esa-sceva/satcom-chunk-collection huggingface dataset:
# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## Materials and Methods
### Training
#### 2.3.1 YOLOv7-Pose  
At present, the overall human pose estimation is divided into Top-down and Bottom-up. In order to solve the problem of traditional key-point detection algorithms, whether based on heat-map or detector-based processing, which are more dependent on computing resources and take a little longer time, the YOLO-based key-point detection algorithms that have emerged in recent years have been proven to have obvious speed and accuracy improvements in human posture recognition [40].  
The latest YOLO-pose human pose estimation algorithm based on feature points [41] follows the bottom-up method [42; 43; 44]; that is, the exact location of the key points of the  
Figure 10: The result of key-point labeling and recognition box generation.  
Figure 9: Marker objects fixed on the bolt model.  
human body in the image is found through image post-processing, and then the key-points without the identity of each figure are classified in a single shot. Through YOLOv5's object detection framework, key points were combined with character anchor points, and the character was divided into a single instance. This method can estimate human posture without relying on the heat map.  
Based on the latest YOLO-pose human key-point detection algorithm, the YOLOv7-pose method adopted in this paper has two advantages:  
1. The optimization of the loss function for evaluating the prediction. The target keypoint similarity (OKS) is a common index to evaluate key points. L1 losses have traditionally been used to detect key points. However, L1 losses are not necessarily suitable for optimal OKS. Similarly, L1 losses do not take into account the scale of the target or the type of key points. Since heat map is a probabilistic map, it is not possible to use OKS as a loss in a pure heat map-based approach. OKS can be used as a loss function only when regressing to the key-point location. Therefore, the YOLOv7-pose uses scale-normalized L1 losses to perform key-point regression and obtain excellent accuracy.
2. The optimization of the input layer structure. The YOLOv7, relative to YOLOv5, innovatively uses a multi-input stacked convolution structure to optimize the feature extraction in both the backbone and the enhanced feature extraction part (head). Additionally, the number of feature layers is increased from 3 to 4 on YOLOv7-pose, which improves the feature extraction ability of the algorithm at different scales. Finally, the extracted 4 feature maps are classified and regressed using the IKeypoint detection head to obtain the locations of key points. The framework of the YOLOv7-pose is shown in Figure 11.  
In consideration of the above advantages, we chose YOLOv7-pose as the bolt key-point detection algorithm. Since the default human body detection framework of YOLOv7-pose  
Figure 11: The framework of YOLOv7-pose.  
uses 17 key points, in order to adapt it for the detection of bolts, we modified the algorithm with 6 key points.
It can only be applied to narrow indoor entrances, but not to commercial streets with wide outdoor entrances. At the same time, it is impossible to use the large number of front-down monocular cameras in the city. Compared with vertical downward stereo camera, front-down monocular camera has wider vision, and because it can see the positive human face, it can better take into account security, criminal investigation and abnormal behavior early warning tasks. In front-down cameras, the image plane is about 45 degrees from the ground. The occlusion between the targets in front-down cameras is not as serious as that in front cameras. At the same time, the scale difference between targets is relatively small. There is a negative correlation between the size of the pedestrian target and the placement height of the camera. However, due to the challenges of frequent occlusions, illumination changes, target scale changes, different fog concentrations in different distance and so on, pedestrian flow statistics algorithm based on front-down monocular camera puts forward higher requirements for detection and tracking algorithm. Thanks to the rapid progress of deep learning algorithm [6; 7; 8; 9; 10; 11], the accuracy of pedestrian detection is constantly improving. More accurate detection results make the performance of tracking-by-detection method reach a higher level [12], which makes it possible to develop a high-precision pedestrian flow statistics algorithm based on front-down monocular camera. In this paper, we propose a pedestrian flow tracking and statistics method based on front-down monocular camera. Firstly, we use convolutional neural network to detect pedestrians appearing in the camera, and modify the detection results based on intersection over union and aspect ratio. Secondly, we use Kalman filter to build uniform linear motion models for the detected pedestrians. Then pedestrian tracking is accomplished by data association algorithm. Finally, the virtual block method is used to count the target. We test the proposed algorithm using the real scenic spot entrance surveillance video, the F1 score of pedestrian flow statistics has reached 95%. At the same time, we compare the proposed multi-target tracking algorithm with other multi-target tracking algorithms on the 2 DMOT2015 dataset. The MOTA of the proposed algorithm reaches 48.1, and the algorithm has advantages in computing speed. The rest of this paper is organized as follows. Section 2 reviews the related work. The proposed algorithm is described in detail in Section 3. In Section 4, experiments and comparisons are carried out. Conclusion and analysis are presented in Section 5.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Effects of Agro-Wastes on RC Elements
#### 4.3.3 Ductility and Cracking Behavior  
Concrete beams produced with PO shells exhibit higher ductility ratios compared to conventional concrete beams, with an elastic modulus measuring only 25% of that of control concrete. Furthermore, it has been observed that PO shell concrete beams experience twice as many shear and flexural cracks as conventional beams, with closer crack spacing and narrower crack widths [69].  
Concrete beams incorporating CSs behave similarly to conventional concrete beams under torsion but exhibit slightly wider crack widths and less stiffness compared to traditional crushed stone aggregates [84]. The addition of steel fibers exceeding 0.5% significantly enhances the moment capacity and crack resistance of beam specimens [26].  
These findings indicate that AW materials can be effectively utilized to optimize the ductility and cracking behavior of concrete beams. For instance, studies on flax fiber-reinforced coconut fiber concrete beams show that while the inclusion of coconut fibers has a limited effect on flexural strength, it significantly improves ductility performance [85].  
The use of PO shells as lightweight aggregates has also been reported to enhance the strength and durability of concrete. Additionally, beams produced with PO shells demonstrate satisfactory flexural performance and positively influence stiffness and strength properties. Experimental results confirm that these beams exhibit good ductility behavior and maintain crack widths within acceptable limits [70; 74; 86].  
Palm kernel shells, used as coarse aggregates in structural concrete, also present a viable alternative. Beams made with palm kernel shell concrete perform better than conventional concrete beams in terms of cracking, reinforcement stress, and aggregate interlock. Despite having a lower fracture modulus, which may lead to early cracking, these beams exhibit narrower and more closely spaced cracks, along with high ductility [77; 78].
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 3 Method
### Data Augmentation  
In deep learning, CNN models need a considerable number of samples for training to obtain satisfactory results, or else there may be overfitting, and the model's generalization ability may drastically decrease. However, for certain actions, it is difficult to obtain a quantity of available and quality samples, so data augmentation methods are used to enlarge the dataset. Although many data augmentation methods are designed for images, such as scaling and cropping, they are not suitable for motion images. For motion images, the continuity of pixel semantics represents the consecutiveness of the motion information. A random scaling or cropping operation will destroy the authenticity of the spatial-temporal features of the actions and will inevitably result in incorrect recognition or classification.  
Therefore, a data augmentation strategy is proposed to generate skeleton samples and prevent the CNN model from overfitting. The strategy consists of mimicking a person performing an action with different speeds, mimicking a certain action that was executed by people of different sizes and mimicking a certain action that was conducted in different places in the field of view. The detail of the method is described as follows.
## 1 Introduction  
A valid database is important for every scientific study. In most cases, studying animal behaviour relies on animal observations to acquire this database. However, behavioural observations are highly time-consuming. Since the time used for observations is often restricted by practical considerations, different observation techniques are used to estimate the behaviour as effectively as possible by reducing the required analysis time [1]. For example, instead of using continuous sampling, which observes the animals continuously for a certain time-period, time sampling only observes the animals for portions of time with breaks in between. Here, the animals are still observed continuously but not for the complete time-period. Focal sampling observes only one animal continuously to facilitate the observation, and scan sampling periodically analyses snapshots after a determined time interval [2]. As each method has advantages and disadvantages, it is important to choose the appropriate method regarding the behaviour, situation and hypothesis being investigated to achieve a valid database. Thus, several studies have evaluated the validity of sampling methods of different behaviour. It has been shown that focal sampling can estimate behaviour correctly which do not differ significantly between the animals [3] and scan sampling is a very efficient method to estimate behaviour with a long duration such as resting behaviour [4; 5; 6; 7; 8]. For behaviour with a shorter duration, however, the time between intervals has to be adjusted, i.e., shortened, to ensure validity. Thus, it can be difficult to observe social interactions as this behaviour often varies between the animals and only has a short duration. For example, biting behaviour in pigs takes only a few seconds and is easily missed by scan sampling if the time interval between scans is too long. However, shortening the interval as far as to accurately estimate biting behaviour (e.g., scan sampling every 5 s) would be less efficient than using continuous sampling. Thus, time sampling or continuous sampling is recommended to observe short behaviour such as biting behaviour in pigs [5; 9; 10; 11; 12], although there is no recommendation regarding the required observation length. The observation time per day is often determined by practical factors and rarely exceeds 2 h per day [13; 14; 15; 16; 17; 18].  
Therefore, the aim of this study was to analyse the validity of different observation periods of different length comparing continuous sampling and time sampling on the basis of tail-biting behaviour and ear-biting behaviour of pigs.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 3 Simulation Scenarios
### Linear UWA Chain Full-Duplex Network
#### 3.1.1 Small Scale Scenarios  
The small scale scenarios are generally denoted as Small_L_H, where L and H are the pipeline length and the number of hops in the network, respectively. In the small scale scenarios, L varies between 2 and 20 km while, H varies from 2 to 20 hops. 2, 10 and 20 km pipelines configured with 2, 4, 10 and 20 hops are considered for the small scale scenario.
# Numerical Modeling of 3D Slopes with Weak Zones by Random Field and Finite Elements
## 3 Slope Stability Analysis  
Taking the Gangtou tunnel slope as the object of analysis, as shown in Figure 11, the position and shape of each section of the slope are quite different, and the distribution of weak zones inside the slope has spatial variability. The traditional two-dimensional model can only represent the rotation angle of the layer of weak zones of the weak zones in a certain section. However, it cannot reflect the real spatial distribution of the weak zones inside the slope.  
Figure 10: Schematic diagram of typical failure modes of slopes. (**a**) Plastic strain diagram of the slope with weak zones; (**b**) homogeneous slope plastic strain diagram.  
To illustrate the influence of the rotation angle of the layer of weak zones inside the slope on the stability and failure mechanism of the slope, weak zones with a rotation of 0-60\({}^{\circ}\) relative to the \(X\)-axis, \(Y\)-axis and \(Z\)-axis were generated in the 3D slope. The model was about 34 m long, 33 m high and 49 m wide. The number of finite element mesh elements was 399,611 and the number of nodes was 73669. The constraint boundaries around the model were normal bearing constraints, and the bottom of the slope was a complete constraint condition as is shown in Figure 12. Both weak zones and weathered flint strips dolomite are elastoplastic materials, and the Mohr-Coulomb yield criterion was adopted. The rock in the slope shown in Figure 11 is weathered flint-striped dolomite, and the soil in the weak zones is residual cohesive soil. The parameters are shown in Table 3.  
Figure 11: Typical failure mode of Gangtou tunnel slope engineering (the rock in the slope is weathered flint-striped dolomite, and the soil in the weak zones is residual cohesive soil).  
Figure 12: _Cont._
# Spectrum Demand Forecasting for IoT Services
## 5 Study Case Implementation: Colombia
### Spectrum Forecasting: Offered Traffic  
We then proceed to calculate the offered traffic per scenario according to Equation (4) using an average transmission rate, an average link spectral efficiency (obtained from each technology standard document), and an activity factor calculated as weighted average following information from [52]. Cellular technologies (including LPWANs) are also expressed in terms of area spectral efficiency to obtain offered traffic in bits per second per hectare (b/s/ha). While non-cellular technologies use only link spectral efficiency, to obtain offered traffic in bits per second (b/s, per gateway or sink node).
# AI-Driven Waste Management in Innovating Space Exploration
## 3 Traditional Waste Management Practices in Space
### Limited Storage Hampers Resource Recovery  
Conventional waste management techniques in space missions are limited by the finite storage capacity of spacecraft, affecting the choice of recyclable materials and the effectiveness of space trash systems. Space is scarce on missions, and spacecraft or space stations cannot store waste for long periods [60; 61; 62]. On the ISS, waste accumulates for months and relies on periodic resupply missions for removal, highlighting inefficiencies in current water recovery systems. This traditional approach works for low Earth orbit (LEO) missions, where waste can be returned to Earth regularly. However, innovative waste management solutions are essential for more extended missions to the Moon, Mars, or beyond, where resupply is less feasible [63; 64; 65]. These solutions must reduce or repurpose waste to minimise storage needs, especially as mission durations increase. Innovations like closed bio-regenerative life support systems, microbial bioreactors, and in situ resource utilisation are vital for extending mission capabilities by efficiently repurposing a wider range of waste materials.  
Traditional waste management in space also poses environmental risks, particularly with space debris. Discarding waste in space threatens satellite networks and the International Space Station, whether it burns up in Earth's atmosphere or remains in orbit. Uncontrolled disposal increases the risk of space debris, which can collide with operational satellites and spacecraft at high velocities. Over 23,000 pieces of debris larger than 10 cm are tracked in Earth's orbit, along with millions of smaller, untracked fragments that pose significant risks [66; 67].  
As space agencies and private companies pursue deeper missions to the Moon, Mars, and beyond, the inadequacies of traditional waste management methods become more evident. Limited storage, poor resource recovery, and environmental hazards associated with waste disposal highlight the need for innovative solutions to ensure the sustainability of long-duration and deep-space missions [68; 69]. By leveraging advanced technologies, space missions can minimise their environmental impact and enhance waste management capabilities in exploration.
## 3 Weather Data Feature Extraction
As shown in Figures 7-12, the three-layer convolutional neural network is used to extract the features of wind speed and wave height and extract the key spatio-temporal structure features layer by layer, the subgraphs a-o are respectively represented as the feature maps corresponding to 15 output channels. The first layer extracts local change patterns, such as wind and wave mutations or edge features; the second layer identifies the coupling relationship between wind speed and wave height and extracts the mesoscale evolution structure. The third layer aggregates higher-level spatio-temporal patterns, captures the dynamic characteristics of the overall wind and wave system, and provides deep-level feature support for subsequent tasks.
# Inhibition of Peroxidation Potential and Protein Oxidative Damage by Metal Mangifera Complexes
## 3 Results  
### HPLC-ESI-MS Analysis of Reaction Mixtures  
HPLC-ESI-MS analytical results of reaction products are shown in Table 1. The reaction of MF with Cu (II) and Zn (II) sulfates, with the same stoichiometric ratios (1:2), rendered a mixture of two different types of complexes at retention times (RT), 25.8 and 27.5 min and 26.2 and 28.8 min, respectively. However, the reaction of MF with sodium selenite, at the same ratio (1:2), rendered only one peak at RT 33.5 min. The reaction of MF with Zn (II) sulfate (ratio 1:3) rendered a mixture of three different types of complexes with RT 26.4, 28.6, and 41.4 min. The reaction of MF with sodium selenite (ratio 1:3) rendered two peaks at RT 33.3 and 44.5 min. The reaction of MF with Cu (II) sulfate (ratio 1:3) rendered the same results (two peaks) as with ratio 1:2 with similar RTs. Molecular ions \([\mathrm{M}-\mathrm{H}]^{-}\) of all chromatographic peaks indicated that three different types of complexes were obtained.  
Fragmentation patterns (MS1 and MS2) showed the typical fragmentation peaks of MF at \(m/z=421\), 331, 271, and 259 for all peaks, indicating that metal-MF complexes were formed. \([\mathrm{M}-\mathrm{H}]^{-}\) values of high-resolution mass spectra for Peak 2 were \(m/z=906.19\) for Cu (II) and \(m/z=908.03\) for Zn (II). Markedly, the reaction of MF with sodium selenite did not form a complex of such structure, probably because the orbital configuration of the Se atom. The isotopic peak clusters for the \([\mathrm{M}-\mathrm{H}]^{-}\) ions at \(m/z=906.19\) (69% relative abundance) and \(m/z=908.19\) (31%) for Cu (II)-MF complex, and \(m/z=908.03\) (48%), 910.05 (28%), and 912.05 (19%) for Zn (II)-MF complex, indicated the presence of these metal-MF complexes (Figure 1A,B) [22]. \([\mathrm{M}-\mathrm{H}]^{-}\) values of high-resolution mass spectra for peak 3 were \(m/z=904.18\) for Cu (II), \(m/z=906.01\) for Zn (II), and \(m/z=937.60\) for Se (IV). The isotopic peak clusters for the \([\mathrm{M}-\mathrm{H}]^{-}\) ions of Cu (II)-MF and Zn (II)-MF complexes with their relative abundances, as described before, indicated the metal coordination (Cu (II) or Zn (II)) to the ligand. In the case of Se (IV)-MF complex, the isotopic peak cluster for the [M-H] ions were more complex (five peaks). \([\mathrm{M}-\mathrm{H}]^{-}\) values of high-resolution mass spectra for Peak 4 were \(m/z=1389.68\) for Zn (II), and \(m/z=1448.84\) for Se (IV). The isotopic peak clusters for the \([\mathrm{M}-\mathrm{H}]^{-}\) ions of Zn (II)-MF as described before. The isotopic peak cluster for the of Se (IV)-MF complex \([\mathrm{M}-\mathrm{H}]^{-}\) ion (peak 4) at \(m/z=1448.84\) (50%), 1446.71 (24%), 1444.60 (9%), 1450.66 (9%), and 1445.62 (8%) indicated the presence of the Se (IV)-MF complex (Figure 1C).
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 3 Materials and Methods
### Source of the Data  
The Valencian Tourism Agency has deployed over 50 web cameras in the _Comunitat Valenciana_ since 2001, in a project sponsored by Tourism. These cameras are accessible on the tourist portal _Webcams de la Comunitat Valenciana_ ([[https://www.comunitatvalenciana.com/es/webcams](https://www.comunitatvalenciana.com/es/webcams)]([https://www.comunitatvalenciana.com/es/webcams](https://www.comunitatvalenciana.com/es/webcams)) (accessed on 24 November 2024)) and broadcast images of various destinations live throughout the day, offering users the opportunity to view the status of these places in real time. Strategically located in collaboration with municipalities and sector entrepreneurs, they allow users to follow events, festivities, and sports competitions.  
Specifically, the data used were obtained from the town of Morella ([[https://en.wikipedia.org/wiki/Morella_Spain](https://en.wikipedia.org/wiki/Morella_Spain)]([https://en.wikipedia.org/wiki/Morella_Spain](https://en.wikipedia.org/wiki/Morella_Spain)) (accessed on 24 November 2024)), Spain. The geographical location of Morella has been crucial throughout the centuries and historical events, which is why it is currently considered a population of high tourist value. The webcam located in this town is a static camera which broadcasts at a resolution of 1920 \(\times\) 1080 pixels, at a rate of 30 frames per second.  
Currently, the aforementioned camera has been upgraded and features different characteristics from the previous one. Because of this, the amount of data available for the study is limited (from 20 September 2023 to 15 October 2023). Therefore, as a solution, it is proposed to extend the series backwards using probability distributions, starting from the statistics of the original series. This extension allows initializing the anomaly detection models.