrowhead framework general recommendations. The consumer interface generator was also implemented as a Java Spring project (available: [[https://github.com/[PERSON]/InterfaceTranslation-velocity](https://github.com/[PERSON]/InterfaceTranslation-velocity)]([https://github.com/[PERSON]/InterfaceTranslation-velocity](https://github.com/[PERSON]/InterfaceTranslation-velocity)), accessed on 6 December 2021) and acts as a support system in the local cloud. The generator is still a work-in-progress project, and the code is susceptible to change.
# A Comprehensive Review of Multimodal Analysis in Education
## 4 Applications in Education
### Educational Games and Simulation-Based Learning  
This category includes research that explores the integration of MMLA within interactive learning environments, such as digital games or simulations. The main aim is to create interesting learning experiences that get students to think more deeply and improve their performance. Multimodal data from gameplay, physiological sensors, or motion tracking are commonly used to assess learning progress, emotional reactions, and behavioural patterns during simulation-based activities.  
For example, [PERSON] et al. [11] implemented a scaffolded game-based learning science unit with immersive VR to enhance the acquisition of scientific knowledge in middle school students. Their study found that students using the immersive VR version demonstrated greater improvement in targeted science knowledge compared to those using a non-immersive desktop version, highlighting the benefits of whole-body movements and multimodal meaning-making for conceptual understanding. Similarly, [PERSON] and [PERSON] [12] designed an embodied augmented reality (AR) learning game for fourth-grade geometry education, integrating computer vision-based gesture recognition to support embodied learning of geometric concepts. Their results revealed significant cognitive gains in students' understanding of angles and shapes, demonstrating the potential of embodied interactions for enhancing spatial reasoning and engagement in mathematics.
# An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
###### Abstract  
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address this problem, in this paper, we first revisit the heteroscedastic modeling (HM) for image classification with noise labels. Rather than modeling all images in one fell swoop as in HM, we instead propose a novel framework that considers the noisy and clean samples separately for chest X-ray image classification. The proposed framework consists of a Gaussian Mixture Model-based noise detector and a Heteroscedastic Modeling-based noise-aware classification network, named GMM-HM. The noise detector is constructed to judge whether one sample is clean or noisy. The noise-aware classification network models the noisy and clean samples with heteroscedastic and homoscedastic hypotheses, respectively. Through building the correlations between the corrupted noisy samples, the GMM-HM is much more robust than HM, which uses only the homoscedastic hypothesis. Compared with HM, we show consistent improvements on the ChestX-ray2017 dataset with different levels of symmetric and asymmetric noise. Furthermore, we also conduct experiments on a real asymmetric noisy dataset, ChestX-ray14. The experimental results on ChestX-ray14 show the superiority of the proposed method.  
chest X-ray image classification; heteroscedastic modeling; label noise Article  
A Note: [[https://www.mdpi.com/journal/algorithms](https://www.mdpi.com/journal/algorithms)]([https://www.mdpi.com/journal/algorithms](https://www.mdpi.com/journal/algorithms))  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 3 Proposed Solution
### Design Considerations and Requirements  
This section highlights the design factors and requirements for developing our proposed communication solution, ensuring secure communication between Digital Twins and IIoT devices. These considerations are tailored specifically to address the resource limitations of IIoT devices. This section discusses these considerations and defines the in- and out-of-scope requirements.  
In our proposed solution, we carefully considered the limitations of IIoT devices. To address these constraints, we made specific design choices. Firstly, the scheme incorporates a lightweight application protocol tailored to the restricted resources of these devices. Secondly, the underlying cryptographic algorithm is selected based on a NIST-standardizedlightweight encryption algorithm. These choices ensure our solution is optimized for the efficient operation of IIoT devices, enhancing their overall performance and security. Our proposed solution is based on the following design choices that take into account the limited resources Industrial Internet of Things devices have:  
* The application protocol should be lightweight.
* The underlying cryptographic algorithm should be based on a lightweight, NIST-standardized encryption algorithm.
## 2 Related Works
### Field Testing  
For ICVs, renowned test sites include MCity [2] and AstaZero [3].  
Mcity operates the world's first purpose-built test facility for evaluating the performance and safety of Connected and Automated Vehicles (CAVs) and technologies under controlled and realistic conditions. Version 2.0 is a developing digital infrastructure that overlays the physical test facilities and enables remote access and utilisation.  
AstaZero is the world's first full-scale, independent test environment for future automated transport systems. In addition to research and development regarding active vehicle safety and AD, AstaZero's research also focuses on connectivity for vehicles, drones, and industrial applications.  
These comprehensive field testing solutions rely on controlled test sites, which can be costly and complex. Furthermore, they focus more on AD applications and overlook communication testing.  
Compared to urban road traffic scenarios, highways involve fewer types of traffic participants, making it easier to implement field deployment for communication testing. The Belgian highway testbed based on an edge-cloud architecture is one such example [16]. [PERSON] et al. [17] researched the impact of CAM message configurations in ITS-G5 and C-V2X on latency-sensitive C-ITS applications on this testbed.
# Design of a System for Verification of Automatic Guided Vehicle Routes Using Computer Emulation
## 2 Materials and Methods
### Integrating Modules into a Single Solution
\(t_{Ci}\)--time component of the \(i\)-th AGV at the intersection;
5. \(t_{Li}\)--loading-time component of the \(i\)-th AGV;
6. \(t_{Ui}\)--unloading-time component of the \(i\)-th AGV;
7. \(t_{Bi}\)--time component for blocking of the \(i\)-th AGV;
8. \(t_{Wi}\)--task/charging-waiting-time component of the \(i\)-th AGV;
9. \(T_{T}\)--total operating time (e.g., shift length);
10. \(x_{T}\)--average utilisation of all AGVs. Then the time utilisation of the individual \(i\)-th AGV \(x_{i}\) can be defined as:  
\[x_{i}=\frac{T_{T}-(t_{Ri}+t_{Ci}+t_{Li}+t_{Uii}+t_{Bi}+t_{Wi})}{T_{T}}*100\;[ \%] \tag{1}\]  
The following will apply to the average utilisation of all AGVs \(x_{T}\):  
\[x_{T}=\frac{\sum_{i=1}^{n}x_{i}}{n}\;[\%] \tag{2}\]  
By analogy, we can determine the percentages of all other time components. Percentage of \(i\)-th AGV driving time \(x_{Ri}\):  
\[x_{Ri}=\frac{T_{T}-(t_{Ci}+t_{Li}+t_{Uii}+t_{Bi}+t_{Wi})}{T_{T}}*100\;[\%] \tag{3}\]  
The following will apply to the average AGV driving percentage for all AGVs \(x_{R}\):  
\[x_{R}=\frac{\sum_{i=1}^{n}x_{Ri}}{n}\;[\%] \tag{4}\]  
Percentage of delay time at the intersection of the \(i\)-th AGV \(x_{Ci}\):  
\[x_{Ci}=\frac{T_{T}-(t_{Ri}+t_{Li}+t_{Uii}+t_{Bi}+t_{Wi})}{T_{T}}*100\;[\%] \tag{5}\]The following will apply to the average percentage of intersection times for all AGVs \(x_{C}\):  
\[x_{C}=\frac{\sum_{i=1}^{n}x_{Ci}}{n}\;[\%] \tag{6}\]  
Percentage of loading time of the \(i\)-th AGV \(x_{Li}\):  
Figure 8.0  
The following will apply to the average percentage of loading time for all AGVs \(x_{L}\):  
Figure 8.1  
Percentage of unloading time of the \(i\)-th AGV \(x_{Ui}\):  
Figure 8.2  
The following will apply to the average percentage of unloading time for all AGVs \(x_{Ui}\):  
Figure 8.3  
Percentage of \(i\)-th AGV blocking time \(x_{Bi}\):  
Figure 8.4  
The following will apply to the average blocking-time percentage for all AGVs \(x_{B}\):  
Figure 8.5  
Percentage of task-waiting/charging-time for the \(i\)-th AGV \(x_{Wi}\):  
Figure 8.6  
For the average percentage of task-wait/charge-time for all AGVs, the following will apply \(x_{W}\):  
Figure 8.7  
We can create a graph of the load based on the calculation of individual components of the capacity utilisation of logistical facilities.
# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 2 Related Works
### Deep-Learning-Based Methods  
Deep learning methods for vehicle smoke detection can be divided into object detection models and semantic segmentation models from their outputted results.  
For object detection models, [PERSON] et al. [16] proposed a two-stage model for vehicle smoke detection. The first stage is to locate the regions around exhaust pipes where the suspicious smoky region is; in the second stage, a multi-region convolutional tower network is utilized to determine the suspicious region further. [PERSON] et al. [17] applied an efficient spatial attention module in a convolutional neural network to recognize the smoky region. [PERSON] et al. [18] designed a lightweight network based on the YOLOv5 ([[https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)]([https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)), accessed on 1 July 2022) to detect vehicle smoke rapidly when the model is deployed on embedded devices.  
For semantic segmentation models, [PERSON] et al. [19] built a classification-assisted gated recurrent network with feature extraction, semantic segmentation, classification, and a fusion module for smoke semantic segmentation. In the segmentation module, they introduced attention convolutional gated recurrent units for learning the long-range context dependence of features. Additionally, a multi-scale context contrasted the local feature structure, and a dense pyramid pooling module was applied to improve the representation ability of the network. Inspired by fully convolutional networks (FCNs) [20], [PERSON] et al. [21] established a deep segmentation network constructed by two encoder-decoder FCNs with different skip structures which are for extracting global context information about the smoke and retaining fine spatial details of smoke, respectively. [PERSON] et al. [22] presented a dual-branch network composed of deep and shallow branches for vehicle smoke segmentation, where the deep branch is for global prediction, and the shallow branch is for spatial details. Moreover, a pyramid attention structure and skip modules were used in the network to expand the receiving range and integrate multi-scale features.