esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography
## 2 Detection Principle  
In active infrared thermography, a thermal source is applied to provide external heat stimulus. The applied thermal stimulation generates heat which propagates as thermal waves from the surface into the inside of the tested structure. When the thermal waves reach a defect or crack, they will change their propagation rate and produce thermal contrast on the surface above the anomaly immediately [50; 51]. Figure 1 demonstrates the infrared thermography method for impact damage detection.  
The entire process including temperature rise and decay is registered using an infrared camera and stored as a 3D matrix composed by N thermograms, where x and y are the spatial coordinates and t is the time [52]. The data is then processed to improve defect visibility and to perform quantitative characterization of defects. Analysis of the infrared signal series can reveal the occurrence and evolution of the impact damage when compared with a baseline or initial condition.  
The propagation of heat not only depends on several material properties, such as thermal conductivity and density, but also on subsurface anomalies which result in temperature differences on the surface target. Active infrared thermography is particularly advantageous for impact damage detection, as this technique can be applied to the inspection of materials for subsurface defects detection. In addition, active thermography is a clean and non-contract technique, which has been proven successful for non-destructive testing [53; 54; 55].
## 4 Performance Evaluation  
### Simulation Parameters  
In this section, the performance of the proposed opportunistic transmission scheme is evaluated through simulation experiments. This paper compared it with three state-of-the-art schemes: (A) Scheme 1 [21]; (B) Scheme 2 [22]; (C) Scheme 3 [23]. The employed simulation software was the opportunistic network environment (ONE). The mobility model for UAVs and ICVs is the shortest path map-based movement (SPMBM) model [29]. The SPMBM model utilizes a \"shortest path map\", a method from graph theory, to predict and optimize the movement paths between network nodes (i.e., UAVs and ICVs). Therefore, the movement of nodes in integrated UAV-ICV networks is not completely random but  
Figure 3: A diagram of UAV-assisted information transmission.  
depends on the physical distances between nodes and the communication opportunities available. In this model, each node is equipped with the global positioning system (GPS). Additionally, each node is represented as a graph to denote its position within the network, and the shortest paths between each node and all other nodes are taken into account. In the simulations, we considered scenarios where UAVs and ICVs equipped with smart Wi-Fi devices communicate in a real urban environment. Moreover, this paper classified ICVs into three types based on their cooperation level. The ratio of type II nodes to type III nodes is 1:1. Furthermore, the UAVs were defined as non-selfish nodes. When any node encountered a non-selfish node, information could be transmitted directly without an interaction request. However, when interacting with selfish nodes, an interaction request was required.  
The simulation scenario settings are shown in Table 1. In Table 1, OFDMA and LoS are abbreviations for orthogonal frequency division multiple access and line-of-sight, respectively. It should be noted that, to ensure a fair comparison, the simulation settings not only considered the proposed scheme but also included the baseline mechanisms. This allowed us to evaluate the performance of the proposed scheme in relation to existing solutions under the same conditions, ensuring that the results were meaningful and comparable. Moreover, we used Open Street Map (OSM) to export real-world road data and integrated OSM with the event-driven simulator by using the osm2 wkt.jar package and Open Jump. Furthermore, we employed an opportunistic multihop multiple access control (MAC) protocol, which enables nodes to take advantage of brief opportunities to exchange data while they are within transmission range of each other.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 4 EVSP Process
### OBU Leaves the Intersection Process  
When the RSU is in EVSP state, it continuously monitors the state and position of the EV and determines if the EV leaves the intersection. As shown in Figure 12, the RSU starts a timer (\(RSU_{timer}\)), and continuously monitors if it is timeout or violates the maximum green constraint. The purpose of the timer is to prevent the RSU from staying in an EVSP state if information broadcasted by OBU without being received after EV leaves the intersection.  
Figure 12: The algorithm of determining if OBU leaves the intersection.
# Using Allan Variance Based Semi-Parameter Model to Calibrate Pointing Errors of Alt-az Telescopes
## 3 Online Calibration of Pointing Errors  
### Calibration of Geometric Errors  
With D-H convention, various error sources make corresponding impacts on the final LOS pointing. While the exact formula is too complicated to expand, a reasonable approximation is necessary. On account of the fact that each item of the geometric errors exerts a small rotation on telescope LOS vector, small-angle approximations [8] are considered suitable for the error angles, namely:  
Rule 1: Each small rotation angle \(\psi\) caused by geometric errors is considered to be an infinitesimal, namely \(\sin(\psi)=\psi\), and \(\cos(\psi)=1\).  
Rule 2: Ignore high order error items, namely \(\psi\cdot\psi=0\), where \(\psi\) is the angle in radian.  
After applying all rotations of error sources, the actual LOS is determined. The actual azimuth and elevation angle are \(A+\Delta A\) and \(E+\Delta E\), respectively. According to the small-angle approximation, the ultimate pointing error can be given as:  
\[\Delta=R^{T}x+\
u \tag{2}\]  
where \(\
u=\left[\begin{array}{cc}\epsilon&\eta\end{array}\right]^{T}\) indicates the vector of measurement related noises. The transformation matrix \(R\) is denoted as:  
\[R=\left[\begin{array}{cc}f(A,E)&g(A,E)\end{array}\right]=\left[\begin{array}[ ]{cc}1&0\\ 0&1\\ 1&0\\ 0&1\\ -\sec E&0\\ \tan E&0\\ \sin 2E\tan E&0\\ \cos 2E\tan E&0\\ \cos 2A\tan E\sin A&\cos A\cos 2A\\ \sin 2A\tan E\sin A&\sin 2A\cos A\\ \cos 2A\tan E\sin A&-\sin A\cos 2A\\ \sin 2A\tan E\cos A&-\sin A\sin 2A\\ \tan E\sin A&\cos A\\ \tan E\cos A&-\sin A\\ 0&-\cot E\end{array}\right] \tag{3}\]  
Figure 1: Pointing errors for telescopes.  
and \(x\) is the parameter vector to be obtained.
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
###### Abstract  
Progress in fire safety science strongly relies on the use of Computational Fluid Dynamics (CFD) to simulate a wide range of scenarios, involving complex geometries, multiple length/time scales and multi-physics (e.g., turbulence, combustion, heat transfer, soot generation, solid pyrolysis, flame spread and liquid evaporation), that could not be studied easily with analytical solutions and zone models. It has been recently well recognised in the fire community that there is need for better modelling of the physics in the near-wall region of boundary layer combustion. Within this context, heat transfer modelling is an important aspect since the fuel gasification rate for solid pyrolysis and liquid evaporation is determined by a heat feedback mechanism that depends on both convection and radiation. The paper focuses on convection and reviews the most commonly used approaches for modelling convective heat transfer with CFD using Large Eddy Simulations (LES) in the context of fire-driven flows. The considered test cases include pool fires and turbulent wall fires. The main assumptions, advantages and disadvantages of each modelling approach are outlined. Finally, a selection of numerical results from the application of the different approaches in pool fire and flame spread cases, is presented in order to demonstrate the impact that convective heat transfer modelling can have in such scenarios.  
heat transfer; convection; fire; modelling; CFD; LES +
Footnote †: journal: Applied Sciences
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 4 Numerical Application
### Material's Strength for Variant Stress Analysis  
In this section the stress-strength analysis is presented due to the component under analysis being submitted to variant stress. In this case, we have the variant vibration forces generated by the principal stresses \(\sigma_{1}=1188\) Psi and \(\sigma_{2}=330\) Psi. Thus, because the stress now is variant, the \(R(t)\) index must be determined by performing the corresponding Weibull stress/strength analysis. Since the vibration applied stress and the material strength to support the applied stress, both are random, then, the component's reliability is determined by a distribution that represents the applied stress and a distribution that represents the strength [23]. Since in this work, the fatigue vibration stress is analyzed by the Weibull distribution, then, the paired combination Weibull-Weibull approach is selected [24,25] and performed by the following relation,  
\[R(t)=\frac{\eta_{s}^{\beta}}{\eta_{s}^{\beta}+\eta^{\beta}} \tag{24}\]  
where it is required to determine the stress \(\eta\) and the strength \(\eta_{s}\) scale parameters. Additionally, the steps to determine the reliability \(R(t)\) for the used material resistance average \(\sigma_{M}\), are next. Step 1. By using the \(\sigma_{1}\) and \(\sigma_{2}\) stresses values in Equation (25), the corresponding mean is,  
\[\mu=\frac{\sigma_{1}+\sigma_{2}}{2} \tag{25}\]  
\[\mu=\frac{1188+330}{2}=759\,\mathrm{Psi}\]
## 4 Discussion
Post-processing techniques, such as aligning point clouds to reference models, correct inaccuracies and enhance accuracy. Future work could refine this methodology by incorporating baseline measurements from complementary technologies, such as inclinometer data or total station surveys, to provide a more comprehensive validation framework. Furthermore, integrating advanced analytical tools for anomaly detection and expanding its applicability to other tunnel geometries could ensure long-term structural safety and advance the field of tunnel deformation monitoring.
## 4 Experimental Results
### MedicoBERT Adaptative Learning Results  
The MedicoBERT model was pre-trained for 16 days using an NVIDIA 3090 GPU (NVIDIA, Santa Clara, CA, USA), Pytorch 2.4, 3,036,923 texts from the Spanish medical literature, the base architecture of BERT, and a hyperparameter configuration identified through a coarse calibration process.  
Figure 4 shows the model's pre-training process and indicates that MedicoBERT converged on the training and validation data over 40 epochs, indicating that it reached a point of maximum performance. No significant improvement was observed in the subsequent epochs. This behavior suggests that this hyperparameter configuration is optimal for training the Spanish medical language model.  
Table 3 illustrates the best results in terms of perplexity during the training of the MedicoBERT model. These results were obtained through experiments, in which the  
Figure 4: MédicoBERT training.  
number of data used and the model's hyperparameters (epoch, learning rate, weight, and batch size) were varied. Among the hyperparameters analyzed, it was observed that the epoch had the most significant impact on the model's performance during training.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology  
This section investigates the impacts of a newly introduced satellite on the accuracy and integrity of the subset to recognize the valuable satellite for the current subset, then the performance-requirement-driven fast satellite selection algorithm can be proposed according to the investigation.
## 1 Introduction
Compared with the base network CrossVIS, the proposed MLGW-VIS uses box-level labels as supervision, combining projection and color similarity loss terms to refine the prediction mask, and adopts SIR And GWM modules to optimize the network structure. Moreover, video cross-frame learning in CrossVIS is used to supplement the supervision of instance segmentation part for the weakly supervised network, which performs well on the traffic dataset based on Youtube-VIS. The average segmentation accuracy reaches 37.9%, which is 5.7% higher than that of CWVIS. The segmentation speed reaches 34.6 FPS on device 2080 Ti. Compared with the weakly supervised baseline network segmentation, the target positioning ability is better, and the mask segmentation accuracy is also effectively improved. The experimental results on the air cruiser platform show that the proposed method can improve the positioning coordinates and distance accuracy of the air cruiser, and they also verify the feasibility and effectiveness of cross-platform video instance segmentation modeling and deployment development. The remainder of this paper are as follows: Section 2 illustrates the network architecture, Section 3 demonstrates the corresponding experimental results. Section 4 presents the model application on the air cruiser. Section 5 provides some conclusions. Finally, Section 6 describes the limitations and future works.
## 3 Vortex-Induced Vibration Performance with Different Vortex Isolation Plates
opening ratio decreases, and the torsional lock-in region vanishes at an opening ratio of 9.35%. Additionally, when compared to the vortex isolation plate with a 20 mm opening size, the lock-in region of the vortex isolation plate with a 10 mm opening size is slightly expanded. It is noteworthy to mention that the suppression of torsional vibrations at a negative AOA may not be prominent when the opening ratio of the vortex isolation plate exceeds 23.4%. At a \(-3^{\circ}\) AOA, vortex isolation plates with a low opening ratio can still effectively suppress torsional vibrations. With a decrease in the opening ratio, the torsional vibration inhibition effect of the vortex isolation plate with a 20 mm opening size first increases, then decreases, and then increases again. However, the vortex isolation plate with a 10 mm opening size with a low opening ratio amplifies torsional vibrations and its performance is worse under torsional VIVs; specifically, the torsional VIV phenomenon occurs when the opening ratio is 9.35%. The effect of the vortex isolation plate with a 20 mm opening size on torsional vibration inhibition initially increases with a decrease in the opening ratio, then decreases, and finally, it increases again. However, the use of the vortex isolation plate with a 10 mm opening size resulted in torsional vibration amplification. This worsened the performance under torsional VIVs, especially at low opening ratios. Specifically, torsional VIVs were observed at an opening ratio of 9.35%. According to the above phenomenon, it can be concluded that the opening size of the vortex isolation plate has a significant impact on torsional VIVs in twin-box girders. This effect is particularly prominent in vortex isolation plates with the same opening ratio. An inappropriate opening size would worsen the vibration amplitude and the wind velocity of the onset of torsional VIVs. Figure 5: Torsional vortex-induced vibration response at different AOAs using different vortex isolation plates with different opening ratios. (**a,b**) 0\({}^{\circ}\) AOA, (**c,d**) +3\({}^{\circ}\) AOA, and (**e,f**) -3\({}^{\circ}\) AOA; the opening diameter is d = 20 mm in (**a,c,e**), while the opening diameter is d = 10 mm in (**b,d,f**).
## 3 Methodology
### Security Attacks  
The study examines a range of common attacks in vehicular networks, focusing on those identified across multiple research papers, which include denial-of-service, man-in-the-middle, spoofing, and other malicious activities that exploit vulnerabilities in vehicle systems and sensor networks. According to the mentioned attacks in Table 1, they can be classified as per the description and as per the definition of denial-of-service, man-in-the-middle, and spoofing attacks.  
The literature review reveals that a significant portion of the research focused on LiDAR and radar vulnerabilities (72%), justifying the emphasis on these sensor types in the subsequent phase of this study.  
The reviewed literature was categorized by attack type and sensor vulnerability, focusing on areas with the highest prevalence of research:  
* LiDAR/Radar Attacks: Twelve studies focused on spoofing, blinding, and jamming attacks (e.g., [3; 4; 12; 26; 49]).
* Network Threats: Eight studies analyzed man-in-the-middle (MitM), denial-of-service (DoS), and Sybil attacks in V2X systems (e.g., [3; 11; 44; 48]).  
The analysis of sensor-specific vulnerabilities reveals a significant disparity in the susceptibility of different sensor types to various attack vectors. LiDAR systems emerge as the most vulnerable, being susceptible to nine distinct attack types, including blinding, jamming, and relay attacks. Radar sensors follow closely, with eight identified attack types, while ultrasonic sensors are vulnerable to six types of attacks. These findings underscore the critical importance of securing these sensors due to their central role in autonomous decision-making.  
Regarding attack types that are supported by only a few references within our current review, these findings remain significant. While several well-known attacks--such asjamming, blind spot exploitation, sensor interference, and cloaking--are discussed across multiple sources in the literature, certain other advanced or highly specialized vectors are still documented by only a limited number of studies. This does not imply they are unimportant; rather, it reflects either the emerging stage of development for these threats or their niche focus that has yet to attract broader research attention. For example, attacks like falsified-information may represent early-stage or proof-of-concept vulnerabilities that warrant greater attention from researchers and industry alike. Including these types of attacks, regardless of how many references currently address them, is crucial for presenting a forward-looking and comprehensive perspective on the evolving threat landscape. This ensures that potential future risks are not overlooked simply due to limited existing documentation. Our methodology therefore aims to identify both well-established threats--validated by multiple studies--and less widely studied or emerging threats, providing a holistic view of current and prospective attack surfaces in autonomous vehicle sensor systems.
# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 1 Introduction  
Integrating BIM and GIS has emerged as a promising approach for enhancing decision-making, collaboration, urban scale simulation in architecture, engineering, and construction (AEC). BIM provides a comprehensive digital representation of buildings, while GIS offers powerful geospatial analysis capabilities. Combining these two technologies holds great potential for improving urban planning, asset management, and digital twin-based simulation as the viewpoint of digital transformation [1]. For example, if there are BIM models generated in GIS, urban environment simulations can be easily performed.  
However, capturing the digital data of existing building assets and converting them from BIM to GIS is one of the main challenges in the Scan to BIM mapping process for BIM-GIS integration. Building masses, including attributes, are critical components that must be accurately represented in BIM and GIS datasets. The conversion process of this dataset should be scalable and adjustable according to user requirements and processed based on the GIS coordinate system. PCD processing between these systems in Scan to BIM mapping is time-consuming and error-prone, because it is based on manual processes that are not fully integrated [2], often leading to discrepancies and data inconsistencies. Since it is difficult to reuse the algorithm parameter values used in the manually incompletely integrated Scan to BIM mapping process, these problems are repeated in similar projects.  
This paper proposes a flexible Scan to BIM mapping description (SBMD) approach in 3D GIS. This study utilizes ALS technology to capture detailed geometric information from the physical site. The captured PCD is then processed to create highly accurate 3D models that are the basis for integration with GIS.  
The main focus of the proposed methodology is a reusable process for the automatic generation of building mass information from ALS datasets by using SBMD. Using a method that allows users to predefine the parameters of an algorithm and reuse it makes the integration between BIM and GIS more efficient and reliable than manual work. The methodology includes extracting the building footprint algorithm, root height, and mass from the PCD obtained in SBMD for 3D GIS.  
In conclusion, this paper presents an approach for a flexible Scan to BIM mapping description method in 3D GIS. Automatically generating a building mass, including attributes, in a GIS platform offers significant efficiency, accuracy, and decision-making advantages.  
To clarify the research scope, this study analyzes the existing manual-based Scan to BIM mapping process and derives use cases that SBMD can support. The required algorithm models are studied to implement use cases, and the SBMD framework structure is designed to operate within a flexible mapping process. Afterward, the proposed method is implemented as a prototype, and the efficiency compared to manual work is analyzed to review the effect.
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 4 Results
Figure 8: Randomly selected examples from fold 4, including PlanetScope RGB images, segmentation labels, and predictions from DeepLabV3+ (DL), Swin-UPerNet (Swin), and Mask2 Former (M2F).
# An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
## 2 Related Work  
Many face detection approaches have been proposed over the past decades [13; 14]. A recent study shows that the convolutional neural network (CNN)-based [15] deep learning algorithms have achieved great success in many computer vision areas, such as object classification [16], object segmentation [17; 18; 19], as well as face detection. This is because the traditional hand-crafted features, like SIFT [20], HoGs [21], LBPs [22; 23; 24] and ICF [25; 26; 5], are not descriptive enough for face detection in practice, but CNNs can automatically learn features to capture complex visual variations by leveraging a large amount of training data.  
On the other hand, the cascade framework is able to reduce prediction time by rejecting negative samples in an early stage. The traditional cascade framework adopts relatively simple classifiers [9; 27; 28]. To improve performance, recently, cascade convolutional neural networks, which use convolutional neural networks as the basic classifiers, have been proposed. [PERSON] et al. used a cascade framework with CNNs (CascadeCNN) for face detection [11]. Later, [PERSON] et al. proposed a multi-task cascaded convolutional network (MTCNN) for face detection by taking facial landmarks into account [12].  
In this work, we propose an improved convolutional neural network cascade that is able to adopt head-shoulder information to make face detection possible in large scene surveillance.