esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmental Remediation of Metribuzin Herbicide by Mesoporous Carbon--Rich from Wheat Straw
## 3 Results and Discussion
### Morphology Analysis  
The morphologies and structures of WS and WSB were identified by SEM as shown in Figure 1. The raw WS preserves the structural organization of the vegetal cell wall with a lamellar structure on the surface [67]. In opposition, the WSB had a surface with spherical structures, suggesting a carbon-based structure from the raw material biological capillary structures.  
The volatiles are eliminated during the pyrolysis treatment, which improves the porosity of the WSB and affects its adsorption capacity [68; 69; 70]. As shown by the studies of [46; 71], the stem structure of wheat straw is destroyed and the micropore configuration is progressively created after pyrolysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Basic Properties**} & \multicolumn{4}{c}{**Conseququent Analysis**} & \multicolumn{4}{c}{**Atomic Ratios**} \\  & & & & **(wt\%)** & & & **(wt\%)** & \\ \cline{2-9}  & **Ash** & **Moisture** & **C** & **H** & **O** & **N** & **H/C** & **O/C** & **(O + N)/C** \\ \hline WS & 7.98 & 3.45 & 47.15 & 6.56 & 43.98 & 2.05 & 0.139 & 0.932 & 0.976 \\ \hline WSB & 5.68 & 5.13 & 73.25 & 2.61 & 19.67 & 2.79 & 0.035 & 0.268 & 0.306 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental composition of WS and WSB.  
Figure 1: SEM images of (**a**) raw WS (**b**) WS biochar.  
The structural analysis of WS and WSB were established by FTIR spectrometry as presented in Figure 2. Wheat straw biochar shows low intensity bands in regions of 2000-3500 nm\({}^{-1}\), which indicate that the material underwent complete pyrolysis with a loss of oxygen-containing species [19].  
The WSB had relatively high intensity bands in regions of 3500-4000 nm\({}^{-1}\), 1500-1750 nm\({}^{-1}\), 1250-1000 nm\({}^{-1}\) and 1000-500 nm\({}^{-1}\). Peaks around 1750-1689 nm\({}^{-1}\) were attributed to the carbonyl C=O groups, while the peaks at 1850 nm\({}^{-1}\) correspond to C-N stretching in aromatic amines [51]. Peaks around 1189 nm\({}^{-1}\) and 1127 nm\({}^{-1}\) were assigned to C-O and O-C-O stretching vibrations. The bands at 839-729 nm\({}^{-1}\) were characteristic of aromatic C-H groups, while the bands at 1543 nm\({}^{-1}\) and 1435 nm\({}^{-1}\) were representative of the aromatic C-C structure [72]. Being the prevalent groups on the surface of WSB, C-C and C-H gives a hydrophobic configuration with low oxygen-containing functional groups, which maximizes the adsorption mechanism. The carbon content and aromatic structures of biochar manage their rate and sorption capacity [65]. Based on this, the aromatic structures have a strong influence on the biochar's hydrophobic properties and \(\pi\)-\(\pi\) electron interaction can appear. Triazine herbicides can behave as a \(\pi\)-electron donor, whereas aromatic carbon from the biochar surface can serve as an electron acceptor, indicating that a \(\pi\)-\(\pi\) electron donor-acceptor interaction between metribuzin and WSB surface is feasible [14].
## 3 Results and Discussions
### Loss of Flow Accident  
The influence of the pump trip on the mass flow rate was studied for loss of flow accident in the EBR-II fast reactor [35]. In this work, the loss of flow accidents of ADS and CNES are simulated with ARTAP, in which the coolant mass flow in the core drops from 100% to 10% due to primary pump coastdown. Both unprotected and protected cases are considered.
## 3 Example Application
### Prediction of Pre-Camber by GM-BP Combined Prediction Model  
A GM-BP combined prediction model of the pre-camber of a bridge's vertical mould is established according to Section 2.3, and its main steps are as follows:  
1. The five sets of parameter prediction results of the GM(1,1) model are used as the input parameters of a BP neural network, and the actual vertical mould's pre-camber is used as the output parameter of the BP neural network, i.e., the number of neurons in the input layer and the output layer of the BP neural network are five and one, respectively, and, after repeated experiments, the training error is minimal when the number of neurons in the implied layer is six. Therefore, in this paper, we establish a three-layer BP with the structure of a 5-6-1 neural network model. The structure of the neural network is shown in Figure 7.
2. The data from the 1# to 10# beam segments are used as training samples to train the neural network, and the data from the 11# to 13# beam segments are used as test  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Beam Section Number** & \(\mathbf{T}^{c}\mathbf{C}\) & **G/KN** & **L/m** & **h/m** & **f1/m** & **f2/m** \\ \hline
1\# & 14.80 & 1436.00 & 9.5 & 6.89 & 0.0251 & 0.0251 \\
2\# & 9.26 & 1428.64 & 14.85 & 6.44  
samples to check the predicted values. The data are normalised using Equation (12) to obtain the respective matrices \(P\) and \(T\): \[P=\begin{bmatrix}1.000&1.000&0.000&1.000&0.000\\ 0.168&0.944&0.128&0.805&0.268\\ 0.152&0.855&0.170&0.719&0.320\\ 0.135&0.766&0.218&0.636&0.374\\ 0.120&0.678&0.271&0.558&0.428\\ 0.104&0.591&0.330&0.481&0.488\\ 0.089&0.504&0.397&0.403&0.550\\ 0.074&0.418&0.471&0.329&0.615\\ 0.059&0.333&0.555&0.260&0.686\\ 0.044&0.249&0.648&0.190&0.759\\ \end{bmatrix}\] \[T=\begin{bmatrix}0.029&0.165&0.752&0.126&0.835\\ 0.015&0.082&0.869&0.061&0.916\\ 0.000&0.000&1.000&0.000&1.000\\ \end{bmatrix}\]
3. The training parameters of the neural network are set: the maximum number of training iterations is 1000, the learning rate is 0.01, and the network training error accuracy is \(10^{-7}\).
4. After the training samples are fed into the neural network, the test samples are fed into it for the simulation's calculation, and the output result is \(S=[0.8465&0.9127&0.9744]\). Then, the predicted value of the pre-camber of the 11#-13# girder segments of the vertical moulds, \(U=[0.0547&0.0570&0.0592]\), can be obtained via the inverse normalisation of the S matrix. Finally, the correlation factors of the remaining girder segments are fed into the trained neural network, and their respective pre-camber predictions are obtained after inverse normalisation.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Discussion on the Interpretability and Fairness of Evaluation Metrics  
The interpretability and fairness of evaluation metrics are crucial for the development of DVC models.  
1. Interpretability Metrics should provide clear insights into the strengths and weaknesses of the models. For example, a low BLEU score may indicate a lack of n-gram overlap, while a high CIDEr score may suggest good consensus with reference captions. However, it is important to interpret these scores in the context of the specific task and dataset.
2. Fairness Evaluation metrics should be fair and unbiased, reflecting the true performance of the models. This requires careful selection of reference captions and consideration of the diversity of video content. Metrics should also be robust to variations in annotation quality and consistency.  
To address these challenges, future work should focus on developing more sophisticated evaluation metrics that can capture the semantic similarity between captions and account for the diversity of video content. Additionally, there is a need for larger and more diverse datasets with high-quality annotations to support the evaluation and benchmarking of DVC models.
## 3 Results  
On the base of the normalized absolute displacements in the upper limb COM and wrist joint, one can state that the RS's kinematics in all five trials in **motion1** and **motion2** show a higher repeatability under PMC (Figures 3a,b and S1a,b). In contrast, the DMD's kinematics in all three trials are less smooth and less repeatable for both motions (Figures 3c,d and S1c,d). Note that the RS did not perform any trunk motions during testing. On the basis of the mean trajectories (Figures S2 and S3), the following observations were made: (1) under NC, the RS used greater movements of the shoulder, elbow, and wrist joints and had greater COM displacements (Figures S2a and S3a) in comparison to the movements of DMD patient (Figures S2c and S3c); (2) Under PMC, the DMD patient's shoulder joint movements are greater with respect to the ones of the RS (Figures S2b,d and S3b,d).  
By considering the processed EMG data (Figures 4 and S4), increased amplitudes of EMG processed data were found in the UT, AD, and BB muscles of the DMD patient in comparison to those of the RS. Moreover, we revealed that the DMD patient's muscle activity was increased in UT, AD, and BB from the beginning of the movement in **motion2** in comparison to **motion1** (Figures 4c and S4c).  
Figure 4: EMG data processed (mean value of the RMS normalized data): BB (EMG1), TB (EMG2), AD (EMG3), and UT (EMG4): (**a**) Reference subject in **motion1** under natural conditions; (**b**) Reference subject in **motion1** under passive manipulator conditions; (**c**) DMD subject in motion1 under natural conditions; (**d**) DMD subject in **motion1** under passive manipulator conditions.
## 2 Electromagnetic Wave Propagation Behavior and the Principle of the Reflected Voltage  
### Fundamental Theory of Electromagnetic Waves  
Electromagnetic waves are an illustration of the transmission phenomenon of the interaction between electrons and the magnetic field. It means that the electric field at any point in space will generate a magnetic field that varies over time. The changing magnetic field and the electric field are reciprocally transmitted forward to each other, as shown in Figure 1.
## 2 Theoretical Background
### VR and Telepresence  
Telepresence, defined as the feeling of being present in an environment constructed through certain communication media, is one of the key features that characterize VR [25; 26]. In fact, [PERSON] [25] defined VR as \"a real or simulated environment where the user experiences telepresence.\"  
Furthermore, [PERSON] [25] introduced two dimensions that influence telepresence within the VR environment: vividness and interactivity. Vividness, also known as realness, is defined as the richness of the mediated virtual environment. The higher the number of senses the medium stimulates and the better the medium replicates the human sensory experience, the higher the vividness of the VR [27]. Interactivity is defined as the degree to which users of the medium can manipulate the mediated environment in real time. The faster the medium responds to the user's commands, the wider the range of manipulation. The more similar the response of the virtual environment is to that expected in the real world, the higher the interactivity [26]. That is, if users feel that they have control over their actions in the virtual environment, they experience a higher level of interactivity.  
A number of studies have employed telepresence, vividness, and interactivity as key concepts for studying VR adoption. For example, [PERSON] and [PERSON] [28] studied the influence of the use of VR on flow experience and satisfaction. Their study found that users of VR showed a significantly higher level of vividness and interactivity compared with those of traditional 2D media, which led to a higher level of telepresence. They also found that telepresence significantly impacted the users' flow experience, which affected their overall satisfaction. Other studies such as [PERSON] and [PERSON] [29] and [PERSON] et al. [30] found that display quality and interactivity positively affected the presence felt by the user, which affected their continued intention to use VR games and credibility of VR news.  
In the construction safety training context, studies have not yet focused specifically on telepresence. Instead, most studies have focused on comparing the effectiveness of VR safety training and traditional training. For example, [PERSON] et al. [10] tested the effectiveness of a VR safety training system compared with that of the traditional training method. The results showed that while VR safety training showed higher test results for some tasks, such as stone cladding work, it had no observable advantages in overall safety training. Other studies, such as that by [PERSON] and [PERSON] [3], focused on developing and testing a VR training program for construction workers.  
Because telepresence is a crucial factor that distinguishes VR training from the traditional classroom training method, understanding how it affects training effectiveness provides a key implication for our study. Hence, the two dimensions of telepresence are utilized as external variables of the TAM that affect the trainee's beliefs regarding VR safety training.
# New Space Engineering Design: Characterization of Key Drivers
## 3 Results and Discussion
### General Discussion of New Space Design Drivers and Enablers  
Entrepreneurial activities in the space sector are carried out by companies of all sizes. Smaller companies and startups typically introduce a single, innovative solution to the market, which is later integrated into larger systems by major industry players. However, small businesses attempting to enter the promising yet highly competitive space sector struggle due to funding shortages, particularly in the so-called \"Valley of Death\" of technology evolution, which corresponds to TRL4-TRL6 [18].
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 4 Simulation Setup and Results  
In this section, we present the simulation results to assess the performance of the proposed approaches in tackling the multi-objective VMPP. We compare the effectiveness of the MOILP model and the MOTS algorithm with other methods from the literature, described as follows:  
* Multi-Objective Ant Colony Optimization (MOACO): MOACO is a multi-objective optimization method aimed at finding a Pareto-optimal solution set while minimizing energy consumption, resource wastage, and communication energy costs between network elements in the DC [35].
* Multi-Objective Genetic Algorithm (MOGA): MOGA is a multi-objective optimization technique based on genetic algorithms and Bernoulli simulation, designed to minimize both the number of PMs used and resource wastage simultaneously [44].
* First Fit (FF) algorithm: The FF algorithm assigns each incoming VM to the first available PM with sufficient resources to accommodate it.
* First Fit Decreasing (FFD) algorithm: FFD is an extension of the FF algorithm that first sorts the VMs in descending order of their resource requirements (e.g., from largest to smallest CPU demands) before placing them on available PMs.
* Worst Fit (WF) algorithm: The WF algorithm aims to distribute VMs across the available PMs by placing each VM on the PM with the most remaining resources.
goal of the adversary is to compromise the ML-based decision-making process embedded within the application or to disclose sensitive information pertaining to the data upon which the ML model was trained. A widely recognized classification system within the domain of adversarial ML is MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) [2]. The objective of this living knowledge base of adversary tactics and techniques is to increase awareness regarding known vulnerabilities and emerging threats in AI-enabled systems. The MITRE ATLAS taxonomy draws inspiration from and complements the tactics, techniques, and procedures (TTPs) from the MITRE ATT&CK framework [3; 4].  
The challenge that we address focuses on the limitations of current taxonomies and threat modeling methodologies for ML-based applications. Taxonomies offer a hierarchical structure to categorize concepts and knowledge about threats, adversarial tactics, and mitigations. These models do not have the expressiveness necessary for in-depth analysis and automated reasoning about both attacks and defenses, particularly in the context of the ongoing arms race between them [5; 6]. This challenge becomes more pronounced as the body of knowledge, including the academic literature, on the AI threat landscape continues to grow at an unprecedented rate. Instead, our goal is to document important concepts in ontologies. Ontologies go beyond classification by offering a semantically richer machine-interpretable specification language with support for automated reasoning. Hence, the primary objective of this work is to systematically encode a comprehensive body of knowledge from the scientific literature to augment the current taxonomies, such as MITRE ATLAS, with the state of the art. By doing so, we aim to streamline the automated assessment of the attack surface of AI-enabled applications by leveraging enhanced reasoning capabilities, and to contribute to a more robust understanding of the challenges posed by emerging AI risks and threats to the development of enhanced defense mechanisms and an overall improvement of the security posture of these applications.  
The remainder of this paper is structured as follows. In Section 2, we review relevant related work on the use of ontologies in the cybersecurity domain along with efforts to establish a framework for machine learning security. Section 3 discusses our methodology on how we extend existing ontologies to document attacks and defenses for ML-based applications. We evaluate and validate our approach in Section 4 from both a qualitative and quantitative perspective. We summarize our contributions and offer suggestions for further research in Section 5.
## 9 Performance Evaluators
### Energy Efficiency of Proposed Technique  
Energy efficiency has always been a serious issue in traditional wireless sensor networks but as far as the underwater sensors networks are concerned it is even more difficult to maintain the energy level among the sensors due to many underwater-related issues which we have discussed earlier. Therefore, we have made the simulation based on energy efficiency and compare the energy consumed by each considered algorithm during the process of localization and synchronization. As compare to radio communications, acoustic modems consume more energy while transmitting and receiving the data. From Figure 10 it can be seen that STSL and JSL consume more energy as compare to GN-JSL and \(E^{2}JSL\) the reason behind this, is that both STSL and JSL employ a two-way message exchange procedure that consumes more energy. On the other hand, in both GN-JSL and \(E^{2}JSL\), reference nodes broadcast a one-way message for the procedure of localization and synchronization. This makes these algorithm procedures energies efficient. We have considered the energy efficiency according to the error tolerance ranging from 0.01 to 0.09 and it is clear from Figure 10 that energy efficiency of all  
Figure 8: Relationship between the number of reference nodes and clock offset RMSE (ms).  
Figure 9: Relationship between number of reference node and clock skew RMSE (ppm).  
considered algorithm increase with the relaxation of tolerance. These results show that the proposed algorithm has high energy efficiency as compared to all other algorithms for all values of tolerance error. It is concluded that \(E^{2}JSL\) can able to reduce the message overhead which is the most basic requirement of acoustic sensors. As \(E^{2}JSL\) shows high synchronization accuracy in comparison with other considered techniques.
## 4 Experimental Validation and Analysis
### Experimental Validation
#### 4.1.2 TopSIS Method Determines the Weights of the Criterion Layer  
Considering the specialized nature of vehicular experiments, this study focused exclusively on the test data from the vehicle transmitters, with detailed results presented in Table 4.  
Figure 8: Three radio propagation paths (communication links).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Path** & **1** & **2** & **3** \\ \hline Length (km) & 21 & 45 & 36 \\ Normalized difference built-up & & & \\ index (NDBI) & 0.43 & 0.86 & 0.65 \\ (Within the 50 m buffer zones on both sides of the communication path) & & & \\ Morphological description & Patchy sparse distribution & Grid-like high-density Pattern & Clustered high-density aggregation \\ Building density & & & \\ (Within the 50 m buffer zones on both sides of the communication path) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The quantitative measurements of key urban architectural parameters.  
The weighting coefficient matrix \(\mathbf{Y}\) (Equation (55)) is derived from Equations (30)-(38).  
\[\mathbf{Y}=\left[\begin{array}{cccc}0.013&0.075&0.043&0.036\\ 0.013&0.066&0.041&0.041\\ 0.012&0.070&0.039&0.044\\ 0.013&0.085&0.034&0.037\\ 0.012&0.054&0.044&0.043\\ \end{array}\right] \tag{55}\]  
The positive ideal solution \(\mathbf{y}^{+}\) (Equation (56)) and negative ideal solution \(\mathbf{y}^{-}\) (Equation (57)) are determined via Equations (41) and (42).  
\[\mathbf{y}^{+}=[0.013,0.085,0.044,0.044] \tag{56}\]  
\[\mathbf{y}^{-}=[0.012,0.054,0.034,0.036] \tag{57}\]  
The \(\mathbf{Z}^{+}\) (Equation (58)) and \(\mathbf{Z}^{-}\) (Equation (59)) are the weights of \(\mathbf{y}^{+}\) and \(\mathbf{y}^{-}\), respectively, which are obtained from Equations (43)-(46).  
\[\mathbf{Z}^{+}=(0.07,0.45,0.24,0.24) \tag{58}\]  
\[\mathbf{Z}^{-}=(0.1,0.4,0.25,0.25) \tag{59}\]  
Thus, the sub-criterion layer weight vector set \(\mathbf{A_{1}}\) for transmitter performance (\(U_{1}\)) is given in Equation (53). Repeating this process yields the corresponding weight vector sets \(\mathbf{A_{2}}\), \(\mathbf{A_{3}}\), and \(\mathbf{A_{4}}\) for receiver (\(U_{2}\)), antenna (\(U_{3}\)), and communication (\(U_{4}\)) performances in Equations (61)-(63), respectively.  
\[\mathbf{A_{1}}=(0.1,0.4,0.25,0.25) \tag{60}\]  
\[\mathbf{A_{2}}=(0.2,0.2,0.3,0.3) \tag{61}\]  
\[\mathbf{A_{3}}=(0.6,0.4) \tag{62}\]  
\[\mathbf{A_{4}}=(0.5,0.5) \tag{63}\]