esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Decoupling Algorithm for the Dual-Deformable-Mirrors Correction System
## 1 Introduction
This results in larger amplitude and higher spatial-resolution wavefront aberrations that need to be corrected. Therefore, adaptive optical systems must also increase their ability to correct these aberrations with greater accuracy and amplitude. However, different types of wavefront correctors are often limited by technical issues such as material selection, manufacturing processes, and system-integration complexity. As a result, it becomes challenging for adaptive optical systems equipped with a single wavefront corrector to meet the requirements of large travel and high spatial-frequency aberrations. To address this challenge, researchers have explored the use of multiple wavefront correctors with different performance characteristics working together. By combining the characteristics of each corrector, it is possible to achieve high amplitude correction for low spatial-frequency aberrations and synchronous correction for small amplitude high spatial-frequency aberrations. This cooperative approach has proven to be an effective solution to the limitations faced by single wavefront correctors in adaptive optical systems. This article proposes a decoupling algorithm to address the issues associated with coupling between FSM and DM in dual-deformable-mirrors wavefront correction systems. The decoupling algorithm utilizes the mode method to control FSM and DM. By applying the mode method wavefront reconstruction principle, the slope measured by the Hartmann wavefront sensor is converted into Zernike coefficients, which are then used to directly calculate the initial control voltage for the FSM and DM. The final control voltage of the FSM and DM is then determined using a Proportional Integral (PI) controller, enabling independent correction of any order aberration while avoiding cross-coupling effects. This approach significantly improves the performance and flexibility of the system, making it of great significance for enhancing their capabilities. The rest of this paper is organized as follows. Section 2 describes the principle of the dual-deformable-mirrors wavefront correction system and decoupling algorithm. Section 3 conducts a numerical simulation of the proposed algorithm. Section 4 establishes an experimental platform for the dual-deformable-mirrors wavefront correction system for experimental research. Section 5 provides a detailed analysis of the coupling effect. Finally, Section 6 concludes the paper.
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 2 Materials and Methods
### Root Node Automatic Location Algorithm and CAT Construction  
In the QCA system, the centerline is the basis for acquiring vascular structure and calculating vascular diameter. In our work, [PERSON]'s fast parallel thinning algorithm is adopted to extract the centerline iteratively according to the binary segmentation result. Until a skeleton with a width of one single pixel is obtained, the algorithm continuously removes pixels from the outer layer of vessels but never affects the overall shape. However, the simple centerline extraction algorithm does not contain information on vascular diameter and direction, which is the main reason why many studies require manual participation. In order to solve this problem and avoid manual participation, this paper presents a set of root node automatic location algorithms. Firstly, a deep learning network is designed to automatically find the root node. It is a three-value segmentation network based on PSPNet, which is used to classify the areas of the catheter, the blood vessel, and the starting point area of the blood vessel (Figure 4). The annotations used in this study are based on our prior work [39], which aimed at multiclass vessel segmentation and detailed classification of coronary vessels. In this study, only the catheter and the starting part of the coronary artery segmentation were annotated separately, while other classes of coronary arteries were grouped into the same annotated category. This algorithm provides valuable support and facilitates great improvements for the subsequent process. On the one hand, segmenting the catheter in the images is crucial for accurate scale conversion from pixels to millimeters, as the size of the catheter remains constant in millimeters despite variations in pixels across different CAGs. In this study, the diameter of the catheter used for coronary angiography was fixed at 1.67 mm. On the other hand, the segmentation results help to confirm the starting point for the main vessel segmentation and centerline extraction by using a searching algorithm for root nodes.  
Based on the results of the root node-assisted network, the root node can be searched by Algorithm A1. The specific steps of the root node searching algorithm are shown in Appendix A.  
Once the root node and the pixels-to-millimeters scales were determined, the CAT could be built iteratively along the centerline. Every node of the CAT stores information, including its coordinates, the coordinates of the next node, the direction of the vessel, and the diameter of the vessel at that node. In order to calculate the diameter, considering the  
Figure 4: Architecture of root node-assisted network for three-value segmentation.  
current node as the center, a circle with a gradually increasing radius is drawn, making sure that the entire circle is in the segmented vessel. When the circle is exactly tangent to the edge of the vessel, the diameter of the circle is considered the diameter of the vessel at this node.
## 3 Proposed Algorithm and Analysis
### CH Selection Using Two-Level Clustering (TLC)
Furthermore, the network lifetime of the proposed method in terms of throughput is compared with the existing heterogeneous routing method [18]. The throughput of the proposed method was demonstrated 10% higher with 5000 rounds. Figure 5: Distribution of CHs in various algorithms cited in the literature, compared to the proposed method. Figure 6: Residual energy distribution of the network system toward the end of 1000 rounds of information exchange in various algorithms cited in the literature, compared to the proposed method.
# Long-Lifetime Event-Driven Wireless Monitoring System for Pole-Mounted Transformers
###### Abstract  
As smart grids develop rapidly, low-cost monitoring systems for pole-mounted transformers increase in demand. Even though battery-powered wireless monitoring systems appear to provide optimal solutions, they consume large amounts of energy for continuous sampling and data transmission. Operation and maintenance costs then increase owing to reduced battery lifetime and battery replacement. To overcome this problem, this paper presents an event-driven battery-powered wireless monitoring system that monitors abnormalities of a transformer and transmits data only if an abnormality occurs. When the proposed event controller detects an abnormality, it enables a root mean square (RMS) converter and a peak detector for sampling and transmitting the maximum RMS value of the abnormal signal and then falls into sleep mode until the next event to save energy. Simulation and experimental results show that the proposed system enhances battery lifetime by up to two orders of magnitude compared to a conventional battery-powered wireless monitoring system.  
abnormality monitoring; battery powered; pole-mounted transformer; event driven; root mean square (RMS) +
Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Autonomous Penetration Testing Based on Improved Deep Q-Network
## 2 Background
### Modeling Pentesting as an MDP Problem  
To use RL for autonomous pentesting, we model pentesting as an MDP problem that is defined by the tuple \(<\mathcal{S},\mathcal{A},\mathcal{R},\mathcal{T}>\). In the autonomous pentesting task, the state refers to the information observed by the agent on the network environment, including the configure knowledge of all hosts and vulnerability information, etc. The size of the state space is affected by the number of hosts in the network, which is usually exponential [15]. The action refers to the vulnerability exploitation, scanning, and privilege escalation operations performed by the agent on the target host. An action \(a\) can be seen as an attack vector defined by \(<h,o>\), which means the agent takes operation \(o\) on the host \(h\). At any training step, the size of the agent's action space reaches \(O(M\times N)\), where \(M\) is the number of hosts in the network and \(N\) is the number of executable operations for attacking the target. Reward is the driver of the agent. We define the reward function as the value of all compromised hosts minus the cost of all actions:  
\[\mathcal{R}=\sum_{h\in H}value(h)-\sum_{a\in\mathcal{A}}cost(a), \tag{5}\]  
where \(H\) represents the set of compromised hosts, \(\mathcal{A}\) represents the set of actions the agent takes, \(value(h)\) measures the importance of any hosts and returns the value of compromised host \(h\), and \(cost(a)\) returns the cost of action \(a\). Based on the reward function \(\mathcal{R}\), the goal of the agent is to compromise the most valuable hosts with the least costly actions as much as possible, which mimics the hackers in the real world. The transition function certainly keeps unknown for the reason that we attempt to use the model-free algorithm.  
The agent is expected to complete the pentesting process autonomously and learn the optimal attack path that has maximized rewards. The attack path is the sequence of actions or a series of ordered attack vectors, and the search space of attack paths is affected by the network scale, thereby affecting the learning efficiency of the agent. Specifically, when the network size increases, the learning efficiency of the agent is mainly limited by two challenges:  
1. The sparse reward problem. Rewards work as intermediate feedback on progress towards the goal, without which learning becomes impossible [24]. However, there exist only a few sensitive hosts with positive value in the network usually, making rewards become sparse when the network size increases. Thus, the algorithm becomes difficult to converge.
2. Large action space problem. The agent learns optimal policy through trial and error. However, at each training step, an action is determined by two dimensions: the agent not only needs to choose the attack target but also needs to choose the proper operation. Action space increases with network size, which reduces exploration efficiency and increases the trial-and-error cost.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 5 Conclusions and Discussion
In addition, the LEO float solution was employed to enhance PPP-AR. This method was undoubtedly a better choice when the ground tracking network of the LEO system was not perfect enough to solve the LEO FCB to fix the LEO satellite ambiguity, which can reduce the time to first fix of the four-system combined PPP-AR to 5 min on the global scale (thus reducing the contingency caused by selecting typical examples). In addition, it does not require too many LEO satellites; four is enough. Similarly,Hong et al. [36] also used this method to study the rapid precise point positioning enhanced with low Earth orbit satellites, pointing out that (based on the small-scale European stations) the time to first fix using the G single system as well as the G/C/E combined system with LEO satellites added could be reduced by 86.4% and 82.8%, respectively, with respect to systems without additional LEO satellites. [PERSON], [PERSON] and [PERSON] provided the initial idea for this work and wrote this manuscript; [PERSON] designed the algorithm; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the analyses of results; [PERSON] and [PERSON] drew the pictures. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 41974032, 42274019. The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study. The work was supported by the Chinese Academy of Sciences (CAS) programs of \"High Level Talents\". The authors declare no conflict of interest.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## Appendix A Survey on the Societal Value of PMWIs  
This survey has been prepared by the Center for Hydrometeorology and Remote Sensing (CHRS) at University of California Irvine to collect feedback from PMWI data users. The information gathered from the survey will be summarized in the report to the Cooperative Institute for Satellite Earth System Studies (CISESS). The survey aims to evaluate the societal effects of PMWIs in applications such as hurricane tracking, precipitation monitoring, ocean surface wind measurements, cryosphere studies, etc. PMWI data is vital for hydrological and meteorological applications because it allows for all-weather, day-and-night monitoring of key variables. This data enables more accurate monitoring and forecasting of weather patterns, water availability, and potential hazards, leading to improved water resource management, flood prediction, agricultural planning, and disaster preparedness efforts.  
The survey below is intended to collect detailed insights into the applications of PMWI data, particularly on their societal impact. Participants are encouraged to share feedback and suggestions to support future sensor projects and missions. Please include relevant references in your response. Respondents' identities will be kept confidential, and their relevant work and references will be cited in the report.  
Survey Questions:  
1. Name: Your full name.
2. Email Address: Your email address for follow-up and further communication.
3. Workplace and Position: The organization where you work and your current job title.
4. Usage of PMWI Data: Specific types of PMWI data application(s) in your role. Categories:  
* hurricane tracking
* precipitation monitoring
* ocean surface wind measurements
* cryosphere studies
* others
5. Importance Rating: On a scale from 1 to 10, rate the importance of PMWI data to your work or applications.
6. Applications: Describe the products or applications you create using this data.
7. Societal Impact: Discuss the societal impact of your work using PMWI data.
8. Measurement of Societal Impact: Explain how you quantify the societal benefits of your work (e.g., lives saved, economic benefits, cost savings).
9. Evidence of Impact: Provide evidence or data supporting the societal impact mentioned.
10. Supporting References: List any references that support your claims about the societal impact of PMWI data.
11. Limitations of PMWI Data: Identify any limitations you encounter with PMWI data and how these affect your products.
12. Suggestions for Improvement: Provide recommendations for enhancing PMWI data quality, accessibility, or other aspects in future developments.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 3 Experiments and Results  
### Study Area and Data  
Rizhao, Shandong Province, China, is selected as the study area, which is a large-scale urban area containing typical urban forms, as shown in Figure 8. The longitude and latitude range of the experimental area is 119.518\({}^{\circ}\)-119.569\({}^{\circ}\)E and 35.383\({}^{\circ}\)-35.431\({}^{\circ}\)N, respectively.  
To verify the effectiveness of the proposed method, some experimental results using the first domestic airborne array TomoSAR system of the Institute of Aerospace Information  
Figure 8: The optical image of the study area in the UTM coordinate system.  
Research Chinese Academy of Sciences (AIRCAS) are presented. Detailed information on the system parameters is listed in Table 1.
# Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
## 5 The Multi-Agent Particle Environment Experiment
### Algorithm Performance Experiment  
In this section, the M2 ATD3 algorithm and the MASTD3 algorithm are verified with the cooperative communication task and the cooperative navigation task in the particulate environment, and compared with the MADDPG algorithm and the MATD3 algorithm. All the experiments were trained for 60,000 steps. The experiment results are as shown in Figure 6.  
Figure 5: Q-value estimation bias comparative experiment based on cooperative navigation task in MPE. The Q-values estimated by the Q-network and the true Q-values are shown. The results are averaged across 5 runs and 95% CIs of the mean are shown for the estimated values. (**a**) DDPG, (**b**) MATD3, (**c**) M2 ATD3, (**d**) MASTD3.  
Figure 6: Comparative experiments using different Q-based multi-agent algorithms in MPE. Shown is the mean episodic reward over the last 1000 episodes, shaded areas are the 95% confidence intervals of the mean, averaged across 20 trials. (**a**) Cooperative Communication Task, (**b**) Cooperative Navigation Task.  
As shown in Figure 6, in the Cooperative Communication Task and the Cooperative Navigation Task, the performance of the MATD3 algorithm is superior to the MADDPG algorithm, and both the M2 ATD3 algorithm and the MASTD3 algorithm have achieved higher total rewards than the MATD3 algorithm.  
As shown in Equation (14), hyperparameters \(\alpha\) determine the proportion of the clipped double Q value to the average Q value when calculating the updated target value \(y_{i}\), and select the appropriate hyperparameter \(\alpha\) being able to balance the impact of underestimation of clipped double Q and overestimation of average Q.  
As shown in Figure 7, in the cooperative communication task and the cooperative navigation task, the M2 ATD3 algorithm receives lower rewards when the \(\alpha\) is set to 0.2 or 0.8, indicating that the low estimation of Q value caused by the clipped Double Q-Learning or the high estimation of Q value caused by the average Q value estimates will have a negative impact on the algorithm. However, the M2 ATD3 algorithm receives the highest reward when the \(\alpha\) is set to 0.5, indicating that by balancing the two Q-value estimation methods, reducing the bias in Q-value estimation can improve the performance of the algorithm.
## 7 Conclusions  
A study of the main halite-bearing strata of the UK onshore and East Irish Sea areas in which UGS caverns have been constructed or planned has been undertaken to assess their potential for the construction of salt caverns for CAES purposes and their exergy storage potential. Storage depths investigated are between 500 and 1500 m. Revisions to an earlier exergy modelling tool, equations for which were validated by operational data from the Huntorf CAES plant, have led to a series of exergy storage capacity estimates for three differing heat models. Both the'static' one-fill exergy storage capacity and a series of 'dynamic' exergy storage capacities based on various fill and empty rates are derived. From a theoretical storage of over 300 TWh, more realistic storage estimates of many tens of TWh are achieved by way of filtering the estimates based on cavern dimensions and different storage cycles considering UGS projects and operational modes. Significant exergy storage capacity exists for CAES in salt caverns, which could provide important support to the UK electricity grid, requiring 300 TWh per year. As the contribution of intermittent renewables generation to the grid rises, it is suggested that salt cavern storages constructed onshore, rather than porous rock storages located offshore, are likely to be the main CAES storage technology available in the UK at least in the short term.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11114728/s1](https://www.mdpi.com/article/10.3390/app11114728/s1)]([https://www.mdpi.com/article/10.3390/app11114728/s1](https://www.mdpi.com/article/10.3390/app11114728/s1)), S1, Tables S1-S6, S2, Tables S1-S8 and S3.  
Conceptualisation, D.E., J.B., J.W. ([PERSON]) and S.G.; Formal analysis, D.E., D.P., M.D., P.W. and J.W. ([PERSON]); Funding acquisition, D.E., J.B., J.W. ([PERSON]) and S.G.; Investigation, D.E., D.P., M.D., P.W. and J.W. ([PERSON]); Methodology, D.E., D.P., M.D., P.W., J.W. ([PERSON]) and W.H.; Project administration, D.E., J.W. ([PERSON]) and S.G.; Software, D.P., M.D., P.W. and W.H.; Writing--original draft, D.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded from the UK Government's Engineering and Physical Science Research Council (EPSRC), UK grants EP/L014211/1 and EP/K002228/1.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank three anonymous referees for comments on the manuscript all of which improved the final version. Financial support from the Engineering and Physical Science Research Council (EPSRC) funded Grand Challenge [Energy Storage] IMAGES project (EP/K002228) is gratefully acknowledged. This paper is published with permission of the Executive Director of the British Geological Survey (NERC).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results
### Bioengineering Investigations
#### 3.2.3 BR44 Stability under Stirred Conditions  
Figure 6A1,A2 shows light microscopic pictures of the _BR44_ MC during the stability tests in the Corning spinner flasks. During the 14-day incubation period at \(N_{slu}\) (85 rpm), no significant increase in debris or broken _BR44_ MCs was observed, indicating that the _BR44_ MC was stable under culture-relevant conditions. However, the size distribution analysis after 14-days showed a decrease in the MC size distribution (Figure 6B) MCs' mean diameter decreased by up to 37%, suggesting the MCs shriveled during the 14-day incubation period under stirred conditions. However, the MCs' shrinkage can be explained by the biodegradable property of PLGA and was, therefore, to be expected (see also Figure S6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Theoretical \(\mathbf{A_{MC}}\) (cm\({}^{2}\))**} & \(\mathbf{N_{slu}/N_{s1}}\) (rpm) & \(\mathbf{u_{tip}}\) (m/s) & \multicolumn{1}{c}{**Re (-)**} & \multicolumn{1}{c}{**P/V * (W/m\({}^{3}\))**} & \(\mathbf{\tau_{nt}}\) **(10\({}^{-3}\) Pa)**} \\ \hline _BR44_ & 180 & 75/90 & 0.16/0.19 & 2095/2514 & 1.42/2.31 & 8.3/10.0 \\  & 360 & 85/102 & 0.18/0.22 & 2374/2849 & 1.96/3.39 & 9.4/11.3 \\ ProNectin-F & 180 & 35/44 & 0.08/0.09 & 978/1229 & 0.35/0.52 & 3.8/4.8 \\  & 360 & 49/60 & 0.11/0.13 & 1369/1676 & 0.62/0.88 & 5.4/6.6 \\ \hline \end{tabular}
\end{table}
Table 2: Overview of \(\mathbf{N_{slu}/N_{s1}}\) and dependent biochemical engineering parameters. (*) _P/V_ values and (**) local volume-weighted mean shear stress levels were derived from [PERSON] et al. [39]. \(\mathbf{u_{tip}}\) indicates the maximum expected fluid velocities in a stirred bioreactor. **Re** is used to classify the fluid flow regime in a stirred bioreactor. _P/V_ represents an important parameter in terms of process scale-up and the estimation of hydrodynamic stresses. \(\mathbf{\tau_{nt}}\) gives an overview of the hydrodynamic stress acting on the cells in a stirred bioreactor system.  
Figure 5: _BR44_ MC suspension dynamics. Photographic pictures (side and bottom view) of the MC distribution during the suspension studies. \(N_{s1}\) was defined as the impeller speed required just entirely to suspend all MCs. \(N_{slu}\) was defined as the lower limit of \(N_{s1}\), meaning that some MCs were still located at the bottom of the flask, but none were at rest.