esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulations
The WMMSE approach in [5] and ideal WMMSE with perfect CSI provide better sum-rate performance due to higher available sub-bands for each cell network while these methods do not maintain the QoS constraints. The proposed DQN scheme however considers the QoS constraint and achieves a higher sum rate than the full-power and random power allocation methods. Furthermore, the protection of cell-edge users in neighboring transmissions against severe cross-coupling interference in the proposed scheme adds to its performance. As expected, the full-power and random OFDMA allocations tend to display lower sum throughput performance compared to the proposed DQN and WMMSE algorithms. For the random OFDMA-based system, since each user uses the \(P_{max}\), the throughput obtained does not change with the sum-rate requirements. To further elaborate on the performance of our proposed method, we plot the average sum throughput versus the transmit power budget with the absence of a QoS constraint in Figure 4. It can be seen from the Figure that the proposed DQN approach achieves a higher sum throughput than the WMMSE and random OFDMA methods. This setting has been discussed with the reward design in Problem (20). When we ignore the QoS constraint, the reward signal is designed to optimize the utility function of each BS \(i\) and capable of obtaining better sum-rate performance. Indeed, the proposed scheme is aiming to maximize the overall capacity of the entire system under the QoS conditions and has also achieved the better result. From Figure 3, it is evident that with the increase of the transmit power budget, the proposed DQN approach is capable of obtaining a better result with distributed execution while satisfying the QoS requirement. Figure 4: Sum rate vs. power budget ignoring QoS. Figure 3: Sum-rate vs. transmit power budget for different schemes. Figure 5 shows the relationship between the average sum rate and the number of users with different power budgets. From the figure, it is found that the average sum rate slowly decreases with the cell user number increasing for varying transmit power budgets. As expected, when the user's number increases, the average sum throughput performance of the proposed approach decreases gradually because, in such situations, additional interference will generate when the cell users become large. However, as evident from Figure 5, increasing transmit power budget of each user leads to a compromise.
## 4 Test and Result Analysis  
### Test Model and Parameters  
IEEE 33-node power distribution system is applied to verify the proposed coordinated control algorithm (as shown in Figure 7) [39]. Node 1 is the reference node and the voltage is fixed at 1 p.u. The other 32 nodes are user nodes, and the allowed range of each node voltage is [0.9, 1.1] p.u. The test data are based on the generated power of PV and the load in Ota city, on June 1 st, 2007 [40]. The simulation is carried out during one day, 24 h period is divided equally into 2880 slots, i.e., each slot is 30s.  
In this paper, a Li-ion battery is considered as the BESSs. Figure 8 shows the change of SOC in different states. The horizontal axis of Figure 8 is 24 h, indicating a full-day SOC operation. In order to make the BESSs have a charging and discharging margin and prevent over charge and over discharge, SOC should be controlled in \(0.1\leq\) SOC(t) \(\leq\) 0.9.  
In Figure 8, number of 1-5 indicates the BESS operation as follows:  
1. T3, T6: Standby period, voltage and frequency are in the normal range, and no regulation required.
2. T1, T4: Discharging period, BESS is discharged power
3. T2, T5: Charging period, BESS is charged power from the power system.  
We assumed each battery with the capacity of 23 kWh. The initial SOC of the batteries is assumed by the Gaussian distribution on [0.3, 0.4] with the mean value of 0.35, and the standard deviation is 1. The number of candidate nodes which are used to perform frequency regulation compensation is assumed as one third of the total number of BESSs of the node (\(x=n/3\)).  
We assume 100 users in a node, and each user is equipped with a photovoltaic (PV) system. In this paper, the daily load data and PV generation data are referenced in the literature [40], which are showed in Figure 9. The PV output is large during the period of 9:15-11:40 and 12:15-15:10. The peak power is 9032 kW. Daily load curve fluctuates between 2306 kW and 4994 kW.  
Figure 7: IEEE 33 nodes power distribution system.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 7 Conclusions  
This paper proposes a novel multiple-step randomly delayed, robust filter, referred to as the multiple-step, randomly delayed, dynamic-covariance-scaling cubature Kalman filter (MRD-DCSCKF), to effectively handle randomly delayed measurements and outliers. The MRD-DCSCKF uses a state-augmentation approach to break the limitations of the delayed steps and reformulates the state update equations of Kalman filter based on the delayed measurements modeled according to a set of Bernoulli random variables. Meanwhile, the proposed filter relies on dynamic-covariance-scaling, robust kernel to suppress measurement-outliers. The application of MRD-DCSCKF to vision-based spacecraft-relative navigation is investigated, where the relative dynamics are described with the T-H equations, and quaternions and generalized Rodrigues parameters are introduced to estimate spacecraft relative attitudes. The simulation results illustrate that the MRD-DCSCKF is able to detect the effects of the underlying parameters.  
Figure 23: ARMSEs of gyro drift under different perturbing parameters.  
Figure 22: ARMSEs of attitude under different perturbing parameters.  
to precisely estimate the status of the spacecraft with high precision, even with randomly delayed measurements and outliers, as compared to other algorithms.  
Conceptualization, Y.C.; methodology, Y.C.; software, Y.C.; validation, Y.C.; formal analysis, H.Z.; investigation, Y.C. and H.L.; data curation, Y.C., R.M. and H.Z.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., R.M., H.Z. and H.L.; supervision, R.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} VISNAV & Vision-based navigation \\ DCS & Dynamic covariance scaling \\ BRV & Bernoulli random variable \\ PSD & Position sensing diode \\ LED & Light-emitting diode \\ SLAM & Simultaneous localization and mapping \\ ECL & Earth-centered inertial \\ LVLH & Local-vertical-local-horizontal \\ GRP & Generalized Rodrigues parameter \\ MCC & Maximum correntropy criterion \\ SE & State estimation \\ GA & Gaussian approximation \\ PDF & Probability density distribution \\ KF & Kalman filter \\ PF & Particle filter \\ EKF & Extended Kalman filter \\ UKF & Unscented Kalman filter \\ UT & Unscented transformation \\ CKF & Cubature Kalman filter \\ GGLQ & Generalized Gauss-Laguerre quadrature \\ HCKF & High-degree cubature Kalman filter \\ MAEKF & Modified adaptive extended Kalman filter \\ STF & Student's \(t\) filter \\ ORD-CKF & One-step randomly delayed cubature Kalman filter \\ MRD-CKF & Multiple-step randomly delayed cubature Kalman filter \\ MRD-DCSCKF & Multiple-step randomly delayed dynamic-covariance-scaling cubature Kalman filter \\ \end{tabular}
## 3 The Main Parameters and Indices of MOT
### Application Performance Indices
#### 3.4.3 Atomic Loading Time  
In Figure 4, the time taken to load the atoms makes up 76% of the measured dead zone and is a major part of the dead zone time. Additionally, the process of loading the atoms is only a preparation stage and does not collect any useful information. By reducing the time it takes to load the atoms, we can shorten the measurement cycle, increase the data rate of the measurement process, and leave more time for the more important stage of collecting information. Therefore, it is important to consider the time it takes to load the atoms when evaluating the performance of the MOT and to have a more thorough discussion about it.  
Using the MOT in the atomic gravimeter developed by Zhejiang University as an example, Figure 7 shows how the number of atoms changes as the loading time increases during operation [77]. Initially, the number of atoms quickly increases, but then the growth rate decreases after a certain point is reached.  
The time required for the saturation of the number of atoms, which is known as the atomic loading time, as shown in Figure 7, is 2 s.  
Figure 6: The number of atoms at various vibration frequencies and laser intensities.  
Figure 7: Atom-loading process in the MOT.  
Based on the discussion in this section, the performance indicators of the MOT are shown in Table 2.
## 4 Experimental Results
### Computing the Image-Processing Block Size  
Section 3.2 gives an outline of the Fibonacci transfer function, which is the key concept used for embedding the secret data in the proposed approach. The maximum information that we can embed in a particular block of size \(A\times A\) pixels depends purely on the periodicity of the transfer function. The periodicity of the Fibonacci transfer function depends on the transformation matrix used in the algorithm. In this investigation, the transformation matrix used in the transfer function is \(\begin{bmatrix}1&1\\ 0&1\end{bmatrix}\) (refer to Section 3.2). The embedding capacities that can be achieved with various block sizes are given in Table 1. It can be seen from Table 1 that the payload rate decreases as the processing block size of the image is increased. The recovery factor is taken into account, along with the payload rate, to fix the processing block size of the images. The image recovery rate is explained in Section 4.7. Based on consideration of the factors referred to, we chose \(8\times 8\) as the feasible block size for processing the images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sl. No.** & **Block Size** & **Periodicity** & \
\begin{tabular}{c} **Bits Embedded** \\ **in a Block** \\ \end{tabular} &
\begin{tabular}{c} **Payload Achieved** \\ **(bpp)** \\ \end{tabular} \\ \hline
**1** & \(8\times 8\) & 16 & 4 & 0.0625 \\ \hline
**2** & \(16\times 16\) & 32 & 5 & 0.0195 \\ \hline
**3** & \(32\times 32\) & 64 & 6 & 0.0059 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relation between the periodicity of the Fibonacci transform and the block size.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 6 Conclusions  
The current study aimed to provide more detailed information about the life cycle of the surface deformations caused by coal extractions. The DInSAR results have been proven reliable and accurate enough for monitoring the phase of main mine subsidence (\(-1.65\) m over one year) with a \(-4\) cm bias and \(18.3\) cm standard deviation when compared to levelling. The amount of detected surface vertical change is compatible with the amount of the extracted material and follows the stage of actual extraction for eight months, with up to \(90\%\) correlation coefficient. The radar data are within the range of the predefined predictive models, which ensures the minimum influence on the infrastructure and settlements. On the other hand, the new information that the radar measurements bring may improve the modelling strategy by adding an additional source for parameter determination. Another approach may involve the modelled horizontal displacements in the north-south direction in the formalisation of the 3D decomposition procedure. In this way, the solution for the vertical component may also be straightened. Significant enhancement will be achieved by applying the relevant procedure for the decrease of tropospheric artefacts in the radar signal and for georeferencing at particular GNSS stations.  
Conceptualization, M.I. and W.R.; methodology, M.I., W.R., A.B., P.P., and P.G.; software, A.B., W.R., K.S., and P.G.; validation, M.I. and P.P.; formal analysis, M.I. and K.S.; investigation, M.I. and P.P.; resources, P.P.; writing--original draft preparation, M.I., W.R., and P.P.; writing--review and editing, A.B., A.K., P.G., and K.S.; visualization, M.I.; supervision, A.B. and A.K.; project administration, W.R.; funding acquisition, W.R.  
The presented investigation is part of the project EPOS-PL, European Plate Observing System POIR.04.02.00-14-A003/16, funded by the Operational Programme Smart Growth 2014-2020, Priority IV: Increasing the research potential, Action 4.2: Development of modern research infrastructure of the science sector and co-financed from European Regional Development Fund.  
The Department of Weglokoks Kraj \"Bobrek-Piekary\" coal mine provided all documentation, including the maps and results of geodetic surveys.  
The authors declare no conflict of interest.
## 4 Comparison and Discussion
### UWB Approach  
UWB technology has been developed as one of the PHY specifications in the IEEE 802.15.6 standard. Although UWB-based WBASNs inherently have very low power density, interference between UWB transceivers or UWB systems occurs at the receiver [44]. The simulation result shows that ZCD is suitable for MAI and interference cancellation because the correlation period of ZCD is zero. Performance of systems employing SIC is better than non-SIC systems in terms of low bit error rate.  
However, devices working in the narrow band can cause interference with UWB-based WBASNs. This type of interference can be cancelled by using pulses in WBASNs [45]. The simulation results show that the hybrid hermite pulses (HHP) system improves the bit error rate in cases of low SINR (high noise). The simulation also compares BER and SINR between the Gaussian pulse and the HHP system to show that WBASNs with HHP outperform those with the Gaussian pulse system.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 3 Experiment Results and Analysis  
### Experiment Platform  
In order to evaluate the method proposed in this paper, we conduct experiments on the platform shown in Table 2. Here, the image resolution of Hong Kong Observatory (HKO) Image database is 1920 \(\times\) 1080 pixels. The true value of visibility for each image to be trained comes from the visibility meter data provided by HKO. The dataset has a total number of 4841 selected images from database. We have selected 3630 images randomly as training set and the remaining 1211 images are selected as test set.  
According to the needs of the experiment, each image could provide appropriate sub-regions through gray-scale average. The visibility distribution of the image database is shown in Table 3.
## 4 Case Study
### Data Collection  
According to the evaluation index system and evaluation criteria above, the required data of Jiangsu in 2015 were gathered (Table 11). All data about the national average level and total amount were extracted from National Statistical Yearbook (2015). Related data of development level in Jiangsu were collected from Statistical Yearbook of Jiangsu Province (2015), related government development plans and bulletin information.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Index** & **Scoring Criteria** & **Score** & **Rule Explained** \\ \hline \multirow{4}{*}{A51} & Ratio reaches 65\% or more & 5 & \\  & Ratio within 60–65\% & 4 & \\  & Ratio within 55–60\% & 3 & The national average is 56.1\% [44] \\  & Ratio within 55–50\% & 2 & \\  & Ratio below 50\% & 1 & \\ \hline \multirow{4}{*}{A52} & High degree of market competition & 5 & \\  & Within 3–5 & 4 & \\  & Common degree of market competition & 3 & Expert decision \\  & Within 1–3 & 2 & \\  & Poor degree of market competition & 1 & \\ \hline \multirow{4}{*}{A53} & High degree acceptance & 5 & \\  & Within 3–5 & 4 & \\  & Common degree acceptance & 3 & Expert decision \\  & Within 1–3 & 2 & \\  & Poor degree acceptance & 1 & \\ \hline \multirow{4}{*}{A54} & More than 40 m\({}^{2}\) & 5 & \\  & Within 37–40 m\({}^{2}\) & 4 & \\ \cline{1-1}  & Within 33–37 m\({}^{2}\) & 3 & The national average is 33 m\({}^{2}\)[44] \\ \cline{1-1}  & Within 30–33 m\({}^{2}\) & 2 & \\ \cline{1-1}  & Lower than 30 m\({}^{2}\) & 1 & \\ \hline \end{tabular}
\end{table}
Table 10: Evaluation criteria of index system in the development base dimension (A5).  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Data** & **Source** \\ \hline A21 & 297,437/Yuan/Year & Statistical Yearbook of Jiangsu [43] \\ A22 & 88,500 & National Bureau of Statistics of China [44] \\ A23 & 91.7 & Statistical Yearbook of Jiangsu [43] \\ A24 & 6\% & National economy and social development statistical bulletin of Jiangsu [52] \\ A25 & 2.757 trillion Yuan & Statistical Yearbook of Jiangsu [43] \\ \hline \end{tabular}
\end{table}
Table 11: The relative data of quantitative evaluation index.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Scoring Criteria** & **Score** & **Rule Explained** \\ \hline \multirow{4}{*}{A41} & Proportion more than 15\% & 5 & \\  & Proportion within 7–15\% & 4 & \\  & Proportion within 2–4\% & 2 & \\  & Proportion below 2\% & 1 & \\ \hline \multirow{4}{*}{A42} & Number reaches 7 or more & 5 & \\  & Number within 5–6 & 4 & \\  & Number within 3–4 & 3 & The national total is 70 [51] \\  & Number 2 & 2 & \\  & Number 1 or under & 1 & \\ \hline \multirow{4}{*}{A43} & Proportion more than 15\% & 5 & \\  & Proportion within 7–15\% & 4 & \\ \cline{1-1}  & Proportion within 4–7\% & 3 & The national total is 18.43 trillion Yuan [44] \\ \cline{1-1}  & Proportion within 2–4\% & 2 & \\ \cline{1-1}  & Proportion below 2\% & 1 & \\ \hline \end{tabular}
\end{table}
Table 9: Evaluation criteria of index system in the enterprise development dimension (A4).
## 2 Materials and Methods
### Loss Function
#### 2.4.2 Loss Function of WGAN  
Different to the spatial-domain-based network that directly learns the mapping relationship between HR and LR pixel values, the WGAN aims to learn the relationship between the wavelet coefficients of frequency-domain characteristics between HR and LRimages. The WGAN constrains the network through two losses in the spatial and frequency domain of the image.  
\[\text{L}_{\text{WGAN}}=\text{L}_{\text{wavelet}}+\beta\text{L}_{\text{texture}}+\text{L}_{\text{full-image}} \tag{9}\]  
where \(\beta\) is the balance parameter, which is 0.1. The WGAN takes the MSE loss function \(\text{L}_{\text{full-image}}\) as the loss constraint of the full image to make the reconstruction result achieve a balance in smoothness and texture details; it can hardly obtain high-frequency texture details. Therefore, in order to pay attention to local texture information and prevent high-frequency wavelet coefficients from converging to 0, the WGAN adopts \(\text{L}_{\text{wavelet}}\) and \(\text{L}_{\text{texture}}\) avoids the above problems, respectively.  
\[\text{L}_{\text{wavelet}}\big{(}\hat{\text{C}},\text{C}\big{)}=\sum_{i=1}^{ \text{N}_{\text{w}}}\lambda_{i}||\hat{\text{c}}_{\text{i}}-\text{c}_{i}||_{ \text{F}}^{2}=\lambda_{1}||\hat{\text{c}}_{\text{i}}-\text{c}_{1}||_{\text{F} }^{2}+\sum_{i=2}^{\text{N}_{\text{w}}}\lambda_{i}||\hat{\text{c}}_{\text{i}}- \text{c}_{i}||_{\text{F}}^{2} \tag{10}\]  
\[\text{L}_{\text{texture}}=\sum_{i=1}^{\text{N}_{\text{w}}}\gamma_{i}\text{max }\Big{(}\alpha||\text{c}_{i}||_{\text{F}}^{2}+\varepsilon-||\hat{\text{c}}_{i}|| _{\text{F}}^{2},0\Big{)} \tag{11}\]  
where \(\lambda_{i}\) is the balance parameter, \(\text{c}_{i}\) and \(\hat{\text{c}}_{i}\) are the ground-truth wavelet coefficients and the inferred wavelet coefficient, respectively. k indicates the start index of the wavelet coefficients to be penalized for taking small values, \(\gamma_{i}\) represents the balance weight, \(\alpha\) and \(\varepsilon\) are slack values. More attention can be paid to the local texture with large weight assigned to the high-frequency coefficient. At the same time, it can capture the global topology information.
###### Abstract  
This paper investigates the coupled relationship between solid-phase temperature fields and droplet evaporation, focusing on the effects of substrate thermal conduction properties on droplet evaporation behavior. A mathematical model is developed to analyze the impacts of substrate thermal conductivity, thickness, and lower-surface temperature on evaporation rate, surface temperature, and evaporation flux. A dimensionless relative evaporation rate (HCs) is introduced to characterize the influence of substrate thermal conduction. Results show that increasing substrate thermal conductivity enhances droplet surface temperature and evaporation flux, thereby monotonically increasing evaporation rate until it approaches the rate of the evaporative cooling model. Conversely, increasing substrate thickness lengthens the heat transfer path, reducing heat conducted to the solid-liquid interface and decreasing evaporation rate. Changes in substrate lower-surface temperature significantly affect evaporation rate, but HCs remains nearly unaffected. The concept of equivalent substrates is proposed and verified through dimensionless analysis and simulations. It is found that different combinations of substrate thickness and thermal conductivity exhibit consistent effects on droplet evaporation, with minimal relative errors in evaporation rate and total heat transfer at the solid-liquid interface. This confirms the existence of the equivalent substrate phenomenon. Additionally, the effects of droplet properties, such as contact angle and evaporative cooling coefficient (\(Ec\)), on the equivalent substrate phenomenon are explored, revealing negligible impacts. These findings provide theoretical guidance for optimizing droplet evaporation processes in practical applications, such as micro/nanoscale thermal management systems.  
liquid evaporation; heat conduction; evaporative cooling; solid phase Article