esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Problem Formulation
 \tag{10}\]  
where \(A\) is the detector area, \(I\) is the normalized spatial distribution of the transmitted intensity, \(\mathbf{\rho}[n]\) is the radial vector which starts from the beam center and ends at any point on the PD, \(\mathbf{r}[n]\) is the pointing error vector starting from the center of the PD and ending at the center of the beam footprint with norm \(r[n]\). After some derivations, we can obtain the closed-form expression of pointing error loss as follows:  
\[ \begin{split} h_{p}[n]=&\bigg{(}r[n]\sqrt{2\pi W_{ y}[n]W_{x}[n]}\bigg{(}\text{erf}\left(\frac{\sqrt{2}r[n]}{\sqrt{W_{y}[n]W_{x}[n]}} \right)\\ &+\text{erf}\left(\frac{\sqrt{2}(a-r[n])}{\sqrt{W_{y}[n]W_{x}[n]} }\right)\bigg{)}+W_{y}[n]W_{x}[n]\\ &\bigg{(}\exp\left(\frac{-2r^{2}[n]}{W_{y}[n]W_{x}[n]}\right)- \exp\left(\frac{-2(a-r[n])^{2}}{W_{y}[n]W_{x}[n]}\right)\bigg{)}\\ &\frac{1}{W_{y}[n]W_{x}[n]}.\end{split}  \tag{11}\]  
where \(W_{y}[n]\) and \(W_{x}[n]\) denote two different beamwidths of the beam footprint, which can be calculated by using a rotation matrix and the law of specular reflection as in Appendix B. According to the intensity modulation and direct detection (IM/DD) method as in [43], the average capacity between the LS and PD can be obtained by representing the probability density function (PDF) of the received electrical signal-to-noise ratio (SNR) \(f(\gamma[n])\)  
\[c[n]=\log_{2}(1+\varepsilon\gamma[n]), \tag{12}\]  
where \(\varepsilon=\frac{\varepsilon}{2\pi}\)[44], and the received SNR \(\gamma[n]\) can be obtained by  
\[\gamma[n]=\frac{2P_{t}^{2}\mu^{2}h_{a}^{2}[n]h_{p}^{2}[n]h_{t}^{2}}{\sigma^{2 }}, \tag{13}\]  
where \(P_{t}\) is the transmitting power from the LS, and \(\sigma^{2}\) is the Gaussian noise variance, and \(\mu\) is the detector responsivity. Then, to overcome the atmospheric and pointing error loss, and maximize the average capacity by optimizing the phase shifts of the RIS \(\Phi=\{\Theta[n],n=1,2,\ldots,N\}\), and the trajectory of the UAV \(\mathbf{\tilde{G}}=\{G[n],n=1,2,\ldots,N\}\), we formulate a problem as follows:  
Figure 3: The ellipse beam footprint and circular PD with error vector. \[(\mathbf{P}1): \max_{\Phi,\mathbf{G}}\frac{1}{N}\sum_{n=1}^{N}c[n]\] (14) s.t. \[(1)(2).\]  
According to the formulated optimal problem, it can be seen that the constraint of phase shifts problem (1) and the UAV trajectory constraint (2) are independent; the optimal problem can be divided into two independent optimal problems, one is to optimize the phase shift, and the other is optimizing the UAV trajectory to maximize the average capacity. In the following section, the phase shift optimization of the RIS is proposed.
To summarize, the primary contributions of this research paper can be outlined as follows:  
* This paper presents a modified methodology for optimizing the single-stage planetary gearbox through the use of MOO. The methodology takes into account a multitude of conflicting objective functions and formulates critical design constraints. * A new optimization method, HMOBPSO, a hybrid between the PSO and BOA algorithms, has been proposed, which improves optimization performance, especially for complicated multi-objective optimization problems. Hybridization has been performed by introducing velocity-updating expression pieces from the PSO algorithm into BOA's butterfly position update equation. * The proposed HMOBPSO algorithm was tested against NSGA-II and several other well-known algorithms in the literature on ZDT and DTLZ benchmark MOO problems. The HMOBPSO algorithm outperformed other algorithms in terms of convergence and Pareto solution quality. The present paper is structured in the following manner. In Section 2, the formulation of the multi-objective planetary gear-optimization problem is given. Additionally, Section 2.1 presents the establishment of suitable objectives for the optimization problem regarding the planetary gearbox under consideration. The formulation of constraints is explained in Section 2.2. Section 3 presents the formulation of a multi-objective optimization problem. The theoretical underpinnings of the PSO and BOA algorithms are outlined in Sections 3.1 and 3.2, respectively. The hybridization procedure and the relevant theoretical foundations of the proposed hybrid HMOBPSO algorithm are laid out in Section 3.3. Section 4 provides an overview of the results derived from numerical experiments conducted on planetary gearbox optimization, as well as on the DTLZ and ZDT benchmark problems. Ultimately, the findings are summed up and presented in Section 5.
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 1 Introduction
### Aim and Scope of This Review  
This narrative review focuses on the progress of scaffold approaches in regenerative dentistry. It is intended for researchers in this field to facilitate understanding of the context beyond their specific focus on material and method. As regenerative dentistry is just emerging, this review aims to provide a brief overview of the developmental steps that have led to the current state of the art, which could help researchers anticipate further trends in scaffold development. Another aim of this review was to perform literature research and analyze the most influential publications on this topic to examine the most commonly researched materials for scaffolds used in regenerative dentistry by novel utilization of large language models (LLMs) like Bard and ChatGPT.  
The presented literature search aims to identify the most influential papers in the studied topic. It is based on the Field-Weighted Citation Impact (FMCI) indicator, and is limited to the last five years to capture recent advances in scaffold development. Therefore, it provides an overview of novel scaffold approaches in regenerative dentistry and excludes other aspects of regenerative medicine or dental tissue development as much as possible.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Decade** & **Scaffold Type** & **Key Features** \\ \hline \multirow{3}{*}{Early 1990s} & Natural Materials & \\  & (collagen, alginate, silk, & \\  & hyaluronic acid, chitosan) & \\ \hline \multirow{2}{*}{Late 1990s–Early 2000s} & Synthetic Polymers (PLA, PGA) & Controlled pore size \\  & & and architecture \\ \hline \multirow{3}{*}{Mid 2000s} & Hybrid Scaffolds & \\  & (various combinations of natural & \\  & and synthetic polymers) & \\ \hline \multirow{3}{*}{Late 2000s–Early 2010s} & 3D Printed Scaffolds & \\  & (HAP, TCP, PLA, PGLA, & \\  & PCL, collagen) & \\ \hline \multirow{3}{*}{Mid-2010s–Present (last decade)} & Advanced materials & Growth factors, signaling \\  & (composites, hydrogels, & \\  & bioactive materials) & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular} \({}^{\dagger}\) 4D materials can change their shape, properties, or function over time in response to stimuli, promoting dynamic tissue growth and healing. \({}^{2}\) Including 3D printing patient-specific scaffolds and development of decentralized scaffolds.  
\end{table}
Table 1: The advancements in scaffold development over the past three decades.
# Taxonomy of Scheduling Problems with Learning and Deterioration Effects
## 3 Results
### Modeling Approaches for Learning and Deteriorating Effects
#### 3.3.1 Learning Effect
This involves several approaches, and the authors define it as exponential sum-of-logarithm-processing-times-based and position-based learning effects, as in Equation (13). Figure 7:3  
where \(\beta\) is a truncation parameter with \(0<\beta<1\), \(\alpha\) and \(b\) are learning rates, \(\delta\geq 0\), \(\gamma\geq 0\) are parameters obtained empirically [65], and \(\delta+\gamma=1\). DeJong's Learning Effect (DJ-LE)  
The [PERSON] model [68] takes into account the residual skills resulting from previous experiences and implies that part of the process cannot be improved. This model has been viewed as having great relevance in real life [11; 69]. The DeJong learning curve follows the equation below:  
Figure 7:4  
where the parameter \(M\) represents the part of job processing time that is limited by some conditions and cannot be shortened (it represents the \"factor of incompressibility\"). Different values of \(M\) are recommended in the literature. For example, in a single machine problem, the [PERSON] model suggests \(M=0.25\) for labor-intensive jobs and \(M=0.5\) for machine-intensive jobs [69]. Figure 7(a) presents the frequency by the system configuration (two or three more common ones) according to each learning effect approach (in Figure 7(a): _SM: single machine; PM: parallel machine; FS: flowshop_). Figure 7(b) shows the percentage of objective function types per each learning effect approach (in Figure 7(b): _SC: single-criteria objective function; MC: multi-criteria objective function_). Given the nature of the learning process, many aspects must be taken into consideration. Some authors, therefore, attempt to include them in their models without being limited to the principles of position and time. These aspects include, for example, previous experience [70], skills [71], the initial capability, learning ability and job difficulty [50]. In addition, we identified alternative approaches to model the learning effect, using a perceptron neural network learning algorithm [72]. We also found some articles corresponding to the inclusion of rate-modifying activities (RMA) [73; 74; 75; 76; 77; 78; 79; 80], which changes the processing conditions in a system and adjusts the production rate of a resource. Two examples are maintenance activities or rest periods, which usually increase production rates. Figure 7:5 \(Cont.\)
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 3 Methods
### RG-C2f Module  
In the fabric defect detection application of YOLOv8, the backbone network's feature extraction is crucial for improving detection accuracy. However, the traditional BottleNeck residual block in C2f, although excellent in feature extraction, has a large computational and parametric count when dealing with high-resolution images, resulting in slower inference speeds and failing to meet the demands of real-time detection. In addition, the redundant information in the residual block may affect the generalization ability of the model, especially in the complex fabric texture background, which makes it easy to introduce noise and affect the accuracy of defect detection.  
To address this problem, this paper proposes an improved C2f module structure named RG-C2f, as shown in Figure 2. Inspired by GhostNet [19], the RG-C2f module effectively reduces the consumption of computational resources by using inexpensive operations to generate redundant feature maps. Unlike the traditional BottleNeck residual block, the RG-C2f module discards the residual structure commonly used in YOLOv5 and YOLOv8. To compensate for the performance loss caused by removing the residual block, The RepConv [20] operation is applied to the gradient circulation branch to boost feature extraction and optimize gradient flow. Meanwhile, the convolutional kernel fusion technique of RepConv can further improve efficiency in the inference stage, realizing the dual advantages of low computational cost and high inference speed.  
Another advantage of the RG-C2f module is its adjustable scaling factor, which gives it the flexibility to adapt models of different sizes. In fabric defect detection tasks, this means that the complexity of the model can be adjusted according to different fabric types and defect characteristics in order to achieve an optimal balance of performance and efficiency. By introducing the RG-C2f module, YOLOv8's backbone network significantly optimizes computational efficiency while maintaining high detection accuracy, especially when processing high-resolution images, demonstrating faster inference and lower latency and providing a more efficient solution for fabric defect detection.  
Figure 1: Structure of the improved YOLOv8.
# Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information
## 2 System Model
We define the profit of user \(i\) based on the acquired throughput as follows:  
\[g_{i}=\alpha_{i}\cdot\log_{2}(1+\sum_{j=1}^{m}C_{ij}) \tag{5}\]  
where \(g_{i}\) is the throughout profit of user \(i\) and \(\alpha_{i}\) is the constant related to the user experience. The expense contains the fee to acquire the bandwidth and the cost caused by the network load represented respectively in Equations (6) and (7). \[f_{i}=\sum_{j=1}^{m}b_{j}\cdot q_{ij} \tag{6}\]  
\[D_{j}=\left\{ \begin{array}{ll}\frac{\beta_{j}}{W_{j}-Q_{j}},&Q_{j}<W_{j}\\ \infty,&Q_{j}<W_{j}\end{array} \right. \tag{7}\]where \(\beta_{j}\) is a constant related to specific access technology. Therefore, from Equations (5)-(7), the utility of the mobile user is defined as follows:  
\[ \begin{split} u_{i}^{F}=g_{i}-f_{i}-\sum\limits_{j=1}^{m}D_{j}\\ =\alpha_{i}\cdot\log_{2}(1+\sum\limits_{j=1}^{m}C_{ij})-\sum \limits_{j=1}^{m}b_{j}\cdot q_{ij}-\sum\limits_{j=1}^{m}\frac{\beta_{j}}{W_{j }-Q_{j}}\end{split}  \tag{8}\]  
The proposed Stackelberg game-based model contains two stages. In the first stage, different networks declare the price strategy profile to all users so that they can make actions to request bandwidth from the connected networks. As a result of the limited network resource, the game among users comes to a noncooperation game with incomplete information, and the Bayesian Nash equilibrium is the final solution. In the second stage, according to the equilibrium strategy of users, the network will adjust its price strategy to obtain the optimal utility. In the next section, we will analyze the follower game firstly, and, subsequently, the behaviors of networks in the leader game are studied.
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## Materials and Methods  
### Ecosystem Model for GPP Simulation  
The Boreal Ecosystems Productivity Simulator (BEPS), hourly version, is a process-based ecosystem model to simulate water, energy, and carbon budgets [47; 48; 49; 50; 51; 52]; it was used in this study to simulate cotton GPP. BEPS is in development for the last 20 years [53]. Although BEPS was initially developed for boreal ecosystems, it was expanded and used for temperate and tropical ecosystems in regional and global scales [54; 55; 56; 57; 58; 59; 48; 51] and for the estimation of winter wheat yield in China [18], as C3 plants share the same photosynthesis theory at the leaf level, i.e., [PERSON]'s leaf-level biochemical model [60], and BEPS has a \"two-leaf\" approach to upscale the ecosystem GPP to canopy level for various ecosystems including crops [49; 61].  
In BEPS, the canopy-level photosynthesis (GPP; \(A_{canopy}\)) is simulated as the sum of the total photosynthesis of sunlit and shaded leaf groups [49]:  
\[A_{canopy}=A_{sun}(g_{sc\_sun})L_{sun}+A_{sh}(g_{sc\_sh})L_{sh} \tag{1}\]  
where the subscripts \"sun\" and \"sh\" denote the sunlit and shaded components of the photosynthesis (A) and LAI (\(L\)), whereas \(g_{sc}\) is the stomatal resistance for carbon molecules. The sunlit and shaded LAI are separated by Equation (2) [49; 61].  
\[\begin{array}{l}L_{sun}=2\cos\theta\big{(}1-e^{-0.5\Omega L/\cos\theta}\big{)} \\ L_{sh}=L-L_{sun}\end{array} \tag{2}\]  
where \(\theta\) is the solar zenith angle, and \(\Omega\) is the clumping index. In this study, the Sentinel-2 data were integrated into the GPP simulation through the LAI product as described in Section 2.4.  
In BEPS, the vegetation biophysical and biochemical parameters are defined for plant function types including crops. For crop GPP simulation (cotton in this study), the clumping index was set to as 0.8. A detailed description of BEPS is provided in the Supplementary Materials.
## 3 Results
### Calculation of Cell Reynolds Numbers  
The bulk Reynolds number of the side-view flow chamber is 1.11 and 3.56 for low shear condition (91.25 \(\mu\)L min\({}^{-1}\)) and high shear condition (292 \(\mu\)L min\({}^{-1}\)), respectively. Cell Reynolds numbers were calculated by Equation (3) and cell Reynolds numbers of TCs were about 4-5 times those of PMNs.  
Figure 4 shows the change of cell Reynolds numbers of adherent PMNs and TCs in the presence of sFg. Large variations appear for both cell Reynolds numbers of PMNs and those of TCs under high shear condition with a range of 0.01-0.017 and 0.06-0.075, respectively. The curves of cell Reynolds numbers of PMNs and those of TCs under low shear condition become much flatter, with a range of 0.003-0.006 and 0.02-0.03, respectively. The lowest cell Reynolds numbers of PMNs and TCs appear at the state of \"firm adhesion\" under both low and high shear conditions.  
When sFg was converted to sFn, the curves of cell Reynolds numbers of PMNs and those of TCs show a flatter pattern under both low and high shear conditions. Cell Reynolds numbers of adherent PMNs vary from 0.004 to 0.005 and from 0.013 to 0.015 under low and high shear conditions, respectively. Cell Reynolds numbers of adherent TCs vary from 0.02  
Figure 4: Cell Reynolds numbers of adherent cells in the presence of soluble fibrin (ogen). (**A**) Above an adherent PMN; (**B**) Above an adherent TC. Solid squares (\(\bullet\)) represent a low shear condition (91.25 \(\mu\)L min\({}^{-1}\)) with sFg, solid circles (\(\bullet\)) represent a high shear condition (292 \(\mu\)L min\({}^{-1}\)) with sFg, open squares (\(\circ\)) represent a low shear condition (91.25 \(\mu\)L min\({}^{-1}\)) with sFn, and open circles (\(\circ\)) represent a high shear condition (292 \(\mu\)L min\({}^{-1}\)) with sFn.  
to 0.03 and from 0.07 to 0.08 under low and high shear conditions, respectively. The curves of cell Reynolds numbers of different cells under the same shear flow condition shear the similar pattern, with the lowest cell Reynolds numbers appearing when a TC collides with an adherent PMN.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 5 Responses of Segmental Tunnel
### Acceleration Responses of the ST Ground
#### 5.1.2 Differences of Amplification of Acceleration along Depth  
Percentage differences of acceleration responses along depths between the FF model and ST model are shown in Figure 18. The percentage difference in this paper is defined as the percentage difference of peak accelerations between the FF model and ST model. The percentage difference, \(\eta\), is determined by the following formula:  
\[\eta=\left|\frac{a_{S}-a_{F}}{a_{F}}\right| \tag{12}\]  
where \(a_{S}\) and \(a_{F}\) are peak acceleration for the ground of SF model and FF model, respectively.  
Effect of model tunnel on acceleration response of ground can be learned from Figure 18. In the cases of the Sin-2, Sin-3, and Sin-6, the differences of acceleration response in model soil between the ST model and FF model are less than 6%. In the cases of the Sin-9 and Sin-11, except for the surface soil layer, the differences are less than 6.5%. In the cases of the Sin-14 and Sin-17, the maximum difference is 32%.  
Effects of the tunnel on acceleration response of the ground: When the frequency of sinusoidal excitation is no more than 9 Hz, the differences of acceleration responses in model soil between the ST model and FF model are less than 10%, and when the frequency of sinusoidal excitation is more than 9 Hz, the differences tend to fluctuate more widely, as shown in Figure 18.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Modal Amplitude**} & \multicolumn{6}{c}{**Frequency of Sinusoidal Excitation**} \\ \cline{2-9}
**(Absolute Peak Value)** & **2 Hz** & **3 Hz** & **6 Hz** & **9 Hz** & **11 Hz** & **14 Hz** & **17 Hz** \\ \hline \multirow{4}{*}{Mode order} & 1 & 9.66 & 7.78 & 2.64 & 2.17 & 1.84 & 1.51 & 1.38 \\ \cline{2-9}  & 2 & 2.80 & 1.59 & 0.95 & 0.96 & 0.74 & 0.59 & 0.57 \\ \cline{2-9}  & 3 & 1.90 & 1.15 & 0.51 & 0.57 & 0.53 & 0.51 & 0.46 \\ \cline{2-9}  & 4 & 1.45 & 0.83 & 0.50 & 0.44 & 0.40 & 0.39 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Amplitude responses of vibration modes under different input frequency.  
The presence of the tunnel induces a relatively high effect on acceleration responses of the ground within excitation frequency varying from 9 to 17 Hz. Similar findings have been made in the study from [PERSON] [26]. However, this finding has not been verified by any experiments.  
Figure 17: _Cont._
## 1 Introduction
Nowadays, the development trends of ophthalmic imaging modalities are focused on improving the system resolution and providing functional imaging ability to achieve accurate early diagnosis and quantitative classification of ophthalmic diseases. While ophthalmic disease diagnoses and treatment evaluations are typically made based on separate information acquired with more than one technique, there is also a need for a multi-modal system that is capable of providing adequate and comprehensive information. Multi-modal systems will not only improve the examination efficiency by reducing patients' moving from one examination system to the others, but also reduce the space and equipment investment. To date, researchers have developed a variety of multi-modal systems to combine the merits of each imaging modality for human eye imaging [36; 37; 38; 39; 40; 41; 42; 43; 44; 45], among which, however, most are focused on posterior segment imaging [36; 37; 38; 39; 40]. Hybrid systems for anterior human eye imaging are relatively rare: few researches have demonstrated the integration of OCT system into SLB for both anterior and posterior segment imaging to improve the examination efficiency [41; 42]; multi-modal systems combining OCT and corneal topography have been developed for anterior segment biometry measurements and applied to eyes with keratoconus [43; 44]; a Scheimpflug camera combined with corneal topography has been used to measure pre/post-refractive surgery eyes [45]. All of these techniques offer only the structural information. In this study, we developed a multi-modal non-invasive anterior eye imaging platform by combining our customized UHR-OCT and microvascular imaging system based on a functional slit-lamp biomicroscopy to provide both structural and functional information in ophthalmic clinical practice. The imaging platform was capable to perform: (1) real-time high-resolution OCT images of human cornea/conjunctiva with the UHR-OCT module; (2) conjunctival vessel network imaging and high-resolution conjunctival blood flow videography with the microvascular imaging module; (3) fluorescein staining and slit-lamp imaging with the SLB module. Customized software was also installed to achieve quantitative vessel parameters. Experiments were performed on a healthy human subject and a keratitis patient to demonstrate the feasibility of our multi-modal system.