esa-sceva/satcom-chunk-collection huggingface dataset:
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
###### Abstract
In the majority of these experiments, only the video component is processed and the audio component is left unimpaired [24]. Among the few studies that have explored the overall audio-visual quality of sequences with distortions in both audio and video components, we can cite the works of [PERSON] [19], [PERSON] [25]. More recently, the study presented by [PERSON] et al. [18] who conducted experiments in which the content had compression distortions in both audio and video components. Although their results confirmed the dominance of the video component on the overall quality, the audio component had a considerable impact on quality for some types of media content. In summary, although several studies explored how users perceive audio-visual quality in common multimedia scenarios, most of these studies ignore the audio component and the effect of audio distortions on the overall audio-visual quality. Additionally, the complex interactions of audio and video components and the different factors influencing the perception of quality have not been properly studied. This work presents a compilation of the results from three psychophysical experiments where participants assessed the overall quality of a diverse set of audio-visual sequences. All three experiments used the immersive methodology, including audio and video impairments. In Experiment 1, artifacts were added to the visual component, while the audio component remained as is. In Experiment 2, the visual component was kept untouched while artifacts were added to the audio component. Finally, in Experiment 3, both video and audio components were impaired by audio and visual artifacts. This paper performs a detailed analysis of the experiments and compares their results, binding conclusions and insights among all three experimental scenarios. Our main goal is to present a wider picture of the audio-visual quality of video sequences and attempt to drive global conclusions. The remainder of this document is divided as follows. In Section 2, a summary of some relevant multimedia perceptual experiments is presented. In Section 3, a description of the rationale behind the study and the experimental design is presented. In Section 4, visual and audio degradations are presented. In Section 5, experimental details are described. In Sections 6-8, all three experiments are described and their corresponding results are presented. Section 9 presents a comparison and a general discussion on the results from all three experiments. Finally, the overall conclusions of the study are presented in Section 10.
## 3 Results and Discussion
### Delamination-Buckling Simulation Results  
As mentioned previously, one of the main objectives of this study was to gain a better appreciation of the predictive capability of the described approaches in simulating a more complicated response. The interest was to determine whether the simulation techniques could capture the response of the 3D magnesium/FRP FML introduced earlier; specifically, when the FML is subjected to an in-plane impact loading, which would potentially cause delamination buckling of its magnesium skins. In this phase of the study, the MIXED model's response is compared to that of the COHESIVE model, qualitatively (i.e., by comparison of the delamination propagation paths) and quantitatively (by comparison of the resulting axial-load shortening curves). The models consider the presence of a delamination (crack) located at the mid-plane, through-the-thickness of the adhesive. In addition, the effect of two parameters on the delamination behavior is analyzed; the parameters are: (i) the through-thickness position of the delamination, and (ii) the ratio of the cohesive strength of the adhesive to the interfacial strength (i.e., the properties used in the xFEM and cohesive elements' material models).
## 2 Materials and Methods  
### Orbital Dynamics Model  
#### 2.1.1 General Model  
Taking the maneuver into account, the satellite's equations of motion in the Earth-centered inertial (ECI) coordinate system can be generally formulated as follows:  
\[\left\{\begin{array}{l}\frac{d\mathbf{r}}{dt}=\mathbf{v}\\ \frac{d\mathbf{v}}{dt}=-\frac{\mu}{r^{3}}\mathbf{r}+\mathbf{a}_{\text{NS}}+\mathbf{a}_{\text{3B }}+\mathbf{a}_{\text{AD}}+\mathbf{a}_{\text{SRP}}+\mathbf{a}_{\text{TD}}+\mathbf{a}_{\text{RE} }+\mathbf{a}_{\text{TH}}\end{array}\right., \tag{1}\]  
where \(\mathbf{r},\mathbf{v}\) are the position and velocity vectors of the satellite, \(\mathbf{r}\) is the magnitude of \(\mathbf{r}\), and \(\mu\) is the Earth's gravitational constant. \(-\frac{\mu}{r^{3}}\mathbf{r}\) denotes the central gravitational acceleration from the Earth, and indicates the thrust acceleration. The other terms of acceleration represent the perturbation ones, specifically, \(\mathbf{a}_{\text{NS}}\)--perturbation due to the Earth's non-sphericity, \(\mathbf{a}_{\text{3B}}\)--the gravitational attraction of the third body, such as the Sun and the Moon, \(\mathbf{a}_{\text{AD}}\)--atmospheric drag, \(\mathbf{a}_{\text{SRP}}\)--solar radiation pressure, \(\mathbf{a}_{\text{TD}}\)--tidal force, and \(\mathbf{a}_{\text{RE}}\)--relativistic effects. Given that maneuvers are usually executed in the local orbit frame, the thrust acceleration can be expressed by  
\[\mathbf{a}_{\text{TH}}=M_{RTN\to ECI}\left[\begin{array}{l}a_{R}\\ a_{T}\\ a_{N}\end{array}\right], \tag{2}\]where \(a_{R}\), \(a_{T}\), \(a_{N}\) are the radial, along-track, and cross-track components of the thrust acceleration in the radial-transverse-normal (RTN) frame, \(\mathbf{a_{RTN}}=[a_{R}\ a_{T}\ a_{N}]\). \(M_{RTN\to ECI}\) is the coordinate transformation matrix that maps vectors from the RTN frame to the ECI frame:  
\[M_{RTN\to ECI}=\left[\frac{\mathbf{r}}{|\mathbf{r}|^{\prime}},\ \frac{(\mathbf{r}\times\mathbf{v}) \times\mathbf{r}}{|(\mathbf{r}\times\mathbf{v})\times\mathbf{r}|^{\prime}},\ \frac{\mathbf{r}\times\mathbf{v}}{|(\mathbf{r}\times\mathbf{v})|}\right], \tag{3}\]  
where \(\mathbf{r}\), \(\mathbf{v}\) are column vectors with dimensions \(3\times 1\).
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion  
### Dataset  
To validate our automatic point of interest recognition and tracking pipeline, we have labeled one race, specifically the 2023 edition of _Omloop Het Nieuwsblad_. This Flemish race, situated around the Flemish Ardennes, includes several well-known segments such as the _Muur van Geraardsbergen_. Throughout the race, numerous other POIs, such as churches and windmills, are highlighted, making it a representative example for many other Belgian and European races.  
For this race, a total of 10 POIs have been annotated with their names, coordinates, reference images, start times, and end times. The reference images are internet-sourced, aiming to simulate a realistic usage scenario. However, as discussed in Section 3.1, using reference images from previous race editions might yield better recognition results. The ground truth start and end times are based on the broadcasted times of the POI in the final television broadcast, i.e., when the director switches to and from the helicopter view. Relying on the director's broadcast decisions ensures an objective way to validate the performance of the recognition pipeline in detecting POIs in advance and ensuring that the tracking does not fail prematurely. Note that we only use the final broadcast to determine these ground truth times; the recognition and tracking itself is still performed on the raw helicopter stream. This approach was preferred to manual labeling based on the helicopter stream, which would require subjective rules for determining when a POI is in view. Additionally, the inner bounding boxes of the POIs have been annotated at the start, middle, and end of their ground truth broadcast times. These annotations allow the  
Figure 9: Overview of the semi-automatic point of interest recognition and tracking methodology.  
evaluation of the used tracking algorithm, ensuring that it remains accurate throughout the broadcast duration. The inner bounding box is used instead of the outer to allow for a more relevant evaluation of the MedianFlow tracker, which relies on the inner bounding box.
## 4 Discussion
### Evaluation of Environmental Factors in the Field  
A similar PAR behavior to that observed during the day of our trial was found by [PERSON] et al. [5] in Nuevo Leon (Mexico). The differences in the PAR transmittance between the pilot greenhouses (Tables 10 and 11) and those obtained by a spectroradiometer in the laboratory (Table 1) may be due to the degradation and accumulation of dust on the covers and the deck angle [62], since when the analyses of the optical properties were performed with the spectroradiometer, the samples were cleaned before the analysis, while once placed in the greenhouses for field evaluation, they were fully exposed to degradation and dirt. According to [PERSON] [63], the accumulation of dust and dirt is one of the main sources of solar transmission loss in greenhouses. It can reduce it by up to 40%.  
The thermal inversion effect observed in November (Figure 7) under the yellow and colorless EPCs may be related to the higher transmissivity in the NIR region (94.55% and 86.93%, respectively) compared to the blue and grey EPCs (61.45% and 62.83%, respectively), which presented more opacity. Nevertheless, in Figure 8, it can be seen that there was a high relationship (R\({}^{2}\) = 0.95) between the % of NIR transmission and the maximum temperatures. Specifically, the improvement in the use of the blue and grey EPCs with respect to the decrease in the maximum temperatures was in the range of 6-9 \({}^{\circ}\)C. Related to the temperature inside the greenhouse, [PERSON] et al. [64] carried out a trial in the region of Murcia with anti-thermal covers, where they obtained a reduction in the maximum temperatures of 4.5 \({}^{\circ}\)C compared to a conventional colorless cover with similar characteristics to the colorless EPC used in this study. These differences may be because we worked in pilot greenhouses that did not contain any crops, and therefore the effect of the plastic films was more evident.
## 5 Discussion
This might be due to the necessity of substantial data volumes for accurately estimating trends in network events concerning nominal features. We also performed the Mann-Whitney U test to compare the temporal features between \(t_{s}=60\) s and \(t_{s}=120\) s. We found that the temporal features using WT and PE showed statistical significance (\(p<0.05\)). However, for the temporal features with one-hot encoded nominal variables, we could not find any statistical significance. By analyzing the January data using the Granger causality test, we found that 88.8% of the \(\mathcal{P}\) and \(\mathcal{E}\) features were significant for normal and attack events with the time scale (\(t_{s}=60\)). With the time scale (\(t_{s}=120\) s), 50% of the \(\mathcal{P}\) features and 88.8% of the \(\mathcal{E}\) features were significant for normal and attack events. We also found that all \(\mathcal{W}\) features were identified as significant for \(t_{s}=60\) s and \(t_{s}=120\) predicting normal and attack events. Overall, the time scale (\(t_{s}=60\) s) yielded better in evaluating the features and forecasting normal and attack events. By analyzing the \(\mathcal{N}\) features, we found that 'S2 (\(p=0.0029\)), 'ICMP (\(p=0.0026\)),' and 'SNMP (\(p=0.0005\))' were significant in predicting normal events. We observed that the variables (i.e., source and destination bytes) showed different results. In detail, the \(\mathcal{P}\) and \(\mathcal{W}\) features from the variables were significant in predicting network events. However, the \(\mathcal{A}\) features from the variables were not significant. We conducted an assessment of the temporal features using LSTM. Additionally, we compared two different embedding dimensions to determine an optimal dimension for analyzing the network data. While the optimal embedding dimension could not be determined clearly, the evaluation results showed different patterns depending on time scales and embedding dimensions. Specifically, with the time scale (\(t_{s}=60\) s) and the embedding dimension (\(e_{d}=4\)), we found better forecasting performances to predict normal events in eight of twelve months (66.7%) and attack events in nine out of twelve months (75%). However, when using the time scale (\(t_{s}=120\) s), we found that the embedding dimension (\(e_{d}=3\)) presented better forecasting performances for normal events (88.3%--ten months out of twelve) and attack events (58.3%--seven months out of twelve). These findings suggest a potential avenue for future research to explore the relationship between time scales and embedding dimensions.
## 3 Results
### Correlation and Regression Analyses
The Landsat 7 models generally yielded low slopes, except for the WLO location subcategory model (slope: 0.40, R\({}^{2}\): 0.38, RMSE: 1.5 \(\upmu\)g/L). Landsat 8 models had slopes between 0.11 and 0.43 and RMSE between 1.3 and 12.7 \(\upmu\)g/L. The best Landsat 8 model for WLO data yielded a slope of 0.43, RMSE of 1.4 \(\upmu\)g/L, and R\({}^{2}\) of 0.39. Sentinel-2 models generally outperformed those for the other satellites, with slopes from 0.14 to 0.54, RMSE from 1.2 to 7.0 \(\upmu\)g/L, and R\({}^{2}\) from 0.16 to 0.52. We attribute this to Sentinel-2's superior spatial, spectral, and radiometric resolution. Most models for Sentinel-2 were atmospherically corrected using ACOLITE or DOS1. For the subcategory OM, Sentinel-2 exhibited the best performance (slope: 0.52, RMSE: 1.2 \(\upmu\)g/L, R\({}^{2}\): 0.52) and hence provided the best option for the lower Chl-\(a\) concentration range due to its higher sensitivity (higher radiometric resolution) to variations in water colour.
## 2 State of the Art
### Layout Problem for Aerospace Systems  
The creation of a new spacecraft involves a series of steps, beginning with conceptual studies and culminating in its manufacturing [99]. At the conceptual stage, the focus is on evaluating different possible designs for the spacecraft's electrical and mechanical systems to select one that meets the mission's goals. Therefore, the optimal layout of aerospace systems is crucial, as it significantly influences the overall performance of the system [100].  
The pioneering works by [PERSON] et al. [101; 102] are among the earliest to propose numerical optimization methods for automating the equipment layout determination process in the conceptual phase of spacecraft design.  
Several papers have focused on optimizing the structural layout of aerospace systems [103; 104; 105; 106]. Moreover, a series of studies [107; 108; 106; 16] investigated the effectiveness of various numerical methods when applied to the optimization of the locations of a given set of components within telecommunication satellites. These studies primarily focused on positioning equipment based on the spacecraft's mass properties, such as the position of the mass center and the magnitude and direction of the principal axis of inertia, while adhering to geometric constraints. In [109; 110; 111], thermal considerations and the minimization of wiring between equipment were introduced as additional objectives in the exploration of candidate solutions within the design space.
# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 3 Materials and Methods
### Dataset Construction
Given that the joint configurations at the moment of collision differed for each collision point within the collision point template with the same spatial pose, in order to create clear distinctions, we collected data on the collision point template at different heights and different relative angular deviations between the charger and the charging port to represent the different joint configurations of the robot. The specific parameters are shown in Table 1. Here, the term 'height' refers to the relative height of the charging port compared to the robot base. Initially, data collection was conducted without any angular deviation between the charger and the charging port. We established a collision point template at intervals of 20 mm, collecting collision vibration data for each collision point within the template. To ensure the reliability of the data for a single collision point, five collision experiments were conducted under the same conditions, resulting in 1445 samples for a collision point template at a single height. Additionally, to enhance the generalization ability and stability of the trained model, collision samples from every set of three adjacent heights were aggregated to create a dataset. This process resulted in three distinct datasets, identified as C1, C2, and C3, each comprising 4335 samples. Furthermore, we considered cases where there was an angular deviation between the charger and the charging port. Due to limitations in the yaw angle precision of the experimental platform, experiments were conducted with angular deviations of \(1^{\circ},2^{\circ}\), and \(3^{\circ}\) based on the height of C1. The corresponding datasets were labeled as C4, C5, and C6, with each dataset containing the same number of samples as C1. Furthermore, to simulate the presence of noise, we introduced varying intensities of noise on the basis of C1-C6. The intensity of the noise was defined using the Signal-to-Noise Ratio (SNR). Specifically, we introduced additive Gaussian noise with SNR values of \(-9,-7,\ldots,9\). Figure 2: Illustration of the charging plug-in process.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 6 Conclusions  
We propose a decentralized trust evaluation framework for IoT networks using IOTA ledger technology. Our approach addresses the critical vulnerabilities of centralized trust models, such as single points of failure and privacy breaches. By decentralizing the trust assessment process and integrating both direct and indirect trust metrics, we ensure comprehensive and reliable evaluation of trustworthiness. The use of IOTA for storing trust values guarantees the immutability and security of trust data, preventing any node from transmitting fake values. Our experimental results demonstrate significant improvements in energy efficiency, scalability, and end-to-end delay compared to traditional centralized models. The decentralized nature of our framework enhances the overall security posture of IoT environments, making them resilient to various cyber-attacks. Additionally, the secure and immutable storage of trust scores ensures data integrity and confidentiality, which are paramount in maintaining the reliability of IoT systems.  
In future, this framework can be further strengthened through integration with emerging AI technologies, particularly large language models (LLMs) and edge intelligence. AI agents can dynamically adjust trust weights (\(\lambda,\rho,\omega_{1}-\omega_{3}\)) in real time based on contextual factors such as environmental conditions, device behavior, and threat detection. Additionally, LLMs could be leveraged for intelligent anomaly detection, trust decision explanations, and federated trust summarization across large IoT ecosystems. This synergy could pave the way for adaptive, explainable, and self-healing trust frameworks in future smart cities, autonomous systems, and decentralized AI infrastructures.  
Conceptualization, A.A.; Methodology, A.A.; Validation, K.C.; Writing--original draft, A.A.; Writing--review & editing, A.A.; Supervision, K.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article. The NS-3 simulation code used to generate the results is available from the authors upon request.  
The authors declare no conflicts of interest.
# SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
## 2 Materials and Methods
### Polysonnography  
An overnight hospital PSG (Embletta MPR PG(r) Natus Medical Incorporated. Pleasanton, CA, USA) confirmed the diagnosis of OSAS. Electrocardiograph, thoracabdominal movements, respiratory airflow, finger oxygen saturation, and body position, along with other standard indicators, were obtained. Results were classified according to the criteria of the American Academy of Sleep Medicine (AASM) for the Scoring of Sleep and Associated Events [11]. The severity of OSAS was categorized according to the Apnea Hypopnea Index (AHI) as follows: none/minimal (AHI \(<\) 5 per hour); mild (\(\geq\)5 AHI \(<\) 15 per hour); moderate (\(\geq\)15 AHI \(<\) 30 per hour); severe (\(\geq\)30 per hour).
## 2 Materials and Methods
### Self-Organizing Networks  
Usually, mobile network design requires three phases. First is network planning, taking in account certain aspects such as coverage, interference, quality of service, and frequency reusing. Second comes monitoring: using KPIs to evaluate the current state of the network. Those KPIs are analyzed in the final phase, optimization, changing parameters such as cell direction and intensity, antenna height, and radio parameters. This process its manual, expensive, and time consuming. In order to solve these problems, an automated technology named SON was designed in order to automatically plan, configure, manage, optimize, and heal mobile networks. This technology is characterized by a life cycle (Figure 2) composed of three phases:  
* **Self-configuration**: Following the idea of \"plug and play,\" this section is responsible for configuring a new node in a network, not only in connectivity but also for transferring the network parameters to that node.
* **Self-optimization**: Responsible for important factors in the network, such as topology, propagation, and interference (the one which is covered in this paper).
* **Self-healing**: In case some nodes fail. This section is responsible for warning the other nodes of that failure in order to reduce the impacts on the network's services.  
Figure 1: In the left, theoretical coverage; on the right, a real coverage example.
## 3 Results and Discussion
### Pollutant Disappearance Experiments
#### 3.1.4. Surfactant Degradation
Analysis of IR spectra of samples after degradation of isonomylphenol by _S. commune_ (Figure S4) reveals significant changes. The intensity of the bands characteristic for deformation vibrations of alkyl fragments at 1379-1456 cm\({}^{-1}\), as well as vibrations of aromatic rings close to 1500, 1600 cm\({}^{-1}\), is significantly reduced. The intensive absorption band at 830 cm\({}^{-1}\), which is attributed to the vibrations of disubstituted benzene rings, completely disappears. Simultaneously, there is a significant decrease in the intensity of antisymmetric and symmetric vibrations of both aliphatic and aromatic CH fragments in the region of 2960-2853 cm\({}^{-1}\). All this indicates the destruction, to a large extent, of the structure of both aromatic rings and alkyl substituents of isonomylphenol. Oxidative destruction of aromatic rings of isonomylphenol is confirmed by the appearance of weak absorption bands at 1727-1739 cm\({}^{-1}\), characteristic of the carbonyl groups of the carboxyl fragment. Subsequent use of GC revealed three metabolites of neonol degradation by the fungus _T. versicolor_ DSM with retention times of 5.50 to 5.76 min. Metabolite with retention time of 5.73 min was detected after degradation of isonomylphenol by _S. commune_. The presence of products with similar retention times suggests that the degradation of neonol _T. versicolor_ DSM and isonomylphenol _S. commune_ at a certain stage can pass along a similar path. pounds in soil biocenoses should be taken into account. It should be noted that oxidized derivatives of PAH are not only more easily available to soil microflora [72] but also are capable of impairing enzyme systems in plants [73].