esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Anatomical Measurements  
Average values (aver.), standard deviations (SD), range (min; max) and norm values (aver \(-\) 1.96 \(\times\) SD; aver + 1.96 \(\times\) SD) for each parameter are shown in the Table 1 (M--male, F--female, R--right body side, L--left body side).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Parameter**}} & \multirow{2}{*}{**Aver**} & \multirow{2}{*}{**SD**} & \multicolumn{2}{c}{**Range**} & \multicolumn{2}{c}{**Norm**} \\ \cline{4-7}  & & & & **Min** & **Max** & **Aver –1.96 \(\times\) SD** & **Aver + 1.96 \(\times\) SD** \\ \hline \multirow{8}{*}{no division} & lateral (mm) & 18.74 & 3.2 & 6.68 & 25.44 & 12.46 & 25.01 \\ \cline{2-7}  & medial (mm) & 18.73 & 3.02 & 8.64 & 24.39 & 12.82 & 24.65 \\ \cline{2-7}  & rostral (mm) & 5.99 & 0.9 & 3.57 & 8.24 & 4.22 & 7.75 \\ \cline{2-7}  & caudal (mm) & 8.92 & 1.37 & 5.77 & 11.75 & 6.23 & 11.61 \\ \cline{2-7}  & area (mm\({}^{2}\)) & 157.77 & 39.07 & 45.36 & 328.8 & 81.18 & 234.36 \\ \cline{2-7}  & line-1 longitudinal (mm) & 20.36 & 3.03 & 7.79 & 26.54 & 14.41 & 26.31 \\ \cline{2-7}  & line-2 transverse (mm) & 7.79 & 0.92 & 5.42 & 9.79 & 5.99 & 9.59 \\ \hline \multirow{8}{*}{right body side} & R lateral (mm) & 18.88 & 3.51 & 6.68 & 25.44 & 12.01 & 25.76 \\ \cline{2-7}  & R medial (mm) & 18.67 & 3.26 & 8.64 & 23.59 & 12.29 & 25.06 \\ \cline{2-7}  & R rostral (mm) & 5.99 & 0.99 & 3.57 & 7.99 & 4.06 & 7.93 \\ \cline{2-7}  & R caudal (mm) & 8.86 & 1.30 & 5.83 & 11.19 & 6.31 & 11.41 \\ \cline{2-7}  & R area (mm\({}^{2}\)) & 160.06 & 44.68 & 45.36 & 328.8 & 72.48 & 247.63 \\ \cline{2-7}  & R line-1 longitudinal (mm) & 20.51 & 3.38 & 7.79 & 26.21 & 13.89 & 27.12 \\ \cline{2-7}  & R line-2 transverse (mm) & 7.82 & 0.93 & 5.87 & 9.42 & 6.00 & 9.63 \\ \hline \multirow{8}{*}{left body side} & L lateral (mm) & 18.59 & 2.88 & 6.83 & 25.05 & 12.95 & 24.24 \\ \cline{2-7}  & L medial (mm) & 18.79 & 2.78 & 10.0 & 24.39 & 13.35 & 24.24 \\ \cline{2-7}  & L rostral (mm) & 5.98 & 0.81 & 4.52 & 8.24 & 4.39 & 7.57 \\ \cline{2-7}  & L caudal (mm) & 8.98 & 1.45 & 5.77 & 11.75 & 6.14 & 11.82 \\ \cline{2-7}  & L area (mm\({}^{2}\)) & 155.48 & 32.69 & 48.74 & 234.96 & 91.41 & 219.55 \\ \cline{2-7}  & L line-1 longitudinal (mm) & 20.21 & 2.66 & 9.11 & 26.54 & 14.99 & 25.44 \\ \cline{2-7}  & L line-2 transverse (mm) & 7.77 & 0.92 & 5.42 & 9.97 & 5.97 & 9.57 \\ \hline \multirow{8}{*}{female} & F lateral (mm) & 18.86 & 2.75 & 12.05 & 22.67 & 13.48 & 24.24 \\ \cline{2-7}  & F medial (mm) & 18.92 & 2.60 & 9.92 & 23.4 & 13.83 & 24.01 \\ \cline{2-7}  & F rostral (mm) & 5.85 & 0.77 & 4.0 & 7.5 & 4.34 & 7.36 \\ \cline{2-7}  & F caudal (mm) & 9.04 & 1.46 & 5.77 & 11.75 & 6.18 & 11.89 \\ \cline{2-7}  & F area (mm\({}^{2}\)) & 164.14 & 43.78 & 78.18 & 328.8 & 78.34 & 249.94 \\ \cline{2-7}  & F line-1 longitudinal (mm) & 20.62 & 2.47 & 13.21 & 25.24 & 15.77 & 25.47 \\ \cline{2-7}  & F line-2 transverse (mm) & 7.83 & 0.94 & 5.96 & 9.97 & 5.99 & 9.67 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of anatomical measurement results for the whole examined group.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 5 Conclusions  
This study introduces a probabilistic approach for health diagnosis and prognosis through an adaptive DBN framework. This framework employs DBNs to predict health conditions and is characterized by its capability to adaptively update parameter probability distributions and regenerate new particle samples, while integrating various sources of inference and cognitive uncertainty. It is suitable for structural health diagnosis and fatigue crack growth prediction under uncertain conditions. The particle filtering algorithm is utilized within the DBN for Bayesian inference to calibrate the physical model. The proposed method has been validated through two experimental data examples, confirming its advantages: (1) It can track the evolution of time-related state variables and reduce uncertainty variables in time-independent states (diagnosis). (2) It allows for the self-calibration of parameter probability distributions and prediction structures through real-time updates, forecasting fatigue crack growth and residual life probabilistically (prognosis), demonstrating excellent stability and robustness. (3) Adaptive updating and regeneration of particles prevent particle depletion, rendering the model's outcomes more realistic compared to traditional particle filtering results.  
In addition, the current model also has some limitations for future enhancement. Firstly, the model's capability is limited to tracking single crack damage, overlooking the interaction and combined effects of multiple crack damages. Secondly, the experimental scenarios used in the model are relatively simple. In contrast, the operational environment of aerospace structures is evidently more complex, posing challenges for accurate simulation and prediction.  
Conceptualization, S.C. and M.L.; methodology, S.C. and Z.W. ([PERSON]); validation, S.C. and Z.W. ([PERSON]); formal analysis, Y.M. and Z.W. ([PERSON]); investigation, S.C.; resources, Y.M. and Z.W. ([PERSON]); data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, M.L. and Z.W. ([PERSON]); visualization, S.C. and Z.W. ([PERSON]); supervision, Y.M. and Z.W. ([PERSON]); project administration, Y.M.  
and Z.W. ([PERSON]); funding acquisition, Y.M. and Z.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.
* The authors received funding for this study from the National Key R&D Program of China (2018 YFA0702800) and the National Natural Science Foundation of China (12072056). This study is also supported by the National Defense Fundamental Scientific Research Project (XXXX2018204 BXXX).
* Not applicable.
* The data presented in this study are available on request from the corresponding author.
* The authors declare no conflicts of interest.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 3 Proposed Method
### Network Model  
In our protocol, the nodes are scattered homogeneously and randomly with a uniform distribution in a rectangular field (100 m \(\times\) 200 m). Figure 1 indicates the overall network model, including a description of the uniform random distribution of the nodes in a disaster application such as a forest fire.  
Our method is configured based on the following assumptions, and the location of the sink is considered to be variable according to the evaluation of the algorithm's efficiency in the target application.  
* The nodes are fixed (stationary) in the network.
* Nodes are not location-aware, but the sink is aware of its position.
* All nodes have the same level of energy (E0) initially, and the network is homogeneous [7].
* The minimum RSSI with the value of \(-97\) db is known as the worst received signal [27].
* The number of CHs (k) is considered to be 5% of all nodes (N) [27; 29; 30].
* Nodes always send their data based on Time Division Multiple Access (TDMA), which is enforceable on IEEE 802.15.4/ZigBee standard. Additionally, we use the Timeout MAC (TMAC) protocol because it is compatible with different sleep schedules in WSNs and provides energy efficiency [27; 43].
* The sink is responsible for clustering and routing. Therefore, the proposed method is centralized.
* The energy consumption of the nodes is estimated based on their distance from the sink or CH (by parameter d) [22].  
Figure 1: The network topology of a typical WSN network for disaster management.  
In [22], the authors evaluate the energy consumption model based on the distance between the sender and receiver (d). In this model, the energy dedicated to sending messages is calculated by the length of the messages (L) and the distance between sender and receiver (d) as follows [22]:  
\[\mathrm{E_{Tx}}\left(\mathrm{l,d}\right)=\begin{cases}L.\mathrm{E_{elec}}+L \mathrm{\varepsilon_{fs}.d^{2}}&,if&d\leq\mathrm{d_{0}}\\ L.\mathrm{E_{elec}}+L\mathrm{\varepsilon_{amp}.d^{4}}&,if&d>\mathrm{d_{0}}\\ \end{cases} \tag{8}\]  
In (8), the energy consumption to send one bit of data is calculated by \(\mathrm{E_{elec}}\). Additionally, sending factors of \(\mathrm{\varepsilon_{fs}}\) and \(\mathrm{\varepsilon_{amp}}\) are valued in proportion to the booster model of the sender-radar. In [22], the consumed and dedicated energy of length L (in bits) will be calculated according to Equation (9) to receive each message.  
\[\mathrm{E_{Rx}}=\mathrm{L.E_{elec}} \tag{9}\]  
It is noteworthy that the initial distance d0 is calculated as follows in Equation (10):  
\[d_{0}=\sqrt{\frac{\mathrm{\varepsilon_{fs}}}{\mathrm{\varepsilon_{mp}}}} \tag{10}\]
## 3 Results
### Effects of Different Sugars on EPS Production of B. velezensis VTX20  
One of the important factors that significantly contributes to EPS production is sugar concentration. EPS production by VTX20 was first assessed by altering glucose concentrations in a range of 0-200 g/L. This revealed that the absence of glucose did not result in EPS production (Figure 2A). The EPS yield was in the range of 3.3-12.5 g/L after 24 h when glucose was supplied from 50-200 g/L into the medium. The highest yield of EPS was 12.5 \(\pm\) 0.25 g/L in the medium with 200 g/L glucose.  
Given that the sugar source is important for EPS production [36], different sucrose concentrations were used to obtain a higher yield of EPS for VTX20. As shown in Figure 2A, the yield of EPS experienced a significant and steady upward trend with 0-200 g/L of sucrose. The EPS yield obtained with 50 g/L sucrose was 17.7 \(\pm\) 3.4 g/L and was the lowest EPS production by VTX20. Differing from glucose, increasing sucrose concentrations significantly enhanced EPS productivity. The highest EPS yield of 65.1 \(\pm\) 4.9 g/L was achieved when 200 g/L sucrose was utilized, which was 5.2-fold higher than that of glucose. In addition, incubation time was also taken into consideration, choosing 200 g/L sucrose as the supplement to the medium. _B. velezensis_ VTX20 grew well and rapidly produced EPS after 12 h (49.8 \(\pm\) 2.6 g/L) (Figure 2B). Observed EPS production slightly increased  
Figure 1: EPS production observed in six selected bacterial strains.  
Figure 2: EPS production by _B. velezensis_ VTX20. **(A)** Effect of glucose and sucrose concentrations on the production of EPSs. **(B)** Time course of EPS production in the medium supplemented with 200 g/L sucrose. The statistics were calculated using a Student’s unpaired two-tailed \(t\)-test using the GraphPad Prism software. Symbols are: ***\(p\leq\) 0.01, ***\(p\leq\) 0.001, and ***\(p<\) 0.0001.  
within 12 to 48 h of incubation time. The highest EPS yield was recorded in the stationary phase reaching a maximum (75.5 \(\pm\) 4.8 g/L) at 48 h. As the incubation time increased, the production of EPS decreased slightly. These results proved that EPS secreted by _B. velezensis_ VTX20 was sugar- and dose-dependent. Sucrose is an efficient carbon source, significantly promoting EPS yields.
###### Abstract  
Under seismic loading conditions, the backfill soil behind retaining walls does not fully reach the limit state, while seismic earth pressure is influenced by wall displacement. The RB (rotation about the base) displacement pattern represents a prevalent deformation mode in retaining walls during operational service. To calculate the seismic non-limited active earth pressure under RB mode, this study first establishes the relationship between critical horizontal displacement (corresponding to a fully mobilized wall-soil interface friction angle) and depth based on numerical simulations, revealing a linear correlation. Subsequently, nonlinear distribution relationships for the mobilized soil internal friction angle and wall-soil interface friction angle with wall-top displacement are derived. Building upon this foundation and considering the failure mechanism of backfill soil under RB displacement, the soil mass is divided into inclined slices. A pseudo-static analytical framework is proposed to calculate both the magnitude and application point of non-limited seismic earth pressure for rigid walls under RB displacement. Validation against experimental data from referenced studies demonstrates the method's rationality. Earth pressure transitions from an initially concave triangular distribution to a linear pattern as displacement progresses. The application point descends from the initial at-rest position (\(1/3\,H\)) with increasing wall-top displacement, subsequently rising as the soil approaches full active limit states, ultimately stabilizing at \(1/3\,H\) under linear pressure distribution. The parameter sensitivity analysis section summarizes that the horizontal seismic coefficient dominates influencing factors, followed by wall displacement, while soil internal friction angle and soil-wall interface friction angle exhibit relatively minor effects. These findings provide critical insights for optimizing seismic design methodologies of retaining structures.  
Keywords:retaining wall; rotational movement about the base; non-limit active earth pressure state; earthquake; mobilized friction angle
## EUV Beamline Experimental Characterization  
### HHG EUV Spectra  
The HHG EUV emission spectra from Argon (Ar) and Neon (Ne) were measured by scanning the gratings of the monochromator. The EUV radiation is measured with the absolutely calibrated NIST EUV diode, placed after the slit of the monochromator. The Ar harmonics are generated with 1.8-mJ pulse energy, focused through a lens with focal length f = 500 mm on the pulsed gas jet, operating at 1 kHz repetition rate. Figure 7a shows the photon flux in the HHG spectrum of Ar. The measured flux is above 10\({}^{10}\) ph/s for all the harmonics in the range H9-H25, with a maximum emission of 5.5 \(\times\) 10\({}^{10}\) ph/s at H17 (26.4 eV), corresponding to 0.23 nJ/pulse at 1 kHz repetition rate. The flux emitted by the EUV source (before the monochromator) can be estimated to be \(\sim\)2.5 \(\times\) 10\({}^{11}\) ph/s at H17, taking into account the monochromator spectral transmission [23]. The Ar spectrum photon energy ranges from 10 eV to 45 eV. The HHG spectrum of Ne is obtained using higher pulse energy \(\sim\)3 mJ/pulse, and shorter focal length f = 300 mm. Figure 7b shows the HHG spectrum of Ne with photon energy extending to \(>\)H45 (70 eV). A maximum photon flux of 3 \(\times\) 10\({}^{8}\) ph/s is measured at the H29 (45 eV). The EUV flux measurements show long-term stability over at least 48-h operation, allowing long TR-Spin-ARPES data acquisition runs.  
Figure 7: HHG spectra of (**a**) Ar and (**b**) Ne, generated with the fundamental TiS laser \(\lambda\) = 800 nm (1.55 eV). The absolute photons flux is measured after the monochromator slit.  
The HHG EUV spectra of Ar and Ne were also produced with 400-nm driving laser pulses [47]. The 1.2-mJ 400 nm pulses are obtained through second harmonic generation of fundamental laser and then focused on the gas jet with a f = 300 mm lens. Figure 8a shows the HHG spectrum in Ar, which has a maximum photon flux of 1.2 \(\times\) 10\({}^{11}\) ph/s at H7 (21.7 eV). Figure 8b shows the HHG spectrum in Ne, with a maximum photon flux of 5.5 \(\times\) 10\({}^{9}\) ph/s at H15 (46.5 eV). The EUV source excited by 400-nm laser can emit higher photon flux, due to the wavelength-scaling effect, and also provide higher energy resolution, which is useful for TR-Spin-ARPES measurements. The spacing between harmonics is increased (Figure 8). Ref. [48] takes advantage of this larger spectral spacing to replace the monochromator with an Al filter which transmits mainly H7 (21.7 eV) with bandwidth of ~150 meV.
# Quadrotor Formation Strategies Based on Distributed Consensus and Model Predictive Controls
###### Abstract  
In this study, the distributed consensus control and model predictive control (MPC)-based formation strategies for quadrotors are proposed. First, the formation-control problem is decoupled into horizontal and vertical motions. The distributed consensus control and MPC-based formation strategy are implemented in the follower's horizontal formation control. In the horizontal motion, the leader tracks the given waypoints by simply using the MPC, and generates the desired formation trajectory for each follower based on its flight information, predicted trajectory, and the given formation pattern. On the other hand, the followers carry out the formation flight based on the proposed horizontal formation strategy and the desired formation trajectories generated by the leader. In the vertical motion, formation control is carried out using only the MPC for both the leader and the follower. Likewise, the leader tracks the desired altitude/climb rate and generates the desired formation trajectories for the followers, and the followers track the desired formation trajectories generated by the leader using the MPC. The optimization problem considered in the MPC differs for the horizontal and vertical motions. The problem is formulated as a quadratic programming (QP) problem for the horizontal motion, and as a linear quadratic tracker (LQT) for the vertical motion. Simulation of a comprehensive maneuver was carried out under a Matlab/Simulink environment to examine the performance of the proposed formation strategies.  
Quadrotor formation control; consensus control; model predictive control; linear quadratic tracker; quadratic programming Article
# Advancing the Solar Radiation Pressure Model for BeiDou-3 IGSO Satellites
## 3 Results
### Performance of SRP Models for BeiDou-3 IGSO Satellites
#### 3.2.1 Orbit Assessment Based on Estimated Clocks
The signal presented in the BeiDou-3 IGSO clock fitting residuals (Figure 6) can be also observed in the Galileo FOC (Full Operational Capability) satellites during eclipse seasons [37]. Previous studies confirmed that the deterioration of Galileo FOC clocks estimates was attributed to the unaccounted thermal radiations from the spacecraft body, and this modeling deficiency can be compensated by the estimation of the \(D_{s}\) parameter during eclipse seasons and also keeping \(D_{s}\) active during Earth's shadow transitions [22; 23; 37]. We found that adding the \(D_{s}\) term in the ECOM1 + BW model and keeping it active in Earth's shadows., i.e., the ECOM1D + BW model, is very effective for eclipsing BeiDou-3 IGSOs. The mean of daily clock residual RMS of eclipsing BeiDou-3 IGSOs for the ECOM1D + BW solution is given in Table 5. Compared with the ECOM1 + BW solution, the clock residual RMS of eclipsing BeiDou-3 IGSOs decreases by 0.5 cm (13.3%) on average. The elevated clock residual RMS is well reduced, and the RMS statistics inside and outside the eclipse season are on the same level (Figure 6). To further highlight that the clock estimates benefit from the consideration of the \(D_{s}\) term, the clock residuals of satellite C39 on DOY 74-75, 2021 (\(\beta\approx-1.8^{\circ}\)) for the ECOM1 + BW and ECOM1D + BW solutions are shown in Figure 9. It can be seen that the stability of clock estimates based on the ECOM1 + BW model does not conform to the characteristics of PHM clocks on BeiDou-3 IGSOs. When switching the ECOM1 + BW model to the ECOM1D + BW model, the clock residuals become very stable. The above results clearly indicate that the orbit modeling defects of eclipsing BeiDou-3 IGSOs can be covered by the introduction of the \(D_{s}\) term also active in Earth's shadows.
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
A method was described for generating plan-like, reflexive obstacle avoidance behavior in a mobile robot in [8]. The paper showed experiments using a simulated vehicle with a primitive range sensor. As the vehicle explored its surroundings, it adapted its responses to sensory stimuli so as to minimize the negative reinforcement arising from collisions. The problem of a mobile robot learning to navigate an unknown environment while avoiding collisions was considered in [9]: an uncertainty-aware model-based learning algorithm that estimates the probability of collision together with a statistical estimate of uncertainty. In order to learn collision avoidance, the robot must experience collisions at training time. The vehicle accelerates in velocity in settings where it has high confidence. In [10], a tailored design of state and action spaces and a dueling deep Q-network (a deep RL network for autonomous navigation and obstacle avoidance) were proposed for unmanned surface vehicles. In [11], a method for enabling a quadrotor equipped with a monocular camera to autonomously avoid collisions with obstacles in unstructured environments was given. They proposed a deep RL-based method for obstacle avoidance that uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered without collisions. The paper [12] focused on the application of RL to obstacle avoidance in dynamic environments for the control of a mobile robot. An intelligent controller was proposed by integrating RL with the behavior-based control architecture and applied to the obstacle avoidance. The problem in which many autonomous systems are designed with a strong reliance on black box predictions from deep neural networks, which tend to be overconfident about unseen data, was considered in [13]. The importance of predictions that are robust to this is evident for safety-critical applications, such as collisions. The paper used an RL framework to form uncertainty-aware navigation around pedestrians. Our aim in this paper is to use RL in the planning layer of the architecture, to produce a trajectory between two orientations that maintains the spacecraft in the pointing set throughout the maneuver. The approach combines and extends that described in [5]; we describe the system kinematics on \(SO(3)\), and then formulate the problem as an optimal kinematic control problem on a matrix Lie group. An application of [PERSON]'s maximum principle (PMP) gives the necessary conditions for optimality in the form of extremal equations, which as in [5] contain a number of unknown parameters. RL is applied to augment the shape of the curve to avoid a forbidden region, by selecting the value of the parameter such that the path remains in the pointing set. These amended parameter values are then used to construct a new trajectory that maintains the satellite in the pointing set.