esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Supervisory Control and Data Acquisition Systems
### The Need for Advanced Visualization in SCADA  
As water supply networks become increasingly complex, there is a growing need for more intuitive and immersive visualization tools. Traditional 2D interfaces, while effective for basic monitoring, often fail to provide a comprehensive understanding of spatial relationships and system dynamics [15]. This limitation can hinder the ability of operators to identify issues, plan maintenance, and respond to emergencies effectively. The integration of 3D visualization into SCADA systems addresses these challenges by offering the following:  
* Enhanced Spatial Awareness: 3D models provide a realistic representation of physical infrastructure, enabling operators to visualize the system in its entirety [16].
* Improved Data Interpretation: Complex data, such as pressure gradients and flow patterns, can be more easily understood when represented in a 3D environment [15].
* Immersive Interaction: Operators can navigate and interact with the system in real-time, improving situational awareness and decision-making [17].
* Training and Simulation: 3D visualization can be used to create virtual training environments and simulate scenarios for contingency planning [18].
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
## 2 The Nested Structure with a Double Octagonal Inner Wall
### The Establish of the FE Model  
The finite element (FE) method has been proven to have sufficient precision and is often utilized for the crashworthiness analysis of tubes [41]. In this paper, the characteristic  
Figure 1: The parameter and structural configuration of SCOD: (**a**) the sketch for thickness parameters; (**b**) the sketch for dimension of middle wall.  
is analyzed by using the nonlinear explicit FE analysis program LS_DYNA. The simulation model of SCOD is shown in Figure 2.  
From Figure 2, the simulation construction of SCOD can be divided into three components: motional plate, SCOD tube, and stationary plate. To save computational costs, the moving plate imposes a load at a constant speed of 3 m/s [39]. The fixed plate is restricted to six degrees of freedom, and the SCOD tube is placed in the fixed plate. When the moving plate moves at a constant speed, the fixed plate limits the longitudinal displacement of the SCOD in the longitudinal direction to ensure successful crushing. To guarantee that the tube is not crushed and that the endergonic particularity of the model can be observed, the tube is compressed by 60% of its original length, and loading is stopped.  
The Belytschko-Tsay shell element with one center integration point and five through-thickness integration points is employed to establish the FE model of SCOD. The convergence analysis of mesh is obtained in Figure 3. As shown in the analysis results, the variation of MCF has a slight change with the increase in mesh size, but the solving time is greatly reduced. To ensure sufficient accuracy and computational efficiency, we decided to adopt the mesh with the size of 1 mm \(\times\) 1 mm to construct numerical models. Because the plate hardly deforms during the collision which can be regarded as a rigid body, both of the plates are simulated with MAT_RIGID (Mat_20), and all nodes will maintain a constant relative displacement in this condition. The SCOD tubes are simulated with MAT_PIECEWISE_LINEAR_PLASTICITY (Mat_24) which is a model that reflects the elastic-plastic mechanical properties of materials. This type of material has an arbitrary stress-strain curve, and any strain rate dependence can be defined. In addition, the strain rate can be explained by the Cowper-Symonds model. The \"AU-TOMATIC_NODE_TO_SURFACE\" is applied to define the contact between SCOD and the plate, and the dynamic and static friction coefficients are both 0.3. And the \"AUTO-MATIC_SINGLE_SURFACE\" contact algorithm is employed to define the self-contact of the SCOD to avoid the occurrence of a pressure breakdown phenomenon. The dynamic and static friction coefficients of self-contact are both 0.3 as well. The material of the SCOD tube is aluminum alloy AA6061O, and the constitutive model and material characterization are shown in Table 1.  
Figure 2: The simulation model of SCOD.
## 2 Materials and Methods
### Water Quality and Fish Performance  
The temperature (\({}^{\circ}\)C) and dissolved oxygen (DO, mg L\({}^{-1}\)) were measured daily using a multiparameter probe (YSI, model Pro-20, Yellow Springs, OH, USA). Salinity (ppt) was measured twice a week using a multiparameter probe (YSI, model Pro-20, USA). The pH was measured daily using a bench pH meter (Mettler Toledo, FEP20, Barueri, Brazil). The total alkalinity (mg CaCO\({}_{3}\) L\({}^{-1}\)) was monitored according to the methodology presented by APHA [31] and was measured twice a week. Sodium bicarbonate was used to maintain alkalinity above 150 mg L\({}^{-1}\). Total ammoniacal nitrogen (TAN, mg L\({}^{-1}\))  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ingredients**} & \multicolumn{4}{c}{**Experimental Diets**} \\ \cline{2-5}  & **Control** & **T5** & **T10** & **T15** \\ \hline Fish meal & 40.00 & 40.00 & 40.00 & 40.00 \\ Soybean meal & 33.00 & 32.00 & 31.00 & 30.00 \\ Wheat bran & 12.00 & 8.00 & 4.00 & 0.00 \\ Gelatin & 2.00 & 2.00 & 2.00 & 2.00 \\ Soy oil & 2.00 & 2.00 & 2.00 & 2.00 \\ Fish oil & 2.00 & 2.00 & 2.00 & 2.00 \\ Cellulose & 4.00 & 4.00 & 4.00 & 4.00 \\ Vitamin/mineral mix & 5.00 & 5.00 & 5.00 & 5.00 \\ Ulva meal & 0.00 & 5.00 & 10.00 & 15.00 \\ \hline Proximal composition (\%) & & & & \\ \hline Protein & 42.44 & 41.82 & 41.37 & 39.98 \\ Lipids & 8.23 & 8.07 & 8.05 & 8.64 \\ Ash & 16.28 & 17.45 & 19.01 & 20.02 \\ Gross energy & 17.63 & 17.34 & 17.32 & 17.42 \\ \hline \hline \end{tabular} Gross energy: Expressed in MJ/kg wet weight of feed.  
\end{table}
Table 2: Ingredient composition and proximal composition of experimental diets (% of dry matter) containing _U. lactuca_ meal at different levels.  
and nitrite (mg L\({}^{-1}\)) were analyzed according to the methodology of [PERSON] [32] and [PERSON] [33] on a daily basis until the system stabilized and then twice a week. Nitrate (mg L\({}^{-1}\)) and phosphate (mg L\({}^{-1}\)) were analyzed weekly using the methodology described by [PERSON] and [PERSON] [34].  
The following formulae were used to evaluate animal performance:  
Initial body weight (g): initial biomass of live animals (g)/total number of animals;  
Average final weight (g): final biomass of live animals (g)/total number of animals;  
Weight gain (g): final weight (g) \(-\) initial weight (g);  
Specific growth rate (% d\({}^{-1}\)): 100 \(\times\) [ln (final weight (g) \(-\) initial weight (g))/(cultivation time)];  
Feed conversion rate (FCR) =\(\sum\)ration offered (g)/(final biomass (g) \(-\) initial biomass (g));  
Survival (%) = (final number of animals/initial number of animals) \(\times\) 100.
## 4 Results and Discussion
### Mechanical Tests  
The mechanical properties of the PEC films in the form of their tensile strength, elongation at break, and Young's modulus were calculated from their stress-strain observations and the results are shown in Table 3. Tensile strength evaluates the strength and elongation at break point indicating stretchability of the film before breakage. The maximum stress in TM-PEC %1 (2.24 Mpa) was higher than that of TM-PEC %0.4 (0.94 Mpa). This may be caused by increasing the amount of chitosan and alginate and interactions between functional groups leading to a more crosslinked and confined and less elastic structure.  
The modulus of elasticity or Young's modulus describes the stiffness of materials and is one of the most important properties of solid materials. From [PERSON]'s law, the modulus of elasticity is defined as the ratio of the stress to the strain [62]. The Young's modulus (stiffness) of the PEC film increased with increasing concentration of chitosan and alginate. Increase in film stiffness can be assigned to the formation of new functional groups which affect polymer interaction and orientation in the film [3].
## 5 Numerical Characterization
### Bouc-Wen Model  
Although the Bouc-Wen model does not take into account the energy dissipation explicitly, it produces continuous curves that fit--better than the polygonal model--the actual shape of the Q-\(\delta\) curves obtained experimentally. Moreover, it is implemented in well-known FEM software for structural analysis such as SAP2000 (Walnut Creek [CA], USA) [28] or OpenSees (Berkeley [CA] USA) [29]. The main parameters to define the Bouc-Wen model in SAP2000 are based on [PERSON]'s formulation [30]: (i) the initial stiffness \(k_{0}\), (ii) the ratio of postyielding stiffness to the initial stiffness \(\alpha^{\prime}\), and (iii) the transition parameter from linear to nonlinear range \(n\). OpenSees has implemented a refined version of the Bouc-Wen model [27] that includes additional parameters to control the shape of the hysteresis loops (\(\beta^{\prime}\) and \(\gamma\)) and the strength degradation (\(\Delta\
u\) and \(\Delta\eta\)). These parameters were calibrated in this study to match the normalized \(Q\)/\(Q_{y}\)_versus_\(\delta\)/\(\delta\)/\(\delta\)/\(\delta\)/\(\delta\)/\(\delta\)/\(\delta\)/\(k_{0}\) = 1, \(\alpha^{\prime}\) = 1/14, \(n\) = 1.2, \(\beta^{\prime}\) = 0.5, \(\gamma\) = 0.5, \(\Delta\
u\) = -6\(\cdot\)10\({}^{-4}\) and \(\Delta\eta\) = 1\(\cdot\)10\({}^{-3}\). The Bouc-Wen equation implemented in OpenSees in terms of stress, \(\sigma\), and strain, \(\varepsilon\), is shown in Equation (8):  
\[\sigma\ =\ \alpha^{\prime}k_{0}\varepsilon+(1-\alpha^{\prime})k_{0}\varepsilon. \tag{8}\]  
Here, \(z\) is a variable that represents the hysteretic deformation and is related with the time derivative of the strain, \(\dot{\varepsilon}\), by the following expression:  
\[\dot{\mathbf{z}}\ =\ \frac{1-|z|^{n}\left(\beta^{\prime}sgn\left(\dot{z}z \right)+\gamma\right)\Delta\
u}{\Delta\eta}\dot{\varepsilon}, \tag{9}\]  
Figure 23: Modellisation of the Bauschinger part: (**a**) \(\beta\) values, (**b**) \(\alpha\) values.  
Figure 22: Idealisation of the Bauschinger parts.  
where \(|z|\) indicates the modulus and \(sgn\) the sign. A detailed explanation can be found in [31]. More sophisticated Bouc-Wen-based models have been proposed in the literature to take into account the pinching and strain hardening effects [32; 33; 34; 35]. However, a simplified model is adopted in this study since the realistic range of displacements expected on structures equipped with dampers (\(\mu<9\)) are below the displacements associated with the onset of the second order effects that cause the pinching of the hysteretic loops (\(\mu>12\)).
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 3 Results and Discussion
### Location of Impact  
Given the hexagonal geometry of the honeycomb core when orientated perpendicular to the impact direction, it could be expected that the exact location of the impact might lead to different energy dissipation patterns. To explore this, three distinct models were developed where the location of impact is changed according to the schematics in Figure 19. Model T-30, with an Al2024-T3 alloy honeycomb core, was used to analyse the effect of changing the location of impact on the dissipation of kinetic energy of the debris particle. The debris particle was fired at a velocity of 6500 m/s at a single edge of the honeycomb structure, a double point and at the mid-point. The details of these models are listed in Table 2. The results in Table 3 show that the highest kinetic energy dissipation occurs for model L-MP, where the debris particle impacts the mid-point of the honeycomb. However, the energy absorption history is similar across all three models, as can be seen in Figure 20, which indicates that very little variation is seen between simulations at different debris impact locations. This is additionally supported by the maximum difference in energy dissipation of 5.2% between the three models. Further, Figure 21 shows little visible variation in particle fragmentation, demonstrating that using the honeycomb structure in this orientation is a suitable method for dissipating kinetic energy, irrespective of the exact location of the impact.  
Figure 19: Location of impact on Whipple shield relative to honeycomb.  
Figure 20: Kinetic energy of debris particle against time for varying debris impact locations.
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 3 Results
### Cascading Use for EoL: Non-Demolition, Reuse, Recycling, Energy Recovery and Landfill  
An important theme across the literature review is the cascading use of mass timber products at their EoL, based on the above scenarios available, mentioned by 23 studies. According to the EU publication on study of \"the optimized cascading use of wood\" [56], cascading use refers to \"the efficient utilization of resources by using residues and recycled materials for material use to extend total biomass availability within a given system\". It promotes the optimal use of the same wood material multiple times before it is converted into energy or landfilled [57]. Cascading can maximize wood's carbon storage capacity by prioritizing its use for \"higher-value\" and relatively long-lasting products like construction materials, and only resorting to \"lower-value\" applications, such as energy production, when the optimal options are exhausted [58]. In terms of LCA calculations with cascading principles in mind, [PERSON] et al. [46] have suggested expanding the system boundaries of the product's LCA to link to its second life cycle in order to assess LCA over multiple lifecycles.  
An ideal hierarchy in cascading use of EoL scenarios is concluded in Figure 4.
# Neutrosophic Linear Equations and Application in Traffic Flow Problems
## 3 Neutrosophic Linear Equations and Neutrosophic Matrices
### Operations of Neutrosophic Matrices  
For neutrosophic matrices, all the classical matrix operations (e.g., matrix addition, matrix subtraction, and scalar multiplication, matrix transpose, matrix inverse, and so on) can be used to perform the neutrosophic matrix operations.  
**Example 1**.: _Assume there are two neutrosophic matrices \(A\) and \(B\):_  
\[A=\left[\begin{array}{cc}I&3\\ 5I&9\end{array}\right]\;\text{and}\;B=\left[\begin{array}{cc}I&2I\\ 3I&5\end{array}\right].\]  
Thus, we can introduce the following matrix operations:  
\[A+B\text{: }A+B=\left[\begin{array}{cc}2I&3+2I\\ 8I&14\end{array}\right]; \tag{2}\] \[A-B\text{: }A-B=\left[\begin{array}{cc}0&3-2I\\ 2I&4\end{array}\right];\] (3) \[3A\text{: }3A=\left[\begin{array}{cc}3I&9\\ 15I&27\end{array}\right];\] (4) \[A^{T}\text{: }A^{T}=\left[\begin{array}{cc}I&5I\\ 3&9\end{array}\right];\] (5) \[A\times B\text{: }A\times B=\left[\begin{array}{cc}(9+I)I&15+2I^ {2}\\ (27+5I)I&45+10I^{2}\end{array}\right];\] (6) \[A^{-1}\text{: }A^{-1}=\left[\begin{array}{cc}-3/(2I)&1/(2I)\\ 5/6&-1/6\end{array}\right]. \tag{1}\]
## 3 Experiments  
In this section, the detection results in spatial domain, frequency domain and spatial-frequency domain are firstly shown intuitively. Then, through a series of ablation experiments, the optimal parameters are given. Finally, we compare our final results with other floating object detection methods. All experiments are conducted in an unmanned surface cleaning robot shown in Figure 4. The robot is composed of an image processing unit, a navigation unit, a conveyor caterpillar, etc. The visual system consists of two HIKVISION B12-IPOE surveillance cameras, and the CPU of the image processing unit is Intel (R) Xeon (R) E5-2660 2.20 GHz. Note that no GPU is involved in running the program. The programming libraries used are OpenCV 4.0.1 and Eigen 3.3.7.  
Experimental images were sampled from 10 different water surfaces, and each area has 40 images resized into \(350\times 350\) pixels. Interference factors in these samples include different illumination intensities, waveform shapes, turbulence levels, reflection sizes, etc. Of these 400 source images, 280 were for training and 120 for testing, and all of them have appropriate ground truth labels. Note that in the testing set consisting of 120 source images, 140 distinct floating objects are labeled as positive samples, because some images contain multiple floating objects. Additionally, there are 10 images (1 image each water scene) without any floating objects.
## 5 Experimental Results and Discussion
### Comparison  
#### 5.2.1 Prefix-Tunning and Fine-Tunning  
In comparison to the fine-tuning method, we compare fine-tuning and prefix-tuning based on three different pre-trained models under the optimal prefix length of 3 tokens, which is determined in the ablation experiment above. The experimental results are illustrated in Figure 5, where \"FT\" refers to fine-tuning all pre-training parameters while \"PT\" represents freezing the pre-trained parameters and optimizing only the prefix parameters. The results indicate that models using the prefix-tuning method outperform models using the fine-tuning method under the same pre-training parameters. Specifically, models based on BERT, ALBERT, and UniLM increase in AUC of 0.00413, 0.00578, and 0.0450 under the prefix-tuning method, respectively. Additionally, ACC improves by 0.0082, 0.0234, and 0.0309, respectively. F1 score improves by 0.0364, 0.0717, and 0.0413, respectively.  
In addition, the number of parameters required to be trained for BERT, ALBERT, and UniLM in fine-tuning are 101,601,040, 3,629,570, and 96,488,976, respectively.  
However, all these parameters are frozen in prefix-tuning, reducing the number of trainable parameters to 244,512, 20,376, and 122,256 in BERT, ALBERT, and UniLM, respectively. This indicates that FAP-Prefix effectively reduces the complexity and training time. In addition, the results indicate that FAP-Prefix brings the downstream task modeling closer to the pre-training pattern. This method enables the model to quickly access the knowledge gained during pre-training. It also accelerates and improves the accuracy of model training with a limited number of parameters.
## 4 Result and Analysis  
The ECS simulation data was obtained for the four operating conditions shown in Table 1. To develop a TL-based fault diagnostic algorithm, two of the conditions were arbitrarily selected as the source and target domains. A summary of the chosen conditions is presented in Table 3, with the source domain being taken at a low cruising altitude of 28k ft and consisted of both simulated PACK data and associated labels. The target domain was taken at the higher cruising altitude of 41k ft, which consisted of only simulated PACK data and no labels. The intention of choosing only one set of operating conditions as the source and target domain is so that the diagnostic algorithm developed in this transfer scenario can then be tested, in Section 5.1, for its ability to generalise when presented with data at new operating conditions to which the algorithm has not been exposed.
## 3 Simulation Modeling and Verification
### Polishing Trajectory Space Optimization and Planning
#### 3.2.3 Polishing Trajectory Space Optimization  
According to the Preston removal principle, the material removed during polishing represents the cumulative amount at a point on the processed surface over a given period of time. Single-point removal is the cumulative removal in the overlapping region of two adjacent polishing trajectories. Polishing trajectory spaces that are excessively large or overly small can result in uneven material removal depth and poor surface quality.  
The analysis suggests that the quality and consistency of the polished surface improve with a smaller peak difference in the material removal profile between the overlapping and non-overlapping regions of the polishing trajectory. This relationship is shown in Figure 10a-c. A curve fit with an R\({}^{2}\) value of 0.99736 was obtained by simulating the optimum space for polishing trajectories on surfaces of different curvatures and fitting the data with curvature, as shown in Figure 10d. This high R\({}^{2}\) value indicates that this relationship can be used to determine the optimum polishing trajectory space for surfaces of different curvatures in subsequent polishing processes.  
Figure 9: Theoretical material removal profiles for surfaces of different curvature.
# Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
## 2 Materials and Methods
### Mathematical Models  
#### 2.3.1 Governing Equation  
Using the Navier-Stokes equation, the propagation of the internal solitary wave and the action of the riser were numerically simulated. The governing equations for the flow field are as follows:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho\overline{u}_{i})}{\partial x _{i}}=0 \tag{1}\]  
\[\frac{\partial\rho}{\partial t}=0 \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho\overline{u_{i}})+\frac{\partial}{\partial x _{j}}(\rho\overline{u_{i}u_{j}})=-\frac{\partial\overline{P}}{\partial x_{i}} +\mu(\frac{\partial^{2}\overline{u_{i}}}{\partial x_{j}\partial x_{j}})+\frac{ \partial(-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}})}{\partial x_{j}}+\rho g _{i} \tag{3}\]  
\[-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}=\mu_{t}(\frac{\partial\overline{ u_{i}}}{\partial x_{j}}+\frac{\partial\overline{u_{j}}}{\partial x_{i}})- \frac{2}{3}(\mu_{t}\frac{\partial\overline{u_{i}}}{\partial x_{i}})\delta_{ij} \tag{4}\]  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\epsilon} \tag{5}\]  
\[\frac{dk}{dt}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}c_{k}\frac{k^{2 }}{\epsilon}+v\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+P_{k}-\epsilon \tag{6}\]  
\[\frac{d\epsilon}{dt}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}c_{ \epsilon}\frac{k^{2}}{\epsilon}+v\bigg{)}\frac{\partial\epsilon}{\partial x_{ j}}\Bigg{]}+c_{\epsilon 1}\frac{\epsilon}{k}P_{k}-c_{\epsilon 2}\frac{\epsilon^{2}}{k} \tag{7}\]  
where \(i\) and \(j\) are three directional components in the Cartesian coordinate system; \(t\) is the time, \(p\) is the fluid density; \(\mu\) is hydrodynamic viscosity coefficient; \(F_{i}\) is the volume force; \(P_{k}\) is the turbulence generation term; and \(c_{\mu},c_{k},c_{\epsilon},c_{\epsilon 1}\), and \(c_{\epsilon 2}\) are empirical constants.