esa-sceva/satcom-chunk-collection huggingface dataset:
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
## 5 Discussion  
In view of the problems that the bearing capacity and compression performance of the retractable U-shaped steel supports in the roadway under complex conditions are difficult  
Figure 19: The sectional design principles for yielding U-shaped steel support.  
to give full play to, and the deformation and damage are serious, many scholars [13,20,30] have carried out research on the relationship between the torque and preload of the support cable, the slip law of different sectional joints, and the sliding mechanism of the lap section of the U-shaped steel support, but there is no relevant research on the sectional design method of the U-shaped steel supports. In order to make up for this research gap, a full-scale indoor test of retractable U-shaped steel supports with different sectional modes was carried out by using the self-developed full-scale stent test system, the influence of different sectional modes on the bearing performance of the stents was quantitatively analyzed, the numerical tests of retractable U-shaped steel supports under different load modes and different cross-sectional forms were carried out; meanwhile, the sectional design methods and field engineering suggestions of retractable U-shaped steel supports were proposed.  
Based on the sectional design method for yielding U-shaped steel supports proposed in this study, it is possible to optimize support structure configurations that are better adapted to site-specific engineering geological conditions. This method enables the full utilization of the yielding performance and load-bearing capacity of the support system, effectively preventing premature instability failure of the support structure. Consequently, it extends the service life of the supports while simultaneously enhancing the safety and economic efficiency of roadway support engineering projects.  
Research Trends: To thoroughly investigate the internal force distribution characteristics and their evolution mechanisms in support with various segmentation configurations, our research team proposes to conduct a systematic theoretical analysis. By establishing mechanical models of the support and deriving analytical expressions for internal forces, this study aims to develop calculation formulas for arch internal forces applicable to diverse working conditions. The anticipated outcomes are expected to provide theoretical foundations for the optimal design of a support.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.2 Validation of RCFBS Using Numerical FEA  
The receptance matrix determined using the FBS method is validated through numerical FEA. The validation process involves comparing the dynamic response predictions based on the receptance matrix of subsystems A, B, and C using the RCFBS approach with the results obtained from the FEA [29]. In this section, the stiffness and mass matrices for  
Figure 3: Reduction of a three-substructure coupling scheme into a two-substructure system. Figure 2: Assembly of system ABC, comprising substructures A, B, and C, along with their corresponding generalized coordinates and degrees of freedom (DoFs). each subsystem are derived. For subsystem A with 2 DoFs, the stiffness matrix \(\left[K_{B}\right]\) and mass matrix \(\left[M_{B}\right]\) are calculated as follows:  
\[ \begin{split}\left[K_{A}\right]=\frac{\mathrm{E}\mathrm{I}_{A}}{ \mathrm{L}_{A}^{A}}\begin{bmatrix}12&-6\mathrm{L}_{A}\\ -6\mathrm{L}_{A}&4\mathrm{L}_{A}^{2}\end{bmatrix},\\ \left[M_{A}\right]=\frac{\mathrm{m}_{A}}{420}\begin{bmatrix}156&-22\mathrm{L}_ {A}\\ -22\mathrm{L}_{A}&4\mathrm{L}_{A}^{2}\end{bmatrix}.\end{split}  \tag{9}\]  
Similarly, for subsystem B with 4 DoFs, the stiffness \(\left[K_{B}\right]\) and mass matrix \(\left[M_{B}\right]\) are derived from the following:  
\[ \begin{split}\left[K_{B}\right]=\frac{\mathrm{E}\mathrm{I}_{B}}{ \mathrm{L}_{B}^{A}}\begin{bmatrix}12&6\mathrm{L}_{B}&-12&6\mathrm{L}_{B}\\ 6\mathrm{L}_{B}&4\mathrm{L}_{B}^{2}&-6\mathrm{L}_{B}&2\mathrm{L}_{B}^{2}\\ -12&-6\mathrm{L}_{B}&12&-6\mathrm{L}_{B}\\ 6\mathrm{L}_{B}&2\mathrm{L}_{B}^{2}&-6\mathrm{L}_{B}&4\mathrm{L}_{B}^{2}\end{bmatrix},\\ \left[M_{B}\right]=\frac{\mathrm{m}_{B}}{420}\begin{bmatrix}156&22\mathrm{L}_{B }&54&-13\mathrm{L}_{B}\\ 22\mathrm{L}_{B}&4\mathrm{L}_{B}^{2}&13\mathrm{L}_{B}&-3\mathrm{L}_{B}^{2}\\ 54&13\mathrm{L}_{B}&156&-22\mathrm{L}_{B}\\ -13\mathrm{L}_{B}&-3\mathrm{L}_{B}^{2}&-22\mathrm{L}_{B}&4\mathrm{L}_{B}^{2} \end{bmatrix}.\end{split}  \tag{10}\]  
For subsystem C, which also has 2 DoFs, the stiffness matrix \(\left[K_{c}\right]\) and mass matrix \(\left[M_{c}\right]\) are computed as follows :  
\[ \begin{split}\left[K_{c}\right]=\frac{\mathrm{E}_{L}}{\mathrm{L}_ {c}^{A}}\begin{bmatrix}12&6\mathrm{L}_{c}\\ 6\mathrm{L}_{c}&4\mathrm{L}_{c}^{2}\end{bmatrix},\\ \left[M_{c}\right]=\frac{\mathrm{m}_{c}}{420}\begin{bmatrix}156&22\mathrm{L}_ {c}\\ 22\mathrm{L}_{c}&4\mathrm{L}_{c}^{2}\end{bmatrix}\end{split}  \tag{11}\]  
Eventually, the receptance matrix of each subsystem is determined by the following:  
\[\left[H_{i}\right]= \begin{bmatrix}-\omega^{2}\left[M_{i}\right]+\left[K_{i} \right]\end{bmatrix} ^{-1}. \tag{12}\]  
where index i represents subsystems A, B, and C. The global receptance matrix for the entire system ABC is obtained by assembling the global mass and stiffness matrices:  
\[\left[K_{\mathrm{ABC}}\right]=
## 3 Experimental Section  
### Materials  
Completely biogenic lubricating greases (1-13) and two reference semi-biogenic lubricating greases (R1, R2) were produced by Fuchs Europe Schmierstoffe (Mannheim, Germany) and Fuchs Lubritech (Kaiserslautern, Germany). Moreover, four completely biogenic lubricating greases (S1-S4) from Pro2 TecS (University of Huelva, Huelva, Spain) were investigated in this study. All biogenic lubricating grease samples were formulated with different biogenic oils, e.g., high-oleic sunflower oil (HOSO), glycerol, castor oil, and a combination of HOSO and castor oil, to form the base oils. Moreover, different natural products, e.g., beeswax, corncob grits, natural cellulose, and lignin, were used as thickener agents. For confidentiality reasons, we are not able to give information on the percentage of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Range**} & \multirow{2}{*}{**Friction State**} & \multirow{2}{*}{**Wear State**} & \multicolumn{4}{c}{**Process Parameter**} \\ \cline{5-6}  & & & \(\epsilon_{R}^{*}\) **(J/mm\({}^{3}\))** & \(\tau_{R}\) **(MPa)** & \(I_{h}\) **(–)** \\ \hline \multirow{2}{*}{0} & Fluid friction & \multirow{2}{*}{Zero wear} & \multirow{2}{*}{\(10^{10}\)–\(10^{7}\)} & \multirow{2}{*}{\(10^{9}\)–\(10^{-3}\)} & \multirow{2}{*}{\(<\)\(10^{-13}\)} \\  & (rheological deformation and shear) & & & & & \\ \hline \multirow{2}{*}{1} & Fluid friction/mixed friction & \multirow{2}{*}{Solid body wear level 1} & \multirow{2}{*}{\(10^{9}\)–\(10^{6}\)} & \multirow{2}{*}{\(10^{3}\)–\(10^{-3}\)} & \multirow{2}{*}{\(10^{-13}\)–\(10^{-7}\)} \\  & (rheological and elastic deformation and shear) & & & & & \\ \hline \multirow{2}{*}{2} & Mixed friction & \multirow{2}{*}{Solid body wear level 2} & \multirow{2}{*}{\(10^{6}\)–\(10^{4}\)} & \multirow{2}{*}{\(10^{3}\)–\(10^{-3}\)} & \multirow{2}{*}{\(10^{-11}\)–\(10^{-5}\)} \\  & (rheological and plastic deformation and shear) & & & & \\ \hline \multirow{2}{*}{3} & Solid friction & \multirow{2}{*}{Solid body wear level 3} & \multirow{2}{*}{\(10^{6}\)–\(10^{2}\)} & \multirow{2}{*}{\(10^{3}\)–\(10^{-2}\)} & \multirow{2}{*}{\(10^{-10}\)–\(10^{-3}\)} \\  & (elastic and plastic deformation and shear) & & & & \\ \hline \multirow{2}{*}{4} & Solid friction & \multirow{2}{*}{Solid body wear level 4} & \multirow{2}{*}{\(10^{4}\)–\(10^{1}\)} & \multirow{2}{*}{\(10^{3}\)–\(10^{-2}\)} & \multirow{2}{*}{\(10^{-8}\)–\(10^{-3}\)} \\  & (separating deformation and shear) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Friction and wear conditions with associated tribological process parameters (adapted from [PERSON] [23]).  
Figure 1: Graph of the basic energy equation of wear (adapted from [PERSON] [23]).  
each thickener in Fuchs greases. More details regarding the biogenic grease samples from Pro2 TecS can be found elsewhere [35; 36]. The compositions of all the biogenic grease samples are depicted in Table 2.
## 5 Simulation Results  
In this section, we run simulations in MATLAB and provide simulation results in terms of the ergodic rate and outage probability. Since the LEO SATCOM would be characterized as multiple coverage in the future, we set \(M=6\) and \(K=3\) throughout the simulation. That is, each beam-edge UT could be covered by six satellites. The normal gain of transmit beams onboard is assumed as \(E_{S}=45\) dB. Let \(\eta_{3 dB}=2.5^{\circ}\) and \(\eta_{m,m}=0.1^{\circ}\) for the beam-center UT. Then, \(\eta_{k,m}=\eta_{3 dB}=2.5^{\circ}\) for the beam-edge UT. We assume the system works in L band where \(f=1\) GHz. The orbit height is \(d=700\) km. In this case, \(d_{m,m}=700\) km and \(d_{k,m}=d/\cos(\eta_{k,m})\).  
For comparison reasons, we provide simulation results of the following schemes:  
* \"ESR-DC, sim\": numerical simulation of ergodic sum-rate for co-carrier-based DC;
* \"ESR-SC, sim\": numerical simulation of ergodic sum-rate for co-carrier-based SC;
* \"ESR-non, sim\": numerical simulation of ergodic sum-rate without cooperative transmission;
* \"ER-DC-edge, sim\": numerical simulation of ergodic rate for UT \(k\) in co-carrier-based DC;
* \"ER-SC-edge, sim\": numerical simulation of ergodic rate for UT \(k\) in co-carrier-based SC;
* \"ER-non-edge, sim\": numerical simulation of ergodic rate for UT \(k\) without cooperative transmission;
* \"ESR-DC, exp\": analytical expression of ergodic sum-rate for co-carrier-based DC;
* \"ESR-SC, exp\": analytical expression of ergodic sum-rate for co-carrier-based SC;
* \"ER-DC-edge, exp\": analytical expression of ergodic rate for UT \(k\) in co-carrier-based SC.
* \"OR-DC-edge, sim\": numerical simulation of outage probability for UT \(k\) in co-carrier-based DC;
* \"OR-SC-edge, sim\": numerical simulation of outage probability for UT \(k\) in co-carrier-based SC;
* \"OR-non-edge, sim\": numerical simulation of outage probability for UT \(k\) without cooperative transmission;
* \"OR-center, sim\": numerical simulation of outage probability for beam-center UT;
* \"OR-DC-edge, exp\": analytical expression of outage probability for UT \(k\) in co-carrier-based DC;
* \"OR-SC-edge, exp\": analytical expression of outage probability for UT \(k\) in co-carrier-based SC;
* \"OR-center, exp\": analytical expression of outage probability for beam-center UT.  
Specifically, \"ESR-non\", \"ER-non-edge\", and \"OP-non-edge\" are considered as benchmarks where the beam-edge UT randomly accesses one of the satellites without using the cooperative transmission [14; 15]. In addition, the co-carrier scheme is still applied in the above baseline methods.
# Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line
## 5 Simulation and Discussion
### 48-h Simulation  
The previous simulations verified the effectiveness of the proposed methods in single task allocation. In order to further verify the actual scheduling performance of the proposed methods in an assembly line with the continuous production state, we conducted a 48-h simulation in this section. Under different numbers of AGVs, the simulation was run for 48 h under GA-IP0, PSO-IP0, GA-IP1, PSO-IP1, HRbest, GA-IP2, PSO-IP2 and IPSO-IP2, respectively. In the first 24 h, the assembly line was in the preheating state, and the material demand of each station was not stable. In 24 to 48 h, the assembly line went into steady production, which could better simulate the actual production scene. So, we took the throughput of 24-48 h as the performance indicator. The simulation results of each method were compared, shown in Figure 8.  
As shown in Figure 8, when the number of AGVs was below 12, the throughput of all methods failed to reach the maximum system throughput. It was caused by the inadequate handling capacity of the AGV system. As the number of AGVs increased, the throughputs of all methods improved. When the number of AGVs was 12, the throughput of IPSO-IP2 proposed in this paper first reached the maximum system throughput. Moreover, it could be seen that IPSO-IP2 always had the highest throughput under different numbers of AGVs. It proved the effectiveness of IPSO-IP2 to solve the problem considered. Furthermore, before the number of AGVs reached 18, the throughputs of IPSO-IP2, GA-IP2 and PSO-IP2 were always higher than those of GA-IP1 and PSO-IP1. It showed that heuristic individuals were beneficial for the algorithm to find the optimal solution. Finally, the throughputs of GA-IP1 and PSO-IP1 were always higher than those of GA-IP0, and PSO-IP0, which proved the validity of the task binding strategy.  
It can be found that when IPO or IP1 was used as the initial population, the throughput of PSO was better than that of GA. It shows the strong global exploration ability of PSO. However, when IP2 was used as the initial population, the throughput of PSO is worse than that of GA. It was because most heuristic solutions in IP2 were probably local opti  
Figure 8: The 48-h simulations under different number of AGVs and different methods.  
mum traps. In this case, algorithms were easy to fall into local optimum in the early stage. The mutation operation could make GA try to search for a better solution from the neighborhood of the local optimum, while PSO without mutation operation was more likely to converge here.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 7 Endoscopy
### Segmentation  
Remarkable research is performed using GANs and the LIRE framework as the basis for polyp segmentation. Another study explores an encoder-decoder approach for the real-time segmentation of polyps from colonoscopy, achieving high recall, precision, F2, and accuracy scores on the KvasirCapsule-SEG dataset.
# Effect of Rock Abrasiveness on Wear of Shield Tunnelling in Bukit Timah Granite
## 5 Conclusions  
In this study, the effects of rock abrasiveness on the wear of disc cutters and slurry discharge pipes were investigated. The radial wear of disc cutters and the thicknesses of slurry discharge pipes were determined regularly at the TBM site located in Singapore.  
The wear of the disc cutter was measured based on the wear coefficient, and the wear coefficient of the disc cutters was investigated in various weathering grades of Bukit Timah granite. The wear coefficients of the disc cutters decreased with increasing weathering grade. The wear coefficient distribution of disc cutters can be approximated using a log-normal distribution, and a correlation between the _CAI_ values and the wear coefficient was proposed.  
Furthermore, the average wear rates of slurry discharge pipes on Bukit Timah granite were determined from the loss of the pipe thicknesses. The wear rates in G(I) to G(IV) grade ground were similar or insignificantly different. The wear rates in G(V) grade and mixed ground were 1.55 times higher than those of G(I) to G(IV) grade ground. The correlation between the slurry discharge velocity and the wear rate was derived. A linear relationship between the wear coefficient and the wear rate  
Figure 13: Correlation between the wear coefficient and the wear rate: (**a**) weathering grade G(I) to G(IV) ground; (**b**) G(V) and mixed ground.  
of slurry discharge pipes exists between G(I) to G(IV) weathering grades of Bukit Timah granite. In contrast, a U-shaped parabolic relationship was established in the G(V) and mixed zone ground.  
The accurate assessment of the effect of rock abrasiveness both on the wear of disc cutters and the wear rate in slurry discharge pipes, investigated in this paper, will play a key role in elucidating the factors that determine wear rate of excavation equipment, particularly in TBMs, and will encourage future research into preventing wear associated with rock abrasiveness in order to maximize lifespan and minimize construction costs and time.  
Writing--Original Draft Preparation, T.Y.K.; Writing--Review and Editing, S.S.L. All authors have read and agreed to the published version of the manuscript.  
The research fund of Hanyang University (HY-20170000002431) supported this study, and the authors greatly appreciate the financial support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 7 Conclusions and Future Perspective  
The key features of the radiation of a three-dimensional directional ultra-wideband (UWB) antenna, which demonstrates unique performance characteristics, are investigated. The main distinguishing feature of this antenna is the presence of a wide matching band, as well as the high stability of the radiation pattern over the entire operating frequency range. This makes the antenna especially promising for broadband applications that require stable and predictable radiation characteristics.  
A detailed analysis of the antenna parameters and characteristics was carried out based on the theory of characteristic modes. During the study, it was possible to identify two main groups of significant modes that jointly form the radiation properties of the antenna. The first group of modes is responsible for providing broadband matching, which allows for minimizing the standing wave ratio (SWR) at the antenna input. The second group of modes affects the stability of the radiation pattern, ensuring its invariance with frequency changes. The combined interaction of these modes ensures efficient radiation of the antenna in a given frequency range, maintaining high technical parameters.  
At present, we are further modifying the antenna described in this article. The main objective of the current work is to extend the operating bandwidth towards lower frequencies. To this end, an in-depth approach is used, based on the use of additional attributes of the characteristic mode theory. In particular, a more thorough study of the energy balance of significant characteristic modes is required. In addition, methods for optimizing the antenna geometry are considered, aimed at enhancing the influence of low-frequency modes and improving the matching in the lower part of the operating range.  
It is expected that the results of the current research will not only improve the antenna characteristics, but also expand its applicability in new areas where efficient radiation at low frequencies is required while maintaining a wide bandwidth and stability of the radiation pattern.  
Conceptualization, V.P.B. and Y.B.; methodology, Y.B.; software, G.D.; validation, A.M., G.D. and V.P.B.; formal analysis, Y.B.; investigation, V.P.B.; resources, A.M.; data curation, G.D.; writing--original draft preparation, A.G.; writing--review and editing, V.P.B.; visualization, A.M.; supervision, Y.B.; project administration, V.P.B.; funding acquisition, Y.B. All authors have read and agreed to the published version of the manuscript.  
The research was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2024-557 dated 25 April 2024).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### k-Nearest Neighbors Method  
The k-nearest neighbors method is a supervised machine learning algorithm used to solve a regression problem that performs well with a small amount of data.  
In practice, the KNN method is more often used in classification problems, but currently the regression version of the k-nearest neighbors algorithm is also common. It is a good basic algorithm to try first before considering more advanced methods.  
The algorithm finds the distances between the query and all examples in the data by choosing a certain number of examples (k) closest to the query, then averages the labels in the case of a regression problem.  
The k-nearest neighbors algorithm follows:  
1. Input:  
Training examples \(\{x_{i},y_{i}\}\)  
\(x_{i}\) are values of training examples attributes;  
\(y_{i}\) are actual values of the output characteristic.  
Test point x for which we are making a prediction.
2. Forecasting:  
Calculating the distance \(D(x,x_{i})\) to each training example \(x_{i}\);  
Selection of \(k\)-nearest instances and their labels \(y_{i1},\ldots,y_{ik}\);  
Determination of the mean value \(\overline{y}\) for \(y_{i1},\ldots,y_{ik}\) by Formula (1):  
\[\overline{y}=\frac{1}{k}{\sum\limits_{j=1}^{k}{y_{i}}} \tag{1}\]  
where \(k\) is the number of nearest instances, \(y_{i}\) is the actual value of the output parameter.
## 6 Conclusions  
We used thermal and chemical analyses to comprehensively characterize the interaction between runway de-icing products (RDP) and ice during ice melting tests. Existing standard practice results in excessive product usage. By applying Montreal Airports' current application concentration, we observed that each salt grain reacts individually with the ice. Correlating results obtained from infrared thermography and Raman spectroscopy provided a better understanding of the dynamic exchange between RDP and ice, shedding light on ice melting kinetics and variations. Notably, the intensity of observed peaks in Raman spectroscopy decreased as RDP was diluted, indicating increased ice melting.  
This spectroscopic approach allowed real-time measurements directly on the runway, eliminating the need for sample collection and preparation typically associated with other methods like FTIR or DSC. Visual tests confirmed the observed reactions within a specific time frame, demonstrating the effectiveness of our approach.  
These insights significantly contribute to our understanding of RDP-ice interactions in a laboratory environment which can be transposed to natural airport conditions. Future research should use an airport traffic simulator to study the mechanical activation of solid RDP since the standard ice melting test omits this parameter, simulating interactions with spreader truck wheels and passing aircraft. Such simulations will help us understand how traffic accelerates the dissolution process, thereby offering ideas on how to further improve the efficacy of the applied de-icing product, but may also enable us to look at the anti-icing properties of the products used following different winter contaminations. Additionally, further exploration of Raman spectroscopy could provide deeper insights into the molecular dynamics of RDP-ice interactions, aiding the development of more sophisticated AI-driven tools for improved runway de-icing strategies.  
Conceptualization, C.C.; Methodology, C.C. and J.-D.B.; Validation, J.-D.B., M.M. and G.M.; Investigation, C.C.; Writing--original draft, C.C. and M.M.; Writing--review & editing, J.-D.B., M.M. and G.M.; Visualization, J.-D.B. and M.M.; Supervision, J.-D.B. and G.M.; Project administration, G.M.; Funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [CRIAQ] grant number [O2 HPA]; [NSERC] grant number [CRDP] 537834-18].  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Anti-icing Materials International Laboratory (AMIL). This research was conducted with the support of the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ), the Ministere de l'Economie et de l'Innovation du Quebec, and the support provided by Montreal Airports, WPred, and Nachur Alpine Solutions.  
The authors declare no conflict of interest.