esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Canopy Measurements  
#### 2.4.1 SIF and Canopy Reflectance Measurements  
For canopy reflectance and SIF measurements, we used a UAV-mounted dual field-of-view spectrometer system, a Piccolo Doppio [57; 58; 59]. This system houses two spectrometers: a QE Pro (Ocean Optics Inc., Orlando, FL, USA) (R: 639-805 nm, SI: 0.18 nm, FWHM: 0.31-0.35 nm) that was used for SIF measurements and a Flame spectrometer (Ocean Optics Inc., Orlando, FL, USA) (R: 344-1019 nm, SI: 0.39 nm, FWHM: 1.33 nm) that was used to measure canopy spectral reflectance. Irradiance and radiance were collected with a 25\({}^{\circ}\) field-of-view bifurcated fibre using a cosine-corrected fore optic.  
While measuring, the UAV hovered for approximately 1 min above each measurement plot at around 8 m height, resulting in an approximately 1.77 m radius footprint on the plot. The flights were conducted on three separate days in 2018, July 10 (two flights), July 11 (three flights), and July 25 (four flights), between 09:30 and 13:00. The UAV also housed a nadir-looking GoPro--camera to be able to later control that the measurements were all done in appropriate positions. In addition to the measurement plots, a bare soil plot was also measured as a \"zero plot\" to provide validation for our SIF measurements.  
25 spectra were averaged together and used to calculate plot-wise radiometric SIF and spectral reflectance factors as the ratio of upwelling radiance to downwelling irradiance using Matlab (The Mathworks Inc., Massachusetts). Integration times were algorithmically optimized, depending on the sky conditions. SIF was retrieved using the Spectral Fitting Method (SFM) [60] at spectral range 685.93-691.17 nm for oxygen B-band (hereinafter, and for better comparison with leaf level data, addressed as FY685 (C), where C stands for canopy) and 756.57-768.84 nm for oxygen A--band (hereinafter addressed as FY760 (C)). Further details about the flight plan and SIF retrieval methods can be found in [PERSON] et al. [28].  
For logistical reasons, it was not always possible to time the canopy spectral measurements with perfect clear sky conditions. Accordingly, some of the spectra data, especially during the early measurements, was collected under a variable cover of thin clouds (see Appendix D, Figure A14). In order to account for the potential impact of variable irradiance on the analysis of red and far-red SIF, we estimated and used the SIF yield instead, as follows:  
\[FY\;685\;(C)=\frac{F685\;(C)}{E_{PAR}} \tag{4}\]  
\[FY\;760\;(C)=\frac{F760\;(C)}{E_{PAR}} \tag{5}\]  
where \(E_{\text{PAR}}\) is the irradiance (W/m\({}^{2}\), Appendix D, Figure A14) integrated for the PAR region between 400 and 700 nm.
## 4 Results
### Construction of Regression Model of Ships Delays  
The analysis of the data showed that a multivariate regression is necessary to obtain answers to the objectives set in the paper. This will allow us to obtain numerical measures that characterize the correlations between the studied variables. It will provide the ability to prognose the value of delays and the costs resulting from them--with the knowledge of the intensity and number of expected vessels covered by the one-way traffic obligation.  
In creating the statistical model, correlations between the intensity of traffic on the waterway and all seven causes of ship delays established during the study were examined.  
The extracted causes of delays were modelled sequentially. Then, the correlations between them were studied. The model presented in this paper was selected on the basis of three statistics. These are: the fitted R\({}^{2}\), the standard error of estimation, and the significance coefficient of the obtained regression equation (\(p\) values). In addition, the statistical significance of the coefficients of the variables in the regression model was evaluated (Table 12).  
Based on the conducted analysis, the variables most strongly associated with the total delays of ships (up to 88.5%) were: traffic intensity, oversize vessels, and vessels carrying dangerous cargo (Table 12). These variables showed statistical significance at the level of \(\alpha<0.05\) (\(p\)-values) (Table 13). Other combinations of variables did not allow for achieving such good statistics of the fitted R\({}^{2}\), for maintaining the significance of the obtained regression model, or for keeping the significance of the variables used to create the model.  
When using the model, the expected intensity on the waterway (the number of expected vessels during a 12-h shift) should be entered in the appropriate column. Then, the expected number of oversize vessels and vessels carrying dangerous cargo should be entered into the model sequentially (Table 14).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type** & **Bulk/General Cargo** & **Tanker** & **Container** \\ \hline Cost USD (day) & 7544.1 & 9702.0 & 5853.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The daily operational cost per type of vessel in the year 2021.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Average Costs (USD)** & **Average Annual Delay (Ship/Days)** & **Annual Losses (USD)** \\ \hline
7699.9 & 82 & 631,391.4 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Average annual financial losses.  
The constructed multiple regression model confirmed that, with respect to the occurrence of delays in ships on the Swinoujscie-Szczecin waterway, the correlation between traffic intensity and vessels subject to a one-way traffic obligation is statistically significant. The multiple factor model, which includes both the intensity on the waterway and vessels subject to one-way traffic, explains up to 88.5% (Table 12) of the total delay (adjusted R\({}^{2}\)). Additionally, the obtained statistical model can be used to calculate potential financial losses resulting from ship delays.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 3 Methodologies
### Optimization Methods  
Grid search and random search are two commonly used hyper-parameter optimization methods in machine learning. Grid search is a method of searching for the best hyper parameters by enumerating all possible combinations of hyper parameters. It finds the best combination by performing a systematic search in a given grid of hyper parameters. Since it needs to try all possible combinations, it is computationally expensive, especially when the number of hyper parameters is large.  
On the other hand, random search is a more flexible approach that randomly selects a set of hyper parameters in a given hyper-parameter space and evaluates them against a given performance metric.  
Experiments have shown that random search could have better performance than grid search because random search can search continuous values and can set a larger search space, so there is a chance to obtain a better model [100]. The relevant experimental results are shown in Figure 8, in the same nine-sampled search, where the grid search was sampled only three times for important parameters, compared to nine random searches. In addition, for cases where only a few hyper parameters play a decisive role, random search is more efficient for searching important parameters. Previous research in the field of machine learning has indicated minimal disparities in the final parameter prediction accuracy between the two methods after many iterations.  
To select an appropriate hyper-parameter optimization method, random search and grid optimization are plotted on the epoch parameter optimization process in the EOR project dataset, as shown in Figure 9, through the comparison of the above grid search and random search methods, according to the size of the dataset and the optimization space  
Figure 8: Experimental distribution for grid search and random search.  
Figure 7: Schematic diagram of a confusion matrix.  
of parameters. Under the same conditions, random search reaches higher accuracy faster than grid search. Although other methods such as automated hyper-parameter tuning, Bayesian optimization, genetic algorithms, and artificial neural networks tuning are also highly effective, in the context of our study, random search was considered an appropriate and practical choice. Random search does not rely on a priori knowledge and therefore is not affected by the initial parameter choice, which can lead to better results in some cases. Moreover, random search can explore a wider hyper-parameter space with limited computing resources, improving the chance of finding a satisfactory solution. In addition, in our specific dataset, we conducted multiple experiments and proved that random search performed well under our dataset and model architecture, so we finally chose random search as the method for hyper-parameter optimization.
## 5 Perceptual Audio Features Extraction Techniques
### Frequency Domain Perceptual Features
#### 5.2.3 Tonality-Related Perceptual Frequency Features  
Tonality is a sound property that is closely related to the subjective perception of the main frequency of harmonic signals, and it allows distinguishing noise-like sounds from sinusoidal-like sounds, and especially those sinusoidal sounds whose frequencies are harmonically related. Contrary to the previous reviewed pitch-based audio features (see Section 4.2.4), psychoacoustical pitch feature incorporates auditory-based models. This is a measure that models human pitch perception (as defined by [PERSON] and [PERSON] [176]), by incorporating a band pass filtering that emphasizes the most relevant frequency band for pitch perception, the use of specific filter bank model (Gammatone) that mimics the frequency selectivity of the cochlea, and use of inner hair-cell models that allows computing autocorrelation functions of continuous firing probabilities. The final feature is computed summing across channels all these autocorrelation functions. Previous approaches, like the work by [PERSON] and [PERSON] [177], combine a cochlear model with a bank of autocorrelators.
## 4 Results
### Myocardial Scar Segmentation and Quantification
#### 4.2.2 Segmentation Performance  
Achieved segmentation performance are shown in Figure 4. With the aim of comparing all the methods under similar working scenarios, only diseased cases were segmented (i.e., healthy slices/patients were not segmented since the algorithms are devised for working on selected pathological cases). Our algorithm obtained the highest Dice index when compared against the reference method ones, achieving a Dice value of 77.22 \(\pm\) 14.3% and considerably outperforming the best ranked reference method (2-SD with Dice 70.49 \(\pm\) 16.48%). Besides, our proposal obtained the lowest Dice variance among all methods. Statistical significance was present in all Dice comparisons. When comparing performance in terms of Hausdorff distances, our method obtained 26.3 \(\pm\) 21.7 mm (Figure 4B). The lowest Hausdorff values were obtained for the 4-SD and 5-SD methods (24.5 \(\pm\) 21.0 mm and 25.6 \(\pm\) 20.8 mm, respectively, \(p<\) 0.05). The achieved homoscedastic Hausdorff distance distributions showed similar variance levels for all the methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Dice (\%)** & **HD (mm)** \\ \hline Coarse & 73.0 (14.5) & 27.5 (21.7) \\ Coarse + Ensemble 1 & 76.3 (14.9) & **26.0 (21.8)** \\ Coarse + Ensemble 3 & 76.9 (14.7) & 26.3 (21.8) \\ Coarse + Ensemble 5 & 77.1 (14.4) & 26.4 (21.8) \\ Coarse + Ensemble 7 & **77.2 (14.3)** & 26.3 (21.7) \\ Coarse + Ensemble 9 & 77.2 (14.3) & 26.2 (21.8) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean (standard-deviation) segmentation performance obtained for the coarse segmentation followed by different ensemble sizes.  
Figure 4: Segmentation performance. FWHM: Full-width at half-maximum; GMM: Gaussian mixture model; n-SD: n-standard deviation thresholding from remote myocardium. Red dots indicate \(p<\) 0.05 with Mann–Whitney U-test.  
methods obtained clinical markers estimations that statistically differed from the expert annotated ones.
## 5 Experiments  
### Performance Evaluation  
We execute the proposed IS algorithm and evaluate the precision and duration of the tail probability calculation of the MMPP/D/1 queue using NS-2. NS-2 is a discrete event simulator utilized to simulate real-time network traffic and topology in order to conduct analysis. Because NS-2 is an open-source project and supports several algorithms in routing and queuing, we can modify the source code for our simulation easily.  
Next, we compare the outcomes of our suggested approach with those of the MC method and the standard IS method. We conduct two scenarios to simulate the MMPP/D/1 queue. In Scenario-1, we consider the special case of the MMPP with two Poisson processes having the same arrival rate \(\lambda_{1}=\lambda_{2}\). Since the two-state MMPP has the same Poisson process, the MMPP/D/1 queue becomes the M/D/1 queue. Then, in Scenario-2, we consider the normal case of the MMPP with two Poisson processes with different arrival rates \(\lambda_{1}\
eq\lambda_{2}\).  
For the two-state MMPP, the average arrival rate is calculated by  
\[\lambda_{mean}=\frac{\lambda_{0}r_{10}+\lambda_{1}r_{01}}{r_{01}+r_{10}} \tag{20}\]  
with the variables \(\lambda_{0}\) and \(\lambda_{1}\) representing the arrival rates of two Poisson processes. The variables \(r_{01}\) and \(r_{10}\) denote the probabilities of transitioning from state 0 to state 1 and vice versa.
## 4 Simulation Results and Discussion
### Load Change with the Proposed Control Scheme  
In order to verify the effectiveness of the proposed self-triggered communication enabled secondary control scheme with sudden load changes, the simulation process is designed as follows: (1) At \(t=0\) s, the microgrid works in islanded mode at beginning, and only the primary control is activated with the reference voltage \(V^{*}_{i}\) and angular frequency \(\omega^{*}_{i}\) (\(i=1,\ldots A\)) set to 380 V and 100 \(\pi\), respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DG1\&DG2 (45 kVA)** & **DG3\&DG4 (34 kVA)** \\ \hline \(R^{s}=0.1\ \Omega\ L^{s}=1.35\) mH & \(R^{s}=0.1\ \Omega\ L^{s}=1.35\) mH \\ \(R^{c}=0.03\ \Omega\ L^{s}=0.35\) mH & \(R^{c}=0.03\ \Omega\ L^{c}=0.35\) mH \\ \(m^{p}=9.4\times 10^{-5}\ n^{Q}=1.3\times 10^{-3}\) & \(m^{p}=12.5\times 10^{-5}\ n^{Q}=1.5\times 10^{-3}\) \\ \(K_{PV}=0.1\ K_{IV}=420\) & \(K_{PV}=0.05\ K_{IV}=390\) \\ \(K_{PC}=15\ K_{IC}=20000\) & \(K_{PC}=10.5\ K_{IC}=16000\) \\ \hline
**Line 1** & **Line 2** & **Line 3** \\ \(R_{\Pi}=0.23\ \Omega\) & \(R_{\Pi}=0.35\ \Omega\) & \(R_{\Pi}=0.23\ \Omega\) \\ \(L_{\Pi}=0.318\) mH & \(L_{\Pi}=1.847\) mH & \(L_{\Pi}=0.318\) mH \\ \hline
**Load 1** & \multicolumn{2}{c}{**Load2**} \\  & 12 kW+15 kVar & 15.6 kW+7.6 kVar \\ \hline \multicolumn{3}{c}{**Secondary control parameters**} \\ \(K^{AVE}_{PV}=0.001\) & \(K^{AVE}_{IV}=8\) & \(K^{AVE}_{PQ}=0.001\) & \(K^{AVE}_{IQ}=0.025\) \\ \(\varepsilon^{V}=0.2\) & \(\varepsilon^{Q}=0.08\) & \(Y^{V}=4\) & \(Y^{Q}=1.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values for simulation.  
Figure 5: Block diagram of the islanded microgrid test system.  
to update the control inputs for \(V^{AVE}\) and \(Q^{AVE}\) at triggering time instants according to the relative state difference between agents. When the consensus is achieved, the control inputs are set to 0 and stop to update. Figure 8 illustrates the triggering time instants of neighboring DGs from 1 s to 2.5 s. As seen in Figure 8, since each linked pair of agents is equipped with a local clock, the neighboring DGs can exchange information independently when the local clock reaches 0. Before the consensus is achieved, the time intervals between the two consecutive triggering time instants are determined by the relative state difference of neighboring DGs. Compared with the conventional continuous-signal-based method, our proposed scheme provides the advantages of reduced communications while meeting the performance requirements of the secondary control.
## 4 Results and Discussions
### Contour Surface Milling Test  
To illustrate the effectiveness of the presented model in predicting surface roughness in machining process, another milling test was conducted for part contour surface, as shown in Figure 15. The machining the part contour was carried out using 4-tooth end mill with diameter of 10 mm under the following cutting parameters; cutting depth 1.0 mm, spindle speed 6000 rpm, and feed rate 1800 mm/min. The vibrations of spindle tool along the milling path were assessed using the accelerometer mounted on the spindle housing. Surface roughness at specific points was measured, respectively, as listed in Table 9. It is found that the surface roughness at different point scatters from 0.526 to 0.584 um, with mean value and deviation (0.5528 \(\pm\) 0.0249), and the vibration level induced in machining also changes with the feeding of cutter with mean value and deviation (0.024467 \(\pm\) 0.002817). Experimental results indicate that vibration of spindle tool is one of the factors contributing the variation of the surface roughness at different points although machined under the same cutting conditions. The surface roughness at the selected points can also be predicted by ANN model, in which the vibration level along with cutting parameters are fed in prediction model. The average prediction accuracy of the surface roughness by ANN model MLP 4-15-1 is \(\sim\)96% compared with the measured values. The validation tests clearly verify the superior performance of the ANN models in predicting the surface roughness in end milling operation. This can further be used as the prototype to develop on-line surface roughness recognition system in future work, as shown in Figure 16. Machining vibration assessed from accelerometer can be displayed in different ways such as the time domain with vibration level in RMS or frequency domain with vibration level as function of frequency. Besides, mathematical models derived from regression analysis or ANN model can be implemented in system for predicting the surface roughness in process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **R** & **RMSE** & **MAPE** \\ \hline (a) Regression model \#1 & 0.8157 & 6.91\% & 9.73\% \\ (b) Regression model \#3 & 0.8172 & 6.85\% & 9.63\% \\ (a) ANN MLP 3-13-1 & 0.8766 & 5.78\% & 7.77\% \\ (b) ANN MLP 4-15-1 & 0.9065 & 4.89\% & 6.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparisons of the statistical values of the prediction models.
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 2 Methodology
### Preprocessing of Ropeway Data
#### 2.2.1 Filtering by Elevation  
Elevation filtering is intended to remove the vast majority of ground objects from the complex original point cloud data, laying the groundwork for subsequent segmentation and extraction. In contrast to towers and ground object points, cableways are discontinuous in the vertical direction. In elevation filtering, low-ground objects and ground points are removed, linear structures (such as rivers and roads) are eliminated from the results of the cableway extraction, and redundant data are reduced. The following are the steps involved in the implementation:  
* The meshing of point clouds. This is divided into grids and all points within each grid are normalized to the horizontal plane of the grid's lowest point, as shown in Figure 3a;
* As shown in Figure 3b, the elevation threshold can be determined by drawing the \"elevation-point-figure map\" of the normalized point cloud;
* Finally, the points within the target area and the points outside the target area are segmented based on their elevation threshold, as shown in Figure 3c.
## 6 Numerical Experiments  
To evaluate the performance of the proposed multi-sided ascending-price auction approach for providing the multi-domain network slicing service, real-world data are leveraged in the simulation experiments in this section. Our main objectives in this section are to evaluate the gap between the proposed multi-sided ascending-price auction mechanism and the optimal trade mechanism, which is used as an upper bound without desirable economic properties, and to evaluate the influence of different parameters on the performance. To demonstrate the effectiveness of the proposed multi-sided ascending-price auction mechanism, a second-price auction is used as the baseline in this section. The second-price auction has the desirable truthfulness property and has been proven effective in previous relevant studies [35]. However, since previous game theory-based solutions do not fit to our newly defined problem as shown in Table 2 in Section 2, the standard second-price auction is not applicable and a variant is designed as follows. The service providers from the service chain route with \(K\) domains are paired up randomly and act as a coalition to compete with the service provider from the service shortcut route in a standard second-price auction approach.
# Effectiveness of Bonding Steel Elements with Polyester-Coated Paint
###### Abstract  
The aim of the paper is to assess the impact of the effectiveness of bonding steel elements with paint coating. The adhesive joints were made using two types of the adhesives: two-component epoxy resin adhesive based on Bisphenol A and polyurethane. Three types of adhesive joints were made: (i) reference samples, (ii) samples with a paint polyester coating, and (iii) samples with a zinc primer and paint polyester coating. These coatings were applied using the electrokinetic method. A shear strength test of the adhesive joints (EN DIN 1465 standard), a coating adhesion test (ASTM D3359-B standard), and surface wettability tests (based on contact angle) were used. Through analyzing the test results, it can be seen that the strength of the adhesive joints of the reference samples made with epoxy adhesive is 46% lower than that of the specimens with primer and paint coating applied. However, in the case of the adhesive joints made with the polyurethane adhesive, the aforementioned difference in the strength value of the adhesive joints of the reference samples and paint-coated samples with an applied primer is 76%. adherends with a paint coating and a previously applied primer obtained the lowest value of the contact angle (38.72\({}^{\circ}\)) and are characterized by good wettability.  
adhesive joint; paint coating; surface treatment; wettability; steel; shear strength Article