esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Effect of Air Leakage on the Evaporation of Water from Goaf
### Influence of Measurement Point Location on the Amount of Water Evaporation
Figure 7: Evaporation of water without air leakage: (**a**) Diagram of water evaporation when the air leakage volume is 0.02 m\({}^{3}\)/s; (**b**) Diagram of water evaporation when the air leakage volume is 0.03 m\({}^{3}\)/s; (**c**) Diagram of water evaporation when the air leakage volume is 0.04 m\({}^{3}\)/s. If the relative humidity of the air varies from day to day, the relative humidity of the air at the air inlet varies irregularly. However, there is an obvious law that the air relative humidity at the air inlet is always smaller than the air relative humidity at the air outlet. This means that the air flowing from the air inlet through the model of the goaf will take away part of the moisture, which leads to an increase in the relative humidity of the air at the air outlet. At the beginning of the experiment, the relative humidity difference between the air inlet and outlet was the largest. As time changes, the moisture evaporates and is taken away, and the relative humidity difference between the air at the inlet and outlet decreases. When the inlet air leakage volumes are 0.02, 0.03 and 0.04 m\({}^{3}\)/s, the humidity differences at the inlet and outlet at the initial moment of the experiment are 16.49, 17.06 and 14.08%, respectively. When they reached equilibrium after 15, 11 and 7 days for the above three groups of coal body moisture evaporation experiments, the relative humidity differences of the air at the inlet and outlet were 4.05, 5.03 and 1.99%, respectively. Finally, the fitted curves of the air relative humidity difference at the inlet and outlet with time at the inlet leakage volumes of 0.02, 0.03 and 0.04 m\({}^{3}\)/s are y \(=-0.89\times\) x + 15.12, R\({}^{2}\) = 0.89; y \(=-1.05\times\) x + 16.21, R\({}^{2}\) = 0.83; y \(=-1.58\times\) x + 11.45, R\({}^{2}\) = 0.78, respectively. It can be seen that, as the air inlet leakage increases, the greater the rate of change of the air relative humidity difference becomes at the air inlet and outlet. Additionally, the faster the water evaporates from the coal body in the mining area, the shorter the time it takes to reach equilibrium. The corresponding air relative humidity difference change rates are 0.89, 1.05 and 1.58%/d.
## 5 Conclusions and Future Work
### Future Work  
The current SCTD model mainly relies on ratings and textual comments, while user behavior data and contextual information also contain rich signals in practical applications. In the future, cross-modal alignment mechanisms can be designed, such as modeling user-item-context heterogeneous relationships through graph neural networks or unifying multimodal feature representations using comparative learning, to further alleviate data sparsity and enhance model generalization capabilities. Second, the refinement of dynamic modeling needs to be enhanced. The existing temporal dynamic module mainly relies on linear assumptions, which makes it difficult to capture nonlinear mutations in user preferences (e.g., sudden hot events). In the future, the interaction effect between short-term fluctuations and long-term trends can be modeled more accurately by introducing a temporal attention mechanism or memory network, combined with external temporal features. In addition, real-time optimization is a key challenge for dynamic recommender systems. Currently, model training relies on offline batch processing, while incremental learning and fast reasoning are required in online scenarios. In the future, lightweight dynamic updating strategies can be explored, such as initializing model parameters through meta-learning, or using a piecewise architecture to separate static and dynamic components to reduce online computation overhead. Finally, dynamic recommender systems need to frequently collect user temporal behavior data, which may lead to the risk of privacy leakage. In the future, decentralized training schemes under the federated learning framework can be explored to combine differential privacy techniques to protect sensitive user information while ensuring that model performance is not significantly affected.  
Conceptualization, F.Z.; methodology, F.Z.; software, F.Z.; validation, F.Z.; formal analysis, F.Z. and W.L.; investigation, F.Z.; resources, F.Z.; data curation, F.Z.; writing--original draft preparation, F.Z.; writing--review and editing, W.L.; visualization, F.Z.; supervision, X.Y.; project administration, W.L.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was inspired by the National Social Science Foundation of China (NSFC) project 'Research on Data Bias Identification and Governance Mechanisms Based on Digital Twins', Project No. 22 BTQ058.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** We are grateful to Hebei University for their cooperation and assistance.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
## 2 The Home Network as The Universal Center of Reference
### Self-Certified Home Certificate  
The first and more straight-forward option is to let the Home CA create a certificate for itself, _i.e._, it signs its own public key with its private key.  
As a home network is a reasonably controlled environment, it is feasible enough to introduce devices (entities) to this certificate and accept it. To this end, one can use PIN-based techniques to secure the exchange of certificates. Another convenient option is near-field communication together with a trust-on-first-use policy [6].  
Entities within the home network will then trust the Home CA's certificate. Every entity in the local home network can use the thus acquired certificate to verify the certificate of another entity that belongs to the _same_ home network.  
When an entity from _another_ home network must be verified, this is not directly possible as entities only know and trust the own Home CA's certificate.  
One way to overcome this is to build on the social relations that are likely to exist between the owners of home networks. For instance, members of different home networks can meet up in person and exchange their home certificates, using, e.g., an application installed on their smart phones. This can be again done with near-field communication (possibly strengthened by setting up a secure Bluetooth connection _etc._).  
We view this key exchange as a valid approach because trust relationships would likely need to be established between the homes of family members, friends, work colleagues and so on. The assumption of social relations is thus likely to hold, and the result would probably be a Web-of-Trust-like structure between home networks that reflects real-life relationships (Figure 2).  
Within the simplicity of the key exchange mechanism lies its beauty: both partners are mutually identified in person, which allows for a high level of assurance with respect to the correctness of the other Home CA's certificate. Access control is now easy to implement: Home CAs can store different access rights for entities in their own domains as well as in other domains. This can be done with any granularity that the user requires (e.g., enable file-sharing for all entities in home network B, enable it only for some,...). It also allows to store very different trust profiles for other home networks (e.g., enable a service only for home networks that are grouped under 'friends' _etc._). This authorization can also be automated to some degree (if desired) and simplified for ease of use. We have built a framework based upon the _eXtensible Access Control Markup Language (XACML)_ for the management of authorization policies within home networks [7].  
Figure 1: Simple certificate hierarchy inside home networks: the Home CA issues certificates to entities.
## 2 Principle of SM Using a Novel Active Transmitter Detection Scheme with SSD  
### System Architecture  
The architecture of the overall indoor OWC system is shown in Figure 1, which includes the digital signal flows highlighted in the yellow box in Figure 1a and the multi-transmitter-single-receiver-based indoor OWC link configuration illustrated in Figure 1b. At the transmitter side, the proposed digital signals for implementing SM with SSD are generated offline and fed into the optical transmitters \(\text{Tx}_{i}\) for optical modulation, where \(i\in\{1,2\}\) is assumed here to simplify the analysis. These optical transmitters are centralized coordinated via the fiber network, where the generated spatial bits in SM control the optical transmitter activation. Thus, the optical signals from different transmitters, featured by distinct optical power, are projected into the indoor free space in turns at different SM time slots. The divergent free space optical (FSO) signals are then partially captured and optically detected by the single optical receiver Rx for further offline signal recovery.
## 5 Numerical Studies
### Validation of the Enhancement Strategies
#### 5.3.2 Validation of the Acceleration Strategies for Column Generation  
To evaluate the efficiency of the acceleration strategies for column generation, we conduct a comparative analysis among four versions of the algorithm embedded with different column generation procedures: (i) the column generation without any acceleration strategy (\(CG_{no}\)); (ii) the column generation with heuristic label setting algorithm (\(CG_{h}\)); (iii) the column generation with decremental state-space relaxation (\(CG_{d}\)); and (iv) the column generation with all acceleration strategies (\(CG_{All}\)). Table 8 summarizes the computation times in seconds for each instance in Set Ins-30.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{4}{c}{**BPC with Bound**} & \multicolumn{4}{c}{**BPC Without Bound**} \\ \cline{2-7}  & **Obj** & **CPU** & **Node** & **Obj** & **CPU** & **Node** \\ \hline Ins-30-1 & 169.373 & **14.1** & **1** & 169.373 & 21.5 & 9 \\ Ins-30-2 & 162.644 & **59.8** & **1** & 162.644 & 64.5 & 5 \\ Ins-30-3 & 156.911 & **10.1** & **1** & 156.911 & 13.9 & 9 \\ Ins-30-4 & 184.650 & **30.4** & **1** & 184.650 & 30.5 & 1 \\ Ins-30-5 & 190.610 & **12.6** & **1** & 190.610 & 22.0 & 9 \\ \hline Ave. & 172.837 & **25.4** & **1** & 172.837 & 30.5 & 6.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effectiveness of the high-quality upper bound.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{4}{c}{**Computation Time (in Seconds)**} \\ \cline{2-5}  & **CG\_no** & **CG\_h** & **CG\_d** & **CG\_All** \\ \hline Ins-30-1 & 119.5 & 103.9 & 37.5 & **14.1** \\ Ins-30-2 & 2545.1 & 508.8 & 85.0 & **59.8** \\ Ins-30-3 & 776.5 & 683.1 & 40.8 & **10.1** \\ Ins-30-4 & 1703.4 & 348.5 & 40.1 & **30.4** \\ Ins-30-5 & 107.8 & 100.4 & 78.6 & **12.6** \\ \hline Ave. & 1050.5 & 348.9 & \(56.4\) & **25.4** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of acceleration strategies for column generation.  
By comparing the time required by \(CG_{no}\) with that of other methods, it is evident that any acceleration strategy is effective, leading to a noticeable reduction in computation time. Regarding their respective contributions to algorithmic improvement, the decremental state-space relaxation stands out as the most effective strategy in enhancing the algorithm's performance. This strategy remarkably reduces the average computation time from 1050.5 s to 56.4 s, whereas the heuristic label setting algorithm achieves a reduction to 348.9 s. Furthermore, in comparison to employing a single acceleration technique, the joint use of these acceleration strategies yields the most significant enhancement in algorithmic performance. Overall, the analysis above suggests that the acceleration strategies are effective.
## 3 Results and Analysis
### Analysis of the Support Effect of the Central Column  
Due to the inability to conduct an on-site analysis of the support effect of neutral columns on the arch cover in practical engineering scenarios, the study subsequently investigated the support effect of neutral columns on the arch cover during the construction process using arch construction methods. Following the completion of the experiments, the neutral columns were dismantled, and data on the deformation of the arch cover and ground settlement before and after dismantling were recorded. This enabled an exploration of the support effect of neutral columns on the arch cover. The changes in arch displacement and ground settlement after the removal of the central column are shown in Tables 7 and 8 (Figure 10). The table shows that the deformation of the arch and ground settlement increased significantly after the demolition of the central column. The maximum year-on-year increase in the arch settlement was 79.41%, and the maximum year-on-year increase in the ground settlement was 97.20%. The closer to the central position of the arch cap, the more pronounced the increase in settlement. The presence of the central column is crucial for supporting the arch cover and controlling the deformation and settlement of the rock and soil above it.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### Markov Decision Process Modeling
#### 3.3.3 Reward  
Designing a better reward function can expedite the convergence of the model training process. In this study, we evaluate the efficiency of the dynamic destroy operator by considering the total profit of the scheduled tasks, denoted as \(f\), as defined in Equation (6).  
\[\begin{split} G_{t}&=f(TimeInsert(X_{t}))-f(TimeInsert (X_{t-1}))\\ R&=\left\{G_{1},G_{2},\dots,G_{t},\dots\right\} \end{split} \tag{18}\]  
where \(R\) is the discounted cumulative reward, and the reward function \(G_{t}\) is set as the difference in the total profit.
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 3 Control Strategies of the DBI
### Non-Linear Controls  
#### 3.3.1 Finite Control Set-Model Predictive Control  
One non-linear technique employed to control the DBI is based on the FCS-MPC algorithm. This strategy of control was proposed by the authors in [15]. Therefore, a summary of this strategy is presented below.  
The general control diagram of the DBI is based on two cascaded control loops. The first control loop is composed by an MPPT method and a PI regulator for the input capacitor's voltage, as explained in the previous sections. Then, the output of the input voltage controller is the active power reference, which is used in the next control loop. The FCS-MPC strategy is employed in the second control loop for the inductor's current and output capacitor's voltage, as shown in Figure 9. The details of this strategy can be found in [15].  
In this work, the objectives of the cost function were slightly changed. The output current was not considered since the main approach is the individual control of each DC-DC converter. Therefore, the global structure of the cost function has two components:  
\[G=\lambda_{1}g_{1}+\lambda_{2}g_{2} \tag{19}\]  
where:  
\[g_{1}=\frac{1}{i_{\text{LIn}}}({i_{\text{L1}}}^{*}-{i_{\text{L1} }}^{p})^{2}+\frac{1}{i_{\text{L2n}}}({i_{\text{L2}}}^{*}-{i_{\text{L2}}}^{p} )^{2}\] \[g_{2}=\frac{1}{{v_{\text{c1n}}}^{2}}({v_{\text{c1}}}^{*}-{v_{ \text{c1}}}^{p})^{2}+\frac{1}{{v_{\text{c2n}}}^{2}}({v_{\text{c2}}}^{*}-{v_{ \text{c2}}}^{p})^{2}\]  
Figure 9: The control scheme of the FCS–MPC for the grid-connected DBI.  
\(g_{1}\) is the quadratic error of the inductor currents; \(g_{2}\) is the quadratic error of the capacitor voltages; \(\lambda_{1}\) and \(\lambda_{2}\) are the weight factors, which can determine the degree of relevance of each variable in the process. In this case, the main goal is the capacitor voltage and the second goal is the inductor current. Therefore, the weight factor \(\lambda_{2}\) is higher than \(\lambda_{1}\). The weight factors were obtained by an empirical procedure, and the selected values were \(\lambda_{1}=1\) and \(\lambda_{2}=75\).
# Feasibility of Storing Latent Heat with Liquid Crystals. Proof of Concept at Lab Scale
## Appendix A  
Unless otherwise stated, all reagents and solvents were obtained from commercial suppliers and used without further purification. Combustion analyses were made with a Perkin-Elmer 2400 microanalyzer. IR spectra were recorded in a Perkin-Elmer Frontier spectrometer coupled to a Pike GladiATR-210 accessory and 1H NMR spectra on Bruker AV-400 (400.13 MHz) or Varian 500 (499.73 Hz) instruments in CDCl3.  
_Preparation of ethyl 4\({}^{\prime}\)-decyloxy-4-biphenylcarboxylate_. Anhydrous K\({}_{2}\)CO\({}_{3}\) (1 g, 7.235 mmol) was added to a solution of ethyl 4\({}^{\prime}\)-hydroxy-4-biphenylcarboxylate (0.2 g, 0.826 mmol) and decylbromide (0.185 mL, 0.826 mmol) in 15 ml of dry \(N_{N}\)-dimethylformamide. The mixture was heated in an oil bath at 100 \({}^{\circ}\)C under N\({}_{2}\) for 24 h. and then was allowed to cool to room temperature. The resulting white solid was filtered off, washed with water (3 \(\times\) 15 mL) and dried under vacuum (0.225 g, 71% yield). IR: \(\
u\)(C=O): 1712 cm\({}^{-1}\). \({}^{1}\)H NMR (CDCl\({}_{3}\): \(\delta_{1}\) 8.08, \(\delta_{2}\) 7.61, AA'XX' spin system (\({}^{3}\)I\({}_{1,2}\) + \({}^{5}\)I\({}_{1,2}\) = 8.55 Hz), \(\delta_{3}\) 7.56, \(\delta_{4}\) 6.98, AA'XX' spin system (\({}^{3}\)I\({}_{3,4}\) + \({}^{5}\)I\({}_{3,4}\) = 8.84 Hz), 4.39 (q, 2H, \(J\) = 7.29 Hz, O-CH\({}_{2}\)-CH\({}_{3}\)), 3.99 (t, 2H, \(J\) = 6.39 Hz, O-CH\({}_{2}\)-CH\({}_{2}\)), 1.81 (m, 2H, O-CH\({}_{2}\)-), 1.51-1.28 (m, 17H, alkyl chain and O-CH\({}_{2}\)-CH\({}_{3}\)), 0.88 (t, 3H, \(J\) = 6.39 Hz, -CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{3}\)).  
_Preparation of 4\({}^{\prime}\)-decyloxy-4-biphenylcarboxylic acid_. To a solution of ethyl 4\({}^{\prime}\)-decyloxy-4-biphenylcarboxylate (0.2 g, 0.523 mmol) in 15 mL of ethanol was added NaOH (0.2 g, 4.88 mmol) dissolved in 1.5 mL of water. After refluxing for 2 hours, the solution was allowed to cool to room temperature affording the sodium 4\({}^{\prime}\)-decyloxy-4-biphenylcarboxylate salt as a white solid, which was filtered off and washed with water (3 \(\times\) 100 mL). The dried material was then refluxed with glacial acetic acid (20 mL) for 30 min. After cooling the solution to room temperature, the resulting acid appears as a white precipitate, which was washed with water and dried under vacuum. (0.180 g, 88% yield). IR: v(C=O): 1685 cm\({}^{-1}\). \({}^{1}\)H NMR (DMSO-d6) : \(\xi_{1}\) 12.97 (s, 1H, OH), \(\xi_{2}\) 7.93, \(\xi_{3}\) 7.68, AA'XX' spin system (\({}^{3}\)I\({}_{1,2}\) + \({}^{5}\)I\({}_{1,2}\) = 8.42 Hz), \(\xi_{4}\) 7.63, \(\xi_{5}\) 7.00, AA'XX' spin system (\({}^{3}\)I\({}_{3,4}\) + \({}^{5}\)I\({}_{3,4}\) = 8.66 Hz), 3.98 (\(t\), 2H, \(J\) = 6.52 Hz, O-CH\({}_{2}\)-CH\({}_{2}\)-), 1.69-1.22 (m, 20H, alkyl chain), 0.81 (\(t\), 3H, \(J\) = 6.64 Hz, -CH\({}_{3}\)). Anal. Calcd. for C\({}_{23}\)H\({}_{30}\)O\({}_{3}\): C, 77.93%; H, 8.53%. Found: C, 77.68%; H, 8.29%.
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 3 Comparison of Calculation Results
### Compressive Strength of Grounded Material  
The failure mode of the grouted joint with moderate distance of the shear key indicates that the grouted material at the shear bond position is crushed with the slip between the steel pipe and the grouted body, and \(f_{\rm cu}\) plays a significant role in \(f_{\rm bk}\). [PERSON] showed through data analysis that \(f_{\rm bk}\) is directly proportional to the 0.5 power of \(f_{\rm cu}\)[9]. The relationship curve between the shear strength \(f_{\rm bk}\) calculated using different codes and the compressive strength of grouted material \(f_{\rm cu}\) is shown in Figure 5. It can be found that API considers that \(f_{\rm bk}\) and \(f_{\rm cu}\) show a linear relationship, which is not consistent with the results of [PERSON]'s analysis. API overestimates the effect of \(f_{\rm cu}\) on \(f_{\rm bk}\), and \(f_{\rm cu}\) is less applicable in the API code. When the compressive strength of larger or smaller grouted materials is used in the test, the formula in the API code is not necessarily applicable. In contrast, HSE, DNV, and DNV-GL agree that the ultimate shear strength of the grouted junction is proportional to the 0.5, 0.4, and 0.3 power cubic compressive strength of the grouted material, and the DNV code is constantly reducing the impact of \(f_{\rm cu}\) on \(f_{\rm bk}\).  
Figure 4: Relationship between interface shear strength (\(f_{\rm bk}\)) and height-to-distance ratio of shear key (\(h\)/s).  
Figure 5: Relationship between interface shear strength (\(f_{\rm bk}\)) and grouted material strength (\(f_{\rm cu}\)).
## 3 System Model
### Data Preprocessing  
#### 3.3.1 Distance between UE and Cell Centric  
A cell-centered data set over time is a data set that represents the 0.001 ms change of a feature. Using the cell location information in the data set and the location information in the UE data set, the distance and elevation angle between the UE and the cell center per 0.001 s were calculated. The haversine formula was used to calculate the distance between the cell center and the UE on Earth. This mathematical formula is cited from paper [13].  
\[\theta=\frac{d}{r} \tag{1}\]  
\(\theta\) the central angle between the two points, \(d\) is the distance between the two points, and \(r\) is the radius of the sphere.  
\[hav(\theta)=hav(\varphi_{2}-\varphi_{1})+\cos\varphi_{1}\cos\varphi_{2}hav( \lambda_{2}-\lambda_{1}) \tag{2}\]  
\(hav(\theta)\) can be calculated directly from the latitude (\(\varphi_{2}\), \(\varphi_{1}\)) and longitude (\(\lambda_{2}\), \(\lambda_{1}\))of the two points.  
\[hav(\theta)=sin^{2}\bigg{(}\frac{\theta}{2}\bigg{)}=\frac{1-\cos\theta}{2} \tag{3}\]  
Above is the haversine function applied to the central angle, latitude, and longitude. To get the distance (\(d\)) from this function, you need to use the arc-haversine or arcsin function.  
\[d=r\cdot archav(h)=2r\cdot\arcsin\Big{(}\sqrt{h}\Big{)} \tag{4}\]  
The formula applied with the above _archav_ or arcsin is solved and expressed as follows.  
\[d=2r\arcsin\bigg{(}\sqrt{hav(\varphi_{2}-\varphi_{1})+(1-hav( \varphi_{2}-\varphi_{1})-hav(\varphi_{2}+\varphi_{1}))\cdot hav(\lambda_{1}- \lambda_{2})}\bigg{)} \tag{5}\] \[=2 rarcsin\bigg{(}\sqrt{sin^{2}\bigg{(}\frac{\varphi_{2}-\varphi_{ 1}}{2}\bigg{)}+\Big{(}1-sin^{2}\bigg{(}\frac{\varphi_{2}-\varphi_{1}}{2}\bigg{)} -sin^{2}\bigg{(}\frac{\varphi_{2}+\varphi_{1}}{2}\bigg{)}\cdot sin^{2}\bigg{(} \frac{\lambda_{2}-\lambda_{1}}{2}\bigg{)}\bigg{)}}\bigg{)}\] (6) \[2 rarcsin\bigg{(}\sqrt{sin^{2}\bigg{(}\frac{\varphi_{2}-\varphi_{ 1}}{2}\bigg{)}+cos(\varphi_{1})\cdot cos(\varphi_{2})\cdot sin^{2}\bigg{(} \frac{\lambda_{2}-\lambda_{1}}{2}\bigg{)}}\bigg{)} \tag{7}\]