esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Temperature Sweep Test Results  
Figure 7 presents the G* and \(\delta\) of the MFAMs after microwave heating at different temperatures. As can be seen from Figure 7a, G* rapidly decreases with the increase in temperature, indicating that the deformation resistance of the MFAMs drops sharply as temperature increases. The reason for this is that rising temperature accelerates the fluidity of the asphalt binder. The G* of asphalt mastics containing common fillers also has the same varying tendency with temperature [32], which illustrates that magnetite powder does not change the trend. At 46 \({}^{\circ}\)C-52 \({}^{\circ}\)C, the longer the microwave heating time, the larger the G* of the MFAMs. Moreover, the longer the microwave heating time, the faster the G* of the MFAMs decreased with temperature rising. This means that microwave heating has a significant influence on the G* of the MFAMs. However, the influence diminishes gradually with the increase in temperature.  
Figure 5: Change of tan\(\delta_{E}\) and tan\(\delta_{M}\) of limestone filler and magnetite filler with frequency: (a) tan\(\delta_{E}\) and (b) tan\(\delta_{M}\).  
Figure 6: Change of tan\(\delta\) of limestone filler and magnetite filler with frequency.  
As can be seen from Figure 7b, the 8 of AM-0, AM-1 and AM-2 gradually increases at 46 \({}^{\circ}\)C-58 \({}^{\circ}\)C, and that of AM-3 and AM-4 gradually increases at 46 \({}^{\circ}\)C-64 \({}^{\circ}\)C, and that of all MFAMs keep relatively steady at 58 \({}^{\circ}\)C-82 \({}^{\circ}\)C. The results mentioned above indicate that the elasticity of the MFAMs decreases, and the viscosity increases with the increase in temperature. The composition of elasticity and viscosity remains basically unchanged when temperature reaches a certain threshold. The \(\delta\) of AM-2, AM-3, and AM-4 is almost the same and larger than that of AM-0 and AM-1 at 64 \({}^{\circ}\)C-82 \({}^{\circ}\)C, which indicates that microwave heating for over two hours has significant effects on the \(\delta\) of MFAMs.  
Figure 8 depicts the G\({}^{\star}\)\(\times\) (sin \(\delta\))\({}^{-1}\) of the MFAMs after microwave heating at different temperatures. It can be seen that G\({}^{\star}\)\(\times\) (sin\(\delta\))\({}^{-1}\) rapidly decreases with the increase in temperature, indicating that the anti-cutting performance of MFAMs drop sharply with a temperature increase. At 46 \({}^{\circ}\)C-52 \({}^{\circ}\)C, the MFAMs with longer microwave heating time have a larger G\({}^{\star}\)\(\times\) (sin \(\delta\))\({}^{-1}\). The reason for this is that microwave heating hardens the MFAMs. Moreover, the longer the microwave heating time, the faster the G\({}^{\star}\)\(\times\) (sin \(\delta\))\({}^{-1}\) decreased with temperature rising. The effect of microwave heating on G\({}^{\star}\)\(\times\) (sin \(\delta\))\({}^{-1}\) gradually decreased with the increase in temperature.
# Mechanism and Application of Soilbags Filled with Excavated Soil in Soft Soil Subgrade Treatment
## 4 FEM Simulation
### Numerical Results  
Figure 12 displays the validation of the settlement of the soilbag-treated subgrade over time. The simulated data agree well with the measured data, indicating the reliability of the numerical model. Notably, the simulated data are slightly lower than the measured data, especially during the initial consolidation phase. This discrepancy may have arisen from the omission of traffic loads in the numerical analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{\lambda}\) & \(\mathbf{\kappa}\) & \(\mathbf{M}\) & \(\mathbf{e}\) & \(\mathbf{\gamma}\) (kN/m\({}^{3}\)) & \(\mathbf{k}\) (cm/s) \\ \hline
0.54 & 0.12 & 1.01 & 1.432 & 16.8 & 2.85 \(\times\) 10\({}^{-7}\) \\ \hline \end{tabular} \(\mathbf{\lambda}\) is the compression index, \(\mathbf{\kappa}\) is the swelling index, \(M\) is the slope of the critical state line, \(\mathbf{e}\) is the initial void ratio, \(\mathbf{\gamma}\) is the unit weight, and \(\mathbf{k}\) is the hydraulic conductivity.  
\end{table}
Table 2: Model parameters of the soft soil subgrade.  
Figure 13 illustrates the dissipation of the pore water pressure at various depths during and up to 18 months following the completion of the soft soil subgrade construction. It was observed that an increase in the pore water pressure occurred rapidly with the filling of the subgrade during construction. Subsequently, a swift decrease in the pore water pressure was noted in the month after construction, with the reduction reaching up to 40% at a depth of 2 m. This trend continued with a gradual decline in the pore water pressure, culminating in an essential dissipation at 18 months post-construction. In Figure 14, the settlement distribution across different positions of the subgrade treated with soilbags is depicted after 18 months post-construction. The central area of the subgrade surface experienced the most significant settlement, recorded at 11.96 mm. Uniformity was characteristic of the settlement distribution at comparable depths along the subgrade, with no abrupt variations detected. This uniformity is attributed to the alternating layout of the soilbags, which enhanced the structural integrity of the subgrade.  
Figure 12: Settlement validations of soilbag-treated subgrade over time.  
Figure 13: Evolution of pore water pressure at different depths.
## 3 Materials and Experiment Design
### Evaluation Metrics  
To comprehensively evaluate the effectiveness of the proposed FMTL framework, we employed a suite of standard classification metrics tailored to the characteristics of each task. For the sleep staging task, we assessed performance using overall accuracy, [PERSON]'s Kappa coefficient [40], macro-averaged F1-score (MF1), and macro geometric mean (MGM). We also compute class-specific recall and specificity for both tasks. For the SDB severity classification task, we use overall accuracy, macro-F1 score (MF1), and macro-area under the receiver operating characteristic curve (mAUC) to capture both overall and class-level discriminative ability. In addition, we computed per-class recall, precision, sensitivity, and specificity are reported for each severity category to provide insight into the model's clinical utility.  
To assess the generalization capabilities of the proposed framework, we evaluate the model under two distinct settings: local evaluation, in which each client is tested on its own held-out dataset, and cross-client evaluation, where a model trained on one institution is applied to data from another. This dual evaluation strategy enables us to quantify both personalized performance and cross-domain transferability under non-IID conditions. All metrics are averaged across five independent runs with random initialization to ensure statistical robustness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Learning rate & \(1\times 10^{-3}\) & Dropout rate & 0.3 \\ Optimizer & Adam & Weight decay & \(1\times 10^{-4}\) \\ Batch size & 32 & Task loss weights (\(\lambda_{1}\), \(\lambda_{2}\)) & 0.5, 0.5 \\ Local epochs (E) & 5 & Training time limit \(\tau_{k}\) & Uniform [60; 180] seconds \\ Communication rounds (T) & 100 & Bandwidth \(b_{k}\) & Uniform [50; 500] KB/s \\ Client participation (C) & 0.2 \(\times\)\(K\) & Dropout probability \(p_{drop}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training hyperparameters.
## 3 Results and Discussion
### Comparison with State-of-The-Art Models
In Figure 12b, the YOLOv10n, on the other hand, incorrectly recognized two overlapping strawberries at the position \"t\" as a single strawberry and missed the strawberry marked with \"u\", demonstrating poor performance in strawberry detection in complex scenes. In Figure 12 c, the two-stage network of Libra R-CNN failed to identify the strawberries marked with \"v\" and \"w\", and erroneously identified a semi-ripe strawberry marked with \"x\" as an unripe strawberry, highlighting limitations in recognizing the turning stage strawberries. As shown in Figure 12d, our model could successfully and accurately recognize the straw \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **Precision** & **Recall** & **mAP\({}_{0.5}\)** & **mAP\({}_{0.50.95}\)** & **Param. (M)** & **FLOPs (G)** \\ \hline Libra R-CNN * [33] & 86.1 & 81.8 & 87.6 & 71.8 & 34.3 & 293.6 \\ CenterNet [34] & 84.1 & 80.4 & 85.2 & 70.1 & 32.7 & 70.2 \\ EfficientDet-D0 [32] & 83.4 & 77.6 & 80.7 & 66.9 & 3.7 & 7.4 \\ YOLOv5n [35] & 89.4 & 85.1 & 90.4 & 74.4 & 2.5 & 7.1 \\ YOLOv3-tiny [36] & 89.7 & 85.3 & 90.6 & 74.5 & 5.1 & 6.5 \\ YOLOv6n [37] & 89.6 & 85.2 & 90.6 & 74.6 & 4.2 & 11.8 \\ YOLO7-tiny [38] & 89.9 & 84.8 & 90.8 & 74.8 & 6.0 & 13.2 \\ YOLOv8n [39] & 90.1 & 85.2 & 91.0 & 75.1 & 3.0 & 8.1 \\ YOLOv9-tiny [40] & 89.5 & 85.2 & 90.7 & 74.7 & 2.0 & 7.7 \\ YOLOv10n \# [41] & 89.8 & 84.8 & 90.9 & 74.8 & 2.7 & 8.2 \\ RTMDet-tiny [42] & 87.0 & 82.9 & 88.9 & 72.9 & 4.8 & 8.1 \\ YOLOv8n-MobileNetv4 \& [43] & 87.8 & 82.3 & 87.2 & 71.8 & 4.3 & 8.1 \\ YOLOv8n-EfficientViT \& [44] & 88.5 & 83.1 & 87.8 & 73.1 & 4.0 & 9.5 \\ YOLOv8n-GhostNetv2 \& [45] & 88.1 & 82.8 & 88.1 & 72.1 & 6.3 & 8.7 \\ HM-YOLO & 92.1 & 86.5 & 92.7 & 76.3 & 4.1 & 11.4 \\ \hline \hline \end{tabular} Note: Models marked with an asterisk (*) represent two-stage networks, those marked with a bound sign (#) denote end-to-end networks, those marked with an ampersand (&) are models equipped with other backbone networks, and unmarked models are classified as single-stage networks.  
\end{table} 
Table 6: Detection results by using different models on the SBDS dataset. berries at three different stages of ripeness in complex scenarios, demonstrating substantial application potential.
## 5 Use Cases  
To demonstrate the applicability of our approach we set up three use cases and applied the LDAF framework to manage the traffic from/to a communication-constrained device. The first case is a simple DNS query in which a device resolves a given host name to a corresponding IP address.  
The second example represents a realistic case of an IoT device, controlled over the Ethereum network. For this we applied our previously developed prototype system, dubbed Swether [25]. This is an IoT electric meter and switch, which is a part of a broader decentralized application (DApp). The DApp is comprised also of a corresponding smart contract deployed in the Ethereum network and a web--based Ethereum enabled user interfaces to initiate control activities. The application logic in Swether requires access to a fully synchronized Ethereum blockchain (BC) node. In our case the node was set on a remote server. Without LDAF the Swether connects to the BC node's JSON-RPC API over WebSockets. The remote server provides it with key BC functionalities, including indication of chain synchronization, event notifications from the smart contract, and creation, signing, and deployment of Ethereum transactions. The details of this architecture are discussed in [23].  
The third case targets binary transfers from the network to the IoT end devices, as, for example, in software updates and patching. To reduce unnecessary traffic to the device, we implemented a service that, instead of forwarding a complete new binary file to a device, calculates and forwards only the binary difference between the old and new version of the file. As in other cases, the originating file server does not implement such a service and only provides full versions of files. We can apply such a service with Swether, too, e.g., for system updates.
## 3 Simulation of Thermal Processes in the Food Industry Using CFD
### Extraction Process  
The extraction process involves the transfer of soluble compounds from solid food matrices into a solvent, such as water or alcohol. Modeling this process using CFD requires the incorporation of various physical phenomena, including fluid flow dynamics, diffusion, and thermal effects. The primary mathematical models used in these simulations include the Navier-Stokes equations, which govern the motion of the solvent within the food matrix, and [PERSON]'s laws of diffusion, which describe the mass transfer of solutes. Additionally, the energy balance equation is employed to model heat transfer during the extraction process [93]. The generalized form of the energy equation is expressed as Equation (2).  
The diffusion term in the mass transfer equation is typically modeled using [PERSON]'s law (Equation (10)):  
\[J=-D\
abla C \tag{10}\]  
where \(J\) is the diffusion flux, \(D\) is the diffusion coefficient, and \(\
abla C\) is the concentration gradient of the solute [94].  
CFD simulations of extraction processes must resolve concentration gradients and solute diffusion rates within heterogeneous food matrices, where the distribution of solvents and solutes is influenced by both convection and molecular diffusion. By utilizing numerical techniques such as the FEM or FVM, these coupled equations can be discretized and solved iteratively to predict the optimal extraction parameters, including temperature, pressure, and solvent-to-solid ratios. Furthermore, empirical models like the Freundlich or Langmuir isotherms can be incorporated to better predict the solute-solvent interactions and adsorption behaviors within the food matrix during extraction [95]. Optimizing these parameters is crucial to maximizing the yield of target compounds while minimizing the degradation of heat-sensitive molecules, such as volatile flavours, antioxidants, and other bioactive components [96].
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 6 Computational Results
### Instance Characteristics, Benchmarks and Analysis Methodology  
#### 6.1.1 Instance Characteristics  
Instances were generated for this study; these instances are available online: [[https://github.com/ndupin/SiteDepTRTPW](https://github.com/ndupin/SiteDepTRTPW)]([https://github.com/ndupin/SiteDepTRTPW](https://github.com/ndupin/SiteDepTRTPW)) (accessed on 25 October 2021). Instances are named gotic_a_b_c_ex_d.txt where \(a\in\{3,5,8,10,15,20\}\) is the number of technicians, \(b\in\{1,3,5,10,20\}\) is the number of skill types, \(c\in\{10,20,40,50,80,100\}\) is the number of jobs to plan and \(d\in\llbracket 1,8\rrbracket\cup\llbracket 11,18\rrbracket\) distinguishes instances generated with the same previous characteristics. For each instance, jobs have the same penalization cost. Penalties are larger than transportation costs so that the optimal solutions serve a maximal number of jobs. For only instance \(\text{gotic\_15\_20\_40\_ex7.txt}\), it was impossible to realize all the jobs; the other optimal solutions visit every customer. In the instances, localisation of depots and customers is given with 2D coordinates, and the distance from localisation coordinates \((x,y)\) and \((x^{\prime},y^{\prime})\) shall be computed using the following rule (different from [19]).  
\[d\big{(}(x,y),(x^{\prime},y^{\prime})\big{)}=\left\lfloor\sqrt{\lfloor(x-x^{ \prime})^{2}\rfloor+\lfloor(y-y^{\prime})^{2}\rfloor}\right\rfloor \tag{46}\]  
The (symmetric) commuting time between locations \((x,y)\) and \((x^{\prime},y^{\prime})\) is given by the following:  
\[t\big{(}(x,y),(x^{\prime},y^{\prime})\big{)}=\left\lfloor\frac{d((x,y),(x^{ \prime},y^{\prime}))}{\textit{speed}}\right\rfloor \tag{47}\]  
where _speed_ is a single speed parameter defined in the instances.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### Two-Dimensional Examples
#### 3.2.3 2D Stationary Droplet  
In the following example, we considered a stationary droplet placed at the center of the vapor phase domain. Such a problem is usually used to evaluate the stability and accuracy of the multiphase model, as well as to estimate the surface tension using Laplace's law. In the current example, the surface tensions of water at different temperatures are estimated. Therefore, the inter-particle force between liquid and vapor phases can be presented as [35]:  
\[\mathbf{F}_{int}(\mathbf{x},t)=-G\psi(\mathbf{x},t)\sum_{i}\omega_{i}\psi(\mathbf{x}+\mathbf{e}_{i }\Delta t,t)\mathbf{e}_{i}, \tag{102}\]  
where \(G\) is a parameter that controls the strength of the inter-particle force and \(\psi\) is the mean-field potential function. [PERSON] and [PERSON] [36] modified the SC [6,7] model and defined \(\psi\) as:  
\[\psi=\sqrt{\frac{2\{P_{\text{EoS}}-C_{g}^{2}\rho\}}{C_{s}^{2}G}}, \tag{103}\]  
where \(P_{\text{EoS}}\) is the EoS pressure. The R-K EoS is used in the present example. The parameters \(a\) and \(b\) of the R-K EoS can be defined as [37]:  
\[a=1.282\frac{RT_{c}\sqrt{T_{c}}}{\rho_{c}}, \tag{104}\]  
and  
\[b=\frac{0.2597}{\rho_{c}}, \tag{105}\]  
where  
\[RT_{c}=0.27847, \tag{106}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{Nu_{ave}}\) & \(\mathbf{U_{max}}\) & \(\mathbf{Y_{max}}\) & \(\mathbf{V_{max}}\) & \(\mathbf{X_{max}}\) \\ \hline Ref. [33] & 8.814 & 64.81 & 0.850 & 220.57 & 0.0378 \\ Present study & 8.6915 & 63.9960 & 0.8529 & 216.8242 & 0.0399 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Dimensionless results for \(Ra=10^{6}\).  
and  
\[\rho_{c}=\ln(2)\rho_{o}, \tag{107}\]  
where \(\rho_{o}\) is an arbitrary constant. The values of \(R\) and \(\rho_{o}\) are set to be 1 and 20 for water, respectively.  
Table 10 shows the scaling parameters that are used to map the data from the lattice-scale to the physical-scale. The LBM predictions are compared with [PERSON] et al. [38] LBM simulations and with the DDBST GmbH database [39], as shown in Figure 11. It is clear that there is a good agreement between the current predictions with published results.
###### Abstract  
With the development of 5G, 5G optical networks have gradually received increasing attention from scholars. However, most studies have focused on discussing the technical or market aspect. Furthermore, their findings have not provided a panorama of the technologies in the 5G domain, nor have they provided a detailed understanding of the key technologies and development trends. An optical network is an indispensable type of infrastructure for the development of 5G. Therefore, defining key technologies in this domain is particularly crucial. The present study used patents for 5G optical networks as the basis of its analysis and constructed a technology network using a network analysis method. Research results indicated that the key technologies provided by 5G optical networks include wireless communication network facilities and local resource management (H04W88 and H04W72), selection arrangements for multiplex systems (H04Q11), and arrangements enabling multiple uses of the transmission path (H04L5). The maturation of optical component technology has paved the way for multiplex communication system technology to flourish and made it one of the key technologies in the development of 5G. Additionally, an analysis of top patentees revealed that information technology companies are the main force in developing 5G optical network technologies. Thus, driven by the market, 5G optical communication has become the technical focus of the private sector. In this study, the researchers constructed a technology network model to explore the technology development trends, and the results may serve as a reference for the government in observing emerging technologies.  
optical network; fifth-generation mobile communication system; patent analysis; technology evolution; network analysis +
Footnote †: journal: Applied Soft. 2019, 9, 4835; doi:10.3390/appr9224835
## 2 Material and Methods
### Simulation Model  
#### 2.3.1. Pavement Modeling  
(1) C-Class Random Pavement  
The road excitation model describes the degree of roughness of the road surface. In the simulation analysis of the road surface model, there are two approaches to converting the spatial model of the road surface into either a frequency-domain model or a time-domain model. Using the mathematical model of filtered white noise, the time domain model of road roughness [21] can be formulated as follows:  
\[\dot{q}(t)=-2\pi n_{1}uq(t)+2\pi n_{0}w\sqrt{Gq(n_{0})v} \tag{23}\]  
where \(\dot{q}\) represents the vertical velocity spectrum of road roughness and the reciprocal of displacement; \(v\) is the vehicle speed, with 54 km/h (15 m/s) adopted in this paper; \(Gq(n_{0})\) represents the road roughness; \(n_{1}\) represents the lower cutoff spatial frequency, taken as 0.01 m\({}^{-1}\) in this paper; \(n_{0}\) represents the reference spatial frequency, assigned a value of 0.1 m\({}^{-1}\); \(Gq(n_{0})\) represents the road roughness coefficient; the C-class pavement roughness coefficient of the pavement is \(Gq(n_{0})=256\times 10^{-6}\) m\({}^{3}\); and \(w\) is the unit of white noise.  
Assuming that the vehicle moves smoothly along a straight path, it encounters different road surfaces on its left and right sides. Figure 5 illustrates the displacements of the front and rear wheels on both the left and right sides when the vehicle traverses a constructed C-class pavement. The velocity spectrum of this pavement resembles white noise, with vertical irregularities ranging primarily between \(-20\) mm and \(20\) mm.  
(2) Convex Pavement  
When a vehicle is driving on a road, it will encounter raised obstacles, resulting in instantaneous and strong impacts, which will affect the ride's comfort. Following the specifications outlined in the GB/T5902-1986 standard in the \"Automobile Ride Comfort Pulse Input Driving Test Method\", the triangle bump pulse excitation is specifically defined as follows [22]:  
\[q(t)=\begin{cases}2\frac{H}{B}v(t-t_{1}),t_{1}\leq t\leq t_{2}\\ 2\frac{H}{B}(B-v(t-t_{1})),t_{1}+t_{2}\leq t\leq 2t_{2}\\ 0,\textit{else}\end{cases} \tag{24}\]  
In the formula, \(H\) = 0.06 m, where \(H\) is the height of the triangular convex block, \(B\) = 0.4 m, where \(B\) is the width of the convex block, and \(v\) = 36 km/h (10 m/s), where \(v\) is the speed of the vehicle.  
The left wheel is impacted by the convex block at 1 s, and the right wheel is impacted by the convex block at 3 s. The displacement of the tire with the constructed triangular bump is shown in Figure 6.  
Figure 5: C\(-\)class random pavement: (**a**) left and (**b**) right.
## 5 Discussion
### Advantages of the Algorithm  
* Finds a set of points in the configuration space that can reach a given point in the workspace and sorts them by type. This can be used to determine the service coefficient and the robot's mobility factor.
* Transforms trajectories from the workspace into a corresponding set of points with possible solutions in the configuration space. This gives completeness to the solutions, unlike some numerical methods known so far, which find only one solution, one that is affected by the initial configuration;
* Can be used to define regions from the workspace and configuration space with different types of solutions. Once created, a region map for a particular robot (environment with obstacle), can be used repeatedly by motion planning algorithms with different initial and target configurations.
* Finds an IK solution even in singular configurations. Numerical methods based on the Jacobian matrix do not deal with this problem.
* The algorithm is applicable to planar robots with a serial structure.
* The resulting regions are useful for planning the placement of objects in the workspace of the robot.