esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Real-Time Small Pit Defect Detection Network
### Extracting Candidate Image Patches for Pit Detection  
#### 3.2.1 Cropping of Positive Region  
The image processing-based candidate region extraction stage is divided into a process for cropping the positive region in the overall film image and a process for conducting the localization of the pit candidate areas in the cropped region and simultaneously extracting the image patches. Because the detection speed decreases when processing entire 8192 \(\times\) 6000 high-resolution images, the positive regions of the film, excluding the background regions, are extracted from the film images. Figure 5 shows the positive region extraction process. First, to improve the processing speed, the original image in Figure 5a is reduced to 1/nth size using Equation (1), and the color image is converted into a black and white image.  
\[\text{resize}\left(\text{ 1},\frac{\text{W}}{\text{n}}\text{, \frac{\text{H}}{\text{n}}}\text{ }\right) \tag{1}\]  
Here, I is the input image; W and H are the width and height of the input image, respectively. In addition, n is a selectable integer used to reduce the number of computations by resizing the input image. As n increases, the processing speed improves; however, when n is set too large, pit defect information is lost. Because n is a factor that affects pit defect detection rates, this analysis is covered in the ablation study discussed in Section 5. As the pit defects have no specific color information, the three-channel RGB image can be converted into a grayscale image to reduce computations. Next, binarization is applied based on a threshold value to extract the positive regions. We analyzed film images with various lighting conditions and observed that there is a large contrast between the film's positive region and the background. Accordingly, the threshold was set at 20% of the image's maximum brightness value, which can adequately distinguish positive regions in all environments. After binarization is applied, as shown in Figure 5b, a four-connectivity labeling operation, in which connected pixels are divided into individual groups, is conducted such that the black and light regions are divided by region. Finally, the positive region is cropped as shown in Figure 5c.
# PROSIM in the Cloud: Remote Automation Training Platform with Virtualized Infrastructure
## 4 HUBCAP Sandboxing Environment
This provisioning capability is essential for the organic growth and sustainability of the HUBCAP platform, which aspires to become a one-stop-shop for small and medium enterprises wanting to embrace digital innovation through the MBD technology for cyber-physical systems. While building out a broad repository of resources for MBD testing, the HUBCAP platform differentiates between two fundamental resource types: models and tools. A model represents a building block that encodes the dynamics of a physical system using readily available notations and software. A tool is built around one or multiple models and can be seen as a stand-alone software application. Tools also have the ability to communicate with one another inside the HUBCAP environment and the creator of the tool can establish particular licensing options for the end-user such as software-as-a-service agreements. To exemplify this classification, one relevant system is the INTO-CPS three tank model [30], that can be publicly accessed in the HUBCAP sandboxing environment. It represents two connected water tanks, with an inflow of water in the first one and the outflow of the second one is collected by a third tank. A typical control application can use this model to assure that the reference level is followed in one of the tanks considering the system behavior and performance requirements. Conversely, in [31] a smart energy tool is described in detail. The application helps users to quantitatively evaluate investment related metrics for energy systems development with suitable visualization, KPI and data  
Figure 4: A sandbox with a basic OS and two tools. On the right: the sandbox shared storage. Figure 5: Platform architecture: interaction scenario. management options. The authors also emphasize cloud-deployment aspects of the tool with regard to sandbox setup, user authentication and performance tests. The relevance to cyber-manufacturing of the HUBCAP environment is argumented in [32]. The main features that can be used by companies in this field relate to plant simulation, rich 3D visualization and future AR/VR enable maintenance and remote operation applications. The key technical features that support the model and tool integration are listed as the network file system (NFS) and virtual networks (VLAN) inside the environment to isolate sandboxes within suitable integration and communication policies. Consideration of security aspects [33] at the intersection of IoT, CPS and Industry 4.0 systems is vital for the design of robust and reliable solutions. Several types are mitigated by leveraging the HUBCAP platform such as preventing control over communication, data-packet infection and flooding attacks. The virtualization approach can be used also to model and simulate attacks on physical devices and embed measures to protect them in subsequent real implementations.
## 1 Introduction
The readily measurable cutting parameters (feed rate, depth of cut, and cutting speed) are adopted as independent variables. Mathematical expressions relating these variables to flank wear are established using the KAN-A, KAN-B, and KAN-C models, respectively. Furthermore, the physical interpretability of the derived formulas is systematically analyzed from three complementary perspectives: (1) a variable importance assessment, (2) the topological evolution of KAN architectures, and (3) the mathematical relationships between input-output variables. A comparative analysis of KAN-A,KAN-B, and KAN-C is conducted to evaluate modeling errors and identify their underlying causes. Subsequently, MLP-A, MLP-B, and MLP-C models are designed by referencing the topological structures of their KAN counterparts. A comparative study between KAN and MLP frameworks is performed to examine differences in tool wear modeling performance. Particular emphasis is placed on analyzing the effects of network depth, width, and parameter configurations, which reveals distinct sensitivities to architectural variations. The fundamental disparities in modeling capabilities are theoretically interpreted through the lens of KART and Cybenko's Theorem. Finally, this work systematically compares classical wear equations, machine learning methods, and KAN-based approaches, highlighting their respective strengths. Simulations of turning processes are conducted using DEFORM-3D to further evaluate the wear modeling capabilities of KAN-A, KAN-B, and KAN-C. The advantages of the KAN-derived formula, along with its applicability and limitations, are thoroughly discussed. The remaining parts of this paper are organized as follows: Section 2 presents the fundamental principles of KANs and the architectures of the proposed models. In Section 3, the public dataset is described, and three modeling experiments are conducted. Additionally, the mathematical expressions derived from KANs and their physical interpretations are discussed. Section 4 introduces multiple metrics to quantify modeling errors and computational efficiency. A vertical comparison of modeling performance among KAN-A/B/C is performed, with a systematic evaluation of error analysis and performance trade-offs. In Section 5, three MLP-based neural networks are constructed, mirroring the configurations of KAN-A/B/C, respectively. A horizontal pairwise comparison of prediction errors is presented. The underlying causes of performance disparities are analyzed through the theoretical frameworks of KART and [PERSON]'s theorem. Section 6 provides a comparative analysis of classical tool wear equations, machine learning methods, and KAN-based approaches. Section 7 simulates turning operations and employs KAN-A/B/C for tool wear modeling, discussing both the advantages and limitations of the approach. Finally, the research findings are synthesized to form a comprehensive conclusion, and potential future research directions are outlined.
###### Abstract  
(1) Background: tooth agenesis is a very common dental anomaly of the human dentition most often affecting the maxillary anterior region, mandibular and maxillary premolar regions. (2) Purpose: the present study was aimed to evaluate the prevalence and patterns between bilateral and unilateral tooth agenesis among orthodontic individuals and to illustrate the treatment options for missing teeth and the outcome. (3) Materials and methods: Pre-treatment records, photographs and radiographs, of 3000 consecutively treated orthodontic individuals from the post-graduate clinic of Tel Aviv University were surveyed to detect permanent tooth agenesis in either dental arch. The data was recorded according to gender, and location and quantified between unilateral and bilateral agenesis. Descriptive and comparative statistical analysis were performed with t-test and Chi-square test (\(p<0.05\)). (4) Results: permanent teeth agenesis, excluding third molars, was found in 326 individuals (11%), 139 males (43%) and 187 females (57%). Of them, 59% were missing in the maxilla and (41%) were missing in the mandible. A higher prevalence rate of bilateral missing lateral incisors in the maxilla (62 cases), followed by bilateral missing second premolars in the mandible (44 cases) compared with unilateral missing teeth. (5) Conclusions: this study found an overall prevalence of missing permanent teeth in orthodontic patients to be 11%. The female: male prevalence ratio was roughly 32, with a greater tendency in the maxilla than in the mandible. A higher prevalence of bilateral missing maxillary lateral incisors and mandibular second premolar than unilateral missing teeth.  
bliateral tooth agenesis; hypodontia; distribution; dentistry; orthodontics
## 6 Numerical Experiments
### Impact of Service Provider/User Ratios  
In the previous part, an assumption is made that the user number is the same as the service provider number in a single domain, which may not be the case in practice when a large number of users are actually served by a few telecommunications companies. To reflect this market feature, we alter the service provider/user ratio to a smaller value and evaluate its impact on the service provision performance of both the optimal trade and our proposed ascending auction mechanisms when the user number is fixed as \(N=10\) (e.g., a thin market) and \(N=100\) (e.g., a thick market). It is worth mentioning that even when the number of service providers decreases, each remaining service provider is endorsed with a stronger service ability (e.g., a bandwidth value drawn from a larger range)  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Range or Distribution** \\ \hline User number \(N\) & [10, 20, 50, 100, 150] \\ \hline Service provider number \(M\) in a domain & [10, 20, 50, 100, 150] \\ \hline Service provider/user ratio \(\alpha=M/N\) & [1.0, 0.8, 0.6, 0.4, 0.2] \\ \hline Domain number \(K\) in the service chain route & [1, 2, 3, 4] \\ \hline Bandwidth requested by users & Historical traffic values \\ \hline Bid price of users & \(U[1,100]\) \\ \hline Bandwidth provided by service providers & \(U[1,300/\alpha]\) \\ \hline Ask price of service providers in the service shortcut route & \(U[1,100]\) \\ \hline Ask price of service providers in the service chain route & \(U[1,100/K]\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameter settings.  
Figure 6: The comparison with the cases of different user numbers (or service provider numbers in a domain): (**a**) the admission ratio; (**b**) the average served bandwidth per user; and (**c**) the gain from trade per user.  
to maintain the overall service level. Otherwise, the outcome would be obvious and trivial because a lower overall service level would impair the performance of any mechanism.  
The evaluation of different service provider/user ratios is shown in Figure 7. With a more centralized market, i.e., in the case of a smaller service provider/user ratio, the performance of either the ascending auction mechanism or the optimal trade mechanism decreases, but to different degrees. The performance of the optimal trade mechanism is slightly affected, especially for the metrics of admission ratio and served bandwidth per user. However, the performance of the ascending auction mechanism is highly affected, e.g., the admission ratio drops by approximately 50% in Figure 7a. The implication is that a diverse and fully competitive market with a large number of service providers is more favorable for our proposed ascending auction mechanism, in which its performance is closer to the optimal trade mechanism.  
It is also found in Figure 7 that the ascending auction mechanism is less affected in the thick market than in the thin market, which gives another implication that the thick market with more users is more robust to the centralization of service providers.
# Punching Shear Stress in Post-Tensioned Transfer Plate of Multi-Story Buildings
## 3 Theories and Current Design Approach in Punching Shear  
### Design Code for Two-Way Shear under Seismic Force  
Since the transfer structure supports the load from the superstructure, it is essential to ensure adequate deformation capacity regarding lateral forces. For seismic force-resisting members, structural codes and guidelines stipulate that the punching shear force (\(V_{u}\)) due to gravity loads for two-way flat plate systems shall not exceed 40% of the concrete shear strength (\(V_{c}\)) of the slab-column connection [17; 18]. Shear strength of concrete is calculated as the smallest of the followings [19]:  
\[\begin{array}{l}V_{c}=\left(1+\frac{2}{\beta}\right)\frac{b_{0}d\lambda\sqrt {f_{c}}}{6}\\ V_{c}=\left(\frac{\alpha_{s}d}{b_{0}}+2\right)\frac{b_{0}d\lambda\sqrt{f_{c}}}{ 12}\\ V_{c}=\frac{1}{3}b_{0}d\lambda\sqrt{f_{c}},\end{array} \tag{1}\]  
where \(b_{0}\) is the perimeter of the critical section; \(d\) is the effective thickness of slab; \(f_{c}\)' is nominal compressive strength of concrete; \(\beta\) is ratio of long to short dimension of column section; \(\alpha_{s}\) is 30 for the interior column, 40 for exterior column (except corner column), and 20 for corner column; and \(\lambda\) is the light concrete coefficient, which is 1.0 in general.  
The perimeter length of the critical section \(b_{0}\) is proportional to the effective thickness of the slab, since the critical section is located at a distance of \(d\)/2 from the column face in accordance with ACI 318-19, Section 22.6.4.1. Therefore, the concrete shear strength \(V_{c}\) is proportional to the concrete compressive strength and the effective thickness of the slab. The result is that the calculation of punching shear force at the slab-column connection due to gravity load can influence the design and determination of the transfer plate thickness.
## 2 Thermal and Strength Analysis of Compression and Tension Steel Elements in Fire  
### Thermal Analysis  
Designing a structure for an accidental situation, i.e., a fire, is based on an analysis of thermal interactions. With an increase in the temperature of fire gases \(\theta_{g}\), the temperature of the steel element increases \(\theta_{a}\) and its resistance \(N\) decrease. Fire engineering requirements for the assumed time \(t_{f,req}\) are met if either of two conditions is satisfied:  
* \(\theta_{cr}\geq\theta_{a}\)--Check in the temperature domain;* \(N\geq E\)--Check in the resistance domain.  
The temperature domain check involves a comparison of the temperature that the steel element achieves (\(\theta_{a}\)) after a specified fire duration with its critical temperature \(\theta_{cr}\). The disadvantage of this approach lies in some limitations concerning the critical temperature determination and in the fact that this approach cannot be applied to some elements. Consequently, resistance domain checks are more popular. This process involves comparing the resistance of the element (\(N\)) with the effect of actions (\(E\)). The latter is the value of forces generated in the structure due to an external load. To describe the temperature of the fire gases, a standard fire curve was adopted:  
\[\theta_{g}=345\ \log_{10}\!\left(8\ t_{fi}+1\right)\ +\ 20,\ ^{\circ}\!\text{C}. \tag{1}\]  
When the temperature of fire gases \(\theta_{g}\) is known, it is possible to determine the temperature that individual elements of the structure will reach.
## Materials and Methods
### Model Description
#### 2.2.2 Water Balance Calculation  
In the DLM-ecohydro model, the detailed water balance equation considers the lateral water flux by the description of surface overland floor (\(\mathrm{F_{surface}}\)) and sub-surface baseflow (\(\mathrm{F_{subsurface}}\)) at the pixel scale and daily time steps. Once the infiltration process reaches its max capacity [33], the ponded water would appear on the soil surface and move laterally as \(\mathrm{F_{surface}}\). The topography derived lateral subsurface baseflow is the difference between subsurface inflow and outflow within eight neighboring pixels as described in [15]. Water balance calculations are composed with storage changes at unsaturated (\(\Delta\mathrm{W_{unsat}}\)) and saturated (\(\Delta\mathrm{W_{sat}}\)) soil parts which are presented below:  
\[\Delta\mathrm{W_{unsat}}=\mathrm{I-F_{surface}}+\mathrm{C-P-T_{unsat}-E_{floor}}, \tag{3}\]  
\[\Delta\mathrm{W_{sat}}=\mathrm{P-F_{subsurface}-T_{sat}-C},\text{ and} \tag{4}\]  
\[\Delta\mathrm{W}=\mathrm{I-F_{surface}-F_{subsurface}-T_{unsat}-T_{sat}-E_{ floor}} \tag{5}\]  
where I is total infiltrated water which is comprised of through-fall, snowmelt and stem flow [34], P is percolation and represented the water in the unsaturated zone downward to the saturated part under the impact of gravity, while C is capillary rise upward from the saturated to vadose zoneappearing under extreme surface dryness. T\({}_{\text{unsat}}\) and T\({}_{\text{sat}}\) are the transpirations from roots situated in the unsaturated and saturated zones, respectively. E\({}_{\text{floor}}\) is soil evaporation and sublimation of snowpack. The complete soil profile storage change is the sum of two soil zones (Equation (5)).
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 4 Results
### Modeling
#### 4.1.4 Higher Number of Locations  
The last step in the modeling stage is to verify the influence of a superior number of locations, \(m\). The order of operations that are needed to calculate each of the correlations was determined and shown in Table 8 since the rest of the executions are not time-consuming.  
Taking into account the previous values and the characteristics of the computer used, the evolution of the calculation time in the modeling stage is determined by increasing the number of locations, Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **c** & **k** \\ \hline Fragavella & 7.5591 & 1.4133 \\ Labrada & 6.4257 & 1.8760 \\ Lanzos & 3.0646 & 1.3053 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weibull parameters for each of the studied locations for a period of 5 years.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Correlation** & **Obtained Result** \\ \hline [PERSON]’s correlation 1–2 & 0.8040 \\ [PERSON]’s correlation 1–3 & 0.3227 \\ [PERSON]’s correlation 2–3 & 0.3873 \\ Autocorrelation 1 & 0.9562 \\ Autocorrelation 2 & 0.9524 \\ Autocorrelation 3 & 0.9399 \\ Cross-correlation 1–2 & 0.8019 \\ Cross-correlation 2–1 & 0.8015 \\ Cross-correlation 1–3 & 0.3246 \\ Cross-correlation 3–1 & 0.3201 \\ Cross-correlation 2–3 & 0.3873 \\ Cross-correlation 3–2 & 0.3851 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlations obtained in the modeling stage for the study with [PERSON], 1, [PERSON], 2, and [PERSON], 3, for a period of 5 years.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Calculations** & **Order of Operations** \\ \hline [PERSON]’s correlation & \(\frac{m^{2}-m}{2}\) \\ Autocorrelation & \(m\) \\ Cross-correlation & \(m^{2}-m\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Order of necessary operations during the modeling stage.  
Looking at Figure 7, it can be checked that the calculation time of the modeling stage grows with the number of locations, so in a scant number of places, the time needed to reach a result is practically null, as it was said in previous sections, of about 0.6 s for 2 sites or of about 1.5 s for 3. As the number of locations increases, the calculation time rises, and it reaches about 37 min when 50 places are being evaluated. As can be seen in Table 8, this is due to the fact that the number of calculations increases in a quadratic order with the number of locations and, as a consequence, so do the number of operations to be carried out in the modeling stage.
## 3 HD-VLL for Hybrid DS/FH Spread Spectrum Signals
### A Description of the Problem
Considering a high dynamic scenario with simultaneous velocity \(v_{d}\), acceleration \(a\), and jerk \(\dot{a}\), the instantaneous phase \(\varphi_{k}\) of the received signal during the \(k\)th loop update period can be expressed as follows:  
\[\varphi_{k}=\varphi_{0k}+\frac{2\pi f_{cj}}{c}v_{d}(t-nT)+\frac{\pi f_{cj}}{c }a(t-nT)^{2}+\frac{\pi f_{cj}}{3c}\dot{a}(t-nT)^{3}, \tag{7}\]  
where \(\varphi_{0k}\) denotes the initial phase of the \(k\)th loop update period, \(T\) denotes the loop update interval, and \(T=MT_{s}\). By discretizing the analog signal in (7), we obtain  
\[\varphi_{ki}=\varphi_{0k}+\frac{2\pi f_{cj}}{c}v_{d}iT_{s}+\frac{\pi f_{cj}}{c }a(iT_{s})^{2}+\frac{\pi f_{cj}}{3c}\dot{a}(iT_{s})^{3},\;\;i=0,1,2,\ldots,M-1. \tag{8}\]  
The purpose of the carrier tracking loop is to generate a replica signal of the received carrier signal, that is, the error between the local instantaneous phase of the replica signal and the instantaneous phase of the received signal should be as small as possible at each moment. By comparing (6) with (8), it can be seen that (6) only takes into account the effect of velocity on the local instantaneous phase but leaves out the effects of acceleration and jerk. Consequently, there is an accumulated phase tracking error of \(\pi f_{cj}aT^{2}/c+\pi f_{cj}\dot{a}T^{3}/3c\) at the end of the \(k\)th loop update period. This accumulated phase tracking error is little when the dynamic is small; in this case, the impact on the loop can be ignored. However, as the dynamic increases, this accumulated phase tracking error poses challenges to the steady-state tracking of the loop.