esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Recommendation Based on Graph Learning  
Graph is becoming a core area of machine learning. Graph learning is widely used to understand the structure of social networks by predicting potential connections, detecting fraud, understanding consumer behavior, or making real-time recommendations. Graph neural network (GNN) techniques have been widely used in recommender systems because most of the information in recommender systems has a graph structure in nature and GNNs have excellent performance in learning graph structures [41; 42]. [PERSON] et al. [43] proposed a light graph convolution network (LightGCN) model that uses user-interacted item records to enhance user representation and interacted user records to enhance item representation. Multi-layer GNNs can simulate the information transfer process and efficiently establish higher-order connections. [PERSON] et al. [44] designed a novel feature interaction graph neural network (Fi-GNN) to model sophisticated feature interactions in a flexible and an explicit fashion, which provides good model explanations for click-through rate prediction. [PERSON] et al. [45] proposed a new graph-based geographical latent representation (GCLR) that models geographic influences between the POIs and the transition patterns of user sequence behavior based on spatial and temporal features, which can capture highly non-linear geographical influences from complex user-POI networks.  
The GNN methods mentioned above employ pairwise connections between data. However, data structures in real-world applications can go beyond pairwise connections and they can be even more complicated. [PERSON] et al. [46] proposed a hypergraph neural network (HGNN) framework that can deal with complex data correlations by encoding high-order data correlation (beyond pairwise connections) using its degree-free hyperedges. [PERSON] et al. [47] proposed a neural signed hypergraph to extract non-linear relationships among users, items, and features. [PERSON] et al. [48] proposed a hypergraph click-through rate prediction framework (HyperCTR) that learns item representations based on multi-modal information interactions among users and items. However, existing research focuses on learning user interaction characteristics with products during and after purchase (e.g., purchase and online review), and it ignores the interactive features in the process of users' learning about product information through search queries before they make a purchase (e.g., product information search). However, user association with a product is a coherent process that should not be isolated into different nodes. Only sorting the user's process of searching-understanding-purchasing-using products and finding opportunity points from each stage can help the recommender system to better discover the potential needs of users. In this paper, we develop a hypergraph framework to handle the interaction behavior of consumers in the whole process of shopping (i.e., searching-understanding-purchasing-using).
## 5 Context Aware CL Optimization on MAC and NET Layers  
AmI systems require sensor data for context inference. The importance of a piece of sensor data, _i.e._, its usefulness to the current context inference process, can only be known by AmI. However, if sensor nodes can similarly know the importance of any sensor data at any given time through context exchange, the WSN can be optimized based on such knowledge. More specifically, situations such as the published sensor data on the VBs not matching with any data subscriptions from AmI while the required sensor data is delayed or lost due to network congestion can be avoided. The key idea behind this context-aware CL approach is to prioritize communications according to AmI context information.  
Therefore, a sensor node that anticipates its data type will become important for AmI's current context inference process can assign its next data to be published with high priority, and reconfigure its protocol parameters accordingly.  
As a use-case of the proposed framework, two existing algorithms, Dynamic Reconfiguration MAC (DR-MAC) [23] protocol on the MAC layer, and Delay Aware AODV-Multipath (DAAM) [24] routing protocol on the NET layer, have been modified to incorporate the context-aware CL optimization framework proposed in Section 4. The modified protocols are referred to as context-aware DR-MAC, and context-aware DAAM, respectively.
## Nomenclature  
**Symbols**  
\(\mathbf{M}_{\mathrm{N}}\) & Mass matrices of the NS  
\(\mathbf{C}_{\mathrm{N}}\) & Damping matrices of the NS  
\(\mathbf{K}_{\mathrm{N}}\) & Stiffness matrices of the NS  
\(i\) & Integration time step  
\(\mathbf{F}_{\mathrm{E}}\) & Reaction force vector of the ES  
\(\mathbf{F}\) & External excitation force vector  
\(\mathbf{a}_{\mathrm{N}}\) & Acceleration response vectors of the NS  
\(\mathbf{v}_{\mathrm{N}}\) & Velocity response vectors of the NS  
\(\mathbf{d}_{\mathrm{N}}\) & Displacement response vectors of the NS  
\(\Delta t\) & Integration time interval  
\(d_{\mathrm{EC}}\) & Displacement targets for the specimen  
\(v_{\mathrm{EC}}\) & Velocity targets for the specimen  
\(a_{\mathrm{EC}}\) & Acceleration targets for the specimen  
\(\mathbf{I}\) & Interface DOF of the two substructures  
\(t\) & Time  
\(K_{\mathrm{E}t}\) & Displacement feedforward gains  
\(K_{\mathrm{E}t}\) & Velocity feedforward gains  
\(K_{\mathrm{E}t}\) & Acceleration feedforward gains  
\(K_{\mathrm{V}}\) & Open-loop gain of the hydraulic system  
\(s\) & Laplace operator  
\(\zeta_{0}\) & Natural damping ratio of the hydraulic system  
\(\omega_{0}\) & Resonant frequency of the hydraulic system  
\(U\) & Displacement command input  
\(x\) & Displacement response output  
\(K_{\mathrm{E}t}\) & Displacement feedback gains  
\(K_{\mathrm{V}t}\) & Velocity feedback gains  
\(K_{\mathrm{V}t}\) & Acceleration feedback gains  
\(k_{0}\) & Desired equivalent open-loop gain of the system under TVC.  
\(\omega\) & Desired frequency of the system under TVC.  
\(\xi\) & Desired damping ratio of the system under TVC.  
\(F_{\mathrm{E}}\) & Shear force  
\(F_{\mathrm{Total}}\) & Total shear force obtained from the measurement table  
\(m_{\mathrm{up}}\) & Mass of the upper steel plate  
\(\mathbf{a}_{\mathrm{up}}\) & Acceleration measured by the accelerometers installed on the upper plate.  
**Abbreviations**  
STST & Shaking table substructure testing  
TVCM & Three-variable control method  
VPF & Velocity positive feedback  
RTHS & Real-time hybrid simulation  
ES & Experimental substructure  
NS & Numerical substructure  
TLD & Tuned liquid damper  
STTs & Shaking table tests  
RTST & Real-time substructure testing  
DOF & Degree of freedom  
PID & Proportional integral derivative
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 4 Model System
### Task Communication Model  
In this article, edge node devices use Time Division Multiple Access (TDMA) to communicate with mobile terminal users. Edge node devices allocate different time slots for each associated mobile terminal user to transmit data, ensuring that only one user communicates on a specific channel at a time. Therefore, data transmission between different users will not conflict, effectively avoiding interference between channels and improving the communication efficiency and stability of the system.  
(1) Communication Model between Edge Nodes and Mobile Terminals  
According to [PERSON]'s theorem, in an interference-free wireless communication calculation model, assuming that the channel transmission gain is \(g\), and assuming that mobile terminal users do not move within a single time slot, then \(g\) is a constant value. The transmission power is \(P_{n}^{tran,d}\), the noise power spectral density is \(N_{0}\), and the wireless channel bandwidth is \(B_{t}^{m}\). From this, the uplink channel transmission rate \(r_{nm}\) from the terminal device \(n\) to the  
Communication Model between Edge Nodes  
Communication between multiple edge nodes is similar to the communication process between edge nodes and mobile terminal users, and the transmission also requires the use of a channel resource. This is represented as \(r_{m_{1}m_{2}}\):  
\[r_{m_{1}m_{2}}=B_{1}^{m}\times\log_{2}\left(1+\frac{p_{m_{1}}^{trans,e}\times g}{ N_{0}}\right),m\in\mathcal{M},t\in T \tag{3}\]  
When an edge node, \(m_{1}\), lacks the service cache type required for task execution, and it is necessary to offload the task to another edge node, \(m_{2}\), for execution, the migration delay between \(m_{1}\) and \(m_{2}\) can be expressed as  
\[T_{m_{1},m_{2}}^{trans,e}=\frac{D_{n}}{r_{m_{1}m_{2}}},\forall n\in\mathcal{N},m\in\mathcal{M},t\in T \tag{4}\]  
Communication Model between Edge Nodes and the Central Cloud  
When both the associated edge node \(m_{1}\) and the cooperative edge node \(m_{2}\) do not contain the service cache type required for task execution, it is necessary to download the required service cache resources from the central cloud. The transmission delay for the edge node \(m\) to download service cache from the central cloud through a return path is  
\[T_{m}^{trans,e}=\frac{D_{s}}{W_{m}},\forall s\in\mathcal{S},m\in\mathcal{M} \tag{5}\]  
where \(W_{m}\) represents the bandwidth of the return path.
## 3 Results
### Effect of Temperature on the Decomposition Process
#### 3.1.2 Distribution of Decomposition Products  
Figure 4 presents the distribution of major decomposition products at different temperatures. The results indicate that the decomposition of the PMVE/N\({}_{2}\) mixture primarily  
Figure 3: Time evolution of potential energy at 1600–2600 K.  
Figure 2: Time evolution of PMVE and N\({}_{2}\) decomposition at 1600–2600 K.  
produces C, C\({}_{2}\)F\({}_{2}\), CF\({}_{2}\), CN, CO, CF\({}_{2}\)O, F, O, C\({}_{2}\)F\({}_{3}\) and CF\({}_{3}\). The yields of CN, CO, and F increase linearly with temperature. Below 2000 K, the production of C, CN, CF\({}_{2}\)O, F, and O remains low. However, when the temperature rises to 2200 K, their yields increase significantly, which correlates with the accelerated decomposition rate of PMVE at this temperature. At 2400 K, C\({}_{2}\)F\({}_{2}\) and CF\({}_{2}\)O exhibit a saturation growth trend. The yield of C\({}_{2}\)F\({}_{2}\) shows a decreasing trend across all temperatures, likely due to the presence of O radicals, which promote the depletion of C\({}_{2}\)F\({}_{2}\)[14]. The quantities of CF\({}_{3}\) and C\({}_{2}\)F\({}_{3}\) fluctuate, first increasing and then decreasing, suggesting the occurrence of radical recombination and decomposition during the decomposition process. Additionally, carbon (C) was detected during the simulation, and it readily combined with oxygen (O) to form CO. Notably, the presence of particulate carbon can negatively impact the insulation performance of the system.  
Figure 4: \(Cont.\)  
In addition to the aforementioned primary decomposition products, a significant number of low-abundance free radicals and small molecules were also generated during the reaction process. Figure 5 illustrates the maximum quantity distribution of these species in the PMVE/N\({}_{2}\) mixture over a temperature range of 1600 K to 2600 K. The decomposition of the PMVE/N\({}_{2}\) gas mixture also leads to the formation of radicals such as NF, NF\({}_{2}\), C\({}_{2}\)F\({}_{3}\)O, CFO, C\({}_{2}\)N, CF, CF\({}_{3}\)O, and CF\({}_{2}\)N, along with small molecules including NF\({}_{3}\), CFN, C\({}_{2}\)N\({}_{2}\), and C\({}_{2}\)F\({}_{4}\).  
Figure 4: Time evolution of PMVE/N\({}_{2}\) decomposition products at 1600\(-\)2600 K.  
Figure 5: Maximum number of decomposition products of PMVE/N\({}_{2}\) at 1600–2600 K.  
Notably, C\({}_{2}\)F\({}_{3}\)O and C\({}_{2}\)N\({}_{2}\) appear only when the temperature reaches 1800 K, while C\({}_{2}\)F\({}_{4}\) is formed exclusively at 2200 K. The generation of C\({}_{2}\)N\({}_{2}\) and C\({}_{2}\)F\({}_{4}\) is closely related to the presence of CN and C\({}_{2}\)F\({}_{3}\), respectively. Throughout the reaction, small molecules such as C\({}_{2}\)F\({}_{4}\), C\({}_{2}\)F\({}_{2}\), CO, and CF\({}_{2}\)O are produced, aligning with experimental findings reported in previous studies [11]. This consistency further verifies the reliability of the simulation results.
# Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
## 3 Results and Discussion
### Encapsulation Efficiency and Loading Capacity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source of Variation** & **N** & **Yield** & **Encapsulation** & **Loading Capacity** \\  & & **(\%)** & **(\%)** & **(\%)** \\ \hline Drying type & & & \(p<0.01\) * & \(p=0.06\) \\ Air-drying & 36 & - & \(16.20\pm 0.45\)* & \(53.63\pm 3.48\)* \\ Freeze-drying & 36 & & \(28.40\pm 3.52\)* & \(61.62\pm 2.09\)* \\ \hline Mean & 72 & \(49.43\pm 0.87\) & \(22.30\pm 2.01\) & \(57.14\pm 2.13\) \\ \hline \hline \end{tabular} N = number of trials. Results are expressed as mean \(\pm\) standard error. * significant at \(p\leq 0.05\). Values within groups marked with different letters are statistically different at \(p\leq 0.05\).  
\end{table} 
Table 2: _Cont._These results are in agreement with results reported during the encapsulation of marjoram EO [26], where the highest efficiency was achieved when 1.25% alginate and 1.25% whey protein were used, while further increase reduced the encapsulation efficiency. Another factor that significantly influenced the encapsulation efficiency but not loading capacity was the drying type, with freeze-drying resulting in higher values than air-drying. This can be explained by the characteristics of the freeze-drying process during which the sample is rapidly frozen, and the water is removed rapidly under vacuum by sublimation of ice, thus preserving the structure of the alginate bead, as opposed to air-drying during which slow evaporation can result in damage of encapsulated material and reduced encapsulation efficiency [12; 27]. In addition, the low temperature and vacuum conditions during freeze-drying can minimize chemical and physical degradation of the encapsulated material, resulting in better retention of EO. Based on these findings, the optimal conditions for achieving maximum encapsulation efficiency and loading capacity are the use of 1.5% alginate in combination with 0.75% whey protein and applying freeze-drying as a method of choice superior to air-drying. At these conditions, the highest encapsulation efficiency of 51.95%, accompanied by the loading capacity of 78.28%, was obtained, while the process yield at these conditions was 50.70%, which is at the higher end of the values obtained in the present study (Table 1).
## 2 Description of the Experiment
### Equilibrium Model  
The equilibrium model based on the assumption of each plate in the absorption column is considered as a theoretical plate (equilibrium plate) which means that the vapor and the liquid leave any plate at thermodynamic equilibrium [19]. According to [PERSON] et al. [28], the main assumptions of equilibrium model are as follows:  
* Phase equilibrium is presented at each stage;
* There are no chemical reactions between the components of vapor and liquid;
* Entrainment of liquid drops in vapor and occlusion of vapor bubbles in the liquid is negligible.  
In practice, this equilibrium takes place only at the interfaces between the vapor and liquid phases, so the efficiencies such as point and Murphree efficiencies are used in equilibrium model to account deviations from real equilibrium state [26]. For modelling the whole packed column, the packed bed is divided into stags (equilibrium stage). Figure 3 illustrates the typical entry and exit parameters of equilibrium plate stage. The equilibrium model of absorption process consists of well-known and accepted correlations called MESH equations that include the equations of component material balance, the equations of phase equilibrium, summation equations, and energy balance for each stage as following [21; 28; 29]:  
Overall mass balance for stage \(j\):  
\[\mathrm{L}_{j-1}-\mathrm{L}_{j}-V_{j}+V_{j+1}=0 \tag{1}\]  
Component mass balance for stage \(j\):  
\[\mathrm{L}_{j-1}x_{i,j-1}-\mathrm{L}_{j}x_{i,j}-V_{j}y_{i,j}+V_{j+1}y_{i,j+1}=0 \tag{2}\]  
Figure 3: Equilibrium stage model [21].  
Energy balance equations for stage \(j\):  
\[\mathrm{L}_{j-1}H_{j-1}^{L}-\ \mathrm{L}_{j}H_{j}^{L}-V_{j}H_{j}^{V}+V_{j+1}H_{j+1}^{V}=0 \tag{3}\]  
Phase equilibrium  
\[y_{i}-K_{i}x_{i}=0 \tag{4}\]  
Summation equations  
\[\sum y_{i}=1 \tag{5}\]  
\[\sum x_{i}=1 \tag{6}\]  
where \(L\), \(V\) are the molar flow rate of liquid and vapor respectively, \(K\) is equilibrium ratio, \(H\) is enthalpy, \(x_{i},\ y_{i}\) are the mole fractions of component \(i\) in liquid and vapor phases respectively.
## 3 Results and Discussion
### The Leaf Greenness Index (SPAD) and the Photosynthetic Rate
azotofixans + B. megaterium + B. subtilis_, an increase in the photosynthetic rate value occurred with an increase in the amount of rainfall, irrespective of the temperatures occurring during the period of measurements (May). At the BBCH\({}_{51}\) stage, the maximum increase in the photosynthetic rate was obtained at the minimum amount of rainfall and at the maximum temperature in the fertilisation variants of NPK, NPK + _B. subtilis_, NPK + _B. megaterium_ and NPK + _P. azotofixans + B. megaterium + B. subtilis_. As regards the fertilisation with NPK + _P. azotofixans_ at the BBCH51 stage, an increase in the photosynthetic rate was obtained with a minimum amount of rainfall and at a minimum temperature. Figure 4: The photosynthetic index after application of microbial preparations (mean value and standard deviation), (**a**) average for the years, (**b**) average for treatment. Values followed by the same letters do not differ significantly in [PERSON]’s (HSD) test (_p_ < 0.05). PA—_Paenibacillus azotofixans_; BM—_Bacillus megaterium_; BS—_Bacillus subtilis_. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **NPK** & **NPK + PA \({}^{*}\)** & **NPK + BM \({}^{**}\)** & **NPK + BS \({}^{***}\)** & **NPK + PA + BM + BS** \\ \hline \multicolumn{5}{c}{**BBCH\({}_{31}\)**} \\
2017 & 8.18 \(\pm\) 3.04 \({}^{\text{e}}\) & 13.14 \(\pm\) 0.46 \({}^{\text{cde}}\) & 9.33 \(\pm\) 5.35 \({}^{\text{e}}\) & 15.58 \(\pm\) 1.14 \({}^{\text{bcd}}\) & 20.02 \(\pm\) 3.03 \({}^{\text{ab}}\) \\
2018 & 17.23 \(\pm\) 1.00 \({}^{\text{bc}}\) & 20.99 \(\pm\) 0.29 \({}^{\text{ab}}\) & 10.57 \(\pm\) 4.03 \({}^{\text{de}}\) & 17.17 \(\pm\) 0.38 \({}^{\text{bc}}\) & 20.25 \(\pm\) 2.11 \({}^{\text{ab}}\) \\
2019 & 17.75 \(\pm\) 0.07 \({}^{\text{bc}}\) & 23.77 \(\pm\) 0.06 \({}^{\text{a}}\) & 18.58 \(\pm\) 0.33 \({}^{\text{abc}}\) & 19.76 \(\pm\) 0.16 \({}^{\text{ab}}\) & 23.77 \(\pm\) 0.06 \({}^{\text{a}}\) \\ \hline \multicolumn{5}{c}{**BBCH\({}_{51}\)**} \\
2017 & 10.22 \(\pm\) 0.91 \({}^{\text{de}}\) & 27.07 \(\pm\) 5.15 \({}^{\text{a}}\) & 16.85 \(\pm\) 4.10 \({}^{\text{bcd}}\) & 13.75 \(\pm\) 0.65 \({}^{\text{de}}\) & 23.74 \(\pm\) 3.81 \({}^{\text{ab}}\) \\
2018 & 19.53 \(\pm\) 7.13 \({}^{\text{abc}}\) & 25.80 \(\pm\) 0.52 \({}^{\text{a}}\) & 14.93 \(\pm\) 6.07 \({}^{\text{cd}}\) & 19.12 \(\pm\) 1.92 \({}^{\text{a-d}}\) & 27.08 \(\pm\) 0.21 \({}^{\text{a}}\) \\
2019 & 19.12 \(\pm\) 0.71 \({}^{\text{a-d}}\) & 21.55a \(\pm\) 3.75 \({}^{\text{bc}}\) & 5.89 \(\pm\) 0.46 \({}^{\text{e}}\) & 19.33 \(\pm\) 0.12 \({}^{\text{abc}}\) & 20.67 \(\pm\) 0.06 \({}^{\text{abc}}\) \\ \hline \hline \end{tabular}  
\({}^{*}\)PA—_Parenhiucillus azotofixans_; \({}^{**}\)BM—_Bacillus megaterium_; \({}^{***}\) BS—_Bacillus subtilis_. Values followed by the same letters do not differ significantly in Tukey’s (HSD) test (_p_ < 0.05).  
\end{table} 
Table 5: The photosynthetic index after application of microbial preparations (mean value and standard deviation), interaction between the years and treatments (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)).
## 5 Mission Technical Implementation
### Spacecraft Tracking
This is shown conceptually in Figure 11(a), for the exaggerated case of a 10 degree inclination difference, and the example of four limb sounding measurement sequences. Required yaw tracking varies across the orbit; in this situation, it is slightly diverging over the equator and strongly converging over the poles. Predicted realistic rates of required yaw tracking, within the specifications of the actual constellation, are shown in Figure 11(b) for a series of measurement sequences with different positions of the tangent point along the orbit. In this example, the primary and retroreflector spacecraft diverged in the Right Ascension of the Ascending Node (RAAN) by only 0.01 degrees, highlighting the importance of precise orbit maintenance. Figure 11: **(a)** Required yaw pointing when primary spacecraft (PSC) and retroreflector (RSC) are in different orbital planes, for the exaggerated case of a 10 degree inclination difference for illustration (the specified tolerance in reality is 0.01 degrees). The PSC is on a prograde orbit with 80 degrees inclination (red), the RSC are on a retrograde polar orbit with 270 degrees inclination (blue). The red and blue lines represent the respective normal vectors. The thick red dot shows the tangent point, purple lines are sounding paths in the ideal configuration of a single orbital plane, black lines represent real occultations. **(b)** Required yaw tracking during a typical measurement sequence for tangent points at different positions around an orbit (colours), and orbital planes characterized by a 0.01 degrees divergence in RAAN. The shaded area indicates the field-of-view of the limb telescope within which no yaw pointing is required during the occultation.
# Limit Equilibrium Analysis of Landfill Instability Based on Actual Failure Surface
## 3 Results
### Centrifugal Test Case Analysis
#### 3.1.2 Centrifugal Test with Berm  
In order to evaluate the three-wedge theoretical model with a garbage dam, we selected test 5 [29] as the focus case, as shown in Figure 6a. The parameters in the experiment are as follows: \(\gamma=9\) kN/m\({}^{3}\); \(\gamma_{sat}=12.6\) kN/m\({}^{3}\); and, when damage occurs, the water level ratio is \(h/H=0.85\). Similarly, select the shear strength corresponding to 20% failure strain: \(\delta_{A}=\varphi_{sw}=26.4^{\circ}\), \(\delta_{P}=13.8^{\circ}\), \(\delta_{B}=20^{\circ}\), \(c_{A}=c_{sw}=22\) kPa, \(C_{P}=0\), \(\beta=47.5^{\circ}\), and \(\theta=45^{\circ}\).  
Figure 5: Schematic diagram of the two-wedge model of a slope without berm based on the centrifugal test: (**a**) traditional model; (**b**) based on the actual failure surface. It should be noted that the geometry scale marked is the full-scale model corresponding to the centrifugal model.  
Figure 6: (**a**) Three-wedge centrifugal test result (modified from [29]). (**b**) Equivalent actual-scale model. The size unit of the centrifugal test is mm. The geometric size in the diagram is derived from the conversion of centrifugal model similarity relationship.  
On the basis of \(\alpha=90^{\circ}\), according to the experimental data, the following can be calculated: \(W_{A}=1245.73\) kN/m, \(W_{M}=3821.58\) kN/m, \(W_{P}=101.31\) kN/m, \(U_{A}=848\) kN/m, \(U_{M}=2948.4\) kN/m, \(U_{P}=82.5\) kN/m, \(C_{A}=434\) kN/m, and \(C_{B}=C_{P}=0\). Then, substitute these values into Equation (33) to obtain the \(FS\) of the slope, \(FS=1.35\).  
In centrifuge test 5, the actual angle between the failure surface and horizontal plane \(\alpha=102^{\circ}\) and its failure mode is shown in Figure 6b. Substitute the angle of the actual failure surface into the improved three-wedge model to calculate and obtain \(W_{A}=978.6\) kN/m, \(W_{M}=4121.3\) kN/m, and \(W_{P}=101.3\) kN/m; then, we obtain \(FS=1.12\).