esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS
### Primary Challenges in Resource Management
#### 3.2.4 Diversified Users and QoS Requirements  
A mega-LEO satellite constellation is a satellite system designed to serve global users. It caters to a diverse range of users, each with unique quality of service (QoS) requirements.  
Traditional terrestrial users, including fixed stations, portable stations, and relay stations, etc.. These stations primarily support land Internet access, land 4G/5G relay services, land emergency communications, polar scientific communications, overseas agency communications for governments and enterprises, and maritime communications. These users are characterized by their limited mobility and lack of cross-regional resource scheduling.  
High-mobility, large-span, and planning route users, including Internet access for aircrafts, ocean ships, high-speed railways, and other end users. These users are characterized by high resource demand, high mobility, and a large spatial span. Their movement trajectories can be used to optimize resource scheduling. However, due to their fast movement, combined with the high-speed movement of LEO satellites, a significant dynamic Doppler shift occurs between them and the satellites. This has a substantial impact on communication quality and necessitates frequency correction.  
High-mobility, large-span, and on-demand scheduling users. As unmanned aerial vehicle (UAV) technology advances, the transmission of command and control information and reconnaissance data for long-voyage UAVs and missiles relies on space-based communication systems, making them potential users of the MLSCS. These users exhibit strong mobility, random movement, and data transmission capabilities, requiring on-demand (OD) scheduling of resources to meet their dynamic data transmission needs. Additionally, the transmission of command and control information must meet stringent requirements for low bit error rate (BER), low delay, and high security.  
Figure 2: The user distribution of Iridium system. Red dots represent high-density areas, yellow dots represent medium-density areas, white dots represent low-density areas, and areas with no dots indicate no users in that area.  
Special industry users. Users in special industries are also potential beneficiaries of the MLSCS. Through its global interconnection capabilities, the MLSCS enables these industries to establish their own private networks, such as financial, telecom, medical, and education private networks. Each of these networks serves unique purposes and has varying quality of service (QoS) requirements. To meet these diverse needs, it is necessary to integrate a wireless resource scheduling system. This system optimizes resource allocation across different network systems, ensuring the required delay, rate, reliability, and security. For instance, financial private networks can leverage the MLSCS to enhance transaction security and speed, while telecom private networks can use it to optimize network traffic and reduce latency.
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 5 Conclusions and Outlook  
The HERVAT system showed a reliable and reproducible behaviour in terms of ignition, pulse stability and thrust levels. Moreover, the TTRP investigation has revealed that the HERVAT system can operate at a level comparable to other published works.  
In order to further improve the TTRP of our system, it is necessary to investigate the plasma plume behaviour with increasing PPU energy. The magnetic nozzle design also needs to be optimized to better match the thruster geometry. For this, simulations of the magnetic field will be performed and transferred into an optimized magnetic nozzle prototype. Comparisons with magnetic nozzles using permanent magnets are planned.  
Additionally, the circuit losses in the PPU need to be reduced to further increase the ratio of the energy stored in the PPU to the energy dissipated in the arc. For this, it is planned to develop a vacuum rated prototype to minimize the influence of parasite effects from the power transmission lines.  
Conceptualization, M.K. and C.T.; methodology, M.K. and C.T.; software, M.K. and C.T.; validation, M.K. and J.S.; formal analysis, M.K. and J.S.; investigation, M.K. and C.T.; resources, J.S.; data curation, M.K. and C.T.; writing--original draft preparation, M.K. and C.T.; writing--review and editing, M.K., C.T. and J.S.; visualization, M.K. and C.T.; supervision, M.K. and J.S.; project administration, M.K.; funding acquisition, J.S. 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.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Average Thrust** & **Impulse Bit** & **TTPR** & **Magnetic Field** & **Electrical** & **Thrust Balance** & **Reference** \\
**[μN]** & **[μN s]** & **[μN/W]** & **[T]** & **Properties** & **Type** & **Type** \\ \hline
4.4 & 0.15 & 3 & 0 & \(\begin{array}{c}\tau_{pulse}=200\,\mathrm{\SIUnitSymbolMicro s}\\ I_{max}=17\,\mathrm{A}\\ I_{acc}=28\,\mathrm{V}\end{array}\) & horizontal thrust & [9] \\
5–22 & 0.3–1.5 & 2.2–9.8 & 0–0.25 & \(\begin{array}{c}\tau_{pulse}=250\,\mathrm{to}\,450\,\mathrm{\SIUnitSymbolMicro s }\\ I_{max}=40\,\mathrm{A}\\ I_{acc}=27\,\mathrm{to}\,49\,\mathrm{V}\end{array}\) & horizontal torsion & [19] \\
8–18 & 0.5–1.1 & 4.5–8.5 & 0–0.25 & \(\begin{array}{c}\tau_{pulse}=133\,\mathrm{to}\,217\,\mathrm{\SIUnitSymbolMicro s }\\ I_{max}=40\,\mathrm{to}\,45\,\mathrm{A}\\ I_{acc}=27\,\mathrm{to}\,51\,\mathrm{V}\end{array}\) & horizontal torsion & [8] \\ \hline \hline \end{tabular}
\end{table}
Table 2: VAT thruster operating conditions and performance for systems comparable to this work.
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
## 1 Introduction
### Motivation  
This work presents a simple energy model based on the detailed analysis of the main types of energy consumed during the different tasks performed by sensor within a network while in active mode. This model is capable of performing a detailed breakdown of such types of energy according to the node hierarchy in the network considering at the same time both high and low consumption sources. This energy model is characterized by being simple and providing evidence of the energy used by the main tasks of a node in the network. In addition, it reflects the impact of each type of energy on the performance metrics of a network, with which we could optimize routing protocols according to the conditions of the network and its local expenditure (at node level).  
The results in this proposal are established by assigning a node hierarchy based on the traffic load due to the node's proximity to the coordinator node. The novelty in this proposal is the possibility of obtaining the skeleton of each energy expenditure in the network and establishing optimizations for higher consumptions. Furthermore, these network consumptions are classified by the proposed energy model according to their dependency in data packet sending. For example, if the unit's microcontroller is detected to be of high consumption, it can be replaced by a smaller one or for a chip with a better wireless technology. Similarly, if the consumption during data reception or transmission is high, it can be an indicator that the channel exhibits high interference and intermittent links. This way, this simple and easy to implement model intends to be the starting point for specific task node optimization methods and sleeping algorithms.
## 6 Results and Discussion
### Discussion
* In the presence of non-uniform optical intensity distributions, the optimal BER location for all flat and curved arrays in mobile UOWCs will be sandwiched between two or more high-BER regions contingent on the intensity profile and angular distribution of the PDs. * Where designers may want to address localization via the optical link, using received-signal-intensity (RSSI)-based methods may not be feasible as multiple loci produce equal received power. However, isolating the attenuation factor by using two or more distinct wavelengths may be a beneficial strategy. Considering the above, we emphasize the need for array designs that can shape-shift dynamically between flat and convex geometry for the received power in a mobile UOWC to remain optimized. We recognize that this may present challenges in designing housing that can move as well as withstand deep-sea pressures. It will require future research attention.
## 3 Results
### Convolution-GRU Classification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **ACC** & **LP** & **LR** & **SP** & **SR** \\ \hline LDA & 0.5935 (0.0475) & 0.8046 (0.0316) & 0.6494 (0.0423) & 0.2103 (0.0577) & 0.3701 (0.0972) \\ KNN & 0.4463 (0.0581) & 0.8127 (0.0515) & 0.3980 (0.0624) & 0.2109 (0.0327) & **0.6400 (0.0924)** \\ LGR & 0.5870 (0.0439) & 0.7975 (0.0269) & 0.6482 (0.0425) & 0.1961 (0.0519) & 0.3422 (0.0954) \\ SVM & 0.6324 (0.0440) & 0.8075 (0.0234) & 0.7094 (0.0503) & 0.2189 (0.0590) & 0.3238 (0.0908) \\ CNN & 0.6972 (0.0234) & 0.8014 (0.0108) & 0.8264 (0.0300) & 0.2048 (0.0547) & 0.1803 (0.0566) \\ GRU & 0.7019 (0.0353) & 0.7979 (0.0186) & 0.8403 (0.0424) & 0.1873 (0.0749) & 0.1494 (0.0774) \\ C-sGRU & 0.7093 (0.0302) & 0.8018 (0.0175) & 0.8460 (0.0350) & 0.2024 (0.0945) & 0.1617 (0.0924) \\ C-GRU & **0.7222 (0.0290)** & **0.8127 (0.0146)** & **0.8484 (0.0372)** & **0.2670 (0.0609)** & 0.2182 (0.0640) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of the methods in IC classification. Figure 5: Classification results using independent component time series. Convolution-GRU achieved the highest classification accuracy rate of 72.2%, while traditional classifiers performed poorly, and none exceeded 65%. Deep learning models performed similarly in the majority class, however, convolution-GRU performed best in the minority class. The calculated weighted F1 score of each classifier is shown in Table 2. It can be seen that under the weighted F1 score standard, the deep learning model still has great advantages over traditional classifiers. The weighted F1 value of the convolution-GRU network used in this paper reaches 0.6827, while the weighted F1 score of the KNN model is only 0.4801, which is also consistent with our previous analysis.
## 4 Discussion
### Application  
In ML, classification emerges as a vital task, involving the nuanced prediction of target classes for individual data instances [28]. Achieving optimal performance on diverse datasets necessitates the careful selection of suitable individual classifiers. The challenge lies in pinpointing the most suitable data mining or machine learning model tailored to a specific problem. To tackle this complexity, researchers often deploy an array of models to ascertain the utmost performance for a given scenario. AutoML platforms are appealing pre-packaged tools for constructing predictive models using healthcare data [47]. In this study, the AutoML interface was employed to forecast and differentiate the simultaneous co-occurrence of mental health and substance use disorders in women. Notably, AutoML consistently demonstrates enhanced precision and specificity across various research investigations. By harnessing the capabilities of AutoML, the study automated critical tasks encompassing model selection, hyperparameter optimisation, and feature engineering. This streamlined approach simplifies the analytical pipeline and substantiates an elevation in the accuracy of prediction models, marking a promising stride within computational health research.  
Figure 2: Distribution of the performance metric (AUC) of the super learning model.  
A significant novelty of our study represents the statistical analysis using an AutoML interface to predict co-occurring mental and substance use disorders in women. This research serves as a practical demonstration of the presented statistical methods utilising an AutoML interface within a real-world context, offering valuable insights into predictive analytics in health-related domains.  
The research finding showcasing the performance of the super learning model in distinguishing co-occurring mental health and substance use disorders among women carries substantial potential for transformative impact. The super learning model comprised two base learners: a Random Forest model and an XGBoost model, and it outperformed the individual base learners.The super learning model's accuracy in identification would enable early identification of women at risk of co-occurring mental health and substance use disorders. Women who receive substance use treatment that is tailored to their gender experience a longer duration of stay in treatment and have a higher probability of maintaining abstinence after completing treatment [1]. The emergence of more accurate and timely diagnosis has significant consequences for the development of improved treatment techniques, aimed at reducing the complications, illness, and death associated with these disorders [26]. Efficient resource allocation would be facilitated as the model's precision allows healthcare providers to focus on those at elevated risk, ensuring that support and treatment resources are channelled where they can yield the most significant benefits.
## 5 Applications
### Analysis and Design of Refractive Devices and Instruments  
Refractive devices are analysed by geometrical optics in a non-rigorous way. Aberrations, especially high-order aberrations, are generally not taken into account or are very hard to be handled by geometrical optics.  
Higher-order aberrations, such as the 5 th to 14 th Zernike terms (vertical astigmatism, vertical trefoil, vertical coma, horizontal coma, oblique trefoil, oblique sucdrafoil, oblique secondary astigmatism, primary spherical aberration, and vertical secondary astigmatism vertical quadrafoil), can cause difficulty seeing at night, glare, blurring, halos, and starburst patterns, as well as diplopia. Wavefront aberration analysis presents a rigorous way to handle high-order aberrations. Our approach could easily handle Zernike terms, especially when designing refractive devices aimed at minimizing aberrations.
###### Abstract  
Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush-Kuhn-Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.  
energy efficiency maximization; non-orthogonal multiple access systems; Lagrangian multiplier method; inter-cluster dynamic programming +
Footnote †: journal: Applied Sciences
## 4 Numerical Results
### Flow Ripple under Different Design Parameters  
Figure 16 depicts the flow ripple (\(\delta_{\rm q}\)) of the tandem pump (\(\zeta=1\)) under 2000 rpm and 20 MPa regarding different index angles (from 0 to \(\lambda\)), noting that the flow ripple was defined by Equation (25), following the work addressed by [PERSON] et al. [(26)].  
\[\delta_{\rm q}=\frac{Q_{\rm max}-Q_{\rm min}}{0.5\cdot(Q_{\rm max}+Q_{\rm min})} \tag{25}\]  
where \(Q_{\rm max}\) is the maximum flowrate; and \(Q_{\rm min}\) is the minimum flowrate.  
Referring to Figure 16, it was clear and consistent that with respect to a certain index angle, the tandem pump's kinematic flow (\(Q_{\rm kin}\)) yielded a greater ripple while the with-trapped flow (\(Q_{\rm wt}\)) yielded a lower ripple than the actual flow (\(Q_{\rm out}\)). Additionally, as the index angle increased (from 0 to \(\lambda\)), one observed a decrease followed by an increase in the flow ripples of the three types of flow, and the flow obtained the minimum flow ripple when \(\gamma=\lambda/2\) (in red rectangles). Concerning the actual flow (\(Q_{\rm out}\)), the maximum flow ripple was roughly 4.62% when \(\gamma=0\), and the minimum flow ripple was roughly 1.57% when \(\gamma=\lambda/2\).  
Figure 17 depicts the flow ripple of the tandem pump at 20 MPa regarding different displacement ratios (from 0.1 to 1) under the same mean outlet flowrate. The operating speed for the tandem pump with the displacement ratio \(\zeta=1\) was 2000 rpm, and the operating speeds for other displacement ratios were set as Equation (26) for the purpose of maintaining the same mean outlet flowrate.  
\[n_{\zeta=\zeta_{0}}=n_{\zeta=1}\cdot\frac{1+1}{1+\zeta_{0}} \tag{26}\]  
Referring to Figure 17, it can be seen that with respect to a certain index angle (\(\gamma\)), the flow ripple first exhibited a decrease, then followed by an increase as the displacement ratio (\(\zeta\)) increased, and a minimum value of the flow ripple could be achieved by properly selecting the displacement ratio. With respect to a certain displacement ratio (\(\zeta\)), the condition of \(\gamma=\lambda/2\) yielded the minimum flow ripple when \(\zeta\) was greater than 0.4. Hence, as observed, the condition of \(\gamma=\lambda/2\) and \(\zeta=0.5\) yielded the minimum flow ripple under the same outlet pressure and mean outlet flowrate (in red squares), and the related flow ripple yielded 1.41%.  
Figure 16: Flow ripple of the tandem pump (\(\zeta=1\)) under 2000 rpm and 20 MPa regarding different index angles.
## 2 Structural Design of the Platform
### Dense Bead Shaft Joint
#### 2.2.2 Stiffness Chain Analysis  
By analyzing the platform's stiffness chain, the components that affect the platform stiffness are determined and the main links that affect the stiffness of the entire machine are also determined using the finite element simulation method. When a load is installed on the mobile platform, the force transfer block diagram is as shown in Figure 4. The upper offset shaft 1 refers to the upper offset joint shaft connected to the mobile platform, and the upper offset shaft 2 refers to the upper offset joint shaft connected to the leg. The lower offset shaft 1 refers to the lower offset joint shaft connected to the leg, and the lower offset shaft 2 refers to the lower offset joint shaft connected to the slider. The load force is initially transmitted to the slider through the mobile platform and is then transferred from the slider to the base platform via two transmission routes. The order of force transmission in the joint is from the first joint axis \(\rightarrow\) steel ball array \(\rightarrow\) joint seat \(\rightarrow\) second joint axis. The force transmission route in the leg is from the inner shaft of the leg \(\rightarrow\) the steel ball array \(\rightarrow\) the outer seat of the leg.  
To locate the main links among the many factors that affect the overall stiffness characteristics of the entire machine, a mechanical simulation analysis method in which the elastic modulus of each link can be changed is used to perform a comparison study. When the elastic modulus of a component is changed, the ratio of the fundamental frequency of the entire machine to the fundamental frequency of the entire machine at the elastic modulus \(\varepsilon=1\) can be obtained. Then, the contributions of each component to the stiffness of the entire machine were obtained, as shown in Figure 5. The term \(\varepsilon\) refers to the ratio between the modulus of elasticity of a component and its reference modulus of elasticity. According to the preliminary selection of materials for each component, the fundamental frequency (reference fundamental frequency) of the entire machine is calculated, and the elastic modulus of each component is recorded as the reference elastic modulus. The joint shaft stiffness has the greatest influence on the stiffness of the entire machine. Therefore, the joint stiffness is an important indicator in the design process. In addition, for parts with freedoms of motion, such as the joints and legs, improvements should be made in terms of material selection, heat treatment, the machining processes, and the assembly process to improve both the stiffness and the motion accuracy of important components.  
Figure 4: Force transfer analysis: (**a**) Transfer chain of force; (**b**) Block diagram of force transfer process.
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## 3 Hamiltonian Monte Carlo
#### Leapfrog Method:  
[PERSON]'s equations need to be approximated by discretizing time for software execution using some tiny step size, \(\varepsilon\). We do this by assuming that the Hamiltonian has the form \(H(z,p)=U(z)+K(p)\), as in Equation (8), and by assuming that kinetic energy has the form \(K(p)=p^{\mathsf{T}}M^{-1}p/2\), as in Equation (9); moreover, \(M\) is diagonal, with \(m_{1},\ldots,m_{d}\) diagonal components, so that  
\[K(p)=\sum_{i=1}^{d}\frac{p_{i}^{2}}{2m_{i}}\]  
The leapfrog method works as follows:  
1. \[p_{i}(t+\varepsilon/2)=p_{i}(t)-(\varepsilon/2)\frac{\partial U}{\partial z_{i }}(z(t))\]
2. \[z_{i}(t+\varepsilon)=z_{i}(t)+\varepsilon\frac{p_{i}(t+\varepsilon/2)}{m_{i}}\]
3. \[p_{i}(t+\varepsilon)=p_{i}(t+\varepsilon/2)-(\varepsilon/2)\frac{\partial U}{ \partial z_{i}}(z(t+\varepsilon))\]  
For the momentum variables, we begin with a half step, then we perform a complete step for the position variables using the current momentum variables' values, and lastly, we perform another half step for the momentum variables using the current position variables' values.  
HMC Algorithm:  
Each iteration of the HMC algorithm has two steps:  
1. New values are drawn randomly from their Gaussian distribution for the momentum variables, irrespective of the current position variables values.
2. Updating Metropolis is performed using Hamiltonian dynamics to suggest a new state. Starting with the current state \((z,p)\), Hamiltonian dynamics is simulated using the leapfrog method (or some other reversible method that preserves volume) for \(L\) steps, using a step size of \(\varepsilon\). At the end of this L-step trajectory, the momentum variables are then negated, resulting in a suggested state \((z^{*},p^{*})\). This proposed state is accepted as the next state in the Markov chain with probability: \[\min[1,\exp(-H(z^{*},p^{*})+H(z,p))]=\min[1,\exp(-U(z^{*})+U(z)-K(p^{*})+K(p))].\] If the proposed state is not approved (i.e., dismissed), the following state will be the same as the present state.