esa-sceva/satcom-chunk-collection huggingface dataset:
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 4 Proposed Lagrange Optimization Scheme  
Generally, reference pixels are located on the left and top of the chroma block and are often smooth, making them easily acquired and directly applicable in intra modes such as VER, HOR, PLANAR, DC, and DM. However, for modes like LM, LM_T, and LM_L, the referenced reconstruction blocks located in the luma component should first be down-sampled for intra prediction. This process may potentially introduce pixel discontinuity. Subsequently, these discontinuous reference pixels are used for chroma prediction, and the inherent distortion from the luma component may propagate further into subsequent chroma blocks during intra-mode decision. Figure 5 illustrates the proportion of optimal modes under the All-Intra configuration, where the quantization parameters (QPs) are typically used to adjust the bitrate. Four common test QP values \(\in\)[22; 27; 32; 37] are chosen for encoding, and the compression structure is defined as an All-Intra frame case for the conditions of the All-Intra configuration in VVC. It can be observed that the modes of LM, LM_T, and LM_L make up more than 40 percent of all intra prediction modes. Figure 6 shows the optimal modes in coding blocks for the sequence of BasketballDrive, where the mode numbers are represented by corresponding colors. It can also be noticed that the modes of LM, LM_T, and LM_L have a significant portion in the frames under different QPs, indicating that the role of the CCLM is vital in intra coding. Consequently, the corresponding distortion propagation characteristic from the luma to chroma is non-negligible in intra-mode decision, where the RD cost may exhibit a discrepancy. In an effort to improve compression efficiency, the discontinuous distortion propagation should be considered to lessen its negative impact on RDO.  
Figure 4: The framework of RDO in chroma prediction.
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 4 Proposed Approach for Course Recommendation
### Meta-Path Embedding Layer
#### 4.1.2 Simulated Meta-Path Embedding  
Using the method in the previous section, the embeddings generated under each meta-path in the heterogeneous graph can be obtained. In order to gather more neighbor information to improve the learning effect of node feature representation, this module uses GCN to aggregate the information of neighbor nodes to generate more meta-path node embeddings. As Figure 2 shows, a GCN model inside \(MEL\) is used to achieve this task; that is, more meta-path node embeddings are simulated by using GCN to aggregate neighbor embeddings for the node embeddings of the initial meta-path, thereby generating new node embeddings. The schematic of this process is shown in Figure 3.  
For a heterogeneous graph \(G=(V,E)\), \(M^{a}\) denotes an adjacency matrix of \(G\) generated from the set of filtered meta-path sampling sequences \(\hat{X}\); \(M^{a}\) is added as an identity matrix \(I\) for extension, e.g., \(M^{a}=M^{a}+I\). Then, we have a matrix \(D\)  
\[D_{ii}=\sum_{j}M^{a}_{ij}, \tag{2}\]  
where \(D\) denotes a diagonal matrix of the node degree. Then, the GCN is used to generate the embeddings of the simulated meta-path following the process in Equation (3):  
\[\hat{e}^{P}=softmax(D^{-\frac{1}{2}}M^{a}D^{-\frac{1}{2}}LeakyReLU(D^{-\frac{1 }{2}}M^{a}D^{-\frac{1}{2}}e^{P}W_{0}^{P})W_{1}^{P}), \tag{3}\]  
where \(\hat{e}^{P}\) denotes the simulated meta-path embeddings, \(e^{P}\) denotes the original ones, and \(W_{0}^{P}\) and \(W_{1}^{P}\) represent the trainable weight matrix.
## 5 Conclusions  
A thermal multiphase lattice Boltzmann model with normalized Van der Waals EOS was developed in this paper to simulate heat and mass transfer in TFE. Validations of the approach were carried out by simulating some benchmark problems and comparing with published results. One contribution of this paper is that it shows that TLBM can provide details of the liquid-vapor interface in TFE, for example, the maximal mass flux is achieved in the thin liquid film region.  
Figure 14: Overall efficiency of multiple pores with hydrophobic sections: blue squares represent the case without any hydrophobic sections; red circles represent the case with hydrophobic sections; the wall temperature is set as 0.72 for both cases; efficiency is normalized with respect to the case that there is only one purely hydrophilic pore.  
Figure 15: The steady-state density profile (red for liquid and blue for vapor) and streamlines in two cases: (**a**) the solid wall is purely hydrophilic (contact angle is about \(15^{\circ}\)); (**b**) the top section of the solid wall is hydrophobic (contact angle is about \(145^{\circ}\), section length is \(50\Delta x\)) while the remaining part of the wall is still hydrophilic.  
The detailed information may help people in the phenomenon-level understanding and device-level design. The TLBM approach was further used to study a vapor generation problem where TFE is involved. To this end, a replenishment boundary condition was proposed, which ensured the balanced mass in the evaporating pore. Two main factors that influence the overall vapor generation efficiency were mainly studied. One is the number of pores that are used for vapor generation given a fixed thermal concentration, and the other is the hydrophilicity/hydrophobicity of the solid walls. Based on the TLBM simulations, scenarios leading to higher overall efficiency were suggested, which is another contribution of our work. Specifically, distributed evaporating pores with hydrophobic pore ends are shown to have the highest efficiency for vapor generation. However, when the pores are too dense, the overall efficiency will decrease due to the suppressed viscosity.  
Our future work will extend the approach to three-dimensional cases. For the vapor generation problem, the three-dimensional TLBM approach will further enable us to consider the factor of pore shape, and thus will be of more practical value.  
[PERSON] developed the method and wrote the paper; [PERSON] improved the method and contributed to the theoretical analysis; [PERSON] provided the simulation platform and funding support for publication.  
The research work was sponsored by the Fundamental Research Funds for the Central Universities (USRP11855). [PERSON] was also supported by National Natural Science Foundation of China (Grant No. 11472269). Special thanks to [PERSON] and [PERSON] in Khalifa University (Abu Dhabi, UEA) for supporting the work.  
The authors declare no conflict of interest.
###### Abstract  
Solar drying is affordable, requiring low energy and an eco-friendly method. Thus, the present paper studies the efficiency and characteristics of the indirect solar convective drying in the fruits of _Arbutus unedo_ L. as well as its effects on the fruit phenolic compounds. The fruit samples were dried at 60 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. Phenolic compounds were investigated using a Liquid Chromatography platform. Experimental results revealed that the effective moisture diffusivity determined by [PERSON]'s second law varied from 1.51 \(\times\) 10\({}^{-9}\) to 4.68 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s, and the activation energy recorded was 2203.62 kJ/kg. Both the total energy consumption and the specific electrical energy of the dried fruits decreased as temperature increased. The Midilli-Kucuk model was selected as the best-fitted model for drying _Arbutus unedo_ L. Significant effect of temperature on phenolics was observed. The concentration of the phenolic compounds decreased by 15.54, 39, and 40.63% at 60, 70, and 80 \({}^{\circ}\)C, respectively.  
convective solar drying; _Arbutus unedo_ L; energy efficiency; polyphenols
# International Airline Alliance Network Design with Uncertainty
## 3 Airline Alliance Network
### Problem Formulation  
An example of an alliance route network is displayed in Figure 1.The nodes of the network include spokes and hubs. As a complex HS route network, this alliance route network architecture has the capability of connecting the networks of different airlines. The hubs are separated into regional hubs (R) and international gateways (K). Regional hubs connect local airports (i) within an area, while international gateways have connections with different areas. Alliance airlines may choose one or more international gateways from regional hubs to transfer passengers from different areas.  
Compared with an HS network, the constructed alliance route network is characterized below:  
1. The cooperation between alliance partners should meet the limitations of traffic rights.
2. The round-trip passenger flow of international routes usually differs greatly and is asymmetric.
3. Usually, the domestic network of an airline is relatively complete before the airline joins an alliance, and the regional hubs can be given. However, airlines need to choose their international gateways from the set of regional hubs through optimization.
4. One or more international gateways coexist with each area for each airline, and all gateways are interconnected across international areas.  
In order to reasonably reflect the practice and simplify the problem, the proposed model is subject to the following assumptions:  
1. All international gateways are interconnected across international areas. Due to the economy of scale in which passengers converge at gateway airports, a discount factor \(\alpha\) is incorporated into the cost between international gateways.
2. According to the actual transportation situation, international transportation usually does not exceed two transits, so airports other than the international gateways are regarded as \"spokes\" of international routes. Multi-allocation connections between \"spokes\" and international gateways are adopted; \(\chi\) is the discount factor from  
Figure 1: Alliance route network configuration.  
\"spokes\" to international gateways, and \(\delta\) is the discount factor from international gateways to \"spokes\". Generally, \(0\leq\alpha<\chi,\delta\leq 1\).
3. The regional hubs within each area are given, and airlines choose their international gateways from this subset of regional hubs.
4. Inter-area journeys are limited to three legs, that is, if both the origin and destination nodes are \"spokes\", traveling across international areas will necessarily involve a three-leg journey. For example, to travel from i5 to i7 would involve one leg from i5 to K1, a second leg from K1 to K2, and, finally, a third leg from K2 to i7. On the other hand, inter-area journeys contain at least one leg, and this happens when both the originating and destination nodes are the international gateways.
5. Inter-area traffic must all be transported from the originating node to the destination node.
6. The round-trip passenger demand of international routes is usually asymmetric; \(W_{ij}\
eq W_{ji}\).
###### Abstract  
This paper proposes two bio-inspired heuristic algorithms, the MOH-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.  
energy management system; energy optimization techniques; genetic algorithm; moth-flame optimization; smart grid; time-constrained optimization techniques
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods
### Performance Evaluation  
In this study, accuracy, sensitivity, and specificity as defined in Equations (6)-(8) were used to evaluate the proposed model [33]. Here, accuracy describes the total number of SA segments and normal segments that were correctly identified among all of the samples, sensitivity reflects the number of correctly identified SA among all SA segments, and specificity reflects the number of correctly identified normal among all normal segments. In addition, the area under the receiver operating characteristic curve (AUC) is also the evaluation index of this model:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{7}\]  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{8}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) are the number of true positives, true negatives, false positives, and false negatives.
# Development of Robot-Assisted Untact Swab Sampling System for Upper Respiratory Disease
## 3 Experimental Results
### Motion Tracking Accuracy of the Slave Robot  
The accuracy of the slave robot's response to the motion of the master manipulator is an important performance indicator to ensure the control transparency of the telerobotic system. As shown in Figure 8a, infrared (IR) retroreflective markers were attached to the top plate of the slave robot and the end of the control knob of the master positioning device. The trajectories of both markers were recorded at 30 Hz by a 3D position tracker (ftk-500, Atracsys LLC, Puidoux, Switzerland). Although the kinematic structures of the slave robot and the master positioning device are identical, the positions of the markers on the slave robot and master device were different in these experiments, as shown in Figure 8a. Therefore, we transformed the measurement frame (position and orientation) of the master device into a frame identical to that of the slave robot. The tracking accuracy for each axis was defined as the average root mean square error (RMSE) of the difference between the motion trajectories of the master device and the robotic end effector in XYZ Cartesian coordinates. For example, error for \(X\)-axis is defined as,  
\[\epsilon_{x}=\frac{1}{M}\sum_{i=1}^{M}\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x-x_{0}) ^{2}} \tag{1}\]  
Figure 7: (**a**) Definition of the workspace with respect to a life-size head phantom model, (**b**) swab positioning test relative to the borders of the workspace, (**c**) depth from the nostrils to the nasopharynx wall, and (**d**) depth range of swab insertion.  
Measurements with a duration of 60 s were conducted 10 times. Therefore, in the expressions above, N is 1800, and M is 10. The average error on each axis is listed in Table 1. \(\epsilon_{xyz}\) is an error in 3D space calculated from \(\epsilon_{x}\), \(\epsilon_{y}\), and \(\epsilon_{z}\), defined as \(\epsilon_{xyz}=\sqrt{\epsilon_{x}^{2}+\epsilon_{y}^{2}+\epsilon_{z}^{2}}\). Figure 8b shows one set of position tracking results between the master and slave devices on the \(X\)-, \(Y\)-, and \(Z\)-axes over time.
## 4 Discussion
### Parameter Interactions
#### Interactions Between Humidity and Air Velocity  
Relative humidity (RH) and air velocity are important factors in the perception of thermal comfort and indoor air quality. The relationship between these two parameters is often complementary: high air velocity can reduce the perception of humidity and make a space feel cooler, while lower air velocity can accentuate the discomfort caused by high humidity levels. Studies show that maintaining an optimal balance between RH and air velocity is important for ensuring occupant comfort, particularly in spaces such as offices, classrooms, and healthcare environments where both factors can have a significant impact on productivity and health outcomes.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 4 Conclusions  
TiNb and TiNbN films with low Ti content were coated on 316L SS by magnetron sputtering with a view to application for bipolar plates in PEMFC. The electrochemical testing indicates that TiNb coating has the best corrosion resistance in cathode simulation electrolyte solutions. At 0.19 V vs MSE, the stable corrosion current density of TiNb/316L SS is lower than 1 \(\mu\)A\(\cdot\)cm\({}^{-2}\). The ICR measurements show that the contact conductivity of TiNb/316L SS is higher than that of substrate. Compared with 316L SS and the TiNb coating, TiNbN film shows moderate corrosion resistance but the lowest ICR. According to XRD analysis, the main component of TiNbN is a solid solution of cubic NbN and TiN. The morphologies of TiNb and TiNbN characterized by SEM reveal that both coatings provide smooth surfaces. It can be concluded that the TiNb/316L SS is a kind of promising bipolar plate material with high corrosion resistance. Nevertheless, the conductivity of TiNb/316L SS still needs to be improved. In the future, multilayer coatings consisting of TiNb and TiNbN with optimized atomic ratios will be studied with aim to improve material's corrosion resistance and conductivity further.  
Conceptualization, K.S.; methodology, K.S. and X.-F.X.; resources, X.L., Y.Z. and W.-W.L.; writing--original draft preparation, K.S.; writing--review and editing, X.-F.X., J.O.J., S.-B.W. and L.Y.; project administration, X.-F.X., J.O.J. and Q.-F.L.  
This research was funded by National Key R&D Program of China (2016 YFE0102700), Major Science and Technology Projects of Shanxi Province (20181101006) and Innovation Fund Denmark by the project Wind2H (5185-00025B).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Numerical Modelling
#### 2.2.1 Governing Equations  
The continuity equations model (Equation (2)), momentum equations, and the energy equation applied to calculate airflow and heat transfer in this research are shown below:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u)}{\partial x}+\frac{ \partial(\rho v)}{\partial y}+\frac{\partial(\rho w)}{\partial z}=0 \tag{2}\]  
where \(\rho\) is the fluid density (kg m\({}^{-3}\)); \(t\) is time (s); and \(u\), \(v\), and \(w\) are the air velocity components of the x, y, and z directions. The transport equation (Equation (3)), where \(\phi\) represents the concentration of the transport variables, mass (air and CO\({}_{2}\) mass fraction), momentum, and energy [28].  
\[\frac{\partial}{\partial t}(\rho\phi)+div(\rho u\phi)=div\big{(}\dot{\Gamma} _{\phi}grad\phi\big{)}+S_{\phi} \tag{3}\]  
where \(\rho\) is the density, \(u\) is the component of directional air velocity, \(\dot{\Gamma}_{\phi}\) is the diffusivity coefficient for \(\phi\), and \(S_{\phi}\) is the source term.  
Managing the meshing grid to obtain accurate simulation results was challenging. [PERSON] et al. [20] discussed that finer meshes might not improve the accuracy anymore, whereas coarser meshes might still give accurate results for some cases.
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Practical Implications for Landscape Sustainability
#### 3.4.5 Future Studies and Data Monitoring  
The modeling framework presented in this study can be applied in other climatic zones or scaled up using advanced technologies such as remote sensing (e.g., GEDI), GIS-based mapping, and drone thermography. These tools would enable detailed monitoring and strategic optimization of vegetation placement and composition [93; 94].  
In conclusion, this study emphasizes that functionally diverse vegetation, when designed with site-specific climatic and spatial conditions in mind, can offer scalable and transferable solutions for sustainable urban and campus environments.  
These recommendations are not only derived from the empirical findings of the present study but are also supported by a broad range of recent literature that emphasizes the importance of species-specific vegetation design [55; 56; 89], microclimate-sensitive landscape planning [40; 41; 84], and climate-adaptive green infrastructure strategies [61; 62; 88; 94]. Integrating these insights into urban and campus-scale planning can greatly enhance resilience to climate stressors while also improving human well-being and ecological sustainability.  
Despite the model's strong predictive performance and the statistical robustness of the experimental design, several limitations should be acknowledged to ensure a comprehensive interpretation of the findings. Firstly, potential calibration drifts or environmental exposure effects on the sensors over time may have introduced minor inaccuracies in the recorded temperature and humidity data. Although all instruments were calibrated prior to field deployment, undetected deviations could have subtly influenced the measurementaccuracy. Secondly, seasonal irregularities--such as unanticipated precipitation events or abrupt wind fluctuations during data collection--may have acted as uncontrolled variables, potentially affecting the consistency of the microclimatic observations. Lastly, the study assumed uniformity in vegetation maturity and canopy density across all sampled areas; however, slight variations in plant growth stages or structural characteristics might have contributed to differential heat retention and evapotranspiration dynamics. Acknowledging these limitations provides critical context for interpreting the results and highlights the need for further research under controlled and long-term environmental conditions to enhance the generalizability and accuracy of future models.
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
## 4 Analysis of the Accident Risk Propagation Mechanism in the Highway Merging Area
### Simulation Scenario  
This study utilized SUMO to simulate traffic accident scenarios in the merging area. SUMO is open-source microscopic traffic simulation software used to simulate and evaluate different types of traffic systems such as roads, highways, railways, pedestrians, and bicycles. It helps to realize various traffic management measures and simulate dangerous situations.  
In this study, a scenario was constructed which consisted of three lanes on the main road and a 200 m acceleration lane. Two types of accidents, side collisions and rear-end collisions, were simulated in the merging areas and the downstream area, respectively. The location of the accident is shown in Figure 3.  
The impact range of the accidents was calculated using the accident impact range model presented in Equation (5). Based on multiple experiments and in conjunction with a literature reference [33], the accident impact range and parameters in this scenario are presented in Table 1. The impact area was split into three adjacent areas (as shown in Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Accident Type** & \(a\) & \(P\) & \(C_{d}\) (N/m\({}^{2}\)) & \(\xi\) & \(b_{i}\) & \
\begin{tabular}{c} **Impact Range** \\ **(m)** \\ \end{tabular} \\ \hline Side collision & 0.5 & 0.5 & 1000 & 3e-7 & 1 & (0.05, 0.3,1) &
\begin{tabular}{c} (227.3, 413.1, 617.1) \\ (198.6, 360.9, 539.1) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accident impact range and parameters.  
Figure 3: Simulation scenario: (**a**) merging area and (**b**) downstream.