esa-sceva/satcom-chunk-collection huggingface dataset:
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
### Challenges in Development of Separation Methods for Lithium Isotopes Using Electrochemical
#### 2.4.1 Analysis Methods for Lithium Isotopes Using ICP-MS Methods  
ICP-MS methods have been applied in various studies for Li isotope determination [45; 46]. Many of these methods are using inductively coupled plasma-quadrupole mass spectrometry methods (ICP-Q-MS) [45].  
The main challenges of the lithium isotope measurements with ICP-MS are changes of ionization equilibria in the plasma due to ionization of matrix interferences (like Na, Ca, and K). Thus, to reduce the matrix bias effects it is necessary to achieve quantitative separation of lithium from other interfering elements [32; 47]. Nevertheless, because of the small separation factor of the Li\({}^{+}\) and Na\({}^{+}\) ions, the separation of lithium from sodium is difficult to achieve. As many clean-up methods are based on the ion exchange chromatography, small columns are the option of choice, due to the fraction of lithium isotopes on large columns [17]. [PERSON] et al. used ICP-Q-MS to determine the lithium isotope ratios from minerals with a precision of \(\pm\)0.8% (relative to reference material IRM-016 Li\({}_{2}\)CO\({}_{3}\)) [45]. [PERSON] et al. determined lithium isotope ratios from seawater and naturally occurring carbonates, using chromatographic separation of lithium isotopes using a low volume-column filed with cation exchange resin (with 100-200 mesh size and total capacity of 3.4 meq) followed by ICP-Q-MS analysis [17]. The average lithium isotope composition of seawater was 30.75 \(\pm\) 0.41% vs. L-SVEC (\(n=10\)), while for the foraminifera carbonates the recorded isotope compositions were 30.72 \(\pm\) 1.43 % (\(n=5\)) for coretop foraminifera from Caribbean Sea and 30.16 \(\pm\) 1.37% (\(n=4\)) for Gulf of Mexico foraminifera, respectively [17]. Therefore, it was concluded that a precision higher than \(\pm\)1.5% vs. L-SVEC could be achieved for real samples [17]. [PERSON] and [PERSON] optimized the determination of lithium isotope ratios from geological materials with ICP-Q-MS [48]. In this study, a higher precision of \(\pm\)1.5% vs. L-SVEC was achieved in conditions of low whole-procedure blank (<0.004 ng) and high matrix tolerance (Na/Li ratio of 100), which allows for low mass consumption (2.5 ng of Li) [48].  
Very recently, [PERSON] et al. developed an inductively coupled plasma-triple quadrupole mass method QQQ-ICP-MS [49]. Using a low volume column filed with cation exchange resin for chromatographic separation, [PERSON] et al. determined the lithium ratio of the seawater to 31.34 \(\pm\) 0.56% (\(n\) = 49), which falls within the values determined previously by [PERSON] et al. [17]. Comparing with ICP-Q-MS methods, this studied achieved a higher precision of \(\leq\)0.6% (2\(\sigma\)) at sub-ng levels of Li per sample [17]. Regarding the separation studies of lithium isotopes, ICP-Q-MS analysis has been used in few studies [36; 38], although MC-ICP-MS methods were preferred.
## 4 Results and Discussion
### Simulation of Different Construction Methods
#### 4.1.2 Construction Impact Stress Analysis
of the pipe material, micro-cracks may develop in the pipe, which may expand over time and eventually lead to pipe rupture. The maximum shear stress caused by the rotary drilling method is 2.71 MPa, which is much smaller compared to the traditional percussion drilling method and is lower than the allowable pressure of the pipeline design. The two construction methods affect the Lei comparative diagram, as shown in Figure 16.  
In summary, rotary piling construction has obvious mitigation effects in terms of both displacement and stress. From a displacement control point of view, the disturbance to the surrounding soil by rotary drilling is significantly smaller than that of percussive piling. In rotary drilling, the continuous rotation of the drill bit acts on the soil, resulting in less lateral displacement, which reduces the displacement and settlement of the neighboring soil layers. In contrast, conventional percussive piling, due to its intermittent impact force action [29; 30], often results in larger lateral displacement and vertical settlement of the surrounding soil, increasing the risk of displacement of neighboring oil and gas pipelines or important buildings. Secondly, in terms of stress distribution, the continuous force of the rotary piling method is more uniform, avoiding stress concentration due to the impact force. Percussive piling generates large dynamic stress waves in the soil around the pile during the construction process [31], which may lead to loosening and structural changes in the soil, thus affecting the overall bearing capacity and stability. In contrast, the rotary drilling  
Figure 16: Impact diagram of the two construction methods compared to the design. Figure 15: (a) Maximum shear stress cloud of oil pipeline for rotary drilling method; (b) maximum shear stress cloud of oil pipeline for traditional percussion drilling method. method maintains the continuity and consistency of the soil structure through a smooth rotary action, which reduces the soil damage caused by stress concentration.
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 2 Methodology
### Record Selection  
The PEER Next Generation Attenuation (NGA)--West 2 Project [43] has been used to define the main database of 210 ground motions. In addition, two ground motions representing the 2016 Norcia earthquake, with epicentral distances of 4.6 km and 26.9 km,respectively, have been extracted from the Italian Accelerometric Archive [44] and included in the aforementioned database. A set of 36 as-recorded ground motions listed in Table 1 has been defined with an average shear wave velocity to a depth of 30 m (\(V_{s,30}\)) falling between 213 m/s and 724 m/s, therefore corresponding to the types of mass B' or C' (according to Eurocode 8 [45]) which are mixed into the set. This latter comprehends highly scattered values of magnitude M\({}_{\rm w}\), ranging from 5 to 7.5, and Joyner-Boore distance included between 0 km and 50 km. The set includes about 50% of near-fault (19 records), ranging from an epicentral distance of 0-10 km, and 50% of far-field records (17 records), or records with an epicentral distance greater than 10 km. The selection exhibits a prevalence of three fault mechanisms: normal, reverse, and strike-slip. Since the frame model used in this study is 2D (see Section 3), two orthogonal directions of the same seismic event are avoided. Record selection also comprehends a wide range of _IM_ and distributed values of \(DCR_{IS}\), with at least one-third of the values greater than 1 [23]. The original set of 36 records was split into two subsets depending on the epicentral distance as shown in Figures 1 and 2, and these were studied separately to investigate the different effects of near-fault versus far-field ground motions. It is well known that the proximity to the fault renders the same ground motions (NF) different from ordinary (FF) ground motions [46]. The near-fault records selection avoids including pulse-like ground motions. Impulsive signals have been identified using the open-source algorithm proposed by [PERSON] and [PERSON] [47; 48]. This can identify pulses at arbitrary orientations using continuous wavelet transforms of two horizontal orthogonal components of a ground motion to identify the orientation that may contain a pulse.
## 5 Conclusions  
Based on GPP measurements from four ChinaFLUX forest ecosystem sites, this study developed an efficient and effective GPP model by comparing various RGSA-optimized algorithms and different variable combinations. With the optimal estimation method selected, this method was applied for inversion analysis on the observed GPP data. In addition, the performance of the selected model was evaluated by comparing the ML-based GPP with RS-based GPP products. The XGB model optimized with RGSA hyperparameters (Section 3.2) showed the highest accuracy (\(R^{2}\) = 0.81, \(RMSE\) = 1.29 g Cm\({}^{-2}\) d\({}^{-1}\), \(MAE\) = 0.97 g Cm\({}^{-2}\) d\({}^{-1}\)) in GPP simulations, achieving a site-scale correlation coefficient of 0.92. Compared to the model without hyperparameter tuning, \(R^{2}\) improved by 5.19% and \(RMSE\) decreased by 9.15% (Figure 10), significantly enhancing predictive accuracy. Using the variable combination of LAI, Temp, NR, VPD, and NDVI, the model achieves superior GPP estimation performance (\(R^{2}\) = 0.83, \(RMSE\) = 1.23 g Cm\({}^{-2}\) d\({}^{-1}\), \(MAE\) = 0.92 g Cm\({}^{-2}\) d\({}^{-1}\)). Compared to using all variables, \(R^{2}\) increased by 2.47% and \(RMSE\) decreased by 4.65% (Figure 10). The GPP data obtained in this study show high spatial consistency with existing RS products (GLASS GPP, GOSIF GPP, and FLUXCOM GPP), accurately capturing seasonal GPP variations in the forest ecosystems. This demonstrated excellent quality and reliability of the developed ML-based GPP model in this study. Overall, this study provides essential technical support for GPP estimation at both methodological and application levels, guiding future research. The findings significantly advance the capacity to quantify climate change effects on forest GPP through the implementation of RGSA, enabling enhanced carbon flux estimations and providing insights into global carbon cycle mechanisms.  
**Author Contributions:** Conceptualization, Q.N. and Q.L.; Data curation, R.G.; Investigation, G.B. and X.L.; Methodology, Q.N.; Supervision, Q.L.; Validation, J.X., X.L. and R.G.; Writing--original draft, Q.N.; Writing--review and editing, Q.L. and J.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42461049).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to privacy.  
**Acknowledgments:** The authors would like to thank the editors and the anonymous reviewers for their crucial comments, which improved the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Experimental Data
#### 2.5.2 Simulated Data  
In this paper, the performance of EONND was tested on real and simulated EMG signals. The simulated data was able to compensate for the lack of quantity of real data and the deviation of data labels. More importantly, the parameters of the simulated data could be adjusted to suit the experimental purpose, such as SNR. However, simulating EMG signals is very difficult because it is not possible to accurately describe all the physiological, anatomical, and physical properties of EMG signals [54]. Since the EMG signal model is generally simplified as the superposition of a noisy signal (baseline segment) and EMG  
Figure 5: Schematic of the signal acquisition method and the raw EMG data with labels. (**a**) The simulated EMG signal with precise labels and (**b**) the real EMG signal with rough labels. The green vertical line is the ground truth of the onset points, and the red vertical line is the ground truth of the offset points. The left part of the figure illustrates the data source.  
signal (activity segment), the EMG signal change-point detection can be described as a source separation issue [55].  
Based on the objectives of the experiment, a simple EMG signal model was simulated using NeuroKit2 (a Python toolbox for signal processing in neurophysiology) [56], which was as follows:  
\[Simulated\ EMG=S_{activity}+S_{noise}, \tag{15}\]  
where S\({}_{activity}\) represents the electrical activity generated by each muscle during contraction, and S\({}_{noise}\) denotes the background noise generated mainly by the neighboring muscles, the friction between electrodes and skin, and the ambient noise [31; 57; 58]. As illustrated in Equation (15), the simulated EMG signals with different SNR values could be obtained by changing S\({}_{noise}\) while keeping S\({}_{activity}\) constant. Ten different values of SNR were simulated (SNR = 14.41, 14.12, 12.51, 10.59, 8.91, 7.52, 6.38, 5.45, 4.69, and 4.05 dB). For each SNR value, 90 active segments were simulated at one time, for a total of 10 simulations. The simulated EMG signal is shown in Figure 5a.  
Therefore, a dataset consisting of 4500 real and 9000 simulated activity segments was created. It should be noted that the labels of the real EMG signals in Figure 5b are described as 'rough' labels, as the responsiveness of the experimental device and the steering wheel (ignoring the physiological electro-mechanical delay) determines the temporal bias of the real data labels.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 5 Scenario and Simulation Report
### Scenario 2: SD-WAN Simulation
Physical and medium access losses are two categories of packet loss sources. Physical losses can be caused by signal degradation, coexistence with other technologies, and interference from other networks or devices. Buffer overflows and collisions brought on by competition in the medium or hidden terminals might result in medium access losses. Therefore, every ECS should be examined and simulated as a unique scenario. As mentioned, ECSs are network-based systems that help people exchange information during crises and physical disasters and are able to restore communication when telecommunication networks fail and in off-grid areas. Therefore, in our simulation scenarios, we mostly assume LOS 4G/LTE and LPWAN networks. For VoIP applications, other simulation scenarios must be made. To summarize, the simulation reports, combined with the results from the experimental scenario, prove that the proposed low-cost and open-source architecture can be used for an easy-to-deploy hybrid ECS that is able to send and receive data in emergencies. Moreover, it can be valuable for the command and control in off-grid industrial buildings and critical infrastructures. Figure 9: (**a**) Received Baseband LTE Signal Spectrum; (**b**) Transmitted and Received Image.
###### Abstract  
Super-resolution (SR) technology is essential for improving image quality in magnetic resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency (HR) details from a low-resolution (LR) image. To address this challenge, we develop a gradient-guided convolutional neural network for improving the reconstruction accuracy of high-frequency image details from the LR image. A gradient prior is fully explored to supply the information of high-frequency details during the super-resolution process, thereby leading to a more accurate reconstructed image. Experimental results of image super-resolution on public MRI databases demonstrate that the gradient-guided convolutional neural network achieves better performance over the published state-of-art approaches.  
Keywords:magnetic resonance imaging; super-resolution; gradient information; convolutional neural network +
Footnote †: journal: Applied
# Quantum Implementation of AIM: Aiming for Low-Depth
## 4 Post-Quantum Security Evaluation of AIM
While it may not be unfair, when compared to NIST's estimates [10] based on [PERSON] et al. 's quantum circuit implementation of AES [16], AIM-I, -III, and -V cannot achieve Level-1, -3, and -5, respectively. This is because the cost of implementing AES quantum circuits in [16] is high, which in turn makes NIST's estimated costs for each level overly conservative [10]. Recently, NIST adjusted the [PERSON]'s key search costs for AES variants [11] based on [PERSON] et al's work [14]. Therefore, we use these adjusted costs for our evaluation (Note that these costs have an issue of underestimation, as discussed in [13]). Additionally, we also consider the work of [PERSON] et al. [13] for our evaluation. They presented the lowest [PERSON]'s key search costs for AES (with their depth-optimized circuits); AES-128: \(2^{156}\), AES-192: \(2^{221}\), AES-256: \(2^{286}\). As of now, since these costs are the lowest and do not have any issues of underestimation, we incorporate these costs into our evaluation as well. As a result of comparing the [PERSON]'s key search costs from [11; 13; 14] with the [PERSON]'s key search costs of AIM, AIM-I, -III, and -V reliably achieve Level-1, -3, and -5 (post-quantum security), as \(2^{160}\), \(2^{227}\), and \(2^{292}\) are greater than \(2^{157}\), \(2^{221}\), and \(2^{285\text{ or }286}\), respectively (see Table 7). Additionally, we can consider the required number of qubits for [PERSON]'s key search. Although NIST does not consider qubit counts as a key metric for estimating attack complexity (considering the limit of depth rather than the limit of qubit counts), the qubit count is certainly a significant metric. The estimated costs of [PERSON]'s key search for AIM require a large number of qubits (more than AES) and have a higher attack complexity than AES (for all variants). In this context, we evaluate that AIM can achieve the appropriate post-quantum security for the key sizes (i.e., Level-1, -3, and -5 for 128, 192, and 256-bit keys), and the required number of qubits for an attack is also high. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cipher** & **Total Gates** & **Total Depth** & **Cost** & **\#Qubit** & \(TD\times M\) & \(FD\times M\) & **For Parallel Search** \\ \hline \multicolumn{8}{c}{**(Complexity)**} & \multicolumn{4}{c}{\(TD^{2}\times M\)} & \(FD^{2}\times M\) \\ \hline AIM-I & \(1.612\times 2^{83}\) & \(1.342\times 2^{76}\) & \(1.082\times 2^{160}\) & 25,300 & \(1.351\times 2^{82}\) & \(1.036\times 2^{91}\) & \(1.182\times 2^{150}\) & \(1.39\times 2^{167}\) \\ AIM-III & \(1.306\times 2^{117}\) & \(1.646\times 2^{109}\) & \(1.075\times 2^{227}\) & 88,396 & \(1.517\times 2^{116}\) & \(1.110\times 2^{126}\) & \(1.707\times 2^{216}\) & \(1.827\times 2^{235}\) \\ AIM-V & \(1.645\times 2^{149}\) & \(1.616\times 2^{142}\) & \(1.33\times 2^{292}\) & 108,073 & \(1.752\times 2^{148}\) & \(1.332\times 2^{159}\) & \(1.862\times 2^{280}\) & \(1.076\times 2^{302}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Costs of the [PERSON]’s key search for AIM.
## 3 Methodology
### Problem Formulation  
The path planning problem is defined as finding a collision-free path from a starting position to a target position in an environment with obstacles. In reinforcement learning, the path planning problem can be expressed as a Markov Decision Process (MDP) with the following model: \((S,A,P,R)\)[39]. \(S\) denotes the state space, \(A\) denotes the action space, \(P\) denotes the probability of a state transition, and \(R\) denotes the reward function. For path planning, at each discrete time step \(t\), the agent collects the state \(s_{t}\in S\) and selects \(a_{t}\in A\) according to the strategy \(\pi\) in the action space. Then, the current state transition to the new state according to the state transition probability \(p(s_{t+1}\mid s_{t},a_{t})\). Reward \(r(s_{t},a_{t})\) is given based on the effect of the action. The total reward of a state \(R_{t}\) is defined as the sum of future rewards when considering the future \(T\) under the discounting factor \(\gamma\in[0,1]\):  
\[R_{t}=\sum_{i=t}^{T}\gamma^{(i-t)}r(s_{i},a_{i}). \tag{1}\]  
The action-value function describes the expected return following the acquisition of \(a_{t}\) in accordance with \(\pi\). As an evaluation index of the current action, it can be expressed recursively as:  
\[Q^{\pi}(s_{t},a_{t})=\mathbb{E}_{r_{t}s_{t+1}\sim E}\big{[}r(s_{t},a_{t})+ \gamma\mathbb{E}_{a_{t+1}\sim\pi}[Q^{\pi}(s_{t+1},a_{t+1})]\big{]}. \tag{2}\]  
The task of reinforcement learning is to maximize the \(Q\), i.e., to seek the most profitable action in the current state.
## 2 Path Planning Algorithm Based on the Markov Decision Process (MDP)
### Value Iteration Method  
The general solution of the MDP algorithm can be obtained through the value iteration or strategy iteration [26]. The value iteration method can give the optimal strategy from an arbitrary point to the target, which is suitable for the real-time control of AUVs. Its abbreviated process is described below.  
In Algorithm 1, we use the greedy strategy to compute \(V_{n}(s^{\prime})\), i.e., assuring the maximum reward in every iteration. And then the Bellman Equation (5) is used to solve \(V(s)\). \(\pi(s)\) is solved using the value iteration method. It turns out that the solution is convergent to the optimal strategy \(\pi^{*}(s)\)[27].  
```
INPUT: MDP five-tuple (\(S,A\), { \(p_{s,\pi}^{a}\)}, \(\gamma,R\)), max iteration number \(N\), deviation \(\varepsilon\) ITERATION: \(\forall s\in S,V_{0}(s)=0\) for n in range (1, \(N\)): for each state \(s\) do: \[V_{n+1}(s)=\text{max}_{a\in A}\sum_{s^{\prime}}P(s^{\prime}|s,a)\left(R(s,a)+ \gamma V_{n}(s^{\prime})\right)\] (6) if \(\forall s\mid V_{n+1}(s)\ -V_{n}(s)\mid<\varepsilon\): break for each state \(s\) do: \[\pi(s)=\text{argmax}_{a\in A}\sum_{s^{\prime}}P(s^{\prime}|s,a)\left(R(s,a)+ \gamma V_{n}(s^{\prime})\right)\] (7) OUTPUT: \(\pi(s)\)
```  
**Algorithm 1**The Value Iteration in the MDP Algorithm
## 2 Data and Methods  
### Data  
In this study, three case images without cloud or snow downloaded in Geospatial Data Cloud [23] were used of which two were obtained in summer when vegetation is lush and one was obtained in winter when vegetation is witnessed and bare soil is widely distributed. The image sizes are 480 rows \(\times\) 480 columns with a pixel size of 30 m, and their (5, 4, 3) false colour composites are shown in Figure 1.  
Typical ground objects in these case images include vegetation, bare soil, water body, and impervious surfaces, and present more representative and complexely. In the (5, 4, 3) false colour composite image with a 2% linear stretch, the vegetation is red in summer such as Cases 1 and 2 and easily distinguished from impervious surfaces because of their different spectral features; bare soil is exposed in winter, such as in Case 3, and is easily confused with impervious surfaces because of their similar spectral features.
###### Abstract  
The use of continuous-thrust propulsion systems allows spacecraft to cover complex space trajectories and to complete missions that would be difficult using chemical thrusters. Among the continuous-thrust propulsion systems proposed in recent decades, solar electric thrusters occupy an important position thanks to the maturity reached by this technology. Technological advances in the miniaturization of spacecraft components allow an electric thruster to be installed even in a small and standardized vehicle such as a CubeSat. In this context, the BIT-3 RF ion thruster is an interesting option that has been recently employed in some space missions for the study of the lunar surface. In the recent literature, the performance of a CubeSat equipped with a propulsion system based on the BIT-3 has been studied considering a simplified model in which the thrust magnitude has a fixed value or varies continuously within a prescribed range. However, the operating levels of a BIT-3 are finite in number. This paper studies the transfer performance of a BIT-3-propelled CubeSat considering the actual operating levels that can be provided by such a thruster. The work analyzes the optimal transfer towards asteroid 2000 SG344 when the electric power is obtained through solar arrays.  
BIT-3 RF ion thruster; CubeSat; solar electric thruster; spacecraft trajectory design; low-thrust orbit transfer; optimal control law; asteroid 2000 SG344. Article