esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 5 Conclusions  
In this work, a real-time conveyor detection method based on the multi-scale feature fusion network is developed, which can realize remote on-line monitoring of conveyor belt operation and avoid the interference of a harsh and complex environment. The standard convolutions are replaced with the depthwise separable convolutions, which have 8x less computational cost. Meanwhile, a new weighted loss function is designed to optimize the network, which improves the pixel accuracy from 53.32% to 78.92%. Compared with the other three representative networks, the effectiveness of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Pixel Accuracy** & **Testing Accuracy** & **Testing Speed** & **Training Time** \\  & **/\%** & **/mm** & **/FPS** & **/h** \\ \hline FCN-8s & 34.58 & - & 2.2 & 2.43 \\ Proposed method & **78.92** & **3.2** & **13.4** & **3.07** \\ UNet & 69.51 & 7 & 2.1 & 3.52 \\ Deeplab v3 & 71.44 & 4.6 & 1.3 & 8.74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance indicators.  
proposed method is verified, while the processing speed of 13.4 FPS can be achieved, and the real-time performance can meet the requirements of various production scenarios. The final test error is less than 3.2 mm, which meets the application requirements of the industrial field. In the future, we will continue to improve our algorithms for superior performance.  
[PERSON] provided the data and conceived the idea; [PERSON] designed the algorithm, carried out the experiments and wrote the paper; [PERSON] provided good ideas and revised it critically for important intellectual contents. All the authors have approved the final version to be published.  
This work was supported by the Natural Science Foundation of Beijing Municipality (No. 4182038), the National Natural Science Foundation of China (No. 61671054), the Fundamental Research Funds for the China Central Universities of USTB (FRF-DF-19-002), and the Open Project Program of the National Laboratory of Pattern Recognition (No. 201800027).  
The authors declare no conflict of interest.
## 5 Conclusions  
This study designed an active support system for a 4m SiC primary mirror, which could effectively reduce the surface error caused by various loads on the primary mirror and improve the imaging quality of the telescope. Firstly, the principle and structure of the designed system were explained. Then, a method for calculating the correction force through bending modes was derived, and the primary mirror pose correction based on the six-hardpoint positioning mechanism was verified. In response to the problem of protrusions and dents on the primary mirror surface caused by the force on the hardpoints, a method to compensate for the force on the hardpoints was proposed, reducing the rms of the corrected primary mirror surface from 50.4 nm to 12.4 nm. To achieve better results in the surface shape correction based on the bending modes, the relationship between the order of the bending modes used in the surface shape correction and the correction effect was studied. The results showed that using the first 30 orders of bending modes could effectively reduce the amplitude of the correction force and ensure a good surface shape accuracy. Finally, a performance evaluation was conducted on the designed system. We used the first 30 orders of bending modes to correct the deformation of the primary mirror under gravity, thermal load, and wind load. Under these three working conditions, the primary mirror surface error met the criterion of rms \(<\)\(\lambda\)/30 (\(\lambda\) = 632.8 nm) after the correction. In summary, the designed active support system could meet the surface shape accuracy index of the primary mirror. This study not only verified the application of active optics technology based on the eigenmode in large-aperture SiC mirrors, but also improved the relevant theoretical research of active optics and bending modes. The research has innovation and engineering significance.  
There are two main aspects for future work planning. We plan to study the impact of dynamic wind loads on the primary mirror surface shape in a future work and to attempt to correct the low-frequency components. In addition, we also plan to conduct research on the thermal control system of the 4m SiC primary mirror.  
Conceptualization, Z.Y.; methodology, Z.Y. and X.W.; software, Z.Y.; validation, Z.Y., X.W. and F.W.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y., X.W. and F.W.; supervision, F.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 12133009).  
Not applicable.  
All data included in this study are available upon request through contact with the corresponding author.  
The authors declare no conflict of interest.  
Figure 26: Zernike distributions of surface before and after correction (\(\theta\) = 0\({}^{\circ}\)).
## 2 Background
### Related Work  
There are many reports in the literature concerning either \"computational auditory scene analysis\" (CASA) [29] or \"acoustic scene classification\" (ASC) [30; 31]. The aim of CASA is to isolate individual audio objects within a complex scene, whereas the purpose of ASC is to identify an acoustic environment wherein a given auditory event took place. Therefore, despite their nomenclature similarity, such studies are fundamentally different compared to the work presented in this paper, which is focused on the classification of the spatial audio scenes.  
[PERSON] et al. [32] recently published a study investigating the subjective classification of complex auditory scenes in spatial audio recordings reproduced using a multichannel loudspeaker system. In contrast to this work, their research aim was to characterize audio objects \"inside\"sound scenes (within-scene categorization) rather than to perform a differentiation between scenes (between-scene classification).  
The purpose of many listening tests involving binaural hearing, reported in the literature, has often been limited to the localization of individual audio sources [8; 16; 18; 22]. They are normally undertaken under highly controlled laboratory conditions. To maintain the ecological validity of the experiments, in this study the listening tests were carried out under more realistic conditions, similar to those encountered by typical listeners while auditioning audio recordings from the Internet. To the best of the authors' knowledge, no large-scale listening test (involving a large group of listeners) has been conducted regarding the perception of spatial scenes exhibited by binaural recordings under ecologically valid conditions.
# Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
###### Abstract  
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.  
Keywords:object detection; YOLOv5; CNNs; vehicle detection +
Footnote †: 2021: 14.11. [[https://doi.org/10.3390/41400114](https://doi.org/10.3390/41400114)]([https://doi.org/10.3390/41400114](https://doi.org/10.3390/41400114))
## 3 Application to Immersed Tunnel  
### Project Overview  
The Hong Kong-Zhuhai-Macao (HZM) Bridge, which spans the Lingding Bay in Pearl River Estuary, is a large-scale cross-sea passage connecting Hong Kong, Zhuhai City of Guangdong Province, and Macao in China. However, the Dangan Islands area outside the Pearl River Estuary is located at the intersection of the Littoral Fault Zone and the NW Pearl River Estuary Fault Zone, which is a potential source area of strong earthquakes [38].  
Figure 5: Displacement of half-space subjected to SV wave incidence at 30\({}^{\circ}\) (unit: m): (**a**) \(t=0.46\) s; (**b**) \(t=0.88\) s; (**c**) \(t=1.13\) s; (**d**) \(t=1.45\) s.  
Figure 6: Time history of the displacement of plane SV wave incidence at 30\({}^{\circ}\): (**a**) the center point of the bottom; (**b**) the center point of the top.  
The seismic safety evaluation report of the HZM Bridge engineering site also classifies the area as a Chinese basic seismic intensity zone of VIII. Hence, it is necessary to conduct seismic analysis on the HZM immersed tunnel.  
The main project of the HZM Bridge is about 29.6 km long, and the length of the submarine immersed tunnel is about 5990 m, consisting of 33 sections. Each section is assembled with five or eight segments, for a total of 252 segments. The length of each standard section is 180 m, with the maximum buried depth being about 43.6 m. The length, width, height, and lining thickness of the segment are 22.5 m, 37.95 m, 11.4 m, and 1.5 m, respectively. Horizontal shear keys are arranged on the roof and floor of immersed tunnel. The length of horizontal shear key is 2.5 m, the height is 0.8 m, and the extension length is 0.71 m. Vertical shear keys are arranged on the side wall and middle wall, where the height of the side-wall shear key is 2.5 m, the height of the middle-wall shear key is 2.1 m, and the width and extension length of the vertical shear key are 0.8 m and 0.6 m, respectively (Figure 7).
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 1 Introduction
This integration enhances feature extraction by capturing both spatial and temporal information and improving the model's sensitivity to important features. * **Feature Fusion for Improved Classification:** The features extracted from the GSTCAN for skeleton and motion data, as well as Bi-LSTM-CA branches for sensor data, are fused and passed through a fully connected layer for classification. This fusion allows the system to leverage complementary information from both streams, improving the overall understanding of human motion and increasing fall detection accuracy. * **State-of-the-Art Performance:** The proposed system was rigorously evaluated on the Fall Up and UR Fall datasets, achieving a classification accuracy of 99.09% and 99.32%, respectively, significantly outperforming existing methods. This demonstratesthe system's robust performance and its potential for real-time fall detection and continuous healthcare monitoring. The paper code and description are available at the following link: [[https://github.com/musaru/Fall_Multimodal/tree/main/Multimodal_Fall3](https://github.com/musaru/Fall_Multimodal/tree/main/Multimodal_Fall3)]([https://github.com/musaru/Fall_Multimodal/tree/main/Multimodal_Fall3](https://github.com/musaru/Fall_Multimodal/tree/main/Multimodal_Fall3)) (accessed on 9 April 2025). The remainder of the paper is divided into sections. Section 2 describes the literature review, Section 3 describes the datasets used, Section 4 details the proposed system, and Section 5 details experimental results and discussion. Finally, the paper concludes with a conclusion in Section 6 and future directions.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 3 Resistance of Concrete to External Chloride in the Presence of Sulphate
### Effect of sulphate on Chloride Diffusion  
Table 4 shows data from different studies [15; 76] using pure sodium chloride solution and a combined sodium chloride-sulphate solution. Non-steady-state chloride diffusion in plain CEM I and slag-blended mortars decreased due to the presence of sulphate in the attacking solution. As discussed earlier, chloride penetration is reduced due to the preferential reaction of sulphate ions with the aluminates in hydrated cements to form ettrinigite, thus, filling pores. This condition may not hold for longer-term exposure, as more ettrinigite formation may lead to expansion and possibly cracking, which, in turn, can increase chloride penetration at later periods [15]. This finding is supported in other studies, which showed lower chloride diffusion in combined solutions than pure chloride solutions [16; 143]. The slag-blended cement in [16] contained 50% slag compared to 30% slag used in Ukpata and Ogirigbo. Nevertheless, the trends of chloride diffusion from pure to combined salt solutions remained similar.  
On the contrary, [PERSON] [144] reported that the presence of sulphate in a combined solution of chloride and sulphate accelerated chloride diffusion in the short term but inhibited chloride diffusion in the long term. These changes may be explained by hydration dynamics and the resultant pore filling, which may inhibit diffusion in the long term [145]. This filling effect was confirmed by [PERSON] [146]. [PERSON] [147] also concluded that the corrosion of iron was aggravated in the presence of sulphate. Still, there are a number of recent studies concerning simultaneous attacks on concrete or mortars by combined chloride and sulphate solution, which did not consider the impacts of chloride and sulphate on each other [148; 149; 150; 151].
## 3 Sublook Generation and Temporal Sublook Coherence Evaluation
Figure 7: (**a**) TSC; (**b**) mean coherence; and (**c**) \(D_{A}\) maps over El Forn de Canillo test site for the GB-SAR dataset. Figure 8: (**a**) TSC; (**b**) mean coherence; and (**c**) \(D_{A}\) maps over El Forn de Canillo test site for the sliding Spotlight TerraSAR-X dataset. The figures on the right represent a zoom of the area highlighted with a white rectangle in the figures on the left. As seen before, El Forn de Canillo is mainly a vegetated area only containing a few man-made structures and rocky areas suitable for the PSI processing. In general, the highest quality points detected from all of the estimators correspond with these areas (reddish hue regions). All estimators follow the road, the buildings and the rocky and bare surfaces present in the area of interest for both sensors. Going into further detail, some considerations deserve special attention. Notice how Figures 7b and 8b evidence the loss of spatial resolution when employing the mean coherence approach. Despite the reduced ML factor employed (\(5\times 5\)), the resolution loss is evident for both sensors. This fact will lead to a significant loss of pixels and details in the later PSI processing, as shown later. Despite this negative aspect, notice how the ML carried out during the coherence estimation reduces the interferometric phase noise, and this fact is translated into a better identification of the persistent scatterers available with respect to the other estimators, which work at full resolution. Due to the intrinsic low-pass filtering related with the ML carried out during the coherence estimation, this approach shows the \"cleanest\" results. However, this does not mean that some high-quality points surrounded by non-coherent clutter have been lost. Furthermore, Figures 7c and 8c show how the \(D_{A}\) estimator exhibits the worse performance in terms of detection capabilities. In general terms, the \(D_{A}\) estimator presents low values on the man-made structures, as it should be, but also on some low-quality vegetated regions, if compared with the TSC approach. This fact is more noticeable in the TerraSAR-X dataset, where the road in Figure 8c is hardly recognizable, presenting surrounding pixels with low \(D_{A}\) values. Notice how the TSC estimator better preserves the details and presents a better contrast between high-quality and low quality areas. As seen hereinafter, this fact will prevent the inclusion of outliers if the pixel selection thresholds are relaxed in order to improve the pixels' density. This strategy is typically employed to obtain a sufficient network of pixels for reliable PSI processing when the number of high-quality pixels is low, as is the case of this work.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
###### Abstract
The following are the main contributions of this research paper:  
* Collecting real-time data on undesirable plants in wheat crops, which are then used as input data for deep learning models after pre-processing. * Efficiently raining deep learning-based models for weed detection so that they can later be used in real-time detection models. * Providing a comparative inference time in addition to detection accuracy, which suggests the suitability of the specific model in terms of inference time as well. This paper is organized as follows. Section 2 discusses the work carried out by other researchers in this area. Details about data collection techniques, pre-processing, and prediction models are presented in Section 3. Section 4 concerns the experiments and results, and the discussion is concluded in Section 5.
# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
## 4 Case Study
### Extraction of Fault Diagnosis Knowledge  
According to the method shown in Section 3.2, the fault diagnosis ontology model is developed based on the heterogeneous knowledge sources, including fault criteria analysis, physical structure analysis, FMECA, and the maintenance logic decision tool. The fault criteria analysis of the seven typical gas path fault modes has been summarized in Table 3. The other knowledge sources in this case study will be described as follows.  
As a reliability analysis method, RCM aims to determine the maintenance strategy and fault information in a structured way. The RCM process is completed by two analysis tools: FMECA and the maintenance logic decision tool [35]. The first step is to analyze the fault modes, effects, criticality, and consequences using the FMECA tool. The second step is to select a correct maintenance strategy using the maintenance logic decision tool. In the FMECA process, the fault effects on different levels are analyzed based on the knowledge of physical structure analysis. In this work, three level effects (local effect, higher level effect, and system effect) correspond to three levels of physical structures. As shown in Figure 10, the gas turbine gas path system is divided into three levels of physical structures (system, subsystem, and component) to locate the fault effects. Consequently, the above three level structures are considered as the data properties of the _FaultLocation_ individual.  
The _system_ data property has the value of _\"Gas turbine gas path system\"_. The _subsystem_ data property has the value of _\"Turbine\"_, _\"Combustor\"_, and _\"Compressor\"_. The _component_ data property has the value of _\"stator blade\"_, _\"rotor blade\"_, _\"thrust bearing\"_, etc.  
The FMECA knowledge of the typical gas path fault modes is summarized in Table 5. The fault effect, fault criticality, fault consequence, and detectability of seven gas path fault modes are analyzed and stored in the _FaultEffect_, _FaultCriticality_, _FaultConsequence_, and _FaultDetability_ sub-classes.  
The fault criticality indicates the risk of the fault mode according to the fault severity and fault frequency. The fault criticality is divided into three levels, including low, medium, and high.  
Generally, there are four kinds of fault consequences, including hidden consequence, safety consequence, operational consequence, and non-operational consequence. The explanations of the four fault consequences are as follows:  
1. Hidden consequence, which cannot be detected by a monitoring system and can be found by periodic testing. Therefore, the fault modes with hidden consequence are undetectable.
2. Safety consequence, which involve the casualty of human beings.
3. Operational consequence, which cause economic loss due to the adverse effect on operational capability.  
Figure 10: Physical structure of the gas turbine gas path system.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 2 Related Work
### Orchestration Methods and Software for Multi-Robot Systems  
Due to the foreseen relevance of multi-robot systems, our literature review identifies several efforts to establish coordination mechanisms. This coordination is crucial for the success of joint operations in intelligent agents, as identified in [34]. In this aspect, intelligent agents must be able to carry out problem solving in a planned and rational way, but being flexible in their behavior according to the environmental characteristics of the surroundings.  
Most orchestration methods rely on newly developed frameworks for popular already existing languages. Examples of this can be found in [35], a Python framework to manage robot teams at a high level of abstraction. This work also proposes several hierarchies, highlighting the primary-secondary or peer-peer organization. However, all tests remain in simulation and it looks like no further progress has been made to continue this project. Scala language also seems to be adequate for these frameworks. The contribution of [36] is based on aggregate computing, presenting an API for the Scala language that allows the description of high-level behaviors such as moving and performing group behaviors. Authors evaluate the use case of 'find-and-rescue' in order to show the ability to execute complex behaviors. While simulation results are favorable using the 'Alchemist' simulator, authors consider actual deployments for future work. A parallel can be drawn with [37], where the attribution of tasks to agents is described with a high degree of mathematical precision. Nevertheless, the feasibility of implementing this proposal for actual agents, or indeed its capacity for simulation, remains ambiguous.  
The Resh programming language is proposed in [38]. Resh aims for the orchestration of robot swarm systems. It offers the orchestration of large concurrent systems and largely asynchronous tasks, allowing the developer to focus on the tasks and actions that the robots must perform, letting the language to handle all other scheduling tasks. However, Resh is not designed for tasks that require strict control of the robot swarm. Using the 'Resh Interpreter' and one 'Robot Agent' per robot, it seems possible to interact with ROS-based robots, although authors do not provide an extensive documentation about the list of platforms supported and whether Resh support for ROS-enabled platforms can be expanded anymore.  
Perhaps the most complete solution in this area can be found in Aerostack 2 [39]. Built on top of ROS 2, Aerostack enables high-level and collective behaviors on diverse drone platforms. Despite the existence of contributions targeting ground robots such as [40] and [41] (among others), none of those have attained the same level of popularity as the initial one.  
In conclusion, as identified in [11], there is a clear lack of implementations that could actually be deployed on real life. This is where our contribution lies, as we additionally consider both robots and sensor nodes together.