esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 5 Conclusions  
We have developed a rapid trajectory planning approach that first employs grid discretization to simplify obstacle spatial representation, followed by lateral and longitudinal grid merging techniques to reduce the number of grids required for obstacle characterization. Subsequently, it dynamically constructs the drivable corridor by implementing four-direction synchronous expansion in open spaces while switching to single-direction iterative expansion when the expandable area becomes constrained. Simulation results demonstrate that, compared to the classical DC planner, our approach reduces collision avoidance constraint construction time by over 95% at reasonable grid resolutions, while the overall solving time of the optimizer decreases by more than 60%, all while maintaining the same planning success rate. Furthermore, the solving time remains stable as the number of obstacles increases, demonstrating the robustness of the proposed method.  
Although selecting an appropriate resolution can balance accuracy and efficiency, the current process relies on empirical tuning and lacks a systematic theoretical foundation. Future work could focus on developing an adaptive grid resolution adjustment mechanism based on scene characteristics, enabling the system to dynamically adjust resolution in response to varying environmental complexities. Moreover, the proposed method is not directly applicable to scenarios involving dynamic obstacles. Extending the approach to accommodate dynamic environments represents a promising direction for future work.  
**Author Contributions:** Conceptualization, W.W. and T.Z.; methodology, W.W., Z.S. and H.L.; investigation, W.W., Z.S. and H.L.; writing--original draft preparation, W.W., T.Z. and Z.S.; writing--review and editing, W.W. and T.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Yuelushan Center for Industrial Innovation, grant number 2023 YCII0126.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
**Acknowledgments:** We sincerely thank Professor [PERSON] for his valuable comments and insightful suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Flow and Transport Simulation
#### 2.2.2 Prediction and Remediation Simulations  
The prediction simulation computed the evolution of the groundwater flow and the concentration of 1,4-dioxane from their initial conditions based on the history of the illegal waste dumping site (Table 1). The initial groundwater flow was determined based on the distribution of the groundwater level (GL) (Figure 1). The initial concentrations of 1,4-dioxane for the assumed contamination source areas 1-5, shown in Figure 1, were obtained by applying a constant annual input rate of 1,4-dioxane from the waste/contaminated soils for each grid within each source area, whereas the initial concentration was set to zero for the other locations. The input rate was set to zero to simulate the removal of waste/contaminated soils after excavation. The total input volume of 1,4-dioxane for each area is listed in Table 3, which indicates that Source 1 is where the largest amount of 1,4-dioxane was discarded. To simulate the permeability changes due to the installation of the impermeable wall, the absolute permeability at the corresponding location after the wall installation was changed to 1.00 \(\times\) 10\({}^{-15}\) m\({}^{2}\). Additionally, when monitoring wells were added, the corresponding grids were changed to a constant flow-rate boundary at a prescribed discharge rate (pumping rate). More specifically, the pumping rates  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Parameter** & **Unit** & **Input Values** & **Reference** \\ \hline \(K\) & Permeability & m\({}^{2}\) & 1.4 \(\times\) 10\({}^{-12}\) & [35] \\ \(\varphi\) & Porosity & - & 0.3 & \\ \(\mu_{0}\) & Viscosity of 1,4-dioxane & Pa-s & 1.31 & \\ \(\mu_{w}\) & Viscosity of water & Pa-s & 1.138 & \\  & Molar density of & kmol/m\({}^{3}\) & 18.36 & \\ \(\rho_{w}\) & Molar density of water & kmol/m\({}^{3}\) & 17.83 & \\  & Molecular diffusion & m/s & 1.0 \(\times\) 10\({}^{-9}\) & \\  & coefficient & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic 1,4-dioxane information and geotechnical information from the site.  
were 10 m\({}^{3}\)/day for pumping well 1, 31 m\({}^{3}\)/day for pumping well 2, and 23 m\({}^{3}\)/day for pumping well 3, respectively [37].  
In the remediation simulation, the 40-year evolution of groundwater flow and the concentrations of 1,4-dioxane from the final conditions in the prediction simulation were computed. We conducted two types of remediation simulations considering passive and active treatments with pumping [37, 38]. The concentration of 1,4-dioxane in the studied area was expected to decrease during the passive and active treatments due to dilution and removal, respectively, by naturally occurring and pumping-induced groundwater. In the remediation simulation with active treatment, in addition to preexisting pumping wells 1-3, the monitoring wells were used as new pumping wells at a constant pumping rate of 250 m\({}^{3}\)/day. Table 4 summarizes the pumping rates of each well.
## 3 Methods
### GAM-MLP Information Fusion
#### 3.4.1 Human Pose Feature Information Fusion
The fully connected layer further processes and transforms the outcome from the self-attention layer. After the self-attention and fully connected layers, the input and output are combined through a residual connection, while the neural network training process is expedited via batch normalization. This helps to prevent issues like gradient vanishing or explosion, ultimately improving the model's generalization capabilities. Multiple transformer blocks can be stacked together to construct a deeper neural network, further enhancing the model's overall performance. Self-attention is a core component of the transformer model, playing a crucial role in machine learning tasks in natural language processing and other domains. It can associate and interact with information from different positions in the input sequence to capture the long-term dependencies in the sequence. The self-attention [31] calculation processes a sequence \((x_{1},x_{2},\dots,x_{n})\), with each \(x_{i}\) being a vector. The self-attention layer yields an output sequence \((y_{1},y_{2},\dots,y_{n})\), where each \(y_{i}\), is also a vector. The self-attention mechanism can be explained as follows:  
Figure 6.1  
where \(X\in R^{n\times d}\) represents the input sequence, \(W_{Q},W_{K}\), and \(W_{V}\in R^{d\times d_{k}}\) are weight matrices that map input vectors to query, key, and value vectors. The output of self-attention is a weighted average of input position vectors, with weights calculated through similarity (dot product) between position vectors after scaling and Softmax normalization. Residual connection addresses gradient vanishing in deep neural network training by allowing input data to flow directly to subsequent layers during forward propagation. This preserves input information, alleviating information loss and distortion. This concept applies beyond convolutional neural networks and enhances various neural network structures' training. Finally, the weighted feature group representations were added together to obtain the final attention fusion representation. The proposed group attention module and transformer module empower the neural network to focus on different input parts, enhancing adaptability across tasks and scenes. In activity recognition, it aids the network in emphasizing activity-related input information, improving accuracy and generalization.
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 2 Related Work
### SGX Performance Profiling  
Similar to our proposal that conducts comprehensive performance profiling to identify metrics and values associated with the performance of SGX-ported applications, there also have been attempts to benchmark SGX applications and the SGX runtime [18; 25; 26]. [PERSON] et al. [25] quantify engineering effort to port legacy code of real-world applications to SGX and compare the performance of ported applications running atop various SGX frameworks [14; 15]. [PERSON] et al. [18] figure out the actual available EPC memory space for an SGX application is around 93 MB in practice due to the reserved memory for metadata. [PERSON] et al. [26] provide a set of toolchains to precisely identify the critical factors related to SGX during the performance analysis of SGX-ported workloads. The above studies have focused on analyzing the intrinsic performance penalty of using SGX or the cost of manual tuning for improving performance, while our approach enables performance optimization with minimal engineering effort.
## 3 Resveratrol and Free Radicals
### Resveratrol and the Nrf/ARE Pathway  
Nrf2 is formed by six Nrf2-ECH homology (Neh) domains and it is one of the most significant regulators of the antioxidant response. Generally, Nrf2 is bound to Kelch-like ECH-associated protein 1 (Keap1) through the Neh2 domain, which is also a substrate for ubiquitin E3 ligase that promotes proteasome-mediated degradation of Nrf2. Under conditions of oxidative stress, the oxidation of Keap1 induces a conformational change that causes the subsequent detachment of Nrf2 and its translocation to the nucleus, where it binds AREs. Formation of the Nrf2-ARE complex then promotes the expression of genes that code for antioxidant and phase II detoxifying enzymes, such as GPx, heme-oxygenase 1 (HO-1), SOD, CAT, and NADPH dehydrogenase quinone 1 (NQO1).  
Resveratrol promotes the production of these enzymes by stimulating Nrf2 phosphorylation through a PI3K/Akt-dependent mechanism, thus causing the release of Nrf2 from Keap1 and its subsequent translocation into the nucleus [64,65]. Generally, NQO1 activation plays a significant role in preventing breast cancer [66, 67, 68, 101]. In particular, estrogens are metabolized by CYP1A1 and CYP1B1 to catechols, whose oxidation products are responsible for the formation of DNA adducts [102]. The induction of NQO1 stimulated by resveratrol facilitates the reduction of quinones to catechols and their subsequent inactivation by the catechol-O-methyltransferasease (COMT) enzyme (Figure 3).  
Quinones formed by the estrogens' metabolism can also be detoxified through conjugation with glutathione (GSH) by the glutathione-\(S\)-transferase (GST) enzyme [103].  
The most common estrogen present in the breast is 17\(\beta\)-estradiol (E\({}_{2}\)). This hormone is hydroxylated to catechol-estrogens, such as 2- and 4-hydroxyestradiol (2-OHE\({}_{2}\) and 4-OHE\({}_{2}\), respectively). The catechol-estrogens are then methylated by the COMT enzyme, forming 2-methoxy-E\({}_{2}\) and 4-methoxy-E\({}_{2}\), respectively [104].  
Figure 3: Mechanisms by which resveratrol can prevent estrogen-initiated breast cancer (with permission from [68]).
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 2 Background
### Quantum Neural Network  
Quantum circuit-based networks, as demonstrated by [PERSON] and [PERSON]'s method [37; 38], utilize quantum gate circuits featuring rotation and controlled-NOT gates. These circuits process quantum states as inputs, and a weight update process, akin to traditional forward propagation algorithms, is presented in complex number representation. [PERSON] and [PERSON] introduced an innovative approach to constructing neural networks based on quantum gate circuits. Their methodology employs fundamental quantum computing components, specifically a single-qubit quantum rotation gate and a controlled-NOT gate. These gates collectively form the neural network, with quantum states serving as input. The network's weight update process adheres to a complex-valued formulation derived from the traditional forward propagation algorithm. The network architecture, visually depicted in accompanying diagrams, showcases the integration of quantum gates in the neural network framework.  
Variational quantum circuits, pivotal in quantum machine learning, comprise three essential components. First, the feature map \(F\) facilitates the mapping of classical data points \(x\) onto a \(z\)-qubit quantum state \(|\varphi(x)\rangle\). This state preparation is expressed as  
\[|\varphi(x)\rangle=F(x)|0\rangle^{\otimes z} \tag{1}\]  
Later, an ansatz, denoted by \(\alpha\) and parameterized by a vector \(\theta\), operates on the quantum state through a sequence of entangling and rotation gates. The resulting quantum state is then determined as  
\[|\varphi(x,\theta)\rangle=\alpha(\theta)|\varphi(x)\rangle \tag{2}\]  
Finally, the observable \(O\) is measured, recording eigenvalues corresponding to the resultant quantum state. The QNN diagram is shown in Figure 1.  
In the realm of machine learning, the variational quantum circuit is executed multiple times with specific inputs \(x\) and parameter vectors \(\theta\). This iterative process allows the circuit's expectation value, denoted by \(f\), to approximate the desired output:  
\[f(x,\theta)=\langle\phi(x,\theta)|O|\phi(x,\theta)\rangle \tag{3}\]  
This expectation value serves as a crucial approximation for the model's output in various machine learning applications, where the variational quantum circuit offers a quantum-enhanced approach to computational tasks.
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 2 Video Monitoring Platform Description
### Platform Orchestration
and, according to specific algorithms and policies, decides how to manage resources of the network devices. More specifically, it has the following main functions: * It is responsible for the implementation of the requests coming from the platform front-end; this is done by configuring and managing the VDTs. More specifically, the NFVO is invoked every time the front-end: (a) receives a connection request from either a client or a video transmitter; (b) a client requests a new connection to a video transmitter; in this case a path has to be created from the video transmitter to it; (c) a video transmitter starts the transmission of a new flow;
* It monitors the network status, the usage of wireless links in the mesh network, the RAM/CPU usage of each computation node and each virtual machine running networking or application functions, with the targets of deciding allocation of physical and virtual resources that will be assigned to specific requests, and selecting the most efficient end-to-end paths between video transmitters and receivers. The policies implemented by the NFVO can refer to a wide range of aspects, like energy consumption, end-to-end delay between source and destination of data flows, number of hops of the path, overall number of virtual machines active at the same time, quality of service (QoS) and quality of experience (QoE), Service Level Agreement (SLA) with the customers, etc. The implemented policies may aim at achieving consolidation, by minimizing the number of drones involved in the forwarding procedure of the media streams from the transmitters to the destination clients. The computational load model described in Section 4 is one of the key elements to design these policies.
# Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder
## 3 Results and Discussion
### Coupled Mechanical-Chemical Activation Mechanism  
Figure 9 shows the mechanism of coupled mechanical-chemical activation of copper slag. The vitreous in the copper slag was a three-dimensional and distorted network structure that possessed high energy and could be activated under certain conditions [36, 37]. Mechanical grinding increased the fineness and specific surface area of copper slag powder and exposed the active mineral components to the alkaline environment of cement-based materials. The main components of the copper slag powder were silicate crystals and amorphous phases with silicate tetrahedral polymerization networks (Figure 9a) [38]. The vitreous network structure of copper slag powder was dissociated in the alkaline environment (Figure 9b). Subsequently, the polymerization network structure of the copper slag powder was destroyed, and the internal Ca2+, Al3+, Fe2+, Fe3+, [AlO4]5-, and [SiO4]4- were precipitated into the pore solution of the hydration products. Ca2+ reacted with [SiO4]4- to form C-S-H gel with high iron content, and the hydration products were deposited on the surface of the copper slag (Figure 9c) [39]. With the continuous hydration reaction, the C-S-H gel continued to form and gradually diffused to the gap between the hydration products and the unreacted copper slag (Figure 9d).
## 3 Methodology
### Long Short-Term Memory  
Figure 3 illustrates the internal architecture of the LSTM model. Let us assume that \(I\), \(P^{\prime},\ Q^{\prime},\ Q,\) and \(P\) represent the input of the LSTM cell, output of the previous layer, input of the cell state, output of the cell state, and output of the LSTM cell, respectively. During forward propagation, the forget gate shown in the figure, which differentiates data that needs attention from those that can be overlooked, will calculate its output as:  
\[G_{1}=\mathit{sigmoid}(I\times U_{1}+P^{\prime}\times W_{1}) \tag{4}\]  
where _sigmoid_ is the activation function and \(W_{1}\) and \(U_{1}\) represent the corresponding weights of two inputs \(I\) and \(P^{\prime}\), respectively. The reason behind the multiplication by _sigmoid_ is to set its output between 0 (zero) and 1 (one). Equations (5) and (6) define theworking process of the input gate and have the power of setting up the values (setting values tending to 1 (one) in the memory and removing those tending towards \(-1\)) in the memory state. Multiplying by both the \(sigmoid\) and \(tanh\) functions facilitates this process:  
\[G_{2}=sigmoid(I\times U_{2}+P^{\prime}\times W_{2}) \tag{5}\]  
\[G_{3}=tanh(I\times U_{3}+P^{\prime}\times W_{3}) \tag{6}\]  
Then, the output of memory state \(Q\) has the following form:  
\[Q=Q^{\prime}\times G_{1}+G_{2}\times G_{3} \tag{7}\]  
Similarly, Equation (8) governs the functionality of the output gate. The final output of LSTM cell \(P\) has the form:  
\[G_{4}=sigmoid(I\times U_{4}+P^{\prime}\times W_{4}) \tag{8}\]  
\[P=tanh(Q)\times G_{4} \tag{9}\]  
The equations stated above govern the working principle of the LSTM cell during forward propagation. Now, during back propagation, which aims to tune the variables to reduce the errors, the LSTM model operates by calculating \(\frac{\delta P}{\delta U_{1}}\), \(\frac{\delta P}{\delta U_{2}}\), \(\frac{\delta P}{\delta U_{3}}\), \(\frac{\delta P}{\delta U_{4}}\), \(\frac{\delta P}{\delta W_{1}}\), \(\frac{\delta P}{\delta W_{2}}\), \(\frac{\delta P}{\delta W_{3}}\), and \(\frac{\delta P}{\delta W_{4}}\). Their subtraction from the old weights provides the final weights of the LSTM cell as:  
\[W_{new}=W_{old}-\frac{\delta P}{\delta W} \tag{10}\]  
\[U_{new}=U_{old}-\frac{\delta P}{\delta U} \tag{11}\]
## 4 Discussion
### Examples of Protocol Applicability  
In the last two decades, studies have shown that, in addition to movement in the sagittal plane, there are important movements in the frontal and transverse planes [9; 45; 46]. Recently, 3D motion analysis has become a crucial tool in addressing rehabilitation processes related to cycling [16].  
Patellofemoral syndrome (\"cyclist's knee\") is the most common cause of knee pain in the cyclist [21]. This pain is caused by increased pressure in the patellofemoral joint. Factors like low or anterior saddle position, or excessively long cranks, lead to higher values of the knee flexion angle at the top of the pedal stroke, producing an increase in the patellofemoral contact pressure [22].  
Maltracking knee is often accompanied by a decrease in gluteus-medius-generated force, the appearance of contralateral pelvic tilt, valgus knee deviation, and foot pronation [47]. Detection of this behaviour is essential to reduce or eliminate knee joint pain. The most common treatment is to increase recruitment of the gluteus medius and vastus medialis obliquus. In addition, it is necessary to improve the alignment of the foot and ankle on the bicycle to improve the mechanics of the knee and, consequently, reduce pain. The implementation of the proposed protocol can be of great use in clinical practice by allowing for the quantification of kinematic deviations in the pelvis, knee and foot, some of which, as mentioned above, occur outside the sagittal plane.
## 2 Research Area and Methodology  
### Site Overview  
The study area is located at an industrial site in Baoshan District, Shanghai, situated within Quaternary alluvial clay deposits of the Yangtze River Delta. Geomorphologically classified as part of the dish-rim highland east of the ancient Songbei \"Gangchen\" paleo-coastal ridge, this open estuarine plain features gentle topography with minimal geological variation. The terrain slopes slightly northwest to southeast, maintaining an average surface soil thickness of approximately 300 m. As shown in Figure 1, the site is surrounded by functional zones including Phase I coal-refining facilities, water management infrastructure, and tar distillation operations. This investigation focuses on the red T-shaped roadway marked in Figure 1, comprising two main routes: a 200 m NW-SE axis and a 100 m NE-SW corridor.  
Formerly housing chemical raw material production facilities, this site exhibits potential subsurface leakage of light non-aqueous phase liquids (LNAPLs) from undocumented underground infrastructure, posing significant environmental risks, including soil contamination and groundwater pollution. To address these challenges, this study implements ERT for long-term real-time monitoring of soil and groundwater resistivity variations. The system enables the following: early leakage detection through resistivity anomaly identification, visual tracking of remediation progress in contaminated zones, and predictive monitoring of potential pollutant migration pathways.
# Contradiction Detection with Contradiction-Specific Word Embedding
## 4 Experiments
### Results and Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Test Data**} \\  & **Acc(all)** & **Acc(contra)** \\ \hline Illinois-LH & **84.43** & 75.97 \\ TreeRNN & 74.89 & 69.86 \\ TreeRNN & 76.88 & 71.00 \\ \hline SVM:shallow features & 76.46 & 72.92 \\ SVM:CWE + shallow features & 82.95 & 77.22 \\ LSTM-RNN:CWE & 56.07 & 61.39 \\ LSTM-RNN:CWE + shallow features & 75.79 & 70.69 \\ \hline CNN:CWE + shallow features & 82.60 & 80.28 \\ CNN:CWE* + shallow features (All) & 84.27 & **82.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of contradiction detection on the test dataset. Contradiction-specific word embedding (CWE) can be fine-tuned during training the model, CWE* represents the fine-tuned CWE. Acc(all) stands for the average accuracy over three categories and Acc(contra) stands for the accuracy of the contradiction category. features, which are the differences of the word order and the unaligned word number, are both helpful in three-category classification, but play a minor role in identifying contradiction category. CWE and task-independent word embedding such as Glove are compared in the contradiction detection task on the test dataset, as shown in Table 6. The proposed model is utilized by incorporating shallow features for comparison. We also use [PERSON]'s embeddding [8] to make a comparison with CWE in the same model. We aim to verify two things, which are: the necessity of learning a contradiction-specific word embedding for contradiction detection task and the advantage of our method in learning embedding. The results show that CWE performs better than Glove and [PERSON]'s embedding in a contradiction detection task.