esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiment by MATLAB
### Multi-Formation Simulation
The horizontal axis represents time. The vertical axis is the formation position error. The comparison can be implemented between Figures 11b and 12b. Then, it can be concluded that the convergence time of the formation control using the group strategy is less than that of the algorithm without the group strategy under the same formation. Figure 11: (**a**) Trajectories of multi-robot on the plane without grouping strategy; (**b**) Formation position error of each following robot. Figure 10: (**a**) Trajectory of rectangular formation by GCF; (**b**) Position error of rectangular formation over time. In order to better show the feasibility of the algorithm, the comparison is performed with other algorithms. It takes the spiral formation of the two algorithms, which are the same as the leader-follower formation method, in their respective optimal states. Among them, the practical formation control (PFC) is the method for an anonymous multi-robot system with unknown nonlinear disorders [2]. The trajectory diagram and system error diagram of the robot are obtained by applying PFC, as shown in Figure 13. The trajectory diagram and system error diagram of the robot are acquired by applying GCF, as shown in Figure 14.  
The x-axis and y-axis are the coordinate changes during the motion of each robot in Figures 13a and 14a. The curves of formation position error over time are shown in Figures 13b and 14b. The horizontal axis represents time. In Figure 13b, \(r\) represents the trajectory radius of the robot centered on the point 'target' as the vertical axis. \(\alpha\) represents the rotation angle of the robot in motion as the vertical axis. According to Figures 13 and 14, the comparison results are shown in Table 1 for forming spiral formation. Figure 12: (**a**) Trajectories of multi-robot on the plane by GCF; (**b**) Formation position error of each following robot. Figure 13: (**a**) Trajectories of nine robots on the plane by PFC in spiral formation; (**b**) Formation position error of each following robot. To sum up, according to Figures 8-10, the simulation results show that the multi-mobile robots using the algorithm control law in this article can dynamically form formation during operation on the different formation problems. Through the comparison of the two algorithms in Table 1, it can be seen that the convergence time of the algorithm in this article will be 25% less than that of PFC. Compared with PFC, the number of robots is poor. However, the overall comparison of the formation control algorithm in this article is acceptable in theoretical analysis and simulation. Therefore, the formation control algorithm proposed in this article has effectiveness and feasibility.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 2 Materials and Methods
### Discounted Cash Flow Analysis
#### 2.4.4 Sensitivity Analysis  
To evaluate the robustness of the techno-economic performance under uncertain or regionally variable conditions, a one-way sensitivity analysis was conducted using the Fujian Province scenario as the baseline. Fujian was selected due to its highest availability of bamboo residues among the ten provinces analyzed, making it a representative high-resource case for hard carbon production.  
Several technical and economic parameters were varied individually while keeping other variables constant, in order to assess their marginal impact on the MSP of hard carbon. The tested parameters and their respective low, baseline, and high values are summarized in Table 3.  
Labor salaries were varied across the minimum and maximum average levels observed among the ten selected provinces. The income tax rate ranged from 15% to 35%, with the lower bound reflecting preferential tax policies for certified high-tech enterprises in China. The bamboo residue procurement cost was varied between 200 and 500 CNY/ton, as previously established from regional market sources. Electricity prices were also varied based on the observed industrial tariff range (35-110 kV users) across the ten provinces.  
Technical parameters such as the scaling factor (used in the CAPEX estimation), sulfuric acid price, hard carbon yield, and overall electric heating efficiency were also perturbed to reflect plausible process variability or supplier differences. Transportation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Low** & **Base** & **High** \\ \hline Labour salary (CNY/year) & 4294 & 5638 & 6724 \\ Equity percentage & 0.2 & 0.4 & 0.6 \\ Average transportation distance (km) & \(-\)20\% & Baseline & +20\% \\ Income Tax Rate & 0.15 & 0.25 & 0.35 \\ Transportation price (CNY/tkm) & 0.2 & 0.3 & 0.4 \\ Scaling factor & 0.6 & 0.7 & 0.8 \\ Bamboo residue price (CNY/ton) & 200 & 350 & 500 \\ H\({}_{2}\)SO\({}_{4}\) price (CNY/ton) & 500 & 750 & 1000 \\ El price (CNY/kWh) & 0.45 & 0.62 & 0.78 \\ Hard carbon yield & \(-\)20\% & Baseline & +20\% \\ El efficiency & 0.5 & 0.7 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivity parameters values.  
related factors--including unit transport cost and average distance--were varied by \(\pm\)20% around the baseline.
## 5 Analysis on Bearing Capacity of PHC Modular Pile
### Effect of Nodular Diameter  
To investigate the influence of nodular diameter on the bearing capacity of the PHC nodular pile, the diameter of PHC nodular pile D was set to be 500 mm, 525 mm, 550 mm, 575 mm and 600 mm, respectively, and the diameter of pile shaft was set to be 450 mm. The simulated load-displacement responses of the PHC nodular pile with different nodular diameter are arranged as shown in Figure 10.  
It can be seen in Figure 10 that the pile head displacements of the PHC nodular pile with different nodular diameters all increased stably with the pile head load, and no plunging failure occurred because of the exitance of nodules along the pile shaft. It can also be seen in Figure 10 that the bearing capacity of the PHC nodular pile would increase with the increase in nodular diameter, and the ultimate capacities were 2359 kN, 2711 kN, 3049 kN, 3261 kN and 3583 kN, respectively, as the nodular diameter increased from 500 mm to 600 mm. The ultimate bearing capacity of the 450 (600) mm PHC nodular pile was 1.5 times the ultimate bearing capacity of the 450 (500) mm PHC nodular pile. Hence, the increase in nodular diameter could largely improve the bearing capacity of the PHC nodular pile. Nevertheless, it should be noted that the increase in nodular diameter would also increase the disturbance to the surrounding soil in the pile installation process, and the time needed for the recovery of soil strength after pile installation would also increase. The time needed for the recovery of soil strength after pile installation and the ultimate bearing capacity of PHC nodular pile after the recovery of soil strength should be comprehensively considered for the design of the driven PHC nodular pile in engineering projects.
## 2 Methods
### Phase-Based Effective Connectivity Estimation Approaches Considered in This Study
#### 2.3.3 Granger Causality  
We also characterize the simulated and EEG data using Granger causality (GC). Like TE, GC is derived from [PERSON]'s definition of causality, and the two measures, in their original forms, are equivalent for Gaussian variables [36]. Briefly, for two stationary time series \(\mathbf{x}=\{x_{t}\}_{l=1}^{T}\) and \(\mathbf{y}=\{y_{i}\}_{l=1}^{T}\), the Granger causality from \(\mathbf{x}\) to \(\mathbf{y}\) is defined as:  
\[\text{GC}(\mathbf{x}\rightarrow\mathbf{y})=\log\left(\frac{\text{var}\{ \mathbf{e}\}}{\text{var}\{\mathbf{e}^{\prime}\}}\right), \tag{12}\]  
where \(\mathbf{e},\mathbf{e}^{\prime}\in\mathbb{R}^{T-o}\) are vectors holding the residual or prediction errors of two autoregressive models, and \(\text{var}\{\cdot\}\) stands for the variance operator. The errors in \(\mathbf{e}\) come from an autoregressive process of order \(o\) that predicts \(\mathbf{y}\) from its past values alone. On the other hand, the errors in \(\mathbf{e}^{\prime}\) come from a bivariate autoregressive process of order \(o\) that predicts \(\mathbf{y}\) from the past values of \(\mathbf{y}\) and \(\mathbf{x}\)[11]. If the past of \(\mathbf{x}\) improves the prediction of \(\mathbf{y}\) then \(\text{var}\{\mathbf{e}\}\gg\text{var}\{\mathbf{e}^{\prime}\}\) and \(\text{GC}(\mathbf{x}\rightarrow\mathbf{y})\gg 0\), if it does not, then \(\text{var}\{\mathbf{e}\}\approx\text{var}\{\mathbf{e}^{\prime}\}\) and \(\text{GC}(\mathbf{x}\rightarrow\mathbf{y})\to 0\). In addition, in analogy to the concept of phase TE, we define \(\text{GC}^{\theta}(\mathbf{x}\rightarrow\mathbf{y},f)=\text{GC}(\theta^{x} \rightarrow\theta^{y})\), where \(\theta^{x}\) and \(\theta^{y}\) are instantaneous phase time series obtained by filtering \(\mathbf{x}\) and \(\mathbf{y}\) at frequency \(f\), as a measure within the framework of GC that captures phase-based interactions.
## 2 Control Architectures for WSN: A State of the Art
### Control Techniques in SDWSN Context  
Using centralized software leads to a global view of the network and thus global optimization. Software-defined networking (SDN) represents a promising paradigm for network management, characterized by the separation of control and data transmission functions. This separation facilitates centralized decision-making and flexible network management. This approach is particularly well suited to WSN, where research proposals have been implemented as SDWSNs [15]. Several studies have been carried out in this field, exploiting new architecture in a variety of ways. Various software solutions implementing the concept of Network Digital Twin are proposed to control [16; 17] or to monitor the energy [18] of the SDWSN.  
In [19], [PERSON] et al. present an algorithm for optimizing energy management in WSNs. This algorithm divides the time interval of each sensor into two distinct periods: one for beacon transmission and the other for execution. During the beacon period, the sensors remain active to send beacon data to the central controller. Based on this information, the controller adjusts the sleep state of the nodes, updates the network topology, and then transmits directives to the relevant nodes. During the execution period, the nodes follow these directives, choosing whether to remain active to send the main data or to go into sleep mode.  
Similarly, [PERSON] et al. [20] introduce a network management method designed for load balancing. This method is activated when a node's energy drops below a certain threshold, or when its congestion becomes excessive. At this point, the affected node communicates directly with the central controller (SDN controller), which plays a crucial role in processing reconfiguration requests. It analyzes the current state of the network and develops new routing configurations tailored to the specific needs of the affected nodes.  
In addition, [PERSON]. [18; 21] propose methods for centralized prediction of energy consumption in SDWSN. These approaches use a mathematical model based on Markov chains to estimate the energy consumption of sensor nodes. Using this model, the SDN controller can predict the energy consumption of the nodes and detect if a network reconfiguration is needed.
## 3 Results
### EXE Promotes Basal Autophagy  
Autophagy is another cellular catabolic process and examined by measuring autophagy induction protein BECN-1, autophagosome formation proteins ATG7 and LC3B-II, and autophagy flux protein p62 and lysosomal protease CTSL. Our results showed that BECN-1 and ATG7 expression levels did not differ among groups (Figure 5A-C). On the contrary, LC3B-II and an LC3B-II/I ratio were elevated in the EXE-DOX group compared to other groups (Figure 5A,D), but neither p62 nor CTSL differed among groups (Figure 5A,E,F).  
Figure 4: Effects of EXE on DOX-mediated proteolysis signaling pathways in soleus muscle. (**A**) Representative western blot images of anabolic and proteolytic signaling proteins. (**B**) Quantification of total protein expression and phosphorylation levels of p-AKT\({}^{\text{Set}473}\), AKT, and p-AKT\({}^{\text{Set}473}\)/AKT ratio. (**C**) Quantification of total protein expression and phosphorylation levels of p-mTOR\({}^{\text{Set}2448}\),  
mTOR, and p-mTORSee2448/mTOR ratio. (**D.E**) Quantification of MSTN and SMAD 2/3 protein expression, respectively. (**F**) Representative western blot images of proteolytic proteins. (**G**) Quantification of total protein expression and phosphorylation levels of p-FOXO3 aSer253,FOXO3a, and p-FOXO3 aSer253/FOXO3a ratio. (**H,I**) Quantification of MAFbx/atrogin-1 and MuRF-1 protein expression, respectively. All target proteins were normalized by Ponceau-stained total proteins (\(n\) = 6-7/group). Asterisks denote significant differences: \(p\) < 0.05 (*), \(p\) < 0.01 (**), and \(p\) < 0.001 (***). Values are presented as mean \(\pm\) SEM. SED: Sedentary; SAL: Saline; DOX: Doxorubicin; EXE: Endurance exercise; mTOR: Mammalian target of rapamycin; Forehead box O3a: FOXOO3a; MAFbx: Muscle atrophy F-box; MuRF-1: Muscle ring finger protein-1.  
Figure 5: Effects of EXE on autophagy against DOX treatment in soleus muscle. (**A**) Representative western blot images of autophagy proteins. (**B**) Quantification of BECN-1 protein levels. (**C**) Quantification of ATG7 protein levels. (**D**) Quantification of LC3B-II and LC3B-I protein levels and LC3B II/I ratio. (**E**) Quantification of p62 protein levels. (**F**) Quantification of CTSL protein levels. All target proteins were normalized by Ponceau-stained total proteins (\(n\) = 6-7/group). Asterisks denote significant differences: \(p\) < 0.05 (*), \(p\) < 0.01 (**), and \(p\) < 0.001 (***). Values are presented as mean \(\pm\) SEM. SED: Sedentary; SAL: Saline; DOX: Doxorubicin; EXE: Endurance exercise BECN-1: Beclin-1; CTSL: Cathepsin L.
## 3 Methodology
### Performance Evaluation and Comparison  
#### 3.6.1 Comparative Methods  
To validate the effectiveness of contrastive-learning-based feature representation, several classification comparisons utilizing different time-series feature representations were performed. The baseline classification is an XGBoost-based classifier only using completely **Clean** Sentinel-2 images (referred to as XGB-Clean). Using all available Sentinel-2 **time-series (TS)** images, an LSTM-based time-series classifier and an XGBoost-based time-series classifier (referred to as LSTM-TS and XGB-TS, respectively) were constructed. Using time-series **Feature Representation (FR)** generated from the proposed contrastive learning framework, an LSTM-based classifier and an XGBoost-based classifier (referred to as LSTM-FR and XGB-FR, respectively) were also built. In addition, GridSearchCV was used to conduct hyperparameter tuning for the XGB-Clean, XGB-TS, and XGB-FR classifiers.  
For classifiers, sample sets were randomly divided into training, validation, and testing sets in a ratio of 6:2:2, for both the proposed type-wise TS2 Vec model and time-series classifiers.
## 3 Theory
### Gaussian Beam  
Assuming that the THz beam emitted from a PCA is a Gaussian beam (and this is a very good approximation of actual beam) [27; 44; 45], it can be modeled as follows [27; 29; 43; 44; 45; 46; 47].  
According to Figure 2, the Gaussian beam parameters can be defined and linked to each other. \(W_{0}\) is the beam waist size at the focal plane (\(z=0\)). The beam waist \(W(z)\), along the \(z\) direction, enlarges and can be expressed by the following equation:  
\[W(z)=W_{0}\sqrt{1+\left(\frac{z}{Z_{R}}\right)^{2}} \tag{3}\]  
where \(Z_{R}\) is called Rayleigh distance and represents the distance along the \(z\) axis where \(W(Z_{R})=\sqrt{2W_{0}}\). It is defined as follows  
\[Z_{R}=\frac{W_{0}{}^{2}\pi f}{c} \tag{4}\]  
where \(f\) is the frequency and \(c\) the speed of light in vacuum. Note that, as shown in Figure 2, the waist sizes, both in the focal region (\(W_{0}\)) and outside (\(W(z)\)), refer to the Gaussian width \(r\) associated to an intensity reduction of a factor \(1/e^{2}\). Moreover, the beam divergence angle \(\theta\) can be defined as the ratio between \(W_{0}\) and \(Z_{R}\):  
\[\theta=\frac{W_{0}}{Z_{R}} \tag{5}\]
## 5 Conclusions
If more frequent updates are needed, or if what is required is not in Open-SURFEX (DrHOOK, FA/LFI formats, GAUSSIAN grid), you are invited to follow the procedure to get an SVN account and access real-time modifications of the code (see the instructions at the first link). The developments presented in this study stemmed on SURFEX version 8.0 and are now part of the version 8.1 (revision number 4621). **Data Availability:** The ERA-5 datasets are distributed by ECMWF ([[http://apps.ecmwf.int/datasets/](http://apps.ecmwf.int/datasets/)]([http://apps.ecmwf.int/datasets/](http://apps.ecmwf.int/datasets/)), ECMWF, last access: June 2018). The ECOCLIMAP dataset is distributed by CNRM ([[https://opensource.umr-cnrm.fr/projects/eccolimap](https://opensource.umr-cnrm.fr/projects/eccolimap)]([https://opensource.umr-cnrm.fr/projects/eccolimap](https://opensource.umr-cnrm.fr/projects/eccolimap)), CNRM, 2013). The SURFEX model code is distributed by CNRM ([[http://www.umr-cnrm.fr/surtex/](http://www.umr-cnrm.fr/surtex/)]([http://www.umr-cnrm.fr/surtex/](http://www.umr-cnrm.fr/surtex/)), CNRM, 2016). The satellite-derived LAI GEOVI observations are freely accessible from the Copernicus Global Land Service ([[http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)]([http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)); last access: June 2018). The ESA CCI surface soil moisture dataset is distributed by ESA ([[http://www.esa-soil](http://www.esa-soil)]([http://www.esa-soil](http://www.esa-soil)) moisture-ccirog/, last access: June 2018, 25). The satellite-driven model estimates of land evapotranspiration are freely accessible at [[http://www.gleam.eu](http://www.gleam.eu)]([http://www.gleam.eu](http://www.gleam.eu)) (last access: June 2018; [46]). The upscaled estimates of gross primary production are freely accessible at [[https://www.bgc-jenna.mpg.de/geodb/projects/Home.php](https://www.bgc-jenna.mpg.de/geodb/projects/Home.php)]([https://www.bgc-jenna.mpg.de/geodb/projects/Home.php](https://www.bgc-jenna.mpg.de/geodb/projects/Home.php)) (last access: June 2018; [48]). In situ measurements of soil moisture are freely available at [[https://www.ncdc.noaa.gov/crm](https://www.ncdc.noaa.gov/crm)]([https://www.ncdc.noaa.gov/crm](https://www.ncdc.noaa.gov/crm)) (last access: June 2018; [49]). In situ measurements of streamflow are freely available at [[https://nwis.waterdata.usgs.gov/nwis](https://nwis.waterdata.usgs.gov/nwis)]([https://nwis.waterdata.usgs.gov/nwis](https://nwis.waterdata.usgs.gov/nwis)) (last access: June 2018, USGS).
# SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
## Appendix A A list of symbols used in this study.
\begin{table}
\begin{tabular}{l l} \hline \(B\) & The loss function for training. \\
**C** & The positional encoding matrix. \\ \(d\) & The scaling factor for self-attention operation. \\
**F** & The combined embedding representation matrix for tokens and semantic labels. \\ \(f_{1}\) & The mapping from a token to a numerical vector. \\ \(f_{2}\) & The mapping from a semantic label to a numerical vector. \\ \(h\) & Number of heads. \\ \(H(\textbf{X})\) & Multi-head self-attention operation for matrix **X**. \\ \(\textbf{K}\) & The key matrix for single-head self-attention operations. \\ \(\textbf{K}_{i}\) & The \(i\)-th key matrix for multi-head self-attention operations. \\ \(L\) & Number of queries in the training dataset. \\ \(M_{1}\) & The dimension of numerical vectors produced by \(f_{1}\). \\ \(M_{2}\) & The dimension of numerical vectors produced by \(f_{2}\). \\ \(M\) & \(M=M_{1}+M_{2}\). The dimensions of **C**, **F** and **U** are \(N\times M\). \\ \(N\) & Number of tokens from an input SQL query. \\ **Q** & The query matrix for single-head self-attention operations. \\ \(\textbf{Q}_{i}\) & The \(i\)-th query matrix for multi-head self-attention operations. \\ \(S(\textbf{X})\) & Self-attention operation for matrix **X** based on **Q**, **K**, **V**. \\ \(S_{i}(\textbf{X})\) & Self-attention operation for matrix **X** based on **Q**, **K**, **V**. \\ \(s_{j}\) & The \(j\)-th query in the training set. \\ \(\mathcal{S}\) & The test set for the experiments, where the length of each query is below \\ \(\mathcal{S}\) & 1000 characters. \\ \(\mathcal{T}\) & The training set for the experiments, where the length of each query is below \\ \(\mathcal{T}\) & 1000 characters. \\ \(\mathcal{U}\) & The test set for the experiments, where the length of each query is below \\ \(\mathcal{U}\) & 1000 characters. \\ \(\mathbf{U}\) & The encoded representation matrix produced by the input embedding section. \\ \(\mathcal{V}\) & The set of vocabularies for the SQL-specific tokenizer. \\ **V** & The value matrix for single-head self-attention operations. \\ \(\textbf{V}_{i}\) & The \(i\)-th value matrix for multi-head self-attention operations. \\ \(\textbf{W}_{K}\) & The weight matrix for the computation of **K**. \\ \(\textbf{W}_{K_{i}}\) & The weight matrix for the computation of **K**. \\ \(\textbf{W}_{M}\) & The weight matrix for combining the results of different single-head \\ \(\textbf{W}_{Q}\) & The weight matrix for the computation of **Q**. \\ \(\textbf{W}_{Q_{i}}\) & The weight matrix for the computation of **Q**. \\ \(\textbf{W}_{V}\) & The weight matrix for the computation of **V**. \\ \(\textbf{W}_{V_{i}}\) & The weight matrix for the computation of **V**. \\ **X** & Input matrix for self attention operations. \\ \(z\) & The estimated probability that the input query is malicious. \\ \(\eta\) & The threshold value for determining whether the input query is malicious. \\ \hline \end{tabular}
\end{table} 
Table 1: A list of symbols used in this study.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Maize Fields Characterization
Likewise, the SAR backscattering can detect differences between rainfed and irrigated fields, being sensitive to the vegetation water content. The soil moisture data described in Section 2.2.4 were used to check for rainfed maize. For each field, the mean soil moisture was calculated for the period from May to July. The mean NDVI was calculated likewise on each field for the same growing period. According to the assumption that irrigation allows maintenance of higher levels of soil moisture during dry periods, as well as higher NDVI responses, the mean soil moisture and NDVI were compared for the fields within the irrigation districts and those far from irrigation infrastructures.