esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Covalent Modification Cycle and Brink Controller (BC)  
#### 3.1.1 Covalent Modification Cycle (CMC)  
Consider the basic framework of the covalent modification cycle (CMC) depicted in Figure 2, where an activator \(X_{a}\) binds to an inactive species \(U^{*}\), and converts it to a chemically form \(U\). A deactivator \(X_{d}\) binds to an active species \(U\), and converts it to a chemically form \(U^{*}\).  
Figure 2: Mathematical framework for the covalent modification cycle.  
Its abstract chemical reaction can be described by  
\[\begin{split}& X_{a}+U^{*}\xrightarrow{k_{b1}}C_{1}\\ & C_{1}\xrightarrow{k_{c1}}X_{a}+U\\ & X_{d}+U\xrightarrow{k_{b2}}C_{2}\\ & C_{2}\xrightarrow{k_{c2}}X_{d}+U^{*}\end{split} \tag{4}\]  
ODEs of abstract chemical reactions (4) can be obtained by  
\[\begin{split}&\frac{d[C_{1}]_{t}}{dt}=k_{b1}[X_{a}]_{t}[U^{*}]_{t }-k_{c1}[C_{1}]_{t}\\ &\frac{d[C_{2}]_{t}}{dt}=k_{b2}[X_{d}]_{t}[U]_{t}-k_{c2}[C_{2}]_{t }\\ &\frac{d[U^{*}]_{t}}{dt}=-k_{b1}[X_{a}]_{t}[U^{*}]_{t}+k_{c2}[C_{2 }]_{t}\\ &\frac{d[U]_{t}}{dt}=-k_{b2}[X_{d}]_{t}[U]_{t}+k_{c1}[C_{1}]_{t} \\ &\frac{d[X_{a}]_{t}}{dt}=-k_{b1}[X_{a}]_{t}[U^{*}]_{t}+k_{c1}[C_{1 }]_{t}\\ &\frac{d[X_{d}]_{t}}{dt}=-k_{b2}[X_{d}]_{t}[U]_{t}+k_{c2}[C_{2}]_{ t}\end{split} \tag{5}\]  
The network diagram in Figure 3 is constructed to intuitively describe the connections between species and multiple reactions in Equation (4), where the entire network consists of binding and unbinding reactions.  
The following results can be given by  
\[\begin{split}&\frac{d[C_{1}]_{t}}{dt}+\frac{d[X_{a}]_{t}}{dt}=0\\ &\frac{d[C_{2}]_{t}}{dt}+\frac{d[X_{d}]_{t}}{dt}=0\\ &\frac{d[C_{1}]_{t}}{dt}+\frac{d[C_{2}]_{t}}{dt}+\frac{d[U^{*}]_{t }}{dt}+\frac{d[U]_{t}}{dt}=0\end{split} \tag{6}\]  
Note that the results \((d[C_{1}]_{t}/dt)+(d[X_{a}]_{t}/dt)=0\), \((d[C_{2}]_{t}/dt)+(d[X_{d}]_{t}/dt)=0\) and \((d[C_{1}]_{t}/dt)+(d[C_{2}]_{t}/dt)+(d[U^{*}]_{t}/dt)+(d[U]_{t}/dt)=0\) imply that the total mass \(C_{1}+X_{a}\), \(C_{2}+X_{d}\) and \(C_{1}+C_{2}+U^{*}+U\) is conserved through the lifetime of the process.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### Impact of VSL and RM on HDV Traffic Flows
#### 2.5.2 VSL Impact on Unstable Traffic Flows  
When the traffic flow density reaches values above the critical density (\(\rho>\rho_{c}\)), the traffic flow becomes unstable (right red side in the approximated triangular fundamental diagram, Figure 3). Interactions between vehicles are then more pronounced. The resulting traffic disruption (caused by, e.g., vehicle braking in a platoon) creates a shock wave that triggers a chain reaction that can produce a complete traffic jam on the urban motorway. Complementing the previous findings, more detailed studies on the effects of VSL on unstable traffic flow are summarized in [6; 65]. It can be seen from [33] that the \(\rho_{c}\) value in the fundamental diagram shifts to a higher value due to the effect of VSL (Figure 3). Therefore, it is possible to place more vehicles on the same length of the motorway segment without the traffic flow going into an unstable condition. This effect was confirmed in the study [6] and is attributed to the effect of speed homogenization mentioned earlier. Regarding the capacity increase, the research results are insufficient, as there is a slight capacity increase in certain locations of the observed urban motorways.
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 5 Comparison with Current Code Equations  
The current standard for reinforced concrete structures provisions [2] recommend a calculation equation for T-shaped structural components in which the additional effect of slabs needed to be considered use the formula given by Equation (35) and illustrated in Figure 12:  
\[b_{a}=\left\{\begin{aligned} \left(0.5-0.6\frac{a}{l} \right)a_{s}\frac{a}{l}&<0.5\\ 0.1\frac{a}{l}&\geq 0.5\end{aligned}\right. \tag{35}\]  
where, \(B\): Total effective beam width corresponding to \(b_{e}\) in this work.  
\(b_{a}\): Effective proportion of slab contributing to the effective beam.  
\(a\): Distance between two side surfaces of two adjacent T-shaped structural components.  
\(b\): Thickness of the vertical supporting structural component corresponding to the thickness of the bearing wall in this work.  
\(l\): Span length of frames corresponding to the slab span length in this work.  
Thus, a comparison of the calculated value of effective beam width for the three specimens from [PERSON] et al.'s experimental test [6,7] using the proposed model and the code equations are summarized in Table 3.  
For these specimens, a is 1000 mm while l is 1500 mm for code equations so that it can be obtained \"\(\frac{a}{l}\)\" equals 0.67 which is greater than 0.5. The effective proportion of slab contributing to the effective beam \(b_{a}\) is consequently calculated equaling 150 mm using Equation (35). Therefore, the effective beam width \(B\) based on the code equations is predicted as 475 mm for both-slab joints (Specimen 1 and 2) and 325 mm for one-slab joint (Specimen 3). However, it has been known, as shown in Table 3, that the effective beam width \(b_{a}\) obtained from the results of the experimental test are 563.86 mm, 558.61 mm, and 446.37 mm for these specimens, respectively. The values calculated using Equation (35) are lower than those obtained from experimental data either for both or one-slab joints. Thus, it can be known that the equations employed in the current code underestimate and are not appropriate to evaluate the effective beam width for initial stiffness at the elastic stage, which might result in giving a higher initial deformation angle prediction which is larger than 1/2000 rad at the elastic stage and consequently, large cracks or shear failure may wrongly be predicted for the design target deformation angle of 1/200 rad (0.05% drift ratio), finally. In contrast, since the proposed prediction model is analytically established from the actual experimental test setup, which can give an appropriate prediction of the effective beam width, the initial stiffness of the wall-slab joints in TWTS structures can be evaluated by the proposed prediction model with more accuracy and higher efficiency than the current code equations.
## 3 Results
### Ranking of the Factors Influencing the Spatial Heterogeneity of Vegetation  
The importance of different factors for the spatial heterogeneity of LAI and GPP in the MHRB can be revealed by the q values from the factor detector. All driving forces had extremely significant effects on the spatial heterogeneity of LAI and GPP (\(p<0.01\)). The ranking of driving factors for LAI was as follows: land use type (X10) > landform type (X11) > humidity index (X3) > slope (X8) > solar radiation (X6) > precipitation (X2) > ETo (X5) > VPD (X4) > elevation (X7) > temperature (X1) > aspect (X9) (Figure 7a). For GPP, the ranking of driving factors according to their q values was as follows: land use type (X10) > humidity index (X3) > landform type (X11) > solar radiation (X6) > precipitation (X2) > slope (X8) > ETo (X5) > elevation (X7) > temperature (X1) > VPD (X4) > aspect (X9) (Figure 7b). LAI and GPP were most significantly influenced by land use, with q values of 0.411 and 0.446, respectively. Humidity index was the most significant climate indicator, with q values of 0.227 and 0.331 for LAI and GPP, respectively. The influences of VPD, temperature, elevation, and aspect on the spatial heterogeneity of LAI and GPP were weak, with q values below 0.1. For LAI and GPP, the rankings of driving factors were similar. However, climatic factors besides ETo had higher explanatory powers for GPP.
# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 4 Experimental Results and Analysis
It can be seen from the figure that the prediction ability of the nPSL method was also good, but it cannot capture dynamic information. Figure 6 shows the standard deviation of MAR. It can be seen from the figure that the deviation of Kgedl was the smallest, which was 154 lower than the following method nPSL. In order to evaluate the effect of path embedding and semantic embedding, we tested Kgedl without path embedding and semantic embedding, which was called Kgedl0. Kgedl0 utilizing path embedding was named Kgedl0 + path, and Kgedl0 utilizing semantic embedding was called  
Figure 5: The Mean Absolute Rank (MAR) link prediction comparison result between Kgedl and baseline methods on the YAGO dataset. Figure 6: The link prediction comparison of STandard DEVIation (STDEV) of MAR between Kgedl and baseline methods on the YAGO dataset. Kgedl + semantic. These methods were tested on the YAGO dataset. Figure 7 gives the comparisons of experimental results, the area under the curve for Kgedl was 17.32%, 30.83%, and 33.76% higher than Kgedl0, Kgedl0 + path, and Kgedl0 + semantic, respectively. The area under the curve for Kgedl0 + path was 16.47% higher than Kgedl0. We can reach the conclusion that path embedding plays an important role in the knowledge graph conflict resolution performance. Figure 8 shows Kgedl's performance at HITS@10 and demonstrates its ability to discriminate, with an average HITS@10 of 0.92, which was 23% higher than the following method nPSL. This shows that in a complex case where there are multiple relationships between entities, other methods can only rely on static entity embedding, and the Kgedl method can update the entity relationships in the knowledge graph to obtain more accurate prediction results.
## 4 Experiments
### Diverse Bubble Diagrams Generation  
This section will validate the diversity of the results generated by DBGNN-VGAE and assess their effectiveness. Figure 11 shows the training curve of DBGNN-VGAE, with good convergence characteristics in all losses. Based on the trained model, we generated bubble diagrams using seven common room combinations, varying the number of bedrooms, bathrooms, and balconies. The generated bubble diagrams with varying input nodes are illustrated in Figure 12. The results indicate that the proposed method successfully creates bubble diagrams based on these varying inputs, demonstrating its superior adaptability. Upon examining each bubble diagram, we observe that the results contain correct door and wall links, with no invalid links. These reasonable outputs confirm that the model has learned effective topological knowledge, allowing it to provide multiple valid bubble diagrams.  
Figure 10: Ablation experiment result of DBGNN.  
Furthermore, we sampled latent variables from a normal distribution to generate dozens of bubble diagrams, as shown in Figure 13. It is evident that, based on different latent variables, the model generates bubble diagrams with significant differences. This demonstrates that VGAE successfully encoded the implicit distribution of bubble diagrams. When combined with DBGNN, the model gained the ability for multi-prediction, effectively addressing the issue of diversity generation.
## 4 Code Verification and Mesh Independency  
In order to reach the mesh independent results, various meshes were examined for case of \(Re=6000\), \(\phi=0.04\), \(\Delta\varphi=10 kV\) and \(Pr=149.54\) as illustrated in Table 1 According to this table the mesh size of \(61\times 121\) guarantees a grid-independent solution. Also the convergence limitation is:  
\[\max_{grid}\left|\Gamma^{n+1}-\Gamma^{n}\right|\leq 10^{-7} \tag{19}\]  
Here \(\Gamma\) is variable \((\Psi,\Omega,\Theta)\). In order to verify the current results, we compared them with those of obtained in [32] (see Figure 2). Furthermore, free convection of nanofluid in a cavity was validated as shown in Figure 3. These figures proved that the current code has good accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mesh size** & **31 \(\times\) 61** & **41 \(\times\) 81** & **51 \(\times\) 101** & **61 \(\times\) 121** & **71 \(\times\) 141** & **81 \(\times\) 161** \\ \hline \(Nu_{av}\) & 6.180284 & 6.195644 & 6.211904 & 6.231994 & 6.235304 & 6.239816 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the average Nusselt number \(Nu_{av}\) along bottom wall for different grid resolution at \(Re=6000\), \(\Delta\varphi=10 kV\) and \(Pr=149.54\), \(\phi=0.04\).  
Figure 3: (**a**) Comparison of the local Nusselt number over the lid wall between the present results and Moallemi and Jang [30] at \(Re=500\), \(Ri=0.4\), and \(Pr=1\); (**b**) Comparison of average Nusselt number between the present results and numerical results by [PERSON] _et al._[6]\(Gr=10^{4}\), \(\phi=0.1\) and \(Pr=6.8\)(Cu-Water).
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 6 Conclusions  
In this paper, we proposed an improved calibration factor derivation method for accurate link-quality determination of mobile communications in various channel environments, and we also suggested a CQI feedback bit reduction scheme for reporting downlink channel state information to BS. The improved calibration method sets the effective interval for the effective SNR to derive the optimal calibration factor used by the MIESM using more valid channels in a wide SNR range. The CQI feedback bit reduction scheme applies a differential CQI technique based on the time correlation between adjacent channels, which change over time, and decreases the number of bits used for CQI feedback.  
The two proposed methods were evaluated and analyzed through simulation. Firstly, the calibration factor derived from the improved calibration method reduced the CQI feedback error rate by 48.8% over the existing calibration factor in the range of SNR \(>\)2 dB. Also, the system throughput of proposed scheme was a maximum of 9.17 dB higher than that of the conventional scheme at SNR \(\geq\)\(-\)7 dB. This means that, by setting the effective interval in the wide SNR range, the newly derived calibration factor performs better than the existing one. Next, using a reduction of the CQI feedback scheme can be used to transmit uplink scheduling information by a reduced number of bits, though the system throughput is almost the same or a maximum of 1.3 kbps lower than the conventional single 4-bit wideband CQI feedback.  
Conceptualization, H.P. and H.L.; methodology, H.L. and H.P.; software, H.L. and H.K.; validation, H.L., H.P. and H.K.; formal analysis, H.L., H.P. and H.K.; investigation, H.L. and H.K.; resources, H.L.; data curation, H.P. and H.L.; writing--original draft preparation, H.L., H.P. and H.K.; writing--review and editing, H.L. and H.P.; visualization, H.L. and H.K.; supervision, H.P.; project administration, H.P.  
This research was supported by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07051108).  
The authors declare no conflict of interest.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Security  
Security is a critical issue in any application. The CoAP is used in the implementation of security services such as authentication, authorization and access control. The details of these implementations are mentioned in this subsection.
## 2 Energy Efficiency Landscape
### Energy Acquisition  
Solar panels are equipped on satellites to harness renewable energy. As they orbit Earth, exposure to sunlight is not always possible for satellites due to Earth's shadow, necessitating the use of rechargeable batteries for power in unit areas. In sunlight, solar energy is converted into electrical energy by solar panels for satellite operations and battery charging. In shadow, reliance on stored battery power is necessary for satellites. Periodic variations in solar energy acquisition are caused by the regular orbital movements of satellites around Earth and Earth around the sun. Figure 1 illustrates this scenario, depicting satellites in varying illumination conditions across the globe, their inter-satellite links, and indicative power levels. The output power of solar panels can be expressed as [25]  
\[P_{S}=\gamma\cdot\eta\cdot S\cdot\cos\theta \tag{1}\]  
where \(S\) is the area of the solar panels, \(\gamma\) is the solar radiation power per unit area, \(\eta\) is the photoelectric conversion efficiency, and \(\theta\) is the angle between the solar panels and the sunlight.
## 4 The Cooperative Optimization Algorithm
### Feedback Component
Hence, an upper bound \(I_{u}^{\text{UB}}\) on the total number of required interactions with user \(u\) for completely identifying all relevant locations for all of his/her use cases is  
\[I_{u}^{\text{UB}}=\sum_{r\in R_{u}}(|V_{w}(r)|+1). \tag{12}\]  
While this value is unknown in a real-world scenario, it allows us to establish a measure of quality on how well our strategy for presenting scenarios to users performs within our testing environments. The same combination of two strategies for generating scenarios w.r.t. an SPR \(r\in R\) as in Reference [16] is used. The first strategy generates scenarios according to the approach described above, i.e, \(S_{r}^{\text{V}}=\{v\in V\mid(r,v)\
ot\in K\}\). The second strategy generates scenarios \(S_{r}^{*}=\{v\in\widehat{X}^{*}\mid(r,v)\
ot\in K\}\) containing all locations from the current best solution that have not been rated yet w.r.t. \(r\). Note that, for users, generally only a fraction of the service point locations in \(V\) is actually relevant to one of their SPRs. Hence, when presenting a user \(u\in U\) two scenarios for each of the user's SPRs, every iteration, the number of user interactionswould quickly exceed \(I_{u}^{\text{UB}}\). Therefore, in the first COA iteration, a scenario \(S_{r}^{\text{V}}\) is generated for each \(r\in R\), but, in successive iterations, scenarios are only generated for subsets of \(R\). More specifically, from the second iteration onward, \(\xi^{\text{V}}\) and \(\varsigma^{*}\) percent of the SPRs \(R_{K}=\{r\in R\mid\exists v\in V:(r,v)\
ot\in K\}\) are randomly selected for generating scenarios according to \(S_{r}^{\text{V}}\) and \(S_{r}^{*}\), respectively, with \(\varsigma^{\text{V}}\) and \(\varsigma^{*}\) being strategy parameters.
## 3 Methodology
### Description of Representative Infrastructure
#### 3.1.2 Communication Network Topology  
The communication network model, as shown in Figure 3, integrates RTU client devices, segmented into two categories based on their connection techniques. The inaugural category utilizes fiber optics. Devices in this group are interlinked via a Passive Optical Splitter (POS), an Optical Line Terminal (OLT), and ultimately an Edge Router (ER). These routers interface with the Wide Area Network (WAN), which can either be proprietary to the company or a public Internet network. In the case of the latter, communication between the RTU and the server is encrypted and safeguarded by a private Virtual Private Network (VPN) tunnel.  
Conversely, the secondary connection method taps into wireless cellular modalities such as Global System for Mobile Communications (GSM), Long-Term Evolution (LTE),  
Figure 2: Considered representative topology of the power distribution network.  
or the Fifth-generation broadband cellular network (5G) broadband cellular standard. RTU configured with a mobile network interface is dependent on a modern. All transmissions are encrypted, leveraging VPN tunnels for fortified security, predominantly when transiting public mobile networks. Occasionally, the choice might lean towards a private mobile network. This WAN is directly linked to both a SCADA operational hub and a data repository hosting the requisite server infrastructure for dialogues with the client RTU. While VPN tunnels have the potential to culminate at an ER, high-demand hardware scenarios might necessitate a server exclusively dedicated to VPN liaisons. The role of the SCADA system extends to supervising and orchestrating operations for client networks as well as RTU. Figure 3 represents the diverse connectivity modalities present in the system. The wireless linkages are represented by \(W\) (\(W_{1}\) through \(W_{n}\)). In contrast, optical conduits are categorized based on their spatial alignment, either above-ground or subterranean. The aerial optical pathways are annotated as AO (\(AO_{1}\)-\(AO_{n}\)), and the subterranean optical counterparts bear the BO (\(BO_{1}\)-\(BO_{n}\)) notation.
## 2 System Model  
Figure 1 shows an RIS-assisted MU-MIMO downlink system. The considered system includes a BS equipped with \(N_{\text{T}}\) transmit antennas, an RIS with \(N_{\text{I}}\) elements, and \(N_{\text{K}}\) UEs equipped single receive antennas. The scattering matrix of the \(N_{I}\) RIS elements connected to the reconfigurable impedance network is represented as \(\mathbf{\Phi}\in\mathbb{C}^{N_{\text{I}}\times N_{\text{I}}}\). The reconfigurable impedance network consists of passive elements that can be adjusted to the system by reflecting the impinging signal. According to network theory [19], the scattering matrix \(\mathbf{\Phi}\) can be expressed as follows:  
\[\mathbf{\Phi}=(\mathbf{Z}_{I}+Z_{0}\mathbf{I})^{-1}(\mathbf{Z}_{I}-Z_{0} \mathbf{I}), \tag{1}\]  
where \(\mathbf{Z}_{I}\) refers to the impedance matrix and \(Z_{0}\) refers to the characteristic impedance and is usually set as \(50\,\Omega\). To maximize the power reflected by the RIS, \(\mathbf{Z}_{I}\) was assumed to be purely reactive as follows: \(\mathbf{Z}_{I}=j\mathbf{X}_{I}\), where \(\mathbf{X}_{I}\in\mathbb{R}^{N_{\text{I}}\times N_{\text{I}}}\) is the reactance matrix.  
It was assumed that the direct channels between the BS and UEs are blocked by obstacles. The channels from the BS to the RIS and from the RIS to UEs are, respectively, denoted as \(\mathbf{G}\in\mathbb{C}^{N_{\text{I}}\times N_{\text{T}}}\) and \(\mathbf{H}=\big{[}\mathbf{h}_{1},\mathbf{h}_{2},\cdots,\mathbf{h}_{N_{\text{ K}}}\big{]}^{T}\in\mathbb{C}^{N_{\text{K}}\times N_{\text{I}}}\), where \(\mathbf{h}_{k}\in\mathbb{C}^{1\times N_{\text{I}}}(i=1,2,\cdots,N_{\text{K}})\) refers to the channel from the RIS to the \(k\)-th UE. In this paper, the 3 rd Generation Partnership Project (3 GPP) TR 38.900 3-dimensional (3D) spatial channel model (SCM) was employed to apply the mmWave wireless communication [20; 21]. In addition, the presence of a line of sight (LOS) component in each channel was considered. The cascaded channel between the RIS and UEs can be written as \(\mathbf{H}_{eff}=\mathbf{H}\mathbf{\Phi}\mathbf{G}\). We denote the transmit signal as  
Figure 1: RIS-assisted MU-MIMO downlink system.  
\(\mathbf{x}\in\mathbb{C}^{N\times 1}\) with the transmit power constraint \(\mathbb{E}\big{\{}\mathbf{x}^{H}\mathbf{x}\big{\}}\leq P_{T}\). Then, the received signal at the \(k\)-th UE is represented as follows:  
\[y_{k}=\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{x}+n_{k}, \tag{2}\]  
where \(n_{k}\sim\mathcal{CN}(0,\sigma_{k}^{2})\) is the additive white Gaussian noise (AWGN).