esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Moon Raw Material Surveying Strategy
### Helium-3 Inventory
The researchers were intrigued by why higher concentrations of helium were observed in the thin near-surface layer than shown by theoretical diffusion models. The disordered atomic packing structure of glass was found to play a key role in capturing and retaining helium. Compared to helium dissolved in the crystal lattice of the lunar soil material, which requires high temperatures to be released, helium contained in bubbles is much easier to extract through the use of mechanical grinding at ambient temperature (e.g., ball mills). It was determined that the upper surface layer of ilmenite is a glassy layer with dense, disordered atomic packing structures, and is approximately 40-50 nm thick. Beneath the glassy layer is an area of partial glass approximately 30 nm thick, followed by a crystalline phase. The glassy layer is formed as a result of irradiation of the surface with high-energy ions/particles from the solar wind or cosmic radiation. The mentioned bubbles [28] have a spherical shape and a diameter of 5-25 nm, and the pressure inside them is estimated at 1 to 39 GPa (for comparison, the pressure on the Moon is 0.3 nPa). The mass of \({}^{3}\)He in the bubbles should be up to 0.26 \(\times\) 10\({}^{9}\) kg, which may constitute from 1/10 to 1/4 of the total \({}^{3}\)He reserves on the Moon. Fusion energy generated by approximately 100 tons of helium-3 could meet the world's energy needs for a year. Thus, the helium retained in the lunar glass bulbs, at current energy consumption, would be enough for humanity for about 2600 years. When looking for easily accessible helium-3 deposits, one should look for places where ilmenite occurs, most of all lunar maria, and that would be strongly exposed to solar wind and cosmic radiation, so that its surface layer becomes glassy, and helium can be stored there in the form of bubbles.
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 2 Preliminaries
### SM4 Algorithm  
As shown in Figure 1, the encryption operation is carried out in the unit of 32-bit wide word, and an iteration operation is called a round, with a total of 32 iterations. Assume that the input \((X_{0},X_{1},X_{2},X_{3})\in Z_{2}^{32}\), round key \(rk_{i}\in Z_{2}^{32}\).  
Figure 1: SM4 encryption process.  
The round function \(F\) can be expressed as follows.  
\[F(X_{i},X_{i+1},X_{i+2},X_{i+3})=X_{i}\oplus\text{T}(X_{i+1}\oplus\oplus X_{i+2} \oplus X_{i+3}\oplus rk_{i}) \tag{1}\]  
Round transformation T: \(Z_{2}^{32}\to Z_{2}^{32}\) is an invertible transformation, which is composed of nonlinear transformation \(\tau\) and linear transformation L, and can be expressed as T(.) = L(\(\tau\).). Nonlinear transformation \(\tau\):\(\tau\) is composed of 4 parallel S-boxes, and S-boxes are the permutation of 8-bit input and 8-bit output, denoted as \(\text{Sbox }(.)\). Assume the input is \(A=(a_{0},a_{1},a_{2},a_{3})\in(Z_{2}^{8})^{4}\), and the output is \(B=(b_{0},b_{1},b_{2},b_{3})\in(Z_{2}^{8})^{4}\). Then \(B\) can be expressed as follows.  
\[B=(b_{0},b_{1},b_{2},b_{3})=\tau(A)=(Sbox(a_{0}),Sbox(a_{1}),Sbox(a_{2}),Sbox( a_{3})) \tag{2}\]  
Linear transformation L: The output of the nonlinear transformation \(\tau\) is the input of the linear transformation L. Let the input be \(B\in(Z_{2}^{32})\) and the output \(C\in(Z_{2}^{32})\), then \(C\) can be expressed as follows.  
\[C=L(B)=B\oplus(B\ll 2)\oplus(B\ll 10)\oplus(B\ll 18)\oplus(B<<24) \tag{3}\]  
The SM4 key extension algorithm is basically the same as the encryption algorithm. Set the initial key of SM4 as \(MK=(MK_{0},MK_{1},MK_{2},MK_{3})\), \(MK_{i}\in Z_{2}^{32}(i=0,1,2,3)\). The round input in the iteration can be represented as \(K_{i}\in Z_{2}^{32}(i\in\{0,1,\ldots,35\})\), \((K_{0},K_{1},K_{2},K_{3})=(FK_{0}\oplus MK_{0},\ldots,FK_{3}\oplus MK_{3})\), \(K_{i+4}=K_{i}\oplus L(\tau(K_{i+1}\oplus K_{i+2}\oplus K_{i+3}\oplus CK_{i}))\), where \(CK=(CK_{0},\ldots,CK_{31})\) and \(FK=(FK_{0},\ldots,FK_{3})\) are fixed parameters of the system (see reference [10] for details). Then the round key \(rk_{i}=K_{i+4}\) (\(i\in\{0,1,\ldots,31\}\)).
###### Abstract  
Peatlands store up to 2320 Mt of carbon (C) on only \(\sim\)20% of the land area in Ireland; however, approximately 90% of this area has been drained and is emitting up to 10 Mt C per year. Gross primary productivity (GPP) is a one of the key components of the peatland carbon cycle, and detailed knowledge of the spatial and temporal extent of GPP under changing management practices is imperative to improve our predictions of peatland ecology and biogeochemistry. This research assesses the relationship between remote sensing and ground-based estimates of GPP for a near-natural peatland in Ireland using eddy covariance (EC) techniques and high-resolution Sentinel 2A satellite imagery. Hybrid models were developed using multiple linear regression along with six widely used conventional indices and a light use efficiency model. Estimates of GPP using NDVI, EVI, and NDWI2 hybrid models performed well using literature-based light use efficiency parameters and showed a significant correlation from 89 to 96% with EC-derived GPP. This study also reports additional site-specific light use efficiency parameters for dry and hydrologically normal years on the basis of light response curve methods (LRC). Overall, this research has demonstrated the potential of combining EC techniques with satellite-derived models to better understand and monitor key drivers and patterns of GPP for raised bog ecosystems under different climate scenarios and has also provided light use efficiency parameters values for dry and wetter conditions that can be used for the estimation of GPP using LUE models across various site and scales.  
carbon flux; eddy covariance (EC); gross primary productivity (GPP); light use efficiency (LUE); peatland; satellite-data-derived models; vegetation indices 2023
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 2 System Model and Problem Formulation
### Problem Formulation  
To formulate the problem of communication overhead reduction in the KCF algorithm, we first make the following assumption.  
**Assumption 1**.: _The network topology keeps unchanged during the estimation processing._  
Let \(J_{i,\text{overhead}}(k)\) denote the communication overhead of node \(i\) in time \(k\), which equals the number of messages received by node \(i\) in time \(k\) under Assumption 1.  
The total communication overhead is  
\[J_{\text{total,overhead}}(k)=\sum\limits_{i=1}^{I}J_{i,\text{overhead}}(k)= \sum\limits_{i=1}^{I}|\text{msg}_{i}(k)| \tag{1}\]  
where \(|\text{msg}_{i}(k)|\) is the number of messages received by node \(i\) in time \(k\).  
Therefore, the problem of communication overhead reduction in the KCF algorithm can be denoted as  
\[\text{min}\sum\limits_{k=1}^{n}J_{\text{total,overhead}}(k)\] (P1)  
where \(n\) is the number of iterations.  
When the number of sensor nodes and the state vector of interested object are given, only the number of iterations will affect the solution of (P1). Obviously, \(n\) is related to the convergence rate of KCF algorithm.  
As described in [5; 22], the estimate error dynamics of the KCF algorithm is a globally asymptotically stable system with a Lyapunov function  
\[V(\mathbf{\eta}(k))=\mathbf{\eta}^{\mathrm{T}}(k)\mathbf{M}(k)\mathbf{\eta}(k)\]and the convergence rate of \(V(\eta)\) is expressed as  
\[\delta V(\eta(k))=V(\eta(k+1))-V(\eta(k))\]  
which is related to the algebraic connectivity of network topology \(\lambda_{2}(\mathcal{G})\). That is,  
\[\delta V(\eta(k))\leq-2\gamma\lambda_{2}(\mathcal{G})\|\overset{-}{\eta}(k)\| ^{2} \tag{2}\]  
According to (2), we can optimize the network topology to improve the convergence rate of distributed estimation. However, since sensor nodes are power-limited, sensor nodes are not preferable to be communicated to nodes far away. In [23], the power relation between an idealized transmitting node and a receiving node behaves quadratically to the communication distance. Therefore, it is essential to constrain the communication range between sensor nodes.  
Hence, the problem formulated in (P1) can be relaxed and converted into  
\[\begin{split}&\max\lambda_{2}(\mathcal{G}^{\prime})\\ &\text{s.t.}|\mathcal{V}^{\prime}|=I\\ &|\mathcal{E}^{\prime}|=K\\ & E(i,\mathcal{N}_{i})\leq D_{i}\end{split}\] (P2)  
where \(E(i,j)\) is the Euclidean distance between nodes \(i\) and \(j\), and \(D_{i}\) is the communication range of node \(i\). Therefore, the algebraic connectivity of network topology is maximized without changing the number of nodes and the number of communication links in the network, in which the constraint on the communication range of two neighboring nodes is incorporated. The optimized network topology is expressed as \(\mathcal{G}^{\prime}=(\mathcal{V}^{\prime},\mathcal{E}^{\prime})\).
## 5 Automated Analysis and QPI
When trained on these features, machine-learning algorithms achieve blind single cell classification with up to 95% accuracy. Indeed, the choice of classification strategy is of vital importance to the outcome of the analysis, as each strategy balances a tolerance for false positives and negatives with computational complexity and speed. Classifiers can be binary, sorting cells into distinct classes, or proportional, assigning a probability to a cell to belong to a specific class. In supervised machine learning, a model classification algorithm is trained using a training sample set consisting of training data labeled, classified, or annotated manually to provide a ground truth. When training is complete, the obtained model is validated on test data of unknown class. The more representative the training data is to the expected test data, the more accurate the classifier In many real-world scenarios, test data may differ significantly from the training set. In the case of building a model for clinical use, this means that each model must be possible to train on samples from one set of patients with known cancer types, while still retaining its classification accuracy on samples from other patients with unknown diagnosis. Achieving this robustness is crucial for any use of machine learning outside of the lab, and although several studies have shown the potential of various classification approaches to distinguish between cells of known types when trained on cells from the same individual [54] or on known cell lines [51], few studies so far have explicitly addressed this problem. Changes detected by image analysis were already visible on day 1 for 0.25 uMetoposide, whereas effects on viability, as detected with spectrophotometry assays, were detected only on day 3 for 5 uM etoposide concentration, leading to the conclusion that the morphological changes observed occur before and at lower concentrations than the reduction seen in cell metabolic activity or viability assays [51]. Moreover, [PERSON] et al. used a low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted and then used to calculate 15 parameters derived from the cellular 3D morphology and texture [54]. In a very recent study, activated macrophages were analyzed and information of both cellular morphology and molecular content were collected with the help of the combination of Quantitative Phase Microscopy (QPM), Raman spectroscopy, and auto fluorescence imaging [101]. Macrophages are immune cells, which are mostly analyzed with immunological methods. However, here the activation state of macrophages could be detected, in comparison with the cytokine secretion and intracellular expression of molecules related to the immune response.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Experiment Procedure  
This study utilized the joysticks, rudder pedals, and throttle controls jointly developed by Thrustmaster and Boeing to emulate the flight dynamics controlling experience of real pilots. Flight scenarios were constructed using the X-Plane11 software. It incorporates 28,000 aerodynamic models to deliver realistic physical simulations and has been certified by the FAA, placing it among the most advanced flight simulation software available [95]. This experimental setup is widely used in scientific research and engineering experiments in the field of simulated flight [96; 97].  
Before starting the experiment, participants were required to undergo unified training to ensure they had mastered the basic skills necessary for simulated flight. During this process, their individual flight ability was also assessed.  
The specific experimental steps are as follows:  
First, we introduce the participants to the experimental procedures, connect the participants to the ECG and ECG electrodes, and adjust the eye-tracking equipment with the participant's cooperation. Then, we start the flight simulation program, as well as the data recording of the eye-tracking device, ECG, EEG, and pressure-sensing seat cushion simultaneously. We the sequentially guide the participant to complete tasks A, B, and C. Throughout the formal experiment, various physiological data of the participants were synchronously collected, and the detection equipment used is shown in Table 2. After completing each type of task, participants need to fill out the NASA-TLX scale to subjectively evaluate their workload.  
The experimental scenario is shown in Figure 2. Throughout the experiment, the environmental variables, such as noise and temperature, were consistently controlled at the same level.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Task** & **Weather and** **Dynamics Simulation** & **Screen Display** & **Experimental Difficulty** & **Overall Workload Level Expectation** \\ \hline A & Noon; & & & \\ Cloudless; & & & & \\ No Turbulence & & & & \\ \hline B & Noon; & & & \\ Scattered Cumulus Clouds; & & & & \\ Some Precipitation and Storms; & & & & \\ Some Turbulence & & & & \\ \hline C & & & & \\ Thick Cumulus Clouds at 1500 m; & & & & \\ Heavy Precipitation and Storms; & & & & \\ Severe Turbulence & & & & \\ \hline \end{tabular}
\end{table}
Table 1: The specific settings for three types of tasks.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 2 Materials and Methods
### Determination of ATP, GTP, and ppGppconcentrations  
Strains of _B. subtilis_ (LK134, for _rm_B P1 and LK135 for Peg) were grown in the MOPS 20 AA medium supplemented with [32P] KH\({}_{2}\)PO\({}_{4}\) (100 \(\mu\)Ci/mL) until early exponential phase (OD\({}_{600}\sim\) 0.3). Samples were taken until 250 min after OD\({}_{600}\sim\)0.3 (time 0). Samples (100 \(\mu\)L) were pipetted into 100 \(\mu\)L 11.5 M formic acid, vortexed, left on ice for 20 min, and stored overnight at \(-\)80 \({}^{\circ}\)C [29]. After microcentrifugation (5 min, 4 \({}^{\circ}\)C) to remove cell debris, the samples (5 \(\mu\)L) were spotted on TLC (thin-layer chromatography) plates (Polygram(r) CEL 300 PEI, purchased from Macherey-Nagel), developed in 0.85 M (for ATP and GTP) or 1.5 M (for ppGpp) KH\({}_{2}\)PO\({}_{4}\) (pH 3.4) and quantified by phosphorimaging. The identities of ATP, GTP, and ppGpp were verified by comparison with commercial preparations of these compounds run in parallel and visualized by UV shadowing [8].  
To determine the relative ATP/GTP concentrations after novobiocin treatment, LK134 was grown to OD\({}_{600}\sim\)0.3 (time 0) in medium supplemented with [32P] H\({}_{3}\)PO\({}_{4}\) (100 \(\mu\)Ci/mL), and at time 5 min treated with novobiocin (5 \(\mu\)g/mL). Samples were taken at time points 0, 5, 10, 20, and 30 min and processed in the same way as above.
### State of the Art
#### 1.1.2 Analytical Robust Stability Analysis  
Recently, many efforts have been made to present advanced robust stability analysis methods for compensating for imperfections in the classical techniques. The most important methods are \(\mu\)-analysis [19; 20; 21], the edge theorem [22], the Lyapunov function, and linear matrix inequality (LMI)-based approaches [23; 24; 25]. A structured singular value or \(\mu\)-analysis is employed to assess the robust stability of an LCL grid-connected synchronverter where uncertainties in control parameters and grid conditions are modeled as unstructured (nonparametric) uncertainties [19]. In this paper, results have confirmed that \(\mu\)-analysis could identify some effects, which might be unclear when performing an eigenvalue analysis. Moreover, \(\mu\)-analysis concluded that the robustness of the synchronverter would be increased under weaker grid conditions (i.e., a grid with higher impedance), which was exactly opposite to the supposed results from eigenvalue analysis [26]. In [22], the stability robustness of a power system with participating wind power plants in the automatic generation control (AGC) task was analyzed by the edge theorem. In contrast to conventional units, the share of wind power systems was uncertain (but bounded), which limited the application of classical robustness analysis methods. The stability analysis of DC microgrids with uncertain constant power loads (CPLs) was investigated in [23; 24]. An uncertain system was modeled as a linear system with polytopic uncertainties on system matrices in this work. Moreover, robustness analysis was performed using the Lyapunov stability theorem combined with the linear matrix inequalities (LMIs).  
These robust stability-analysis methods are interesting and manifest different operational aspects of emerging power electronic-based power systems; however, they are mathematically complex and a high level of expertise is required to apply them. In addition, their application to a larger scale system with multiple uncertainties and disturbances is not easy at all. Moreover, these methods usually consider a system's worst-case scenario,which might have an extremely low occurrence. Therefore, a power system design based on the obtained results might not be cost-effective and efficient. To better utilize existing assets, and at the same time properly incorporate all system uncertainties and disturbances in the stability analysis, probabilistic stability analysis could be considered as a simple and powerful tool [27; 28; 29; 30; 31; 32; 33; 34].
## 3 Numerical Studies
### Attenuation Performance of Forest Trees Forming a Metawedge  
Existing studies have shown that forest trees arranged at different heights have different vibration mitigation zones of frequency [44; 45]. This study extends the investigation to examine the performance of tree grids arranged in a metawedge pattern in mitigating ground vibrations induced by railway traffic.  
As illustrated in Figure 10, a grid consisting of \(N_{x}\) = 20 and \(N_{y}\) = 30 trees is considered, where the tree height gradually decreases from 20 m to 0.75 m along the \(x\)-direction to form a forward metawedge structure (Figure 10a), where the height increases along the positive y-direction, and a reverse metawedge (Figure 10b), where the height decreases along the positive y-direction. Three height increments of \(\Delta h_{\text{t}}\) = 0.25 m, 0.5 m, and 1.2 m are, respectively, applied within the height ranges of [0.75, 3], [3; 8], and [8; 20]. Other geometric parameters of the tree grid remain unchanged, as listed in Table 3. The train load consists of eight harmonic point loads (corresponding to two carriages), and the parameters of soil, the track, and the tree grid are listed in Tables 1 and 2.  
Figure 11 presents the vertical displacement fields at \(z\) = 0 m (ground surface) for three cases under two carriages (eight harmonic point loads) at \(f\) = 22 Hz: (a) a forward metawedge, (b) a reverse metawedge, and (c) the case without trees. It can be seen that both the forward and reverse metawedges produce a significant ground vibration isolation effect, which is substantially more effective than that of the ordinary tree grid with uniform height, as studied in Section 3.1. Figure 12 shows the ARS values at observation points A (a) and C (b) for the forward metawedge, reverse metawedge, and uniform height configurations in the frequency range of 1-80 Hz. It can be observed that both metawedge configurations provide notable attenuation efficiency within 8-80 Hz. Compared with the uniform height tree grid, the mitigation performance in the frequency range of 10-40 Hz is significantly improved, and the mitigation zone is widened. The difference in mitigation performance between the forward and reverse metawedge configurations is relatively small. Figure 13 presents the vertical displacement fields at \(x\) = 0 m for (a) a forward metawedge and (b) a reverse metawedge under the excitation of two carriages (eight harmonic point loads) at \(f\) = 22 Hz. The metawedge captures incident Rayleigh waves and converts them into bulk shear waves, consequently diminishing vibrations in the region behind the tree array and resulting in broadband attenuation efficacy, consistent with the findings by [PERSON] et al. [41].  
Figure 10: (**a**) The forward metawedge and (**b**) the reverse metawedge with 30 columns of trees whose heights increase as the \(y\) coordinates of the tree grow.
## 1 Introduction
Under the same LS channel estimator, the trained network outperforms the ZF and MMSE equalizers, and the whole receiver is more reliable than the online ELM-based detector in both frequency selective channels in the pool and time-variant shallow sea channels. * To obtain the optimal network structure, the block size of an OFDM symbol is associated with the coherence bandwidth. By testing each network with the input sizes in the range of less than the coherence bandwidth, the optimal hyperparameters can be found. The simulations verify the above configurations. The remainder of this paper is organized as follows. Section 2 describes the UWA OFDM system. Section 3 discusses the UWA channels and the traditional detectors for symbols suffering from them. The network structure and training strategy are described in Section 4, while the result discussions of simulations and sea trials are included in Sections 5 and 6. Section 7 concludes our work.