esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Learning by Building: Interactive Modular Piping 3D CAD Configuration System
### Program Flow and Implementation Concept  
After the context was clarified, the development team continued to follow the extended CommonKADS approach and started to define the models at the concept level. In addition to gathering all relevant data from industry standards and manufacturing expertise in the form of rules and general equations, the developers collaborated with piping engineers to define a workflow for configuring the pipe routings (Figure 5).  
Typically, when adding routings to pre-existing infrastructure and instrumentation that already have calculated capacity, the first step is to define the routing number, the default elbow radius, and the input capacity. The draft diameter is then calculated based on these inputs and chosen according to the project-relevant standards. The configurator then creates a sub-assembly for the piping with the corresponding metadata in the background. This assembly later contains two levels of detail: one with a simplified sweep geometry and one with the actual piping assembly.  
The next step is to define the start and end connectors of the routing both with position and orientation. Therefore, either existing connectors can be selected or the connectors are virtually declared. The configuration system then instantiates the corresponding markers on a layout component level. The drag points are defined in the same way. To simplify the later assembly process and geometric constraining, the configuration system additionally should add work points to each drag-ball position.  
Afterward, when the routing is formally declared, the engineers can start adding predefined segment templates. To do so, the engineer picks the starting and target reference for the new segment. In the background, the configurator instantiates the geometry to the routing assembly, assigns all necessary parameters, and adds geometry constraints to the model. To replace a routing template, the old one is deleted and a new one is inserted the same way. For the majority of routings, three drag points per route are sufficient.
## 3 Results and Discussion
In our own studies, despite lower ratings of the listed sensory characteristics and overall acceptability of the samples with AFX and FPRX, they were scored as good (4 points in a 5 points scale). In contrast, adding green tea powder in production of lamb sausages negatively affected the sensory quality since the score of acceptability reduced in the group of sausages contained green tea while the scores for color, flavor, and odor were higher than in samples without its addition [33]. Conscious consumers expect food with the most natural composition but that is safe for their health and life, hence the need of producers and researchers to look for solutions that will meet these expectations without causing nutritional and organoleptic losses [34]. The proposed solutions should be aimed at maintaining the stability of meat characteristics to prevent undesirable modifications during storage time [35]. The results of our own study indicate that the application of PANA4 in the recipe of meat products resulted in the desired organoleptic characteristics, while ensuring their microbiological quality. Quantitative flavor profiling (Figure 1) revealed that the intensity of light brown color and flavor bouquet were rated as medium in samples AFX and PANA4. These samples were also characterized by the same level of intensity of meat aroma. According to the product specifications, additive AFX contains flavor, and additive PANA4 is added to enhance palatability, which was achieved in experimental trials. The firmness on slicing and biting was optimal in variants with AFX addition. Meat products manufactured with addition of this additive are characterized by higher intensity of pepper flavor. The taste of cooked pork was the least intense in the samples FPRX and, on average, was too intense in the samples AE100. The intensity of this taste is reflected in the organoleptic evaluation, in which the taste and smell of the samples produced with additive AE100 were rated as the lowest of all the variants.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Encryption Performance  
In this section, ShareA is selected for testing. We analyze the proposed scheme's security through the key space, the gray histogram, and the correlation of adjacent pixels.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 4 Model Decomposition and Solution
### UC Master Model  
The master model is the traditional UC model with HVDC transmissions, which is composed of objection Function (17), minimum frequency limit Constraints (10) and (11), generating units'Constraints (18)-(23) and Benders cut constraints generated from all three sub-models. Besides, Constraint (29) is taken into account to realize power balance.  
\[\sum_{i\in N_{G}}P_{G,i}^{t}\times u_{i}^{t}\geq P_{L,sum}^{t}+P_{loss}^{t} \tag{29}\]  
After linearizing the objective function and constraints in the master model, the mixed integer programming method is employed to solve the problem. The master model aims to get optimization of the generators' ON/OFF and power output variables, HVDC transmission power, which would be used as constant in the sub-models. The specific calculation process can be seen in literature [16].
## 2 Model Design
### Finite Element Analysis of Integrated Squirrel-Cage Structural Deformation  
An integrated squirrel-cage flexible support roller bearing includes a roller bearing, a hollow shaft, and a squirrel-cage support structure, and all components are thin-walled, as shown in Figure 2a. The shaft is assembled in the inner ring. The outer ring and the squirrel-cage structure are one integrated component, which means the outer ring is a part of the squirrel-cage structure, and the outer raceway is manufactured on the internal surface of the cylinder of the squirrel-cage structure. A finite element model containing a thin-walled hollow shaft, an inner bearing ring, and an outer ring-squirrel-cage support structure was constructed in ANSYS.  
Boolean operation of the assembly was conducted in order to ensure satisfaction of the hexahedral meshing conditions, and three-dimensional object element meshing is achieved with SOLID45 elements. The mesh size has a great influence on the accuracy of the calculation [19]. The dimensions of contact areas are on the micrometer scale and the overall dimension of the bearing is in the millimeter scale. Thus, after meshing the whole assembly, the contact lines on the inner and outer raceway is re-mashed twice with smaller mesh sizes, so it is more accurate in imposing a load, as shown in Figure 2b.  
To accurately reflect the relationship between the inner ring and the shaft, contact pairs are placed between the inner surface of the inner ring and the outer surface of the shaft, and between the side of the inner ring and the shaft shoulder. The contact method is surface-to-surface, and the friction coefficient is 0.1 [13]. The outer ring and the squirrel-cage structure are one integrated component, thus there is no contact pair. Designating the inner surface and the side surface of the inner ring as the contacting surface and the corresponding shaft outer surface and shaft shoulder as the target surface, respectively, the mutual contacting portion is described by TARGE170 and CONTA174 surface-surface contact elements. Targe170 is used to represent various 3-D \"target\" surfaces for the associated contact elements, and CONTA174 is used to represent contact and sliding between 3-D \"target\" surfaces and a deformable surface defined by this element.  
The contact loads on the raceway from the rollers are calculated using the modified quasi-dynamic model, and the calculated loads are added to the contact line in the FEA model, as shown in Figure 2c,d. The squirrel-cage base surface is restricted to zero degrees of freedom. The shaft's two end faces are restricted to zero degrees of freedom, and the inner ring is restricted on the shaft by contact pairs. There are no other constraints in this model, thus, it can deform under forces. Solving the model, the flexure and torsion deformations of the squirrel-cage structure and the thin-walled ring, as well as the resultant internal stress due to structural deformation, can be analyzed. In addition, the values of \(\delta_{1 jkh}\) and \(\delta_{2 jkh}\) in the modified quasi-dynamic model are obtained.
###### Abstract  
This study evaluated the effect of various adhesive systems, particularly silane application, on the repair bond strength of a nanofill resin composite and associated stress distribution using finite element analysis (FEA). A total of 105 composite specimens (4 \(\times\) 6 mm) were aged by thermal cycling (10,000 cycles), roughened, etched with phosphoric acid, and assigned to seven groups (\(n\) = 15): G1. control--no adhesive; G2. Single Bond Universal Adhesive; G3. composite primer; G4. PQI; G5. Silane + PQI; G6. Clearfil Universal Bond; G7. All-Bond Universal. Shear bond strength was measured using a universal testing machine (1 mm/min), and failure modes were microscopically classified. FEA was conducted under static and fatigue conditions using 3D models built in Fusion-360. Mechanical properties were obtained from technical data and the literature. A 300 N load was applied and contact detection (0.05 mm) and constraint zones were defined. Statistical analysis was performed using one-way ANOVA and Tukey's HSD (\(p\) = 0.05). [PERSON]'s correlation was used to assess the relationship between bond strength and von Mises stress. The highest bond strength was found in G2 (21.54 MPa) while G1 showed the lowest (8.86 MPa). Silane-treated groups exhibited favorable stress distribution and a strong correlation between experimental and simulated outcomes. Silane applications significantly enhance composite repair performance.  
d 2025 15 8159
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 4 Proposed Algorithms  
This section proposes two user-scheduling algorithms to select subsets of users assigned to a particular AP in the considered communication scenario. The users are selected based on two parameters: the minimum distance between a user and an AP in the MDS algorithm (minimum distance scheduling), and the maximum channel gain between a user and an AP in the MCS algorithm (maximum channel gain scheduling). The algorithms are presented in the next section along with a flowchart in Figure 2.  
Figure 2: A flowchart showing the flow of operations involved in the MDS and MCS algorithms.
## 4 Results and Discussion
### Exponential Function FGM Beam  
The material function in the E-FGM beam is expressed using Equation (7). Explicit expressions for FGM beam stiffness coefficients can be solved by integrating Equations (16)-(18) using the limit of integration from 0 to z where E\({}_{1}\) and E\({}_{2}\) are the elastic moduli of the FGM beam at the (z/h = 0) and (z/h = 1).  
\[\mathrm{K_{1}}=\frac{\mathrm{h}(\mathrm{E_{1}}-\mathrm{E_{2}})}{\mathrm{ln} \left(\frac{\mathrm
# End-Users' Augmented Reality Utilization for Architectural Design Review
## 5 Discussion
### Technical Acceptance  
The results of the technical acceptance questionnaire reveal that the AR system was deemed the most comfortable visualization platform to conduct the design review. The user was only required to wear the device and walk around to review the virtual building design. AR system users felt that the technology was useful, easy, satisfactory, and that they would have high intention to use it in the future. However, for long-term use, the physical discomfort users felt this needs to be resolved. Although the technology acceptance level was higher than the 2D screen, the VR and AR systems had a visually induced motion sickness (VIMS) side effect [45]. All VR participants and some AR participants experienced motion sickness when wearing the HMD. Based on the participants' comments, motion sickness increased with the amount and speed of head movements. There have been several attempts to reduce VIMS. Participants who were exposed to pleasant music or scents reported feeling less sick [45; 46]. Reducing the wear time of the HMD has also been suggested as a potential remedy [47]. As such, priority must be given to improve the technology in order to mitigate VIMS and facilitate end-user involvement in design collaboration.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Experimental Design
#### 4.1.6 Updating Cost of Link  
Determining a route for a particular flow through a SDN efficiently is still challenging. The SDN controller must obtain a lot of information in order to accomplish this. The information includes getting a comprehensive vision of the network (i.e., network topology discovery and getting link state information), computing the optimum paths for the flows considering the information of the flows and the network, and reconfiguring the routing table based on the new forwarding rules in the infrastructure plane. Common routing algorithms depend on three concepts to compute the cost of a link. These concepts are static link cost (Hop-to-Hop count, distance, and link capacity), dynamic link cost (available link capacity, and link utilization), and dynamic link cost while minimizing the interference (available link capacity, link utilization, and flow count on a link) [41]. The approach presented in this part explains the computing and updating of the cost of a link in the SDN framework. The cost of a link is calculated based on the inverse relationship between the bandwidth and the latency of the link, where latency is equal to the reciprocal value of bandwidth. The cost of the link is updated recursively. The cost of the link is calculated based on the updateCost algorithm in Section 3.2. An example and figures are explained for this aspect below.  
Suppose that a host (Host) wants to send a set of packets to a server (Server) and there are two different routes between the Host and the Server, as shown in Figure 7, the steps below will be followed to calculate the link cost of both routes:  
1. 1 st route R1 goes through switches S1- > S2- > S4- > S6- > Server
2. 2 nd route R2 goes through switches: S1- > S3- > -S5- > S6- > Server
3. The getAvailablePaths (Host, Server) algorithm will find these two paths (R1, R2) between Host and Server
4. For each link in R1,R2
5. B1 = Available bandwidth of P1 = availableBW(L12) + availableBW(L24) + availableBW(L46)
6. B2 = Available bandwidth of P2 = availableBW(L13) + availableBW(L35) + availableBW(L56)
7. Latency of P1 = D1 = 1/B1; Latency of P2 = D2 = 1/B2.
8. If D1-acceptableLatency > D2-acceptableLatency then bestCost = D2
9. else bestCost = D1
10. Then we get the nearest value to the acceptable Latency
11. If bestCost = D1 then best path is R1 else best path is R2.  
Figure 7: Updating Link Cost.
# Trend Prediction of Event Popularity from Microblogs
## 3 Methodology
### Popularity Time Series Denoising  
The popularity evolution of microblog events tends to be a nonlinear and irregular time series. Therefore, to solve the popularity trend prediction, it is necessary to extract the trend components of popularity time series. Specifically, this paper needs to denoise the time series data and extract the different components for trend prediction. Our proposal decomposes and reconstructs the trajectory matrix of the time series to solve time series denoising through singular spectrum analysis [28]. Here, there are four steps for time series denoising.  
(1) Embedding  
Supposed that the time series of event popularity from microblog is represented by \(X=[x_{1},x_{2},\cdots,x_{m}]^{T}\), then the paper transforms it into a \(p\)-dimensional trajectory matrix \(Y=[y_{1},y_{2},\cdots,y_{p}]^{T}\), where \(2\leq p\leq m\). Consequently, the trajectory matrix can be defined by Formula (6).  
\[Y=\left[\begin{array}{cccc}x_{1}&x_{2}&\cdots&x_{m-p+1}\\ x_{2}&x_{3}&\cdots&x_{m-p+2}\\ \vdots&\vdots&&\vdots\\ x_{p}&x_{p+1}&\cdots&x_{m}\\ \end{array}\right] \tag{6}\]  
(2) Decomposition  
Firstly, the trajectory matrix \(Y\) obtained above is decomposed into \(d\) components, where \(d\) is the rank of matrix \(Y\). And then, the paper can get parameter group \((\lambda_{i},U_{i},V_{i})\) of matrix \(YY^{T}\) by using Singular Value Decomposition (SVD) algorithm. Here, \(\lambda_{i}\) represents the singular value of the matrix, and the symbol \(U_{i}\) and \(V_{i}\) are used to define the left eigenvector and the right eigenvector, respectively. Subsequently the trajectory matrix \(Y\) and its components \(Y_{i}\) are defined by the following Formulas (7) and (8).  
\[Y=Y_{1}+Y_{2}+\cdots+Y_{d} \tag{7}\]  
\[Y_{i}=\sqrt{\lambda_{i}}U_{i}V_{i} \tag{8}\]  
(3) Grouping  
In the paper, \(k\) of the \(d\) components are selected as the popularity trend components, denoted as \(I=\{I_{1},\cdots,I_{k}\}\). Meanwhile, the valuable extraction component \(Y_{I}\) of the time series is represented by \(Y_{I}=Y_{I_{1}}+Y_{I_{2}}+\cdots+Y_{I_{k}}\). Therefore, the other \(d\)-\(k\) decomposition components are considered as the noise of the time series.  
(4) Reconstitution  
By means of diagonal averaging, the valuable trend component \(Y_{I}\) formed in the grouping stage can be converted into the previous time series \(x_{component}=\{x_{I_{1}},\cdots,x_{I_{k}}\}\). Consequently, the original time series \(X\) is represented as the sum of component series data \(x_{component}\) and noise series data \(x_{noise}\).  
\[X=x_{component}+x_{noise}=x_{I_{1}}+x_{I_{2}}+\cdots+x_{I_{k}}+x_{noise} \tag{9}\]
# Plane Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network
## 2 Proposed Method  
### Overview of Proposed Approach  
The most basic flame detection method using CNN is based on iterative learning through a learning dataset for flames, and then the image is classified as being a flame or not through a new input image. However, there is a limitation in classification using CNN, and if the object is simply predicted, the accuracy may decrease when multiple objects exist in one image. In addition, repeated learning through many datasets cannot significantly reduce the ratio of false negatives or false positives, resulting in reduced accuracy. Therefore, through the application of an appropriate image pre-processing process, these problems were offset, and the detection accuracy of the flame was improved.  
In this study, the proposed method to increase the detection accuracy of a flame was divided into two main procedures. First, the flame and non-flame image datasets were collected and trained using Inception-V3 among the CNN models, as shown on the left in Figure 1. When classifying a flame from direct input images without any pre-processing for models learning about a flame, it is difficult to reliably classify flame or non-flame images because they contain non-flame objects or unnecessary background areas. Therefore, the first image pre-processing separates objects from the input image filters the HSV color regions where flames exist. Subsequently, second image pre-processing uses Harris corner detector to detect corner points. Additionally, corner point detection was performed only on areas where corner points existed in a 45 to 135 degree direction, with sharp characteristics at the top of the detected corner points, bounding boxes for dense points, and the area was extracted as a ROI.
## 2 Materials and Methods
### Cytotoxicity Measurement  
Molecule/virus-induced cytotoxicity in the HnAEC culture was determined by detecting lactate dehydrogenase (LDH) released into the basolateral medium using CyQUANT LDH Cytotoxicity Assay kit (Invitrogen, Cat. No. C20300, Waltham, MA, USA). Briefly, 50 \(\mu\)L of basolateral medium of each sample was transferred into a 96-well plate and mixed with 50 \(\mu\)L of reaction buffer. Samples were incubated at room temperature for 30 min, protected from light, followed by an addition of 50 \(\mu\)L of stop solution to each well. Medium from HnAEC cultures that were lysed by Triton X-100 10% overnight was used as maximal LDH activity control. In parallel, fresh MucilAir medium served as background LDH activity control and an LDH positive control was included in each measurement. The absorbance at 490 nm and 680 nm were measured. The 680-nm absorbance values were subtracted from the 490-nm absorbance values to calculate the LDH activity. The percentage of cytotoxicity was determined using the formula: % cytotoxicity = (Sample LDH activity \(-\) Medium LDH activity)/(Maximal LDH activity \(-\) medium LDH activity) \(\times\) 100.
## 5 Seismic Hazard  
The seismic moment was estimated based on the inverted slip distribution, with a maximum at \(3.9\times 10^{20}\) N\(\cdot\)m:  
\[M_{0}=\mu\cdot S\cdot\delta u \tag{5}\]  
\(\mu\): Shear modulus taken as 33 GPa,  
S: Area of the fault,  
\(\delta\)u: Mean slip from strike slip and dip slip.  
The seismic moment release was summed for each 2 \(\times\) 2 km patch in horizontal rows to obtain the variation in moment release with depth, and summed in vertical columns to get the moment release along strike. The slip distribution and the study of the integrated moment by depth and strike show that the main deformation happened in the north part of the fault, where the fault is curving towards the east with a slip reaching 12.6 m (Figure 9D) and most of the moment was released at depths shallower than 4 km.  
Figure 9: Slip distribution model. Inversion based on (**A**) GPS data and three interferograms (Table 1) with all the weights set to 1; (**B**) same data sets but with the relative weights estimated by gABIC (GPS 1, 0.09, 0.10, 0.10 for the three interferograms). All the data sets were included in (**C**,**D**). (**C**) The relative weights are set to 1; (**D**) the relative weights ([GPS, levelling, Benchmarks, Interferogram 1, 2, 3, SPOT] = [1, 10\({}^{-4}\), 10\({}^{-4}\), 0.09, 0.10, 0.10, 0.01]) were estimated by gABIC. The colour scale saturates at 12 m. \(M_{0}\) is the total seismic moment for each model.  
To evaluate the effect of the rupture on the surrounding area, the Coulomb Stress Change (CSC) was calculated using Coulomb 3.3 (Graphic-Rich Deformation and Stress-Change Software) USGS software [37; 38]. CSC represents the transfer of stress. It is possible to estimate, from the CSC, the triggering effect of large earthquakes such as the Chi-Chi event. CSC was estimated on a specified fault (strike, dip, and rake specified) at a depth of 11 km with a friction coefficient of 0.4. Figure 10 shows that the increase of Coulomb stress is concentrated at the north of the Chelungpu fault and towards the north-east which indicates that most of the stress was transfered to faults at the north of the Chelungpu fault. As presented by Reference [16], aftershocks (see Figure 11) mainly occured downdip from the rupture area which is consistent with the increase of Coulomb stress that we observe in Figure 10A. We observe in Figure 10B an increase of stress for depth above 10 km which is consistent with the aftershock distribution.  
Figure 10: (**A**) Coulomb Stress Changes (CSCs) due to the Chi-Chi earthquake. The green lines are the location of the 4 sub-faults. (**B**) Cross section with location presented on (**A**) by the dashed line.