esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Identification by Nearest Neighbor Algorithm of Effective Points to R_Value Calibration Surfaces  
The collected DE-XRT image data was input into our software for identification experiments by means of comparing mean distance between R_value of effective points and R_value calibration surface. Table 4 shows the statistical data for 1# sample, obtained from its position and posture in Figure 7 and calculated \(d_{RM}\) according to Equation (10). For 1# sample identification, 2934 effective points out of 7618 points by means of Section 2.3.1 were judged. When \(d_{RM}\) from Al calibration surface was less than that from Mg calibration surface, Al can be identified. The more effective the points were, the less random the error, indicating the mean effect of effective point \(d_{RM}\) can improve identification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Total Points** & **Effective Points** & \(d_{RM}\,\mathrm{l}_{\mathrm{Al}}\) & \(d_{RM}\,\mathrm{l}_{\mathrm{Mg}}\) & \(R_{M}\) & \(S_{LM}\) & **Identif. Class** \\ \hline
7618 & 2934 & 0.039 & 0.121 & 1.630 & 0.283 & Al \\ \hline \end{tabular}
\end{table}
Table 4: Identification data of sample 1#.  
Figure 11: \(S_{L}\)-\(x\)-\(R\) R_value calibration surface of light metals Al and Mg.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Material** & \(\mathbf{\beta_{1}}\) & \(\mathbf{\beta_{2}}\) & \(\mathbf{\beta_{3}}\) & \(\mathbf{\beta_{4}}\) & \(\mathbf{\beta_{5}}\) & \(\mathbf{\beta_{6}}\) & **RMSE** \\ \hline Mg & 1.390 & 0.386 & 2.375 \(\times\) 10\({}^{-5}\) & \(1.500\times 10^{-4}\) & \(-0.523\) & \(3.970\times 10^{-7}\) & 0.018 \\ Al & 1.561 & 0.103 & 2.601 \(\times\) 10\({}^{-4}\) & \(-6.220\times 10^{-4}\) & \(-0.352\) & \(6.937\times 10^{-7}\) & 0.020 \\ SS & 0.937 & 3.664 & 1.332 \(\times\) 10\({}^{-4}\) & \(3.177\times 10^{-5}\) & \(-3.092\) & \(-1.950\times 10^{-7}\) & 0.027 \\ Brass & 1.413 & 2.572 & 2.108 \(\times\) 10\({}^{-4}\) & \(4.249\times 10^{-4}\) & \(-2.445\) & \(-1.290\times 10^{-6}\) & 0.023 \\ Cu & 1.310 & 3.488 & \(-1.636\times 10^{-5}\) & \(-1.680\times 10^{-4}\) & \(-3.216\) & \(1.238\times 10^{-6}\) & 0.026 \\ \hline \end{tabular}
\end{table}
Table 3: R_value calibration surface coefficients.  
Figure 12: \(S_{L}\)-\(x\)-\(R\) R_value calibration surface of heavy metals Cu, brass and SS.  
Table 5 shows the identification statistics of self-made samples. The laboratory identification accuracy by count basis was 85.2%. If the accuracy was evaluated by weight, the identification results were as followed: 83.8% of Al recovered with 91% purity; 66.7% of Mg recovered with 39.7% purity; 85.7% of Cu recovered with 90.7% purity; and 70% of brass recovered with 68.4% purity. Among them, the purity grade of Mg was particularly low because the weight of 3# sample reached 1458 g, much heavier than those of 1#, 2# and 4# samples. The data of actual waste identification in Table 6 are correct according to experience and visual inspection.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 4 Results  
Departing from the configurations and scenarios described in the previous section, a complete set of experiments were carried out. The respective results are presented next, and discussed in Section 5.  
The hypervolume (HV) indicator [58] will be used to evaluate the results as indicator of multi-objective relative quality, measuring the n-dimensional volume with respect to a pre-defined reference point [59]. The HV values are used to rank solutions and assess global optimization convergence.  
Figures 5 and 6 show the evolution of the Hypervolume for the median execution of every 27 configurations, for overlapping and separated obstacles scenarios, respectively. The reference points used for HV computation were 10 km and 90 km (80 km for scenarios 6 and 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N** & **PS** & **NG** & **  
==ERROR: No output for this page==
The average rankings of every configuration and scenario are compiled in Table 5.  
\begin{table} \
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**C\_1** & **AvR\_1** & **C\_2** & **AvR\_2** & **C\_3** & **AvR\_3** & **C\_4** & **AvR\_4** & **C\_5** & **AvR\_5** & **C\_6** & **AvR\_6** & **C\_7** & **AvR\_7** & **C\_8** & **AvR\_8** \\ \hline
17 & 7.31 & 17 & 7.78 & 13 & 6.41 & 16 & 6.84 & 22 & 9.73 & 16 & 6.96 & 16 & 7.67 & 13 & 9.43 \\
16 & 7.88 & 14 & 8.45 & 16 & 8.00 & 17 & 8.47 & 25 & 10.06 & 17 & 8.43 & 13 & 8.29 & 16 & 9.84 \\
13 & 8.12 & 18 & 8.90 & 17 & 8.37 & 22 & 8.88 & 13 & 11.75 & 13 & 9.69 & 17 & 8.63 & 26 & 10.39 \\
18 & 8.35 & 16 & 8.92 & 14 & 8.69 & 18 & 9.02 & 16 & 11.80 & 18 & 9.96 & 18 & 9.00 & 18 & 10.84 \\
15 & 8.65 & 13 & 9.31 & 22 & 9.61 & 13 & 9.04 & 26 & 11.82 & 26 & 10.22 & 22 & 9.98 & 17 & 10.88 \\
14 & 9.10 & 15 & 9.35 & 18 & 9.78 & 25 & 10.49 & 17 & 12.04 & 14 & 10.35 & 14 & 10.02 & 7 & 11.02 \\
19 & 10.04 & 27 & 11.94 & 23 & 10.96 & 23 & 11.35 & 27 & 12.10 & 25 & 10.80 & 26 & 11.06 & 4 & 11.78 \\
22 & 10.25 & 25 & 12.29 & 24 & 11.65 & 1\end{tabular}\end{table}
+++  
Before going into more detailed analysis, a global perspective of the optimization results is presented in the images depicted in Figure 11, showing some selected glider trajectories resulting from the multi-objective optimization process. Although the trajectories are represented here as 2D, as previously stated, they are in fact 4D as the vehicle evolves in time across X (Longitude), Y (Latitude), and Z (Depth) coordinates during the three-day mission, from start towards the target point (pattern on Figure 1).  
In general, all comparatives follow a similar structure. Scenarios 1 and 8 will be selected as an example to have a closer look to the optimization process.
## 3 Model Validation and Results Analysis
### Inhomogeneous Particle Case  
To verify the modeling capability of the IIM T-Matrix method for particles with inhomogeneous compositions, the results obtained by the IIM T-matrix model are compared with the DDASCAT for a spheroid with a spherical core. In this case, the wavelength of incident light is taken as 0.5 \(\upmu\)m, the horizontal and rotational axis of the outer spheroid are set as \(a=1.0\)\(\upmu\)m and \(b=1.5\)\(\upmu\)m respectively, and its complex refractive index is set to be m\({}_{1}=1.44-0.000\)i. The inner part is a spherical core with a radius of 1.0 \(\upmu\)m, its refractive index is taken as m\({}_{2}=1.20-0.000\)i. The region between the inscribed and circumscribed spheres is divided into 30 layers along the radial direction. The simulation results are shown in Figure 7.  
As can be seen from the figure, the IIM-T-matrix model achieves high calculation accuracy, and the curves of the scattering phase matrix are almost coincided with those of DDASCAT. For F\({}_{11}\), the relative simulation errors are all less than 10% in scattering angles ranging from 0\({}^{\circ}\) to 30\({}^{\circ}\). With the increase of scattering angle, its simulation accuracy decreases, but the relative errors are within 26%. The spatial distributions of the simulation errors of F\({}_{12}\)/F\({}_{11}\), F\({}_{34}\)/F\({}_{11}\), and F\({}_{44}\)/F\({}_{11}\) are similar to F\({}_{11}\). In the forward scattering direction, their simulation accuracy is higher and the absolute simulation errors tend to be 0, while as the scattering angle becomes larger, their simulation accuracy is slightly reduced, but it should be noted that their absolute simulation errors are still within 0.1 in most scattering directions. From the discussion, it can be found that the IIM T-Matrix method can simulate the light scattering by inhomogeneous particles with a high accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Type** & \(\mathbf{C_{ext}/\upmu^{2}}\) & \(\mathbf{C_{abs}/\upmu^{2}}\) & \(\mathbf{C_{sca}/\upmu^{2}}\) & \(\upomega\) \\ \hline EBCM & 11.4129 & 0.2791 & 11.1338 & 0.9755 \\ IIM T-Matrix & 11.3102 & 0.2735 & 11.0367 & 0.9758 \\ Difference/\% & 0.9080 & 2.0475 & 0.8798 & \(-0.0279\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The integral scattering parameters calculated by the IIM and EBCM T-Matrix methods for the cylindrical particle.  
Figure 7: The scattering phase matrix obtained by the IIM T-Matrix method and DDASCAT for the spheroidal particle with a spherical core. The simulation errors of F\({}_{11}\) are denoted by relative errors. For F\({}_{12}\), F\({}_{34}\), and F\({}_{44}\), they are firstly normalized by F\({}_{11}\), and their simulation errors are evaluated by the absolute errors.
## 3 Sources of Uncertainty  
### Uncertainty in Ground Motion Intensity and Method of Intensity Selection  
Intensity measures (IM) are useful, such as the information on the occurrence probability of a ground motion with a given IM level. The typical IM of an earthquake is commonly measured by peak ground motions (i.e., acceleration PGA, velocity PGV, or displacement PGD) or the spectral responses of structure (i.e., acceleration S\({}_{\text{a}}\), velocity S\({}_{\text{v}}\), or displacement S\({}_{\text{d}}\)). The IM of an earthquake reflects the correlation of structural performance or damage. An IM candidate must satisfy two criteria, which are a proper IM to EDP of interest and an available probability function of occurrence [6]. For a 30.5 m-span STS gantry container crane having a long fundamental period ranging from 1.3 s to 2.1 s [1,11,12], the elastic spectral acceleration (S\({}_{\text{a}}\)) is a more proper input variable to be selected compared to the peak ground acceleration. In addition, the spectral acceleration at the fundamental period of the structure relates to uniform hazard analysis that is commonly suggested in worldwide seismic guidelines. For instance, Figure 3 shows an example of two elastic response spectra constructed based on the Korean Design Standard (KDS) for seismic zone I and soil type S\({}_{\text{D-1}}\) and S\({}_{\text{D-2}}\)[13]. It can clearly be seen that the response spectrum of Z1 SD-2 is developed from a higher peak ground acceleration (PGA) than that of Z1 SD-1, they are 0.199 g and 0.214 g for Z1 SD-1 and Z1 SD-2, respectively. However, this does not reflect the actual response of the structure. In particular, in this study, at the fundamental period of 1.35 s of the Korean container crane, the spectral response of acceleration under Z1 SD-1 earthquake is 0.217 g higher than that of Z1 SD-2 of 0.174 g, as shown in Figure 3. In this study, therefore, the spectral acceleration (S\({}_{\text{a}}\)) is a suitable input variable to consider the uncertainty in ground motion intensity and profile.  
Figure 3: A comparison of the structural response using elastic response spectra of Korean Design Standard (KDS).  
Figure 2: Sensitivity analysis by tornado diagram.  
Two criteria for selection of an IM have been proposed by several researchers [8]: The first method strongly correlates with the performance variable of interest (i.e., damage factor). The second method uses hazard information (i.e., the information on the occurrence probability of an earthquake with a given IM level) [4]. In this study, the three S\({}_{\text{a}}\) values selected corresponded to the seismic zone I of KDS [13] for return periods of 100, 500, and 2400 years, considered as the lower bound, median, and upper bounds, respectively. The adjustment of the amplitude according to local site conditions for numerical analyses will be explained in detail in Section 4.2.
## 2 Theory and Modeling
### Boundary Consideration  
As a photon reaches the sea surface, an estimation for total transmission \(t_{t}\) of ocean interface due to specific viewing angle and sea wave conditions of rough sea surface [20; 21] is given:  
\[t_{t}=t_{p}\times t_{r} \tag{9}\]  
where \(t_{p}\) is means the part of transmission under calm sea surface calculated with Snell's law; and \(t_{r}\) is the part of transmission in a wind-driven roughened surface and can be obtained as a function of wind speed \(u\):  
\[\left\{\begin{array}{l}t_{r}=1-1.2\times 10^{-5}\times u^{3.3},\ u\leq 9\ \mathrm{m/s}\\ t_{r}=1-1.2\times 10^{-5}\times u^{3.3}\times(0.225u-0.99),\ u>9\ \mathrm{m/s} \end{array}\right. \tag{10}\]  
Actual measurements of bidirectional reflectance distribution functions (BRDFs) of ocean bottom materials like sand or sea grass canopies have rarely been made. Because of the lack of measurements and models of the BRDF for actual ocean bottom materials, it is usually assumed that a bottom is a Lambertian reflecting surface [22]. In this study, the sea bottom is simply treated as a Lambertian surface, and for that reason lidar bathymetry simulation is not useful for our purposes. Its reflectivity is the same in all directions. A reflection event is forced by multiplying the photon's weight by sea bottom albedo.
## 3 Results and Discussion
### Flow Control Effect by AC-DBD Plasma Actuation  
#### 3.2.1 Force Balance Data  
The drag coefficient of the model, C\({}_{D}\), using the data from the force balance are tabulated in the following Table 1. From Table 1, it can be seen that the use of AC-DBD plasma actuation could only marginally affect the drag coefficient of the articulated lorry model. Drag reduction occurred when the actuation frequency was 9 kHz but the reduction in C\({}_{D}\) compared to the baseline case was 0.001 which is negligibly small. The results obtained suggested that using the AC-DBD plasma actuation cannot reduce drag on the articulated lorry model. In contrast, at 7 kHz, the drag coefficient actually increased by 0.004. Although, it is still negligibly small, it points out the possibility of the drag increase due to the implementation of the AC-DBD plasma actuation.  
Although the results shown in Table 1 confirmed that the use of the AC-DBD plasma actuation could not reduce drag, it is important to understand how the plasma actuation affects the flow characteristics in the wake region so that the design of the actuator can be modified to suit this  
Figure 7: Jet x-velocity at various actuation frequencies at different normalised x-locations downstream of the trailer rear end. x/H = (**a**) 0.2, (**b**) 0.4, (**c**) 0.6, (**d**) 0.8, (**e**) 1.0 and (**f**) 1.2.  
application. To be exact, could the use of AC-DBD plasma actuation actually be able to exert effects on the flow and hence, manipulate the flow in the wake region? To answer, this question, a series of PIV measurements were conducted to visualise and quantify the flow characteristics in the wake region of the articulated lorry model with and without the AC-DBD plasma actuation being implemented. In addition, frequency responses of the flow in the wake region could be derived from the PIV data so that the effect of the AC-DBD plasma actuation in the flow frequency response could be studied.
## 6 Discussion
### Comparative Performance Analysis  
To prove the efficiency of the introduced CAD and its competing capacity, a comparison was executed to compare its performance with the state-of-the-art methods for cervical cancer diagnosis using the Mendeley LBC dataset. The methods described in Table 5 are summarized as follows. The study [69] cropped each photo in the database and used such clipped photos to learn DarkNet-19 and DarkNet-53 individually. Because the attributes extracted out of these CNNs were large, they were fed separately to neighborhood component analysis (NCA) to reduce their sizes. Lastly, the above lowered features were then utilized to construct an SVM classifier, which achieved accuracies of 98.26% and 99.47% for two datasets. In addition, the authors of reference [70] proposed a CAD that utilized a two-step dimension reduction strategy that utilized PCA and the grey wolf optimizer (GWO) that diminished features extracted from numerous CNN architectures. The lowered attributes were used to train an SVM classifier, which generated final predictions. Whilst the reference [71] procured confidence values from the Inception, MobileNet, and InceptionResNet CNNs, and then gathered such ratings using a fuzzy distance-based hybrid approach with multiple distance measures. On the other hand, the study [72] utilized three CNNs, including Inception V3, MobileNet V2, and Inception ResNet V2, with extra layers to discover data-specific attributes. The authors suggest a new ensemble methodology based on the reducing of error values to combine the results of such models using three distance measures. To calculate the final predictions, the authors defuzzified the above distance metrics using the product rule reaching an accuracy of 99.23%. Conversely, in [73], a cervical cell image creation model (CCG-taming transformers) and a classification model that used Tokens-to-Token Vision Transformers (T2T-ViT) with transfer learning was introduced, which attained 98.89% accuracy. Whereas the study [74] employed GoogleNet and ResNet Individually to extract features. Genetic algorithm was then utilized to select features. The 720 selected features were then used to train SVM classifier, reaching an accuracy of 99.07%. The results included in Table 5 confirm the superior capacity of the introduced CAD over other state-of-the-art methods. it is obvious from the table that the proposed CAD achieved greater performance measures than all of the competing approaches. The su
# NO Separation Characteristics in Integrated Electromigration Membrane Reactor
## 3 Results and Discussion
### Effect of Inlet Concentration on Separation Efficiency and Total Mass Transfer Coefficient  
The effect of the NO inlet concentration on the NO separation efficiency and total mass transfer coefficient was shown in Figures 6 and 7, respectively. It can be observed in Figure 6 that the separation efficiency decreased with the increase in the NO inlet concentration under the experimental conditions. When the NO inlet concentration increased from 202 to 612 mg\(\cdot\)m\({}^{-3}\), the NO separation efficiency decreased from 56.4% to 41.2% at an 18 kV discharge voltage. This is the reason why the increased amount of NO negative ions is less than the increased amount of NO molecules in the reactor with the increase in the NO inlet concentration. Some studies [29; 30] have reported a similar changing rule in removing NO using other methods. It can be seen from Figure 7 that the total mass transfer coefficient of NO decreased with the increase in the inlet concentration. It also can be seen in Figure 7 with the increasing of inlet concentration that the total mass transfer coefficient decreased more significantly at a discharge voltage of 18 kV than at a discharge voltage of 12 kV. It may be the reason that the NO separation efficiency decreased as the NO inlet concentration increased (Equations (3) and (4)) [26].  
Figure 6: Effect of inlet concentration on separation efficiency.  
Figure 7: Effect of inlet concentration on total mass transfer coefficient.
## 4 Discussion
### Essential Fatty Acids: Linoleic Acid and Alpha-Linoleic Acid  
LA intake is above the lower FAO/WHO AI for all age ranges in all studies, except in 4 out of 12 studies done in 1-2 year old children (one national survey in France [78] and three local studies in Canada [49], China [61] and Australia [96], for which intake was too low. The EFSA AI for LA, based on LA intake in healthy adults in Europe, is higher than that of FAO/WHO, and LA intake was lower than the EFSA recommendations in about half of the studies for all ages considered, without specificity for geography. Sunflower seed oil contains 80-90% LA and is the most consumed vegetable oil in Turkey [120], which may explain the exceptionally high LA intake in children in this country. However, we did not have access to the original data of this survey to be able to confirm this speculation. At present, the health impact of high LA in early life is unclear. While available preclinical evidence suggests a risk for obesity and worse metabolic outcomes in adulthood in response to high LA (or high LA:ALA ratio) in early life, supportive clinical data are lacking (reviewed in [121]). Future, well-designed studies are warranted to untangle the complex relationship between these FA in early life and their role in long-term health.  
ALA intake is lower than the EFSA and FAO/WHO AI in more than half of the studies for all ages considered, without specificity for geography. To the best of our knowledge, no previous reviews have compared LA and ALA intake among children to the new FAO/WHO recommendation, however, [PERSON] et al., [13] compared intakes in European children of these two essential FA with the EFSA AI and found that in most of studies, LA and ALA intake were lower than the AI (75% of studies in 1-3 years old for both FA; 66% [LA] and 100% [ALA] of studies in 4-9 years old). These findings were based on only 7 studies available at that time in Europe. Our findings for Europe, based on more than 15 studies, most of them published since 2017, are less pessimistic, with LA and ALA intakes being in line with European AI in about 50% of studies.  
In the review of [PERSON], as in our own review, LA and ALA intake are probably overestimated, because several of the recovered studies assessed n-6 PUFA (and not just LA [12 studies]) or n-3 PUFA (and not just ALA [8 studies]). It is estimated that LA represents \(\sim\)97-99% of the dietary n-6 PUFA, while ALA generally represents \(\sim\)80-90% of the dietary n-3 PUFA [13; 54; 73]. Thus, the overestimation is higher for ALA than for LA, reinforcing the finding that ALA inadequate intake is more prevalent than that of LA. While LA and ALA are the two EFA needed for normal growth and development of children [122], according to EFSA, intakes of LA above 1% do not show any additional benefits.
## 2 Materials and Methods
### Machine Learning-Aided Sea Ice Monitoring Methods  
One of the most important tasks of monitoring sea ice is to distinguish sea ice from water. Therefore, the problem of this study can be regarded as a typical binary classification that can be done by using an ML method on a big dataset. The process flow of monitoring sea ice using ML is presented in Figure 4.  
The ML-based sea ice monitoring method includes three steps: (1) feature extraction from the TDS-1 data; (2) the learning process with the training dataset using ML algorithms; (3) the automatic discrimination between data collection over sea ice and over water. A total of seven input variables are used, which are the reference SIC maps and the sequence of six features (i.e., RESC, RESI, RESD, REWC, REWI and REWD) extracted from the TDS-1 data. When the reflection is coherent, the footprint of the TDS-1 DDM is about 6 km by 0.4 km along the track and across track, respectively [25; 26; 30], which is comparable with the reference SIC maps with a grid resolution of 6.25 km. The footprint is much larger for incoherent reflections. The specular point of each DDM is used to match the reference data. In general, the ML is based on two different data sets (training-set and test-set). The training data are pre-labeled using the reference SIC map; thus, the relationship between input parameters and output results can be modeled using suitable ML algorithms. Then, the output results of test data can be obtained using the pre-built model. According to the process of building models, machine learning could be mainly categorized into supervised learning, unsupervised learning and semi-supervised learning. The characteristics of supervised learning is that training data have priori information (results). In this study, the task is to distinguish sea ice from water and the output results of training datasets can be obtained through the reference SIC data. Therefore, two types of supervised learning--DT and RF--are adopted to monitor sea ice.