esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
## 5. Orifice Coefficient of LNG  
The orifice coefficient of the LNG tank with the liquid cargo loaded was calculated while assuming the LNG loading conditions in Equation (1). The orifice coefficients of LNG are listed on Table 6. It is assumed that the density of the LNG is as 498.5 kg/m\({}^{3}\) and the LNG cargo with normal pressure head (25 m) is loaded in the tank. The results a listed in Table 5. Comparing the orifice coefficient under pressure conditions, as in Figure 17, it is found that the coefficient gradually increases as the internal pressure increases. When the area of the crack is less than 14 mm\({}^{2}\), it is possible to check that the orifice coefficient increases gradually due to the roughness of the wall of the crack wall. We can see that the area increased rapidly in sections over 14 mm\({}^{2}\). Based on this result, it can be confirmed that the orifice coefficient is distributed differently depending on the size of the crack as shown in Figures 19 and 20.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen No.** & **Area [mm\({}^{2}\)]** & **Load Case** & **Office Coefficient** \\ \hline \multirow{4}{*}{0} & 1.75 & LC1 & 0.22 \\  & (0.5 \(\times\) 3.5) & LC2 & 0.30 \\  & & LC3 & 0.34 \\  & & LC1 & 0.23 \\  & 3.5 & LC2 & 0.31 \\  & (0.5 \(\times\) 7) & LC3 & 0.35 \\  & & LC1 & 0.24 \\  & 7 & LC2 & 0.31 \\  & (1.0 \(\times\) 7) & LC3 & 0.36 \\  & 14 & LC1 & 0.28 \\
3 & (1.0 \(\times\) 14) & LC2 & 0.33 \\  & & LC3 & 0.38 \\  & 28 & LC1 & 0.4 \\
4 & (2.0 \(\times\) 14) & LC2 & 0.54 \\  & & LC3 & 0.59 \\ \hline \end{tabular}
\end{table}
Table 6: Orifice coefficient according to crack area and pressure conditions.  
Figure 18: Orifice flow rate for Reynold number and aspect ratio. (**a**) Flow rate vs. Re. (**b**) Flow rate vs. AR.
# Arsenic (V) Removal by an Adsorbent Material Derived from Acid Mine Drainage Sludge
## 2 Materials and Methods
### Arsenic Adsorption Batch Test
#### 2.3.2 Adsorption Isotherm  
Adsorption isotherms were determined through adsorption experiments with the same 50 mg/L initial concentration of As(V) but different adsorbent doses. Then, 50 mL of the arsenic-containing aqueous solution was placed into conical centrifuge tubes, and the pH was adjusted to 6.0 by adding 0.1 M HCI and NaOH. Adsorbents were added to the solution at doses ranging from 0.0025 to 0.5 g/L. Conical tubes were continuously stirred at 20 rpm by using a vertical stirrer. After 48 h, the solution was filtered with a PES filter. Arsenic concentrations were determined as described above.  
Adsorption isotherm data were fitted as a Langmuir isotherm (Equation (5)) and Freundlich isotherm (Equation (6)) [22; 23] as follows:  
\[q_{e}=q_{m}\frac{a_{L}C_{e}}{1+a_{L}C_{e}} \tag{5}\]  
\[q_{e}=K_{F}C^{1/n} \tag{6}\]  
where \(q_{e}\) is the quantity of the arsenic adsorbate adsorbed per unit weight of solid adsorbent, \(q_{m}\) is the maximum sorption capacity of the adsorbent (mg/g), \(C_{e}\) is the equilibrium concentration of the adsorbate in solution (mg/L), and \(a_{L}\) is the Langmuir affinity constant. The constants \(K_{F}\) and 1/n reflect the adsorption capacity and adsorption intensity, respectively, of the adsorbent.
## 4 Nanotechnology and Cancer
### Nanoparticles in Cancer Therapy  
Current cancer treatments are constrained to surgical methods, radiation therapy, and chemotherapy, each of which poses the risk of damaging healthy tissues or not eliminating the cancer. Nanotechnology, however, presents a solution to these issues by enabling the precise and targeted delivery of chemotherapy drugs to cancer cells and tumors, contributing to the surgical removal of tumors and enhancing the efficacy of radiation and other established treatment approaches [39]. This progress promises a lower patient risk and increases the likelihood of survival. Further advancements in nanotechnology-based cancer treatment have expanded beyond merely improving drug delivery to creating new treatment options that are only possible through the unique properties of nanomaterials [39]. Nanoparticles' sizable surface area can be tailored with ligands, which can be antibodies, small molecules, peptides, DNA or RNA strands, and aptamers [39]. These ligands can serve both therapeutic purposes and guide the nanoparticle's behavior within the body. The versatility of these properties allows for combination drug delivery, multimodality treatment, and the combination of therapy and diagnosis, referred to as \"theranostic\" action. Nanoparticles used in cancer therapy include liposomes, polymeric nanoparticles, gold nanoparticles, magnetic nanoparticles, and dendrimers [39]. Each type has unique properties that make it suitable for certain applications. For instance, liposomes are often used as drug carriers due to their biocompatibility and ability to encapsulate both hydrophilic and hydrophobic drugs [39].
# Experimental Identification of Characteristic Curves of
###### Abstract  
Supersonic vacuum generators, or ejectors, operate pneumatically to extract air from tanks in industrial applications. A key performance metric for ejectors is the Total Evacuation Time (TET), which measures the time required to reach minimum pressure. This research predicts TET using empirical models that rely on two key metrics: the characteristic curve, which relates absorbed flow rate to the working pressure, and the polytropic curve, which describes the evolution of the polytropic coefficient across working pressures. Accurately capturing both curves for subsequent fitting to polynomial curves is crucial for forecasting TET. Several experimental setups were employed to capture the curves, each of which refined the data and improved the quality of the polynomial fits and coefficients. Multiple setups were necessary to pinpoint the breakpoint, from supersonic to subsonic operation mode, which is a critical factor that affects the characteristic curve and the TET. Furthermore, the research shows an improvement in the TET forecasts for each setup, with deviations between experimental and predicted TET ranging from 7.6% (14.5 s) to a 1.4% (2.6 s) in the most precise setup. Once the models were validated, an optimized ejector design, extracted from an author's previous article, was tested. It revealed a 4% improvement (8 s) in the TET. These results highlight the importance of the mathematical models developed, which can be used in the future to compare ejectors and reduce the need for experimental data.  
experimental study; vacuum generator; one-stage supersonic ejector; characteristic curve; polytropic curve; evacuation time +
Footnote †: journal: Applied sciences  
Article
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 2 Modelling Approach
### Simulation
#### 2.1.2 Wind Turbine Model  
Hourly wind speed data for an entire year can be imported or downloaded from online databases. The wind speed for each 15 min time step was obtained using a random number \(N(0,\sigma_{W})\) (to consider the uncertainty) and first-order autoregressive function model. If the hub height \(z_{\text{hub}}\) (m) was different from the anemometer height \(z_{\text{anem}}\) (m), the wind speed at the hub height \(w_{\text{hub}}(t)\) (m/s) was obtained by converting the measured wind speed \(w(t)\) (m/s) at the anemometer height considering the surface roughness length \(z_{0}\) (m) based on  
\[w_{hub}(t)=w(t).\frac{\ln\frac{z_{\text{hub}}}{z_{0}}}{\ln\frac{z_{\text{anem }}}{z_{0}}} \tag{5}\]  
The wind turbine output power curve supplied by its manufacturer is measured in standard conditions (temperature 288.15 K, sea level, and air density \(\rho_{air\_0}=1.225\) kg/m\({}^{3}\)). For each time step, the wind turbine output curve was converted to the air density of the time step \(\rho_{air}\)(t) (kg/m\({}^{3}\)) by multiplying with the relationship \(\rho_{air}(t)/\rho_{air\_0}\). Turbines with pitch control maintained their nominal power (\(P_{WT\_N}\)) without any changes, as shown in Figure 2.  
The wind turbine output power \(P_{WT}(ws,t)\) (W) at time \(t\) for each wind speed \(ws\) is obtained as  
\[P_{WT}(ws,t)=P_{WT_{curve}}(ws)\rho_{air}(t)/\rho_{air\_0}(1-f_{red\_WT})^{y- 1}f_{WT\_loss}\ \ \forall\ ws<ws_{rated}, \tag{6}\]  
\[P_{WT}(ws,t)=P_{WT_{curve}}(ws)(1-f_{red\_WT})^{y-1}f_{WT\_loss}\ \ \forall\ ws\geq ws_{rated}, \tag{7}\]  
where \(P_{WT_{curve}}(ws)\) represents the output power curve (power vs. wind speed \(ws\)) of the wind turbine when it is new (year 0) under standard conditions; \(f_{red\_WT}\) represents the wind turbine annual reduction factor owing to degradation ([38]); \(f_{WT\_loss}\) represents the efficiency owing to losses in wires, switches, etc.; and \(ws_{rated}\) represents the rated wind speed of the wind turbine (in Figure 2, 15 m/s).  
Several wind turbines can work in parallel; in this case, the total output power is calculated considering the wake effect model proposed by [PERSON] (2018).
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 4 Computational Complexity  
For \(N\) memory elements of input vector and \(M\) dimensions of random Fourier features, the computational load per iteration of KLMS-RFN algorithm is calculated Table 1.  
As shown in Table 1, the computational complexity of the KLMS-RFN algorithm is mainly related to the dimension of the random Fourier features and the input signal vector. When the dimension of input vector is a constant, the computation complexity of KLMS-RFN will be \(O(M)\).  
Consider the following calculation of kernel function \(K(\mathbf{C},\mathbf{x}(n))\), where \(\mathbf{C}\) denotes a dictionary with \(D\) samples and \(\mathbf{e}_{k}(n)\) is a \(D\) dimensional error vector. The computation requirement of computing the Gaussian kernel includes \((N+1)D\) multiplications, \((2N-1)D\) additions, and \(D\)\(exp(\cdot)\) calculations. Therefore, the entire computation complexity can be considered \(O(D)\). For the QKLMS-FB algorithm, the computation complexity of updating significance is \(O(D)\), when D is the network size of QKLMS-FB [17]. According to whether a new center must be added, the corresponding computation complexity of QKLMS-FB will be two times \(O(D)\) or three times \(O(D)\). Therefore, the computational complexity of QKLMS-FB algorithm is not a fixed value during the training process. The detailed comparison of computation complexity between the proposed algorithm and QKLMS-FB is described in the simulation section.
# A DDPG-Based USV Path-Planning Algorithm
## 3 Simulation
### Environment Modeling  
#### 3.2.1 Environment Setting  
In our simulation environment, we configure a map of dimensions 320 by 320, with the unmanned boat's objective being to locate the most efficient route to a specific target point. We represent the state of the unmanned boat with the triplet \((x,y,\alpha)\), where \(x\) and \(y\) are the real-time coordinates of the boat within the simulated terrain, and \(\alpha\) signifies the yaw angle relative to the target.  
Starting from the initial coordinates of \((x_{0},y_{0})\), the actual heading angle is given by \(\theta=\arctan(x_{0},y_{0})\). When considering the current coordinates as \((x,y)\) and the target coordinates as \((p,q)\), we can derive the desired heading through the following formula:  
\[\beta=\arctan(x-p,y-q)-\arctan(x,y) \tag{16}\]  
Figure 3: Diagram before and after action space optimization.  
As the boat carries out actions from the set, each corresponding to a specific heading angle \(a\), the actual heading angle changes accordingly. We can represent this as \(a_{\theta}\), and update the heading angle with  
\[\theta_{\text{new}}\ =\ \theta_{\text{old}}\ +a_{\theta} \tag{17}\]  
The yaw angle \(\alpha\) is then characterized as  
\[\alpha=\beta-\theta \tag{18}\]  
And the steering angle \(\Delta\theta\) is determined as  
\[\Delta\theta=\theta_{\text{new}}\ -\ \theta_{\text{old}} \tag{19}\]
# A Novel Hierarchical Multi-Stable Cylindrical Structure with Superior Energy Trapping
## 2 Structural Design
### Design of Hierarchical Multi-Stable Cylindrical Structure (HMCS)  
Inspired by the bionic multilevel structure, a novel hierarchical multi-stable cylindrical structure (HMCS) based on a cosine curved beam was proposed, as shown in Figure 4. The proposed new structure consists of two traditional multi-stable cylindrical structures (TMCSs) combined through a nested concept, with the inner and outer layers connected by slender strips.  
Figure 3: (**a**) Two different deformation states of the cosinusoidal curved beam under a transverse force \(\mathbf{f}\); (**b**) the geometry features of the unit cell; (**c**) axonometric view of the unit cell.  
Figure 2: Normalized force versus normalized displacement for three different modes.
## 3 Related Work
### Relevant Data Farming Applications  
Although the data farming methodology originates from warfare simulations [14], a recent publication by [PERSON] et al. [7] proves its applicability to manufacturing system simulations. The application is particularly useful in the planning phase when real data are not available. Moreover, in manufacturing, data farming leads to a comprehensive, data-based system understanding instead of focusing on predefined, specific \"what-if\" analyses as carried out in the past [8]. There are already several publications on the application of data farming in manufacturing.  
[PERSON] et al. [31] apply data farming to a demonstrator production line. The experiment design considers quantitative factors such as buffer capacities, categorical factors such as clearance strategies, and different product mixes, resulting in approx. 102.400 simulation runs. [PERSON] et al. [32] apply data farming in the context of supply chains. An exemplary supply chain is analyzed using an experiment plan with 257 different experiments. [PERSON] et al. [33] analyze a data farming case study from vehicle assembly. For this purpose, a simulation model of a 30-station and 14-buffer assembly line was used. Moreover, the simulation model covers 50 workers and 720 different vehicle variants. The factors were separated into decision factors and disturbance factors. First, [PERSON] et al. [33] varied the decision factors based on a Nearly Orthogonal Latin Hypercubes (NOLH) experiment design [34], and then the disturbance factors were also varied. [PERSON] et al. [35] applyclustering algorithms to a simulation database that was created using the data farming methodology. Thereby, the goal is to optimize the operation status of a production system. To do so, [PERSON] et al. [35] compare different order control approaches in combination with a set of order prioritization rules and production volume variation. This results in 1470 simulation experiments, leading to a large simulation database for clustering analysis.  
Many authors use dedicated methods (e.g., NOLH designs) to design data farming experiments. In general, the goal of these methods differs from the traditional goals of physical experimentation. For data farming, large experiment designs with good space-filling behavior are preferred over small designs [36]. Experiment design methods for data farming are not further discussed in this paper, as they are the subject of several publications, such as [16; 37; 38].
## 5 Simulation Results
### The Correctness Verification of AFC-ANF When the Rotor Is Under Acceleration  
The ANF is added to the AFC of the AMBs rigid rotor system, which can filter out the signals present in the rotor vibration signals measured by the sensors that are of the same frequency as the rotational speed. Figure 11a-d show the rotor vibration of the AMBs  
Figure 10: Rotor vibration with or without positive polarity control. (**a**) \(x\) direction of AMBs-A. (**b**) \(y\) direction of AMBs-A. (**c**) \(x\) direction of AMB-B. (**d**) \(y\) direction of AMB-B. (**e**) \(A\) 3D plot of AMB-A. (**f**) A 3D plot of AMB-B.  
rigid rotor system in constant-acceleration operation before and after the addition of ANF. Figure 11e,f show the 3D figure of rotor vibration of the AMB-A and AMB-B ends of the AMBs high-speed motor, respectively.  
In Figure 11, it is observed that, under constant acceleration, the rotor vibration is significantly reduced due to the addition of the ANF, which reduces the rotor vibration at the AMB-A end from 1.5 \(\times\) 10\({}^{-6}\) m to 0.9 \(\times\) 10\({}^{-6}\) m, reducing by 40%; the rotor vibration at the AMB-B end reduces from 1.5 \(\times\) 10\({}^{-6}\) m to 0.9 \(\times\) 10\({}^{-6}\) m, reducing by 40%. After a short time, the rotor speed unbalanced component is filtered out to achieve unbalanced control, and the AFC-ANF does not change the stability of the AMBs-rigid rotor system, which also indicates that the AFC-ANF has a benefit-suppression effect on the unbalanced component.
## 3 Nearest Neighbor Classification of Protein Structures
### Classification Algorithm  
\(k\)-nearest neighbor classification is a scheme that assigns the query to the class to which most of the \(k\) targets belong that are closest to the query. In order to classify, we therefore need to determine the \(k\) structures with minimum distance to the query and assign the superfamily to which the majority of the neighbors belong. As seen in the previous section, we can use bounds to decide whether a structure is closer to the query than another structure. This can be generalized to deciding whether or not a structure can be among the \(k\) closest structures in the following way. We construct two priority queues, called LB and UB, whose elements are \((t,lb(q,t)))\) and \((t,ub(q,t))\), respectively, where \(q\) is the query and \(t\) is the target. Here, \(lb(q,t)\) (respectively \(ub(q,t)\)) is any lower (respectively upper) bound on the distance between \(q\) and \(t\). In our current implementation, we use \(D_{\max}\) as a distance, while lower and upper bounds are \(d_{\bigtriangledown}(q,t)\) (respectively \(d^{\triangle}(q,t)\)) or \(\underline{d}(q,t)\) (respectively \(\overline{d}(q,t)\)) where \(\underline{d}(q,t)\) and \(\overline{d}(q,t)\) are lower and upper bounds based on Lagrangian relaxation. As explained in [8], these bounds can be polynomially computed by a sub-gradient descent method, where each iteration is solved in \(O(n^{4})\) time, where \(n\) is the number of vertices of the contact map graph. However, when the graph is sparse (which is the case of contact map graphs), the above complexity bound is reduced to \(O(n^{2})\). The practical convergence of the sub-gradient method is unpredictable, but an experimental analysis performed by the authors of [8] suggests that 500 iterations is a reasonable average estimation. The quality of the bounds \(\underline{d}(q,t)\) and \(\overline{d}(q,t)\) for the purpose of protein classification has been already demonstrated in [9,11,21].  
The priority queues LB and UB are sorted in the order of increasing distance. The \(k\)-th element in queue UB is denoted by \(t_{k}^{\mathrm{UB}}\). Its distance to the query, \(d(q,t_{k}^{\mathrm{UB}})\), is the distance for which at least \(k\) target elements are closer to the query. Therefore, we can safely discard all of those targets that have a lower bound distance of more than \(d(q,t_{k}^{\mathrm{UB}})\) to query \(q\). That is, \(t_{k}^{\mathrm{UB}}\) dominates all targets \(t\) for which \(lb(q,t)>ub(q,t_{k}^{\mathrm{UB}})\).  
We assume that distances between family members are computed optimally (this is actually done in our preprocessing step when computing the family representatives), _i.e._, \(d(A,B)=\underline{d}(A,B)=\bar{d}(A,B)\) if \(A,B\in\mathcal{F}\). The algorithm also works if this is not the case, then \(d(A,B)\) needs to be replaced by the corresponding Lagrangian bounds at the appropriate places.
## 3 Project Overview and Data Preprocessing
### Optimal Feature Subset Selection
#### 3.3.2 Feature Importance Measure for RF  
In this study, the random forest algorithm was employed to rank the importance of the features and to further reduce feature dimensionality. The calculation result is illustrated in Figure 4c. The significance of the feature variables was evaluated using the mean decrease in impurity (MDI), which is based on the Gini index. Throughout the feature reduction process, the random forest algorithm utilizes its inherent MDI metric to prioritize feature variables, reflecting their respective roles within the random forest algorithm. The Gini index is calculated and averaged to compare different feature variables, identifying those with a more substantial influence and those with a more efficient ability to recognize surrounding rock grades during the classification process.  
\[Gini(p)=\sum_{k=1}^{K}p_{k}(1-p_{k})=1-\sum_{k=1}^{K}p_{k}^{2} \tag{3}\]  
In the formula, \(K\) represents the overall count of sample categories, and \(p_{k}\) represents the weights assigned to category \(K\) for sampling.
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 6 Materials and Methods
### Feature Selection Algorithms
The SBFS algorithm starts with an initial full set of features and sequentially removes a feature that results in better fitness. The process continues until there are no features that can be removed that will result in a better fitness than the current solution. Generalised Sequential Forward Feature Selection (SFFS).The SFFS is used as implemented by the GreedyStepwise implementation in Weka [22]. The SFFS algorithm starts with an initial empty set of features and sequentially adds a feature that results in better fitness. The process continues until there are no features that can be added that will result in a better fitness than the current solution. Pearson Correlation Coefficient Feature Selection (PCFS).The Pearson correlation coefficient is used to determine feature relevance. The implementation makes use of the Weka [22] library. Features are ranked based on relevance with respect to the class, in descending order. One of the disadvantages of using information theoretic criteria is that the approach only assigns a measure of relevance to each feature. A method to determine the number of most relevant features is required. A simple linear combination approach is taken to evaluate the model accuracy for each combination. Given a list of ranked features in descending order, each linear combination of features is considered and the fitness of the solution is evaluated. For example, let the set of all features sorted by decreasing relevance be \(F=\{1,5,2,4,3\}\). The following five solutions will be used to evaluate the model fitness: \(\{1\}\), \(\{1,5\}\), \(\{1,5,2\}\), \(\{1,5,2,4\}\), and \(\{1,5,2,4,3\}\). The feature set with the highest fitness value is selected as the number of features to use. Information Gain Feature Selection (IGFS).The information-gain [27] information theoretic criterion is used to determine feature relevance. The implementation makes use of the Weka [22] library. Features are ranked based on relevance with respect to the class, in descending order. The same process of linear combination evaluation is followed as in the PCFS algorithm.