esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They analysed deformation characteristics through unconfined compression tests and found that the waste rocks with a particle size of 0-15 mm, as aggregates of the paste filling materials, had the largest compressive strength. [PERSON] et al. [22] examined the fractal dimensions of coal gangue particles during compressive loading and found that the fractal dimensions were greatly affected by the axial stress and strength of the waste rocks. By using 3D scanning and other methods, [PERSON] et al. [23] investigated the influences of different particle shapes on gangue particle breakage under compression and revealed that cubic gangue particles had a stronger bearing capacity. [PERSON] et al. [24] replaced partial fine aggregates with gangue to prepare a new concrete, recoding less deformation in the new concrete with a higher compressive strength (56.44 MPa) than in traditional concrete (41.03 MPa). [PERSON] et al. [25,26] analysed the deformation behaviours of gangue particles with different sizes when placed under different axial and lateral stresses. In addition, some scholars also tested the effect of the water environment on the compression deformation of crushed gangue [27, 28, 29, 30, 31, 32] and further explored the environmental effect of the gangue under compression [33, 34, 35, 36, 37, 38]. In summary, studies to date have focused on the influence of material strength on the compressive deformation of waste rock filling materials and the particle morphological variation during the compression process by numerical simulation. However, few studies are concerned with the effects of loading stress and velocity on the compressive deformation of waste rock filling materials. Moreover, the evolution mechanism of the particle breakages of gangue filling materials before and after compression remains vague and poorly quantified. Therefore, in this paper, an experimental system is designed consisting of two modules: sieving and compressive loading modules. Detailed test schemes and procedures are also devised. Quantitative models of compressive deformation and particle breakage of the gangue backfill materials are determined. The effects of different loading stresses and velocities on the compressive deformation and particle breakage behaviour of the gangue backfill materials are investigated and the reasons for such effects are analysed.
## 2 Related Work
### OST-HMDs Capable of Displaying at Optically Correct Depths  
Conventional OST-HMDs are not capable of stereoscopic display at an optically correct depth. Specifically, when a virtual object with a certain depth is displayed by the OST-HMD, it is observed at the depth of the display focal plane and not at the intended depth. Consequently, the vergence position is correctly adjusted at the intended depth whereas the accommodation position is fixed at the depth of the display focal plane. This problem is called the vergence-accommodation conflict problem [10; 11; 12] and is the cause of the failure of OST-HMDs to reproduce the visual effects of transparent objects.  
Numerous studies have been conducted to solve this problem. One such proposed method is the varifocal system [13; 14; 15]. This system enables us to set multiple focal planes for displaying virtual objects. It comprises a varifocal lens or mirror to shift the focal plane between near, medium, and far distances at high speed. However, the number of focal planes that can be set is currently limited by the slow update speeds of the image elements and varifocal lenses. [PERSON] et al. [13] developed a system that can project over a 100\({}^{\circ}\) field of view (FoV) with variable focus by replacing the varifocal mirror with a half-mirror. However, the use a half-mirror-based optical design increases the system size.  
Other systems that involve the use of light fields are cited in [16; 17; 18]. In such approaches, stereoscopic display is provided by reproducing light rays in 3-D space instead of displaying them on a surface display. In principle, light field-based systems can display virtual objects at any distance with optical correctness; however, they cannot currently display at high resolution because of the limited resolution offered by image elements on LCDs and electroluminescence displays (ELD). Additionally, they require significant computational cost, as the display function requires calculations based on ray-tracing instead of polygon-based algorithms.
# How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective
## 1 Introduction
### 1.1 AR Technology  
Globally, the railway industry is focusing on attaining efficiency in MRO by accelerating the maintenance and repair activities through industry specialized augmented reality (AR) technology solutions [4]. The global augmented reality industry for the MRO market was valued at $403.3 million in 2018 and is expected to reach $3319 million by 2024 [4]. AR-based innovations have received a lot of traction in the last few years.  
In just over a decade, AR has matured and proven to be an innovative and effective solution to help solve a number of the critical problems to assist and improve maintenance processes before they are implemented [5]. This technology is based on human-computer interaction and overlays digital virtual information in the real-world environment. The information display and image overlay are context-sensitive and user-dependant, meaning that they depend on the perceived objects in combination with the user's expectations. This technology enables railway companies to examine, monitor, and analyze rolling stock components with great effectiveness and efficiency. The evolution of technologies such as cloud computing, cognitive computing, and machine learning is paving the way for the growth of AR in MRO [6].
## 3 Experimental Results
### Experimental Results
#### 3.2.3 Experiment 3: Experiments on Additional Sensor Measurement Noises
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sensor Distribution** & **Noise Strength** & **Method** & **RMSE** & **CC** \\ \hline \multirow{8}{*}{Symmetric} & 0\% & 1D-att-CNN & 0.7517 & 0.9232 \\  & & ours & 0.4824 & 0.9702 \\ \cline{2-5}  & 10\% & 1D-att-CNN & 1.5608 & 0.6070 \\  & & ours & 0.9217 & 0.8798 \\ \cline{2-5}  & 20\% & 1D-att-CNN & 1.8461 & 0.3677 \\  & & ours & 1.1425 & 0.8142 \\ \cline{2-5}  & 30\% & 1D-att-CNN & 1.8545 & 0.2794 \\  & & ours & 1.2456 & 0.7699 \\ \hline \multirow{8}{*}{Asymmetric} & 0\% & 1D-att-CNN & 0.8211 & 0.9701 \\  & & ours & 1.0579 & 0.9710 \\ \cline{2-5}  & 10\% & 1D-att-CNN & 1.8808 & 0.8375 \\  & & ours & 1.8564 & 0.8791 \\ \cline{2-5}  & 20\% & 1D-att-CNN & 2.5385 & 0.6902 \\  & & ours & 2.3807 & 0.8022 \\ \cline{2-5}  & 30\% & 1D-att-CNN & 2.5211 & 0.6809 \\  & & ours & 2.4250 & 0.7977 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Load identification performances evaluations and comparisons with additional sensor measurement noises. The evaluated loads are the random loads in Section 3.2.1. Extra Gaussian noise with different noise strength are added on the acceleration measurements. We choose to compare the proposed approach with the second-best algorithm in Section 3.2.1. Figure 7: The comparisons of the RMES and CC metrics of different apporach under different additional measurement noise strengths. The first row gives the results for symmetrical sensor distribution, while the second row gives those of the asymmetrical sensor distribution. (**a**) The RMSE for symmetrical distribution under different noise strength. (**b**) The CC for symmetrical distribution under different noise strength. (**c**) The RMSE for asymmetrical distribution under different noise strength. (**d**) The CC for asymmetrical distribution under different noise strength.
## 1 Introduction  
Cracks are ubiquitous in concrete structures, including bridges, pavements, and highways, and are the number one issue to be addressed in road and bridge pavement maintenance [1]. Most pavement defects initially manifest as pavement cracking under repeated pressure, the physical or chemical environment. Some cracks will extend and expand, eventually exposing the internal reinforcement and other structures, and damage their protective layers, which may affect the driving of vehicles or lead to the collapse of roads and bridges, causing hazards to humans or even irreversible hazards [2, 3]. To assess the safety and durability of concrete structures, crack detection plays an important role in the maintenance of concrete structures [1].  
Traditional crack detection relies on the experience of the inspector, as well as the technical level of judgment, or the help of electromagnetic detection [4], radioactivity detection [5], ultrasonic detection [6], and infrared [7], which have been continuously improved. Although they can achieve the expected detection of cracks, the above methods not only increase the detection cost, but also produce a strong subjective factor, and the long-term use of X-rays and gamma rays for detection will produce a large number of hazards to the human body.  
Based on the above, road operation and maintenance companies urgently need some way to achieve the automatic detection of cracks. This is the main objective of many working researchers. For example, the threshold-based approach proposed by [PERSON] [8] is suitable for well-defined large cracks. An entropic dynamic thresholding technique combined with morphological filtering was proposed by [PERSON] [9] to remove false positives. [PERSON] [10] proposed a crack clustering method to classify and detect transverse cracks and longitudinal cracks.  
With the booming artificial intelligence and computer hardware, deep learning-based bridge crack detection technology has achieved great results. However, these crack detection methods require abundant data, and they will take a long time to collect a large amount of road data and then upload them to the computing environment. Cloud-computing has high latency, low bandwidth, and large data storage capacity, which will not produce a positive response to road accidents. Edge-computing has low latency, a fast response, and good protection of data privacy. Considering the drawbacks of traditional crack detection and the advantages offered by edge computing, this paper aims to propose a lightweight crack detection model that can be easily deployed on edge devices to achieve a fast response to cracks. Below, we list several contributions made by this research:  
1. We presented an automatic crack detection algorithm that is deployable to edge devices, i.e., BT-YOLO, which is an improved algorithm based on the combination of Transformer and YOLO. It can achieve high-precision crack detection;
2. We performed ablation experiments, as well as comparative experiments. The effectiveness of each module was verified;
3. We deployed the model for an edge device and tested it to verify its excellent detection capability.
## 3 Materials and Methods  
Traffic prediction and planning in existing telecommunications networks is increasingly oriented towards connecting physical and virtual devices based on the use of current concepts of the M2M communication, Wireless Sensor Networks (WSN), Internet of Things (IoT) and Cyber Physical Systems (CPS). Machines or systems, i.e., intelligent devices in the network, communicate with wireless technologies according to the LTE standards, in a way that the devices forward the collected data in the form of packets through the telecommunication network, thus generating traffic, and applications process them and provide information for users. Therefore, traffic generation is performed in the domain of the provider's network, which provides communication service to users--devices, data transfer in the domain of an external or global communication network, and their processing in the application domain. In addition to the growing number of intelligent devices, the diversity of their application is increasingly present in the networks, which affects the setting of various requirements and restrictions related to data transfer speeds, packet sizes, delays, reliability, unattended operation, etc. These communication requirements have been standardized by the Third Generation Partnership Project (3 GPP) regulatory body. Therefore, e.g., Technical Specification [16] specifies and establishes the characteristics of the physicals layer procedures in the Frequency Division Duplex (FDD) and Time Division Duplex (TDD) modes of Evolved Universal Terrestrial Radio Access (E-UTRA). In [17], the description and definition of the measurements performed by Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) is given. Traffic steering control information is subject to technical specification [18]. Radio Link Control (RLC) protocol specifications are given in [19]. Similar to [17], in [20] the measurement specification performed by E-UTRAN is given. Technical specification [21] is related to improving LTE coverage. Traffic modeling in M2M communications is presented in [22] and [23].  
On the other hand, the European Telecommunications Standards Institute (ETSI), which also owns the LTE as the world's dominant mobile phone technology, is in charge of standardizing the abstract Middleware service layer.  
For the defined subject and objectives of the research, the methodology of the research process was implemented in several successive steps: 1. Data collection; 2. Creating a predictive model--digital data treatment (defining the model metaparameters, model training, model testing, selection of the final model); 3. Results analysis and discussion; 4. Conclusion.
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 4 Results and Discussions
### Application for Engineering Practice  
Through the above parametric study, the effects of the influencing factors on the nonlinear wave-induced uplift force onto pipelines were examined. Based on the numerical results, an empirical formula predicting the nonlinear wave-induced uplift force onto a pipeline is proposed here.  
\[F_{z}=0.0798\zeta-c \tag{17}\]  
\[\zeta=\left[\frac{PD^{\frac{28}{15}}a^{\frac{3}{2}}\gamma_{w}\frac{25}{8}}{(G+ \lambda)^{\frac{1}{6}}e^{\frac{1}{5}}}\right]^{\frac{4}{5}} \tag{18}\]  
\[P=\frac{\gamma_{w}H}{\cosh(kd_{i})} \tag{19}\]  
\[d_{i}=d-S^{2}tan(\alpha)(-0.0002S+0.0317) \tag{20}\]  
\[\lambda=\frac{\
u E}{(1+\
u)(1-2\
u)} \tag{21}\]  
\[a=\left\{\begin{array}{c}10.5+\log{(k_{s}\frac{T}{L})},k_{s}<10^{-4}\\ -\log{(k_{s}\frac{T}{L})},k_{s}\geq 10^{-4}\end{array}\right. \tag{22}\]  
where \(k\) is the wave number; \(c\) is a coefficient and \(c=-942.25\) in the range of this study.  
To verify the effectiveness of this new formula, Figure 20 shows the comparison results of the uplift force between the formula and the numerical model. It can be clearly observed  
Figure 19: Variation of uplift force onto the pipeline with the slope horizontal distances of wave propagation.  
Figure 18: Variations of uplift force onto the pipeline with slope angles at different water depths.  
from Figure 20 that accuracy can be guaranteed when using the new formula to predict the nonlinear wave-induced uplift force onto the pipeline buried in sloping seabed. It should be noted that this empirical formula is obtained by fitting the numerical results of this study, and its applicable range is within the parameter used in this study.
## 2 Test Methods for ME
### Mechanical Testing of Plastics  
Mechanical testing of plastics is used to investigate and determine mechanical properties of polymeric test specimens using standardized test methods compiled, for example, by American Society for Testing and Materials (ASTM), International Organization for Standardization (ISO), or Deutsches Institut fur Normung (DIN). The mechanical behavior of plastic materials is characterized by tension (\(\sigma\)) and elongation (\(\epsilon\)). Depending on the major load types, a distinction can be made between tensile, compression, and bending stresses (see Figure 2). For tensile and compression stresses--so called normal stresses (\(\sigma\))--a force (F) applies vertically on the loaded surface. Bending stresses, as well as normal stresses, can be considered as a superposition of tensile and compression stresses and are calculated by the quotient of bending moment (M\({}_{\text{b}}\)) and section modulus (W\({}_{\text{b}}\)). If the force is not applied vertically but in the surface, shear stresses (\(\tau\)) emerge. The corresponding load types are shear and torsion. For the latter, shear stresses are calculated by the quotient of torsional moment (M\({}_{\text{t}}\)) and polar section modulus (W\({}_{\text{p}}\)) [14].  
Figure 1: (**a**) Schematic representation of material extrusion (ME) principle and (**b**) material transition including relevant coupling mechanisms.  
Figure 2: Overview of different load types.  
Test methods can be further distinguished by the speed during execution. Whereas for static tests, a steady speed is performed, the speed of quasi-static tests is slowly increasing. Dynamic tests, on the other hand, are characterized by a non-uniform load application. Within these ranges of speed, test methods are subdivided according to the major load types (tension, compression, and bending). In addition, for quasi-static tests, torsion and shear tests are conducted as well [15].  
To obtain comparable values for the mechanical properties of different plastics, test methods must be conducted under equal test conditions regarding speed, ambient temperature, and relative humidity. As thermoplastics show viscoelastic material behavior above the glass transition temperature, mechanical properties are significantly affected by the ambient temperature. Besides, some plastics (e.g., PLA and ABS) are hygroscopic, so the moisture content also has a major impact on the mechanical properties. In general, the ambient temperature and relative humidity are set at 23 \({}^{\circ}\)C and 50%, respectively. [14]
## 2 Background and Related Work
### Model Checking
#### 2.1.3 Model Checking with DTMC and PCTL  
Model checking is a formal verification technique used to automatically verify whether a system model satisfies certain properties [14; 15]. For probabilistic systems like those modeled by DTMC, PCTL is used to express the desired properties. The model checking process involves determining whether the system satisfies the specified properties by evaluating the probabilities of different state transitions.  
In the context of GitHub repositories, model checking can be used to analyze and verify key aspects of project activity. For instance, DTMC can model how contributors move between different states, such as creating a pull request, having it accepted, or reverting to a commit state after a rejection. Using PCTL, we can express properties like \"the likelihood that a contributor will remain active after a pull request is rejected\".  
A practical application of model checking in software projects can be seen in the work by [16], where the authors used probabilistic model checking to analyze GitHub repositories. They employed DTMC and PCTL to model and verify various aspects of project activities, such as commit frequencies and pull request handling. Their study demonstrated the efficacy of probabilistic model checking in understanding and predicting the behavior of open-source software projects.
## 2 Related Work
### SPH for Biological Simulations  
In recent years, SPH has become increasingly popular in computer graphics. It has been successfully used for the simulation of various fluid phenomena, including: rigid and elastic solids, deformable objects, spray, foam, tiny air bubbles, granular materials, and other complex scenes that consists of millions of points [33; 34; 35]. Recently, SPH based methods have been used to solve non-hydrodynamic partial differential equations such as the wave equation, the diffusion equation, Maxwell's equations and Poisson's equation [36], as well as to solve electronic structure calculations [37].  
The extension of SPH to simulate biological structures was relatively sparse, with a few examples of blood or biological fluids confined by meshes [38; 39; 40], simulations of a virtual liver [41], lips [42], cartilage [43], and generic biological tissues [44].  
Regarding the simulation of biological soft tissue with SPH, several works tackled the simulation while exposing some advantages of using the method: [PERSON] et al. [45] integrated the effect of internal and external forces, and demonstrated the advantage of using SPH for large tissue deformations; [PERSON] et al. [46] used a variation of SPH, Predictive-Corrective Incompressible SPH (PCISPH) to simulate different types of tissue, both solid and fluid, and introduced contractile fibers based on mass-spring systems; [PERSON] et al. [44] used SPH to simulate tissue that experienced large deformations and damage, to the point of failure. Most of these works results were in agreement with analytical solutions, as well as with Finite Element Method (FEM) solutions.  
These solutions do not consider biophysical models to control the activation and deformation of the tissues. Additionally, the tissues are modeled using mass-spring, elastic, or stress-based models that are not necessarily applicable to biological tissue simulations since they are non-volumetric methods where spring elements connecting point masses must be tuned for each desired scenario [43]. This is an issue present in many other works that simulate skeletal muscles. Current computational models of skeletal muscle models typically focus on simplified phenomenological relationships mimicking the overall (mechanical) behavior of a single skeletal muscle.
## 5 Limitations and Future Work  
Although the proposed framework performs well on the Wisconsin Breast Cancer Dataset, several limitations still exist. The model was trained and tested on just one benchmark dataset. This restricts its applicability to other medical conditions or more diverse data distributions. Future work will focus on testing the model across multiple datasets to evaluate its reliability and potential for use in wider clinical settings.  
Another significant limitation is the current lack of explainability features. In clinical settings, understanding how and why a model makes a specific prediction is crucial for gaining trust of healthcare professionals. While our aim in this work was to improve classification accuracy, future updates will incorporate explainable AI techniques, such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). These will provide clear insights into model decisions and feature contributions.