esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials, Equipment and Context  
### Transgenic Plants  
The species chosen for these experiments is _Arabidopsis thaliana_ (Arabidopsis), a model plant that has a rich genetic and molecular biology background and a strong spaceflight heritage. Arabidopsis is also among the most understood of plants with regards to reactions and adaptation to environmental stresses, and when transformed with reporter gene constructions, Arabidopsis is a proven platform for development of phytosensors based on gene expression.  
The green fluorescence protein used in these plants is referred to as GFP S65T, a \"red shifted\" version of the original jellyfish GFP [14-16]. This GFP was engineered to have a peak excitation at 488 nm and peak fluorescence at 512 nm, and has been deployed widely in the laboratory environment as a reporter for plants [1, 17, 18]. Similar engineered versions of GFP have been deployed in other species [19]. Plants containing a GFP reporter construct driven by the constitutive CaMV 35S promoter, as well as control plants without any GFP, were chosen for comparison in this demonstration project. The construction of these plants, as well as the use of these plants and those with environmentally inducible GFP constructions in a variety of spaceflight relevant experiments, has been detailed previously [2, 8, 13, 20].  
The growth format for the plants in this study is taken from the STS-93 space shuttle experiment [11, 21]. Square format Petri plates containing a semisolid nutrient substrate were planted with sterile seeds, with germination and growth occurring in the plate-vertical orientation. This allows a simple and clearly organized presentation of both roots and shoots for images from the nadir position relative to the surface of the plates. The growth medium contains nutrients and water such that light and a  
Figure 1: The TIS-II GFP imager. A. The imager housing is a compact, light-sealed box approximately 10” 1 x 8” h x 8” w. The camera and controller board are at one end, along with the LED board and excitation wavelength filter. The imager housing is shown resting on its power control module. B. The sample holder places the imaging plate perpendicular to the excitation light and the imaging camera, the lens of which can be seen in the hole through the excitation filter. C. The basic TIS-II Imager diagram, indicating the two main connections to the imager are for power and network access for control and image transfer.  
temperature above 4\({}^{\circ}\)C are the only exogenous requirement for _Arabidopsis_ growth. The plants are grown under the ambient light conditions of the greenhouse until they are inserted into the imager. Plants on nutrient agar plates can remain viable for weeks with no exogenous light (note the date stamps in Figure 4).
## 4 GNSS-Based Location Context Profiling
### Location Context Autoencoder  
To learn a compact representation from the packed features (i.e., image sequences), we naturally choose an autoencoder for embedding [35], and the compact representations are extracted from its central layer. Sequence inputs are handled by incorporating a recurrent neural network (RNN) as the fundamental structure [36]. Given a sequence of inputs and a desired output sequence, the goal of the RNN is to estimate the conditional probability of this output sequence given the input sequence. Incorporating the two structures, we design our autoencoder as a sequence-to-sequence structure with images as input. Both the encoder and the decoder are implemented using gated recurrent unit (GRU) cells. As shown in Figure 3, given the snapshots from time 0 to \(\mathcal{T}\), denoted as \(\mathbf{x}=(x_{0},\cdots,x_{\tau}\cdots,x_{\mathcal{T}})\), where \(x_{\tau}\in\mathbb{R}^{n_{i}\times n_{i}\times\mathcal{M}}\), \(n_{i}\) is the size of a snapshot, and \(\mathcal{M}\) is number of GNSS features, we want to get a compact representation \(\mathbf{v}\in\mathbb{R}^{\ell}\) with \(\ell\) being the latent dimension of GRU (shown as red in Figure 3). The encoder and decoder are defined as transformations \(\Theta\) and \(\Phi\). Here, \(\Theta:\mathbf{x}\rightarrow\mathbf{v},\Phi:\mathbf{v}\rightarrow\mathbf{x}^{\prime}\), where \(\mathbf{x}^{\prime}\) denotes the reconstructed sequence of \(\mathbf{x}\) with the same dimension. The training is conducted with a back-propagation algorithm to find transformations \(\Theta^{*}\) and \(\Phi^{*}\) based on the following objective:  
\[\{\Theta^{*},\Phi^{*}\}=\arg\min_{\Theta,\Phi}\sum_{\tau=0}^{\mathcal{T}}\|x_ {\tau}-x_{\tau}^{\prime}\|_{2}^{2}.\]  
The encoder first applies a convolution layer with filter size \((m_{f},m_{f},\ell)\) to each \(x_{t}\) and flattens it into a vector as an input for the GRU cell. At the decoder side, the semantic vector \(\mathbf{v}\) is set as the hidden state, the output generated by the RNN is reshaped, and a deconvolutional layer applied, with the same filter size as applied at the encoder side to obtain the final output \(x_{t}^{\prime}\). The parameters setting for \(m_{f}\), \(\ell\) and \(\mathcal{T}\) will be studied in Section 5.2.1. Figure 2 shows examples of snapshots and correspondingly generated  
Figure 3: Autoencoder structure.  
representations, based on the six scenarios in Figure 1. Due to space limitations, we only visualize the first three steps in a sequence of length five, and select three features to be the RGB channels of images.
## 4 Trajectory-Planning Method
### Calculation of Fuel Consumption during Transfer (TCFC)
The above geometric relationship is shown in Figure 5.  
The orbital dynamics can be described as Equation (18):  
\[r=\frac{a(1-e^{2})}{1+e\cos(\theta_{0}-\Delta\theta)} \tag{18}\]  
Equation (18) can be expanded as Equation (19):  
\[r=\frac{a(1-e^{2})}{1+e\cos\theta_{0}\cos\Delta\theta+e\sin\theta_{0}\sin\Delta\theta} \tag{19}\]  
Assuming \(k=\frac{DX}{a(1-e^{2})}\), \(\Delta\theta\) is a small amount in a single guidance cycle, which can be obtained by Equation (20):  
\[\Delta\theta=\frac{k(1+e\cos\theta_{0})}{1-ke\sin\theta_{0}} \tag{20}\]  
Based on the transformation relationship between the orbital elements and the state quantity, the updated coordinate components of the target orbit-injection point can be calculated as Equation (21):  
\[\left\{ \begin{array}{ll}X_{\text{ocff}}=0&Y_{\text{ocff}}=\frac{a(1-e^{2})}{ 1+e\cos(\theta_{0}-\Delta\theta)}&Z_{\text{ocff}}=0\\ V_{\text{xocff}}=\frac{\sqrt{\mu/a(1-e^{2})}}{Y_{\text{ocff}}}&V_{\text{yocff}} =e\sin(\theta_{0}-\Delta\theta)\sqrt{\frac{\mu}{a(1-e^{2})}}&V_{\text{zocff}} =0\end{array} \right. \tag{21}\]
## 2 Materials and Methods
### Methods
#### 2.3.2 Meteorological Drought Indices Calculation  
The Mann-Kendall (M-K) test was used to identify the annual trends of GPP and drought indices and their significance. This is a nonparametric statistical test method [44; 45] recommended by the World Meteorological Organization. It presents advantages, since samples do not need specific distributions, and its calculation method is fairly simple. Therefore, the M-K method is widely used to analyze changing trends and qualify the significance of trends in the time series of drought, precipitation, and temperature, among others [46; 47; 48; 49].  
For the statistic \(Z\) of the M-K test, \(Z>0\) indicates an upward trend, and \(Z<0\) indicates a downward trend. When the significance levels are set to 0.01, 0.05, and 0.1, \(|Za|\) is 2.58, 1.96, and 1.65, respectively. At a certain significance level, if \(|Z|>|Za|\), the statistics pass the corresponding significance test.
## 3 Improved Multiobjective Particle Swarm Optimization Algorithm
### Algorithm Improvements  
#### 3.2.1 Improvement of Learning Strategies  
When MOPSO is used to solve the MOPs, its method of selecting the global optimal solution is to select it randomly from the external archive. However, due to the lack of pertinence, the guiding role of \(g_{best}\) cannot be effectively played. In this paper, an improved learning strategy is proposed to deal with \(g_{best}\) in different situations, so that it can be better applied to solve MOPs.  
(1) If \(x_{i}(t)\) is not included in the external archive, then the individuals that dominate \(x_{i}(t)\) in the archive can be used as its global optimal solution. Therefore, it can be considered that \(g_{best}\) of the solution is a linear combination of all the archive individuals that dominate \(x_{i}(t)\).  
It is assumed that there are \(k\) solutions in the external archive that dominate \(x_{i}(t)\): \(a_{1}(t)\), \(a_{2}(t)\), \(\cdots a_{k}(t)\). We obtain a set of weights \(w_{j}\) by random generation, so that \(\sum_{j=1}^{k}w_{j}=1\). Thus, \(g_{best}\) can be expressed as follows:  
\[g_{best}=\sum_{j=1}^{k}w_{j}a_{j}(t) \tag{13}\]  
The velocity updating formula of the particle is expressed as follows:  
\[v_{id}(t+1)=\omega v_{id}(t)+r_{1}c_{1}(p_{ibestd}-x_{id}(t))+r_{2}c_{2}(g_{ bestd}-x_{id}(t)) \tag{14}\]  
(2) If \(x_{i}(t)\) is the solution in the external archive, then there is no individual that dominates \(x_{i}(t)\), and it is a non-inferior solution in itself. It can be considered that there is no global optimal solution, that is, \(g_{best}\) does not exist. The velocity updating formula of the particle is expressed as follows:  
\[v_{id}(t+1)=\omega v_{id}(t)+r_{1}c_{1}(p_{ibestd}-x_{id}(t)) \tag{15}\]  
In the above two cases, only the velocity is defined differently, and the position updating method of particles remains unchanged.
## 3 Simulation Framework
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Step 1**: Set \(i\leftarrow\)1, \(\mathbf{w}_{n,i}\leftarrow\frac{1}{\sqrt{M_{t}}}\mathbf{I}_{dim}\), \(P_{i}\leftarrow\mathbf{w}_{n,i}^{\text{H}}\mathbf{w}_{n,i}\), \(\varepsilon=10^{-3}\) \\ \hline
**Step 2**: \(\mathbf{r}_{n,j,i}=\left(\mathbf{H}_{n,j}\mathbf{w}_{n,i}\right)^{\text{H}},1 \leq l\leq L\) and \(\mathbf{A}\leftarrow\sum\limits_{l=1}^{L}\sum\limits_{l=1}^{L}\left(\mathbf{r} _{n,j,i}\mathbf{H}_{n,l}\right)^{\text{H}}\mathbf{r}_{n,l^{\prime}}\mathbf{H}_ {n,l^{\prime}}\), \(\mathbf{A}\leftarrow\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\text{H}}\) \\ \hline
**Step 3**: \(\mathbf{w}_{n,i+1}\leftarrow\mathbf{V}\mathbf{D}^{1/2}\) and \(P_{i+1}\leftarrow\mathbf{w}_{n,i+1}^{\text{H}}\mathbf{A}\mathbf{w}_{n,i+1}\) \\ \hline
**Step 4**: If \(\frac{\left|\mathbf{tr}(P_{i,1})-\mathbf{tr}(P_{i})\right|}{\mathbf{tr}(P_{i})}\geq\varepsilon\) go to Step 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximization of the SNR in spatial multiplexing transmission mode. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Units** & **Value/Assumption** \\ \hline Frequency & MHz & 2000 \\ Total bandwidth & MHz & 3.84 \\ Number of floors & & 3 \\ Standard deviation of shadow fading & dB & 10 \\ MC snapshots per simulation & & \(10^{6}\) \\ Multipath components (\(L\)) & & 6 \\ Processing gain (\(PG\)) & & 32, 64, 128 \\ Antenna radiation pattern per antenna & Omnidirectional \\ Number of antennas at the transmitter (\(M_{t}\)) & & 2,3,4 \\ Number of antennas at the receiver (\(M_{r}\)) & & 2,3,4 \\ Number of nodes per floor & & 1–21 \\ \(\mathit{SINR}_{th}\) & dB & 3/5/7/10 \\ Floor height (\(F_{h}\)) & m & 3 \\ Transmission mode & & Diversity combining/spatial multiplexing \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters.
## 1 Introduction  
Atmospheric turbulence characterization based on remote sensing of the target-plane laser beam characteristics is typically performed along the optical propagation path between optical transceiver and a target. Assuming the turbulence is uniform (homogeneous) and described by the Kolmogorov power spectrum [1], these characteristics can then be used to estimate the refractive index structure parameter \(\mathrm{C}_{n}^{2}\) averaged over the target line of sight. However, in many cases, atmospheric turbulence is non-uniform [2] and composed of relatively thin, horizontally extended layers of high values of the refractive index caused, e.g., by evening transition of the boundary layer [3] or jet streams and gravity waves in the upper troposphere and lower stratosphere [4]. These layers have frequently been observed with balloons [5] and radars [6]. The knowledge of atmospheric turbulence layer characteristics, such as their strength and position along the optical propagation path, is important for improving the efficiency of laser systems in beam projection and free-space optical communication applications using, e.g., multi-conjugate adaptive optics technique [7].  
In this paper, we present a concept which allows characterization of atmospheric turbulence layers along the trajectory of a moving target as well as real-time evaluation of major laser beam characteristics such as intensity scintillations directly on the remote target. The technique is based on the previously developed target-in-the-loop atmospheric sensing (TILAS) concept [8], which was experimentally proven over a 7-km atmospheric propagation path to a stationary retro-target [9]. Here, we consider a more general case when the TILAS system, a single-mode-fiber-based optical transceiver, is operating with a moving target. As shown in the paper, the use of the moving target offers several advantages for atmospheric characterization such as estimation of strength and position of a spatially localized turbulence layer, as well as evaluation of the turbulence outer scale \(L_{0}\), based on analysis of both the autocorrelation function and variance of the power signal measured by the TILAS system.  
The principal assumptions required for the TILAS technique presented here are as follows. First, the target is supposed to either be unresolved (smaller than the diffraction-limited beam footprint at the target plane), have a glint caused by reflection from the target's surface, or have a small-size retro-reflector attached. It is also presumed that the distance to the target as well as the target's speed are known. Besides, we utilize the Taylors' frozen turbulence hypothesis and assume that the target is moving sufficiently fast, so that we may neglect the impact of the wind along the propagation path.  
The paper is organized as follows. A concept of the TILAS system operating with a moving target with a glint is described in Section 2. Numerical simulation approach for modeling and simulation of the TILAS system is detailed in Section 3. Numerical simulation results are discussed in Section 4. The paper concludes with a summary and suggestions for future work.
# A Novel Hierarchical Multi-Stable Cylindrical Structure with Superior Energy Trapping
## 6 Conclusions  
This paper proposes a novel hierarchical multi-stable cylindrical structure (HMCS). The mechanical properties of the structure and the influence of its geometric parameters were investigated through a combination of numerical simulations and experimental studies. The main findings are summarized as follows:  
1. The HMCS inherits all the characteristic mechanical behaviors of traditional multi-stable cylindrical structures (TMCSs), including energy absorption and snap-through behaviors. Compared with the TMCS, the proposed HMCS has significant increases in both total and specific energy absorption. Specifically, in the case tested, the HMCS exhibited approximately a 200% increase in load-carrying capacity, and achieved a total energy trapping of 485% and a specific energy trapping gain of 225% compared to the TMCS.
2. The height \(h\) and thickness \(t\) of the curved beams are identified as critical parameters influencing the performance of the HMCS. While the peak force and energy absorption exhibit relatively straightforward correlations with \(h\) and \(t\), the valley force shows a more complex relationship with beam thickness. A critical parameter ratio of \(h/t\approx 3.69\) was determined, serving as a threshold for distinguishing between mono-stable and bi-stable responses.
3. A tuning snap-through sequence design of a multilayer HMCS is achieved by tailoring the thickness of the curved beams. The results reveal that the thicknesses of the curved beams dominate the snap-through behavior in multilayer structures. Deformation  
Figure 12: Force–displacement curves of case 4321.  
initiates from the layer with the lowest stiffness and progresses sequentially through layers with increasing beam thickness.  
This work provides a novel strategy for designing high-efficiency energy-absorbing structures. The proposed HMCS offers both theoretical guidance and practical reference for applications in energy absorption, impact mitigation, and advanced mechanical metamaterial systems.  
Y.W., conceptualization, writing--original draft, methodology, software, investigation; M.H., conceptualization, methodology, funding acquisition; Q.T., conceptualization, methodology, funding acquisition, resources, supervision, writing--review and editing, validation; X.C., investigation, software; J.W., validation, software; Q.J., conceptualization, software, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant No. 1220222), Shandong Provincial Natural Science Foundation (grant No. ZR2022 QA104), and supported by the Science Foundation of the National Key Laboratory of Science and Technology on Advanced Composites in Special Environments (grant No. JCKYS2024603C012).  
The data used to support the findings of this study are included within the article. Further data or information is available from the corresponding author upon request.  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this study, a novel, robust flight control system was designed for FWUAVs following curved paths under uncertainties on the latitude plane. Without using any model information, the desired yaw angle can be derived by using path-following errors, flow angles, and the current yaw angle. The model-free approach for deriving the desired yaw angle is easy to implement in hardware for engineering applications, and it dramatically reduces the burden of flight control computers. For the yaw angle control, firstly, though the yaw movement system is full of nonlinearities, couplings, time variations, and external winds, which would seriously degrade flight performance, the designed disturbance estimator can estimate all the uncertainties existing in the yaw movement model accurately for feedback compensation such that the path-following accuracy can be improved significantly. The path-following errors for the x and y directions are only 0.02 m and 2 m, respectively, compared with 0.12 m and 12 m for the NDI approach. Secondly, due to the existence of the disturbance estimator, the frame parameters of FWUAVs are allowed to perturb within a wide range between \(-\)30% and +30%. Through consuming quite a similar amount of input power (the deflection angles of the rudder), almost the same path-following control performance with very small path-following errors can be achieved. The yaw system has strong robustness in dealing with uncertainties, which allows the desired yaw angle of the position system to be tracked precisely. The yaw rate has small fluctuations in situations where external wind fields are encountered. However, the yaw rate can return to a stable value within a short time period.  
Conceptualization, M.T. and W.Q.; Methodology, W.Q.; Validation, W.Q.; Formal analysis, W.Q.; Investigation, Q.W.; Resources, W.S.; Writing--original draft, W.Q.; Writing--review & editing, M.T.; Supervision, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The supporting information is shared and available online by visiting [[https://github.com/maggie48](https://github.com/maggie48) dy/english-nal]([https://github.com/maggie48](https://github.com/maggie48) dy/english-nal) (accessed on 22 October 2023).  
The authors declare no conflict of interest.
## 3 Algorithm Principles
### RIME-Optimized XGBoost Hybrid Model  
XGBoost was chosen as the base model, with its three hyperparameters (number of trees, tree depth, learning rate) optimized simultaneously using RIME. XGBoost adapts to different scales of input samples, thereby enhancing its learning and generalization capabilities. A hybrid model with high learning and generalization abilities is established, as illustrated in Figure 3.  
XGBoost does not have a built-in, fully automated hyperparameter optimization tool. Hyperparameter settings usually depend on experience or are assisted by strategies like early stopping, cross-validation, and grid search.  
However, these methods have flaws or limitations. For example, grid search and cross-validation consume a lot of computing resources and time, while setting empirical parameters requires a lot of prior knowledge and understanding of the model and data. Moreover, hyperparameters influence each other, and traditional optimization methods may not handle this well. They may lead to suboptimal tuning results.  
In this study, we use RIME with MSE as the loss function to simultaneously optimize the number of trees, tree depth, and learning rate of XGBoost. The process includes the following:  
(1) Mapping XGBoost's three core hyperparameters to RIME's optimization goals, forming its search space, where each parameter combination is a potential solution.  
(2) Setting the value range and step size for hyperparameters.  
(3) Using MSE as the fitness function to evaluate each parameter combination.  
(4) Setting the population size and randomly generating individuals within the parameter range.  
(5) Iteratively optimizing based on RIME's calculation mechanism.  
(6) Returning the optimal hyperparameter combination to XGBoost upon convergence or reaching the maximum iteration.  
(7) Training XGBoost and generating predictions.
## 5 Results and Analysis  
### The Effect on the Propagation of Near-Ground Shock Wave  
As shown in Figure 6, the pressure contour images of the shock wave above the concrete ground are given. When \(t=120\) ms, the air shock wave will be stratified when it encounters the ground. Some energy of the shock wave will be transmitted into the ground, and some will be reflected, superimposing with the incident wave near the ground. When \(t=255\) ms, the shock wave under the ground precedes that in the air, this is because the shock wave travels much faster in the solid than in the gas. When \(t=675\) ms, a high pressure area appears near the ground due to Mach reflection. When \(t=1140\) ms, the height of the Mach bar gradually rises, and the pressure near the ground is higher than other areas in the air. It indicates that the ground has an enhanced effect on the propagation of the shock wave, and the enhanced area is mainly concentrated near the ground. In the face of the enhancement effect of the shock wave above different grounds, it is necessary to conduct in-depth research through data analysis.  
Figure 5: Reflected pressure contours images of the shock wave explosion above the rigid ground.  
Figure 6: Pressure contours images of the shock wave above the concrete ground.
## 7 Security of Data Transmission
The reliability of a set of routes \(W\) depends on the reliability of all constructed routes as follows [3]:  
\[1-\prod_{\omega\in W}\bigl{(}1-\Pi_{S,D}^{\omega}(t)\bigr{)}, \tag{29}\]  
where \(\Pi_{S,D}^{\omega}(t)=\prod_{\{a,b\}\in\omega}A_{a,b}(t)\)--reliability of a single route \(\omega\in W\), which is the product of the availability \(A_{a,b}\) of each of the connections between the nodes \(a\) and \(b\) at a certain point in time \(t\). We see that with an increase in the number of routes, the reliability of data transmission increases. In addition, RRNS increases the reliability of data transmission due to excessive noise-resistant coding. RRNS controls not only the situation with the loss of availability of an individual node and connectivity but also damage due to failures and intentional distortion of information.