esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks
## 3 Proposed Framework
### Heterogeneous Graph Transformer with Relation Awareness (HGTRA)
** Likewise, this paper aims to integrate relationships into the message-passing mechanism in order to differentiate the disparities between diverse categories of edges. For any given \((h,r,t)\in t\), the calculation of its multi-head message is carried out as follows:  
\[HMessage(h,r,t)=\lvert\lvert_{i\in[1,h_{n}]}HMSG_{head}(h,r,t) \tag{11}\]  
The detailed expression of \(HMSG_{head}(h,r,t)\) is shown below:  
\[HMSG_{head}(h,r,t)=\left[V_{Linear}i\left(e_{t}^{(l-1)}\right)\right]\left| \left|R^{(l)}(r)\right.\right] \tag{12}\]  
In order to obtain the \((i)\)-th message head, \(HMSG_{head}(h,r,t)\), this paper first applies the linear projection \(V\_{Linear}\) to project the characteristics of the tail entity \(t\). Subsequently, it concatenates the features of \(t\) and the relation \(r\), and connects all \(h_{n}\) message heads to obtain the final heterogeneous message;  
**(d) Heterogeneous Aggregation. ** The final step is heterogeneous aggregation, depicted in Figure 2d, where the heterogeneous multi-head attention and messages are merged into entities. By using attention coefficients to weigh the messages of neighboring entities, we can aggregate information from neighbors with different features and update the vector representation of entity \(h\). The specific formula is shown below:  
\[\widetilde{e_{h}}^{(l)}=\forall(r,t)\in RN(h)\oplus HAttention(h,r,t)\cdot HMessage(h,r,t) \tag{13}\]  
In this context, the symbol \(\oplus\) represents the operation of superimposition. In order to combine the characteristics of names and the features derived from a multi-layer neural network, we employ residual connections [27] to create the ultimate modified embedding, as demonstrated in the subsequent equation:  
\[e_{h}^{(l)}=\varpi_{\beta}A_{Linear(\widetilde{e_{h}}^{(l)})}+\left(1-\varpi _{\beta}\right)N_{Linear(e_{h}^{(l-1)})} \tag{14}\]  
where \(\varpi_{\beta}\) is a trainable weight; and \(A\_{Linear}(\cdot)\) and \(N\_{Linear}(\cdot)\) are linear projections. Finally, based on the entire relation structure \(T_{rel}\) and the property structure \(T_{attr}\), this paper can generate relation-based embedding \(E_{rel}\) and property-based embedding \(E_{attr}\), respectively, and employ them for end-to-end entity alignment tasks.
# An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management
## 4 Discussion
### Reusability of the Proposed System  
The case presented in this study demonstrates the use of the proposed system to integrate sensors and visualize the sensor data from the context of the comfort of a particular space. This section discusses how the system can be adapted to (1) monitor a different space employing the same context view, and (2) monitor a different space with a different context view.  
In general, to adapt the proposed system to a different desired context, one only needs to provide the BIM model and a new set of sensors suitable for the desired context view; to change the calculation formula specified in the custom nodes in Dynamo, one needs to change the formula predefined in the context viewer computation block, which is written in the Python language. A basic understanding of Python is thus a requirement for adaptation. When applying the system to a different space with the same context view (such as comfort PMV index), the sensors and microcontroller can be physically moved to the new target space, and a BIM model provided for that space. When applying the system to a different space with a different context view, such as when concerned about the air quality in an auditorium, the air quality index in the WELL Building Standard [29] can be used as the context view; in this case, a different set of sensors, such as PM2.5, PM10, and carbon monoxide sensors, can be deployed in the auditorium, and the formula in the context viewer computation block of the data visualization process can be changed in accordance with the new air quality index (such as by using the weighted average of sensor values or the number of satisfactory and nonsatisfactory sensors). Of course, in this example, a BIM model of the auditorium would need to be constructed if not already available.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 4 Injection Experiment of 5G Signal Interference into an RA Receiver
### Analysis of Gain Compression Effects on IF Signals  
The 5G signal interference injection experiment was carried out using the experimental setup shown in Figure 8 and the experimental platform shown in Figure 9. Due to the low modulation frequency of the LRA700 altimeter, the frequency of the IF signal obtained after processing by the receiver was not suitable for observation using a spectrum analyzer. Consequently, in the experiment, the simulated altitude of the radio altimeter was increased to 300 m and set in the altitude simulator. The echo signal power received by the RA receiver was measured at \(-66.3\) dBm. The injected 5G signal waveform matched the 5G signal waveform utilized in the simulation, with both employing the 5G NR TM1.1 standard. The center frequency of the injected 5G signal was 4000 MHz, and the bandwidth was 10 MHz. The IF output waveform obtained from the spectrum analysis is shown in Figure 10. In the interference-free state, the output IF signal had a frequency of 175 kHz and a power of 19.33 dBm. The power of the IF signal gradually decreased as the power of the 5G interference signal increased. These results indicate that the 5G interference signal caused the RA to experience the interference effect of IF signal gain compression. This nonlinear effect became more obvious as the 5G signal power increased. This is consistent with the theoretical analysis and simulation results from the previous section.  
\begin{table}
\begin{tabular}{c c c} \hline
**Equipment Name** & **Equipment Model** & **Function** \\ \hline Radio altimeter & LRA700 (Collins, West Palm Beach, IA, USA) & Test object \\ Altitude simulator & ALT8000 (Viva, Kansas, KS, USA) & Generates RA echo signals \\ Signal generator & E8267D (Keysight, Santa Clara, CA, USA) & Generates 5G signals \\ Spectrum analyzer & N9020B (Keysight, Santa Clara, CA, USA) & Display signal spectrum \\ AC Power supply & HY9001 (Hwayunrike, Shenzhen, China) & RA power supply \\ ARINC429 data converter & USB429-1T1R-B (Horntech, Beijing, China) & Receives altitude data \\ \hline \end{tabular}
\end{table}
Table 1: Models and functions of the experimental equipment.  
Figure 8: Diagram of the RA interference experimental setup.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## System Design of SRSS/ILMO
### System Unit Composition of SRSS/ILMO  
The specific system unit composition of SRSS/ILMO is shown in Figure 3. The seabed observation system module includes a seabed observation controller, dedicated configurations, observation sensor interfaces, an underwater acoustic communication module and power supply. The dedicated configurations, including attitude sensor TCM XB, altimeter PA200, pressure sensor, and penetration system, are mainly used for system layout, recovery and attitude monitoring. The observation sensor universal interfaces with universal data interface and conventional power interface are used to carry different observation sensors. The surface relay transmission buoy consists of relay transmission buoy controller, underwater acoustic communication module, satellite communication module, and power supply. The remote monitoring system is an interconnected LAN with clients and servers.  
Figure 3: System unit composition of SRSS/ILMO.  
Figure 2: Networked system structure of SRSS/ILMO.  
As shown in Figure 4, the proposed SRSS/ILMO was used in the project of seabed engineering geology in-situ long-term real-time surveying. The project aims to discover the dynamic change process of the seabed engineering geological environment and its influencing factors by establishing an automatic long-term in-situ continuous observation system. In this project, the seabed observation controller is equipped with the seabed in-situ long-term observation sensors, dedicated configurations, and a seawater battery. Dedicated configurations include the penetration system for placing sensor rods, the pressure sensor used to measure water depth, the altimeter PA200 for measuring altitude off the sea floor, and the attitude sensor TCM XB for attitude measurement of the submarine observing platform. The seabed in-situ long-term observation sensors include a three-dimensional high-density resistivity measurement sensor (referred to as resistivity sensor), a fiber bragg grating based excess pore pressure observation sensor (referred to as FBG-PPS), an acoustic sensor, a sea water turbidimeter, and a sea current meter.
## 4 Results and Analyses
### Convergence of the Preceding Tunnel
It should be noted that, at the moment in which the following tunnel passes over the measured section of the preceding tunnel, the release stress can be effectively reduced by the LR, so the disturbance transmitted to the preceding tunnel has been very small, and  
Figure 8: The shape of the preceding tunnel: (**a**) RR; (**b**) LR; (**c**) TR; (**d**) CR. the reinforcement of the preceding tunnel plays a less obvious role here. Therefore, in accordance with the phenomena observed in the above-mentioned ground settlement and tunnel deformation, only the LR can effectively reduce the disturbance of the excavation to the preceding tunnel. The convergence deformations of the linings for the CR are shown in Figure 8d. The vertical convergence values of the linings are 22.4, 20.3, 19.1, 18.2, 17.6, and 16.5 mm and the horizontal convergence values are \(-12.6\), \(-11.3\), \(-10.6\), \(-10.3\), \(-10.1\), and \(-9.9\) mm for \(t=0.0\)\(D\), \(0.05\)\(D\), \(0.10\)\(D\), \(0.15\)\(D\), \(0.20\)\(D\), and \(0.30\)\(D\), respectively. Compared with other strategies, when \(t=0.3\)\(D\), the convergence of the lining by the CR still remains large, which indicates that the CR has a weaker effect on the control of the development of convergence deformation of the preceding tunnel lining. Based on the results in Figure 8, the maximum convergence under different cases is summarized, as shown in Figure 9. With the increase of \(t\), the maximum convergence decreases under all of the four strategies, but the effectiveness of increasing reinforcement thickness becomes weak when \(t\) is larger than \(0.2\)\(D\). Among all strategies, TR is the most effective at controlling the development of convergence. When \(t=0.3\)\(D\), this approach can reduce the horizontal convergence by 98.4% and vertical convergence by 56.7% when compared with the case of \(t=0.0\)\(D\). The LR shows a similar effect, which results in a reduction of 86.5% horizontally and 54% vertically. In light of these observations, reinforcement of the soil layer around the following tunnel is vital in controlling the impact of construction of the following tunnel on the preceding one.
## 4 Data Processing and Features
### Reinforcement Learning
#### 5.3.1 Q-Learning  
Q-Learning is a Reinforcement learning strategy that, given the current state, determines the next optimal action. It selects this action at random and maximizes its reward potential. Q-learning is a model-free, off-policy kind of reinforcement learning that, given  
Figure 8: Basic structure of reinforcement learning having agent-environment interaction.  
the present state of the agent, will determine the most effective path of action and recommend it. The next action that has to be performed will be decided by the agent based on its current position within the environment. Figure 9 shows the Q-Learning that employs Q-values (also known as action values) to iteratively enhance the learning agent's behavior.
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 6 Case Analysis
### Result Analysis
The GA-ACO algorithm combines the advantages of both algorithms. At the beginning, it uses a wide range of genetic algorithms to search for pheromones, and then uses the ant colony algorithm to help the whole optimization process maintain a faster convergence speed and not fall into local parts optimal, then, the global optimal solution is found around the 60 th generation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Planning** & **GA-ACO** & **GA** & **ACO** \\ \hline Maintenance cost/10,000 yuan & 679.9 & 589.2 & 612.9 \\ Investment cost/10,000 yuan & 358.3 & 278.7 & 298.3 \\ Loss cost/10,000 yuan & 203.3 & 259.3 & 241.7 \\ Fuel cost/10,000 yuan & 1961 & 2231.6 & 2193.9 \\ Pollution compensation cost/10,000 yuan & 78.75 & 109.3 & 92.6 \\ Subsidy cost/10,000 yuan & \(-417.6\) & \(-285.6\) & \(-301.8\) \\ Total cost/10,000 yuan & 2863.7 & 3182.5 & 3137.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average cost of the three algorithms. Figure 10: Optimization algorithm comparison chart. * Considering Load Growth  
Load growth is also taken into consideration in this paper. It is assumed that all loads will increase by 50% in the next 15 years [23]. The number of access nodes to be selected for all the three DGs will increase to 30. What is more, the line capacity will double. The simulation results are shown in Table 6 and Figure 11.  
It can be seen that by considering the load growth, the model can still reasonably reflect the actual situation and this further verifies the effectiveness of the hybrid algorithm in convergence speed and convergence.
## 6 Conclusions  
A conceptual model of the coupled cooling system for auxiliary ventilation and PTI to control heat hazards was proposed. Based on a similar principle, a scale model test platform of tunnel ventilation and cooling was established. The thermal performance of the coupled cooling of ventilation and PTI in a tunnel WF was studied using this platform. The result shows that the coupled cooling system can effectively reduce the temperature in the vicinity of the WF. After the application of insulation, the temperature of the vortex area was lowered by up to 4.5 \({}^{\circ}\)C, and the ATWA was reduced by 1.5 \({}^{\circ}\)C. Simultaneously, the problem of elevated temperatures in the area away from the VDO improved.  
Increasing the ventilation volume, as well as the length and thickness of the PTI layers, can decrease the thermal equilibrium temperature of the workspace. As the distance between the VDO and the WF decreases from 0.6 m to 0.3 m, there is a gradual disappearance of the local high-temperature area, and the ATWA reduces from 30.9 \({}^{\circ}\)C to 28.5 \({}^{\circ}\)C. The best cooling effect was achieved when the ventilation duct suspension height was in the center of the insulation layer. The environmental temperature of the tunnel was significantly impacted by the ventilation velocity, the distance from the VDO to the WF, and the length of the PTI layer. Furthermore, the ventilation velocity of 10.5 m/s, the distance between the VDO and WF of 0.4 m, the insulation layer length of 0.6 m and thickness of 0.02 m, and the ventilation duct height of 0.075 m are the optimal ventilation and PTI parameters.  
Conceptualization, Y.X.; Investigation, Z.L. and J.W.; Resources, Z.L. and G.L.; Writing--original draft, Q.J. and J.W.; Investigation, Z.L. and G.L.; Formal analysis, Y.X. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (52274247), the Hong Kong Scholars Scheme (XJ2023039), the Natural Science Foundation of Hunan Province (2022)J0050), the NHC Key Laboratory for Engineering Control of Dust Hazard (KLECDH2023020101), the State Key Laboratory of Safety and Health for Metal Mines (2022-JSKSSYS-03), the Natural Science Basic Research Program of Shaanxi Province-Outstanding Youth Science Foundation Project (2021)C-48), and the Postgraduate Innovative Project of Central South University (2022 ZZTS0573).  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
Nomenclature  
ATWA average temperature of the working area  
PTI partial thermal insulation  
TIL thermal insulation layer  
VDO ventilation duct outlet  
WF working face
## 6 Exact Monitoring under Imperfect Information Given Uncertain Linear Dynamics
This conflict clause is added to the Boolean SAT problem and forces the SAT solver into (usually non-chronological) backtracking, thus searching for a different resolution of the Boolean structure of the SMT problem. A thorough description of the algorithmic principles underlying this so-called lazy theorem proving approach to SMT can be found in [24; 25]. iSAT is an industrial-strength SMT solver that is commercially available [26] and covers a very general fragment of arithmetic, covering linear, polynomial, and transcendental functions over the integers, the mathematical reals, and (in bit-precise form) the computational floats [27]. Although iSAT [21; 28; 29] is by no means optimized for solving linear constraint systems--its primary field is non-linear arithmetic involving transcendental functions, the above monitoring condition can be checked in approximately 300 ms on a single core of a Core i7 10 th generation running at 1.8 to 2.4 GHz. iSAT would, with essentially unaltered performance, be able to also check error models whose encoding requires non-linear arithmetic, like the mixed absolute-relative error model of Equation (2). In the above case of absolute error, we may equally well apply the dedicated SMT-LA solver MathSAT 5 [15] to the equivalent SMT-lib encoding shown in Appendix A, as only linear arithmetic is involved. The runtime then amounts to just 9.4 ms on an eight-core AMD Ryzen 7 5800X running at 4.4 GHz. As these runtimes have been observed on general-purpose SMT solvers devoid of any particular optimization for the formula structures arising in the monitoring problem, we deem online monitoring in real-time practical even for more complex (deeper nesting of sub-formulae, larger \(\text{duration}(\phi)\)) monitoring conditions and system models (higher dimensionality especially), given the proven scalability of SMT to large-scale industrial problems. For the above model-based monitoring procedure, akin to Lemma 1, we obtain  
Lemma 2: _For systems featuring uncertain affine dynamics, the above monitoring algorithm is exact, where exactness in this setting refers to exact characterization, in the sense of Definition 5, of the truth values possible over \(GT(m_{S_{1}},\ldots,m_{S_{n}})\cap D\) with \(D\) being the set of possible trajectories of the system according to its uncertain linear dynamics._
## 4 Experiment and Results
### Algorithm Comparison  
To further substantiate the effectiveness of the IMOWOA in addressing the dephosphorylation optimization problem in converter steelmaking, a comparison was conducted with several algorithms, including the mature multi-objective particle swarm algorithm [22] (MOPSO), the multi-objective grey wolf optimization (MOGWO) algorithm, known for its strong optimization capabilities with fewer parameters, the novel multi-objective beluga whale optimization (MOBWO) algorithm, distinguished by its distinctive search paradigm [23], the multi-objective crayfish optimization algorithm [24] (MOCOA), and the original MOWOA. The Pareto feasible solution graph for each algorithm is illustrated in Figure 5, while the first optimal solution in the respective solution sets is presented in Table 6.  
As illustrated in Figure 5, the MOPSO, MOBWO, and MOCOA algorithms exhibit relatively poor Pareto optimal solution sets. During the iterative process, these algorithms are trapped in more local optimal solution cases, leading to an insufficient exploration of the search space. On the contrary, the MOGWO and MOWOA achieve a more favorable  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Endpoint P (\%)**} & \multicolumn{2}{c}{**Production Cost (CNY/t)**} \\ \cline{2-5}
**Strategic** & **Mean Value** & **Standard Deviation** & **Mean Value** & **Standard Deviation** \\ \hline MOWOA & 0.0135 & 0.0009 & 3207.15 & 2.30 \\ + Sobol + Reward & 0.0129 & 0.0002 & 3201.27 & 1.54 \\ + Sobol + MOGWO & 0.0130 & 0.0011 & 3215.81 & 8.42 \\ + Reward + MOGWO & 0.0123 & 0.0008 & 3212.16 & 5.52 \\ IMOWOA & 0.0110 & 0.0006 & 3214.51 & 3.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments results.  
trade-off between the two objective functions; however, these algorithms compromise the performance of the objective function f1 during the optimization search process. According to the data presented in Table 6, the MOGWO algorithm successfully obtains a lower endpoint phosphorus content of 0.0128%, while the MOWOA achieves a reduced cost of production cost at 3205.89 CNY per tonne of steel during the iterative process.
## 5 Experimental Setup and Measurements
### Oblique Angle Incidence  
Moreover, the shielding characteristics of the FSS are also measured for various oblique incidence angles up to \(\pm 75^{\circ}\), with a step size of 25\({}^{\circ}\). The frequency response of the FSS shield for TE polarization is illustrated in Figure 9b. It is observed that the stop bandwidth and the notch selectivity slightly increase as the incident angle is varied from 0 to 75\({}^{\circ}\). Furthermore, Figure 9c presents the SE versus frequency curves over angle variations for the TM wave mode. The results are almost analogous and stable for TE polarization. However, a minor deviation in the resonance frequencies is also noticed at 75\({}^{\circ}\) for the X- and Ku-bands, respectively. Hence, the overall angular performance of the anticipated FSS shield is stable and within acceptable limits at the desired frequency bands. Nevertheless,  
Figure 8: (**a**) Fabricated FSS panel with zoomed view given as inset. (**b**) A photograph of measurement setup inside an anechoic chamber.  
minor variations in the results are associated with the finite array size, fabrication, and measurement imperfections.
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Level 2: Analysis Ready Data
#### 3.2.2 Auxiliary Data  
A digital elevation model (DEM) mosaic covering the complete study area is used for enhanced cloud shadow detection, scaling optical depths with altitude, and to perform the topographic correction. A precompiled water vapor database is used for atmospheric correction of Landsat data. The database holds water vapor values for the central coordinates of each WRS-2 frame. If available, day-specific values are used. If not, a monthly long-term climatology is used instead. The FORCE water vapor database component (FORCE WVDB, see Figure 2) can be used to generate and maintain such a database or a ready-to-use dataset may be freely downloaded [40]. The effect of using the water vapor climatology as a fallback option was globally assessed in [41].