esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Constitutive Models of Concrete  
#### 4.1.1 Concrete Damage Plasticity Model  
The Concrete Damage Plasticity (CDP) proposed by [PERSON] et al. [37] and [PERSON] [38] was used. The CDP model assumes isotropic damage and is suitable for applications in which the concrete is subjected to arbitrary loading conditions, such as cyclic loading. It accounts for degradation of the elastic stiffness created by plastic straining that takes place in tension and compression, and for stiffness recovery effects under cyclic loading [35].  
Plasticity and damage are the main phenomena that characterize the nonlinear behavior of cementitious materials. Strain softening, progressive deterioration, and volumetric expansion are the  
Figure 11: Effect of heat treatment on energy dissipation capacity of different composite specimens.  
Figure 10: Effect of heat treatment on composite specimens: (**a**) surface cracking, and (**b**) polyvinyl alcohol (PVA) fiber deterioration.  
major phenomena that describe the plastic performance of concrete. These phenomena lead to the degradation of strength and stiffness of cementitious materials [37; 39; 40]. Similarly, the damage is characterized by the reduction of stiffness. A scaled isotropic damage model from the continuum damage mechanics is introduced in ABAQUS (2012). This model describes the stiffness degradation under uniaxial loading as per Equation (1):  
\[\sigma=(1-d)E_{0}\Big{(}\varepsilon-\varepsilon^{pl}\Big{)} \tag{1}\]  
where \(\sigma\) is the stress, \(d\) is the damage factor, which represents the elastic stiffness degradation and has values in the range between 0 (undamaged) to 1 (fully damaged). \(E_{0}\) is the initial (undamaged) elastic stiffness, while \(\varepsilon\) and \(\varepsilon^{pl}\) are the total strain and plastic strain, respectively. If no damage is considered in the concrete (\(d=0\)), then Equation (1) can be reduced to Equation (2) as follows:  
\[\sigma=E_{0}\Big{(}\varepsilon-\varepsilon^{pl}\Big{)} \tag{2}\]  
Stiffness degradation models are classified into elastic degradation models and plastic degradation models according to the presence of plastic strain [37]. The elastic degradation models are represented by the total strain, implying that no plastic strain exists (i.e., \(\overline{\varepsilon}^{p}=0\), where \(\overline{\varepsilon}^{p}\) is the plastic strain with stiffness degradation). In this case, Equation (1) can be modified as follows:  
\[\sigma=(1-d)E_{0}\varepsilon \tag{3}\]  
where \(\varepsilon\) is the total strain. On the other hand, plastic degradation models have been presented to overcome the problems associated with elastic degradation models [37]. Through the plastic degradation models, the stiffness degradation deals with the plastic deformation instead of the total deformation as in the case of elastic models. It means that, after damage, plastic strain still exists (i.e., \(\overline{\varepsilon}^{p}\
eq 0\)). According to the aforementioned approach, Equation (1) can be rewritten in the form of Equation (4):  
\[\sigma=(1-d)E_{0}(\varepsilon-\overline{\varepsilon}^{p}) \tag{4}\]
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 3 Experimental Results Analysis  
### Experimental Platform and Parameters  
AMU-Net was built on the monAI platform, and it follows instruction from monAI for data processing. PyTorch deep learning framework was implemented to experiment and evaluate all the models. The relevant experiments were performed with NVIDIA GTX 3080 GPU on the Windows 10 platform with 32 Gb RAM. We utilized the Adam optimizer with an initial learning rate of 0.00001, which followed a cosine annealing schedule for dynamic adjustment during training. The model was trained for 200 epochs to ensure effective convergence and performance optimization. The cosine annealing schedule can be defined as follows:  
\[\eta_{t}=\eta_{\text{min}}+\frac{1}{2}(\eta_{\text{max}}-\eta_{\text{min}}) \bigg{(}1+\cos\bigg{(}\frac{T_{cur}}{T_{\text{max}}}\pi\bigg{)}\bigg{)} \tag{41}\]  
The dataset consists of 30 single-phase, mono-modal MRI scans of the entire heart, acquired using respiratory and electrocardiogram gating during free breathing. The target region of interest for segmentation is the left atrium [36; 37]. The dataset was split into 80% for training, 10% for validation, and 10% for testing to evaluate the model's performance.
###### Abstract  
In this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut.  
cutting force; tool wear; surface roughness; ANN-RSM +
Footnote †: journal: Applied Sciences
# A LiDAR-Based Backfill Monitoring System
## 4 Experiment
### Processing Speed  
The time to process each set of scans in different LiDAR ranges is shown in Figure 12. Inside the slightly curvy tunnel, the top space of the retaining wall is larger than that of the other tunnels, causing the progress to end at around 80%. Additionally, the curvature of the tunnel causes the laser beam to be blocked, delaying the start of progress monitoring in such tunnels. While increasing the LiDAR range (e.g., to 200 m) can allow earlier detection of the slurry top surface, it remains constrained by obstructions at corners.  
Overall, as the size of the backfill body increases, the time taken to process each set of scans becomes longer. The time is not affected by the curvature of tunnels, but the number of points in each LiDAR scan takes a toll of the processing speed. Nonetheless, the maximum time to process a set of scans is 20 s. For the straight tunnel with a volume of 8990 m\({}^{3}\), there is up to a 0.006% increase in backfill progress within 20 s, based on a flow rate of 100 m\({}^{3}\)/h. Determining the critical flow velocity of the backfill mixture is crucial for accurately estimating the backfilling time [44]. In conclusion, the backfill monitor is sensitive, and is capable of capturing the rising slurry top surface and effectively monitoring the backfill progress.
# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 6 Experiments and Results
### Comparison with State-of-the-Art Approaches  
#### 6.6.1 ICDAR-19  
Table 6 summarizes the comparison of HybridTabNet and the current state-of-the-art approaches [8; 63] on the ICDAR-2019-TrackA-Modern dataset. Our approach achieved a W.Avg of 0.928, which is close to the state-of-the-art performance. This does not beat the state-of-the-art performance of table region detection because they utilize image processing techniques, such as Dilation and Smudging for effective learning.  
The advantage of our approach is that we did not use any image preprocessing or post-processing techniques on the data. Our model directly takes the raw training data without any image preprocessing technique and learns effective representations. It outputs the accurate table region masks and bounding boxes directly at inference time due to its architectural design and the techniques implanted during its training.  
The aforementioned points make our technique much better than the earlier state-of-the-art methods. Even without utilizing any image preprocessing or post-processing techniques, our model achieved better performance compared with current state-of-the-art approaches.  
Figure 10: HNet results on UNLV dataset. (**a–c**) True positive, False positive, and False negative results, respectively. The blue outline shows the predicted table, and the red outline highlights the ground truth table.  
\begin{table}
\begin{tabular}{c c c c} \hline
**IOU Threshold** & **Precision** & **Recall** & **F1-Score** \\ \hline
0.5 & 0.926 & 0.962 & 0.944 \\ \hline
0.6 & 0.914 & 0.949 & 0.931 \\ \hline
0.7 & 0.914 & 0.949 & 0.931 \\ \hline
0.8 & 0.902 & 0.936 & 0.919 \\ \hline
0.9 & 0.792 & 0.822 & 0.807 \\ \hline W.Avg & **0.882** & **0.915** & **0.898** \\ \hline \end{tabular}
\end{table}
Table 5: HybridTabNet results on UNLV dataset. W.Avg denotes the weighted-average of the respective measure on the IoU threshold.
## 3 Theoretical Model and Computational Analysis of Aberrations in Double Prism Compensation Devices  
When designing and applying compensation devices, it is essential to ensure the rationality of the scheme and analyze and calculate whether the compensation device will impact the entire optical system, the nature of that impact, and whether it remains within a tolerable range.  
Aberration theory is crucial for designers to better understand the fundamental aspects of imaging systems. This section primarily focuses on the analysis of aberrations introduced  
Figure 4: Schematic Diagram of Adjusting the Image Plane with Different Parameters of Dual-Prism.  
by compensation devices. Due to a prism not being an axisymmetric system, but a plane symmetric system, the aberrations it introduces in a beam of light exhibit the same plane symmetry as the prism itself. However, the aberrations produced by prisms have not been widely discussed, and it is valuable to understand the image plane tilt, distortion, and aberrations caused by wedge prisms. This chapter will address this topic. The following discussion will exclude piston terms as they contribute a uniform phase difference that does not affect image quality.
# A Theoretical Foundation for Context-Aware Cyber-Physical Production Systems
## 4 CPPS Models
When an order is released to CPPS, processing of the order can be represented by the evolution of the states of the model \(G_{c}\) of CPPS under a control policy. The requirements of an order can be described by product demands, \(D_{j}\), where \(D_{j}>0\forall j\in J\), and deadline \(\Phi\), where the deadline is specified by a period in the time horizon \(\Pi\). In the model of CPPS, a marking represents the state of the system. Before processing the given order, the CPPS is in its initial state with the initial marking \(m_{0}\). For the given order requirements, the number of different types of products to be produced to fulfill the product demands of the order can be represented by a final marking \(m_{f}\) in the model of CPPS. Whether the order deadline can be met can be calculated by determining whether the marking \(m_{f}\) can be reached from \(m_{0}\) by the deadline \(\Phi\). Throughout the evolution from \(m_{0}\), the CPPS may visit undesirable states. An improper control policy may bring the system to an undesirable state under which some or all the transitions can no longer be fired. Figure 4 shows an example of an undesirable state in which transitions can no longer be fired, with the exception of \(t_{r1}\) and \(t_{r2}\). There is a research issue and question that needs to be addressed: can the CPPS model reach the target (final) state by the deadline without visiting any undesirable state? To answer this question, we formulate the Deadline Awareness Problem and the Future States Awareness Problem. Based on the model of CPPS, we formulate the Deadline Awareness Problem. As the Deadline Awareness Problem is to determine whether the requirements of a given order can be fulfilled by the order deadline, the Deadline Awareness Problem is to determine whether final marking \(m_{f}\) can be reached by the deadline \(\Phi\). Formally, this problem can be stated as:  
**Deadline AwarenessProblem**  
Given the model \(G\) of a CPPS with an initial marking \(m_{0}\), determine whether \(G\) can be brought to the target marking \(m_{f}\) from \(m_{0}\) by the deadline \(\Phi\) under some control policy, where \(m_{f}\) and the deadline \(\Phi\) are specified by the order requirements. For the target marking \(m_{f}\) and the deadline \(\Phi\) corresponding to the given order requirements, the Future States Awareness Problem can be formulated as follows. **Future States AwarenessProblem**  
Given the model \(G\) of a CPPS with an initial marking \(m_{0}\), determine whether there exists a control policy to bring \(G\) to the target marking \(m_{f}\) without visiting any undesirable states, where the marking \(m_{f}\) is the marking corresponding to the given order requirements.
## 4 Bone Grafts and Substitutes for Bone Repair
### Synthetic Bone Substitute Materials  
#### 4.2.1 Calcium Phosphate Ceramics (CaP Ceramics)  
Hydroxapatite (HA) is the most widely used ceramic material for human bone augmentation because it has a chemical composition and a crystalline structure similar to that of bone. Its bioactivity is related to osteoconductive properties, which allow the apposition and migration of osteoblasts to the surface of the material [74; 75; 76]. HA, alone or in combination with an auto-/allo-/xenograft, has been used with adequate clinical success rates in dentistry and orthopedics to support bone regeneration [77]. However, the quality and quantity of newly formed bone following augmentation with synthetic HA only was often considered insufficient. This is why recent research has focused on the production of HA particles with nanometric dimensions, which improves the biomechanical properties and better mimics the composition of natural bone [78; 79]. The nanostructure allows a higher surface-to-volume ratio, favoring the adhesion, proliferation and differentiation of osteogenic progenitor cells [80; 81; 82].  
Tricalcium phosphate (TCP) has two crystallographic forms: \(\alpha\)-TCP and \(\beta\) -TCP. \(\beta\)-TCP is a material that has been widely used as a bone replacement material for many years. It has a faster biodegradation and absorption compared to HA due to its low level of Ca/P ratio, but it also has many desirable properties, such as ease of handling, radiopacity that allows monitoring of healing, good osteoconductivity due to macroporosity that promotes fibrovascular growth and osteogenic cell adhesion, good resorbability compared to bovine bone grafts, low immunogenicity and no risk of disease transmission [83]. While the interconnected porous structure of \(\beta\)-TCP allows for improved vascularity, it also causes poor mechanical strength which makes it suitable as a bone substitute only with other materials, especially hydroxyapatite [84].  
That is why \(\beta\)-TCP and HA are frequently used in combination today, developing biphasic commercial products (Figure 2).  
Therefore, faster and higher bone regeneration rates were obtained compared to using only HA, but also better mechanical properties than \(\beta\)-TCP used alone. In addition, the resorption and osteoconductivity of these biphasic calcium phosphate ceramics can be controlled by changing the HA/\(\beta\)-TCP ratio (Figures 3 and 4) [85].  
Figure 3: Filling the remaining cavity after intralesional curettage of a tumor with granules of synthetic biphasic ceramic (hydroxapatite HA and beta tricalcium phosphate \(\beta\)-TCP).  
Figure 2: Scanning electron microscopy images of different commercial bone substitutes based on calcium phosphate ceramics (Courtesy of Prof. [PERSON], University Politehnica of Bucharest, Faculty of Materials Science and Engineering).
## 2 The Vision of Future Manufacturing Industry Networks
### Open Research Issues  
Future IIoT systems emphasise the autonomous nature of machines, and machine connectivity, messaging, and understanding are the central issues. Relevant investigations go in two focal directions: model-based service architecture for enterprise scale interoperability, and machine connectivity and communication networks and protocols.  
Some model-based MRAs are proposed to pursue manufacturing flexibility, focusing on the modelling of goals, plans, capabilities, products, enterprise resources, business models, etc. Typically, the knowledge-based manufacturing reference architecture for collaborative production control [15], the Cloud Computing and Internet of Things-based Cloud Manufacturing (CCIoT-CMfg) service model integrating IoT and cloud computing for on-demand use and resources sharing [16], and ARUM system for adaptive and responsive manufacturing [14] are exemplar applications. In addition, a product lifecycle management (PLM) interoperability framework is proposed for collaborative product development by modelling of enterprise, business and information system [17]. A service-oriented architecture model is defined for coordinating execution of distributed automatic system management [18]. The relevant investigations focus on system level manufacturing management and address manufacturing flexibility at the enterprise scale with model-driven solutions, rather than the underlying machine flexibility in production line scale.  
With respect to networks and protocols, SDOs and academic pursue interoperability mainly focusing on the communication and networking standards and architectures, such as IEEE 802.11 ahfor non-intensive M2M data exchange in industry [19], Bluewave wireless routing protocol for factory automation [20], M2M communication-based on 3 GPP LTE/LTE-A networks [21], and CoAP/IPv6 over Low Power Wireless Personal Area Network (6 LoWPAN)/REST for smart healthcare [22]. Since the application scenarios of manufacturing industry and some resource constrained device-based IoT applications differ in many aspects. The relevant studies do not achieve integral interoperability at the connectivity, communication, and semantic understanding as a whole to accommodate the specificities of industrial scenarios, and make it well suited for industrial applications.  
In summary, there is a technical gap that restricts the data and information level technologies from truly integration with machine interactions and industrial operations to leverage machine interoperability and high level adaptability. To bridge the technical gap and achieve an interoperable manufacturing architecture, this investigation describes a collaboration-oriented M2M messaging mechanism toward machine interoperability for production line-scale machine collaborations in manufacturing industry and other IIoT applications.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 2 VoIP Application and Scenarios  
VoIP service offerings over IP networks have turned into a most wanted service over wired and infrastructure-based wireless networks in most network installations of various sizes [18]. Fixed equipment like VoIP phones, switches, access points, and routers can guarantee VoIP quality and they can very well support VoIP services. This better quality is usually achieved by reducing the number of hops, delay, jitter and packet loss using routing policies, traffic engineering, traffic shaping, QoS features configuration techniques on infrastructural devices, or networking equipment. However, if such networks are affected by either natural or man-made disasters, the supporting infrastructure for VoIP is equally affected [19]; this then limits affected people's access to emergency services and hence puts their lives in danger. A backup or supportive, self-forming and self-healing network for emergency situations using mobile phones with Wi-Fi capability can respond to such scenarios [20]. There are also other scenarios where the wired network is non-existent or even not possible and traditional wireless networks cannot respond, for example in mobility scenarios, rural areas, isolated areas, hard-to-reach villages, mines, perimeter security, military fields, temporary deployments, etc. A report on community networks [21] has studied such networks across the globe, where Wi-Fi and mesh technologies have been used, among other technologies, to facilitate the development of networks that are for public use in rural and urban installations.
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 1 Introduction
* Because performance analysis of the new generation of mobile networks will not be realistic considering only the free-space propagation model, the capacity bounds analysis in this research work, along with a comprehensive discussion through the numerical results, will provide a key indicator for mobile operators to be well thought out in the future planning of 5G networks. * The following new figures illustrate how the maximum distance and coverage change based on the selected service class. * In addition, the following parts are changed, modified, or added:  
* The Abstract and Conclusions have been changed. The Introduction and related work have been modified, and additional references have been added. * The derivation of the capacity bounds in a multiservice case is given. * A new group of results is added to reflect the effect of the multiservice case capacity bounds on cell coverage and capacity considering different service categories and mobility scenarios. Consequently, the \"Simulation results and discussions\" Section is changed to demonstrate this effect. * The impacts of cell breathing, interference, and environment-specific path loss (via COST-231 Hata extensions) are quantified. * Actionable insights for network planning (e.g., trade-offs between coverage and capacity) are provided. The remainder of this paper is organized as follows. Section 2 includes related work. Section 3 overviews the extended COST-Hata model. The derivation and analysis of the capacity bounds are described in Section 4. In Section 5, 5G performance with multiservice is introduced. The simulation results and discussion are presented in Section 6. Section 7 concludes the paper by discussing future studies. Finally, a list of relevant references is provided. The framework diagram of this paper's structure is given in Figure 1.  
Figure 1: Framework structure and details of the research work.