esa-sceva/satcom-chunk-collection huggingface dataset:
# Partitioning Strategies for Parallel Computation of Flexible Skylines
## 4 Parallel Computation of Flexible Skylines
### Partitioning Strategies
#### 4.1.2 Angle-Based Space Partitioning  
Angle-Based Partitioning [9] (Angular) partitions the space based on angular coordinates, after converting Cartesian to hyper-spherical coordinates. Unlike Grid, Angular does not support any kind of grid dominance.  
The partition of tuple \(t\) is computed based on hyper-spherical coordinates, including a radial coordinate \(r\) and \(d-1\) angular coordinates \(\varphi_{1},\ldots,\varphi_{d-1}\), as follows:  
\[p(t)=\sum_{i=1}^{d-1}\left\lfloor\frac{2\varphi_{i}}{\pi}m\right\rfloor m^{i-1} \tag{3}\]  
where \(m\) is, again, the number of slices in which each (angular) dimension is divided. This amounts to grid partitioning on angular coordinates. Figure 2b shows Angular at work.
## 3 Structuring of the Proposed Forecast Framework
### Performance Assessment  
To assess the prediction performance under different experimental scenarios, two scientific performance metrics are selected for prediction. This paper chooses mean absolute error (_MAE_) and mean absolute percentage error (_MAPE_) as performance metrics, which are used for evaluating the performance of different models in prediction results and can be expressed as follows:  
Mean absolute error (_MAE_):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-y_{i}^{\prime}\right| \tag{21}\]  
Mean absolute percentage error (_MAPE_):  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-y_{i}^{\prime}}{y_{i}}\right| \tag{22}\]  
where \(y_{i}^{\prime}\) is the predicted value; \(y_{i}\) is the target value; \(n\) is the number of prediction data; \(\overline{y}\) is the average value of \(y_{i}\). Generally, the lower values of _MAE_ and _MAPE_ lead to better performance of the prediction task.
## 4 Results and Discussion
### Comparative Analysis  
The comparative study between the existing regression models and the proposed Bi-GRU model is detailed in this section. [PERSON] et al. [38] analyzed the performance of three existing regression models in retail sales forecasting, namely LSTM, Gradient Boosted Regressor (GBR), and Time Delay Neural Network (TDNN). In this study, the existing regression model's efficacy was analyzed by applying it to the Rossmann and Walmart datasets. The experiments carried out on the Rossmann and Walmart datasets state that the GBR model obtains minimal mean NRMSE and ND values when related to other two regression models. The GBR model has a mean NRMSE of 0.12 and 0.01 and ND of 0.20 and 0.10 on the Rossmann and Walmart datasets, respectively. The comparative results between the existing regression models (GBR, TDNN, and LSTM) and the proposed Bi-GRU model are clearly stated in Table 6 and Figure 4.  
On the other hand, [PERSON] [39] analyzed the sales prediction performance of XGBoost, random forest, and Ordinary Least Squares (OLS) models on the Walmart dataset. According to the numerical investigation, the XGBoost model has a minimal MAE of 0.12 and MSE of 0.06, and a high R2 of 0.98 on the Walmart dataset. The obtained numerical results are superior to the random forest and OLS models.  
In addition, [PERSON] [40] performed feature engineering with the XGBoost model for precise sales forecasting on the Walmart dataset. The presented XGBoost model has a minimal RMSSE of 0.65, which is superior to other comparative regression models, like ridge and logistic regression. When related to these existing regression models, the presented Bi-GRU model has a minimal error value and high R2 value on the Rossmann and Walmart datasets. The comparative results between the XGBoost model and the proposed Bi-GRU model are specified in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Models** & **NRMSE** & **ND** \\ \hline \multirow{4}{*}{Rossmann} & GBR [38] & 0.12 & 0.20 \\  & TDNN [38] & 0.15 & 0.29 \\  & LSTM [38] & 0.13 & 0.25 \\  & Bi-GRU & 0.08 & 0.07 \\ \hline \multirow{4}{*}{Walmart} & GBR [38] & 0.01 & 0.10 \\  & TDNN [38] & 0.02 & 0.14 \\ \cline{1-1}  & LSTM [38] & 0.01 & 0.13 \\ \cline{1-1}  & Bi-GRU & 0.01 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative results between the existing regression models and the proposed Bi-GRU model by means of NRMSE and ND.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feature Engineering Techniques**} & \multirow{2}{*}{**Measures**} & **Rossmann** & **Walmart** \\ \cline{3-4}  & & & **Bi-GRU** & \\ \hline \multirow{4}{*}{MRMRR} & NRMSE & 2.14 & 2.22 \\  & ND & 2.10 & 2.09 \\  & RMSSE & 1.96 & 1.82 \\  & MAE & 1.30 & 1.22 \\  & MSE & 1.26 & 1.12 \\  & R2 & 0.90 & 0.94 \\ \hline \multirow{4}{*}{APSO + RFE + MRMR} & NRMSE & 0.08 & 0.01 \\  & ND & 0.07 & 0.03 \\ \cline{1-1}  & RMSSE & 0.08 & 0.08 \\ \cline{1-1}  & MAE & 0.05 & 0.07 \\ \cline{1-1}  & MSE & 0.04 & 0.03 \\ \cline{1-1}  & R2 & 0.98 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
## 3 Simulation Results
### Water-Incident Reflection Pattern  
The pattern of water-incident lidar was plotted in the same way as that of air-incident lidar. Figure 10a presents the percentage of the water-incident energy, which was reflected into water onto the under-side of the sea surface. In the case of 45\({}^{\circ}\) incident and 10 m/s wind, the reflectivity was 34.51%, less than that of unpolarized light (38.08%). The energy was distributed around the specular reflectance quad near \(\theta=45^{\circ}\). Figure 10b presents the horizontal and vertical polarization. The reflected light was depolarized due to scattering. Figure 10c presents 45\({}^{\circ}\) polarization. The circular polarization, shown in Figure 10d, was almost zero.
## 2 Materials and Methods
### Template Mask
#### 2.4.2 How to Create a Template Mask  
Similar to the general segmentation map, the template mask is a binary label map that consists of positive and negative areas. The positive area is created by the rule utilizing the position, diameter, and _orientation_ of breast lesions. The shape of the template mask is one of two types: sphere or ellipsoid. If the lesion has a '_parallel_' property of _orientation_, the diameter perpendicular to the skin is halved, changing the shape of the positive area from a sphere to an ellipsoid. With the center coordinate \((x_{t},y_{t},z_{t})\), diameter \(d_{t}\) and orientation of the lesion, we can define a template mask \(\mathcal{T}(x,y,z)\) as a simplified segmentation label.  
\[\mathcal{T}(x,y,z)=\begin{cases}1&\text{ if }\frac{(x-x_{t})^{2}}{d_{t}}+\frac{(y-y_ {t})^{2}}{\alpha d_{t}}+\frac{(z-z_{t})^{2}}{d_{t}}\leq 1,\\ 0&\text{ else}\end{cases} \tag{5}\]  
where \(\alpha\) is 0.5 if the orientation is parallel; otherwise, \(\alpha=1\).  
An example of a template mask is shown in Figure 4 with a sampled breast cancer. Figure 4a-c show transverse, coronal, and sagittal views of the center of a breast lesion. With lesion diameters of 10 mm, 18 mm, and 15 mm on each axis, the average diameter was 14.3 mm. The centroid of the template mask is located in the same position as the center of the lesion. Since the lesion has a '_parallel_' feature of _orientation_, the positive area shrinks in the skin-perpendicular direction, obtaining the shape of an ellipsoid whose semidiameters are one of 7.15 mm and two of 14.3 mm, as shown in Figure 4d-f.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 5 Neural Network (NN)-Based Phase-Field Brittle Fracture
### Representative Numerical Examples
#### 5.3.1 Single-Edge-Notched Test  
The same benchmark test depicted in Section 4.3.1 is further examined to verify the computational methodology by comparing the predicted results to that of finite element analysis.  
**Case 1:** The first case considers only stress, strain and, driving force as the input to the neural network model. The formulation of the NN-based phase-field model defines a function in the following format:  
\[d_{NN}=FFNN(\varepsilon,\mathbf{\sigma},\mathbf{\mathcal{H}},\mathbf{W},\mathbf{b}), \tag{25}\]  
where stresses are calculated using the finite element method. Only one NN-model is incorporated within the FE-analysis which predicts the phase-field \(d\). The results are promising; however, there are some mismatches between the finite element solution and the NN-model as shown in Figure 15.  
**Case 2:** The second case is similar to the first case; however, there is only one more input to the neural network. Here, the previous value of fracture phase-field \(d\) is also considered as an input to the neural network model. The formulation has the following format:  
\[d_{NN}^{t}=FFNN(\varepsilon^{t},\mathbf{\sigma}_{NN}^{t},d_{NN}^{t-1},\mathcal{H }^{t},\mathbf{W},\mathbf{b}). \tag{26}\]  
In terms of accuracy, this model predicts the relationship between the input and outputs much more accurately than the previous case, see Figure 16. Hence, for the next example, we employ the analysis in Case 2.  
Figure 15: Case 1: Single-edge notched tension test. Fracture phase-field \(d\) curves versus strain \(\varepsilon\) in (**a**) and vs. displacement \(u\) in (**b**).  
In this example, different cases are considered to create the most efficient neural network model for the prediction of the fracture phase-field \(d\) and compared in terms of both accuracy and efficiency. Table 8 depicts the AceGen generated c code size, while Table 9 compares the AceFEM solution times related to the generated code. It can be concluded that incorporating more than one model within the finite element formulation costs more evaluation time during generating the c code. On the other hand, in the AceFEM simulation, there is no significant difference in the total solve time.
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
## 2 Monitoring Methods Used in Valleebre and Sample Results
### Triangulation and Electronic Distance Measurement  
From 1987 to 1995, geodetic measurements with theodolite and EDM (Electronic Distance Measurement) were carried out. Between 1987 and 1992, up to 17 points were triangulated in the toe of the lower unit, mainly for orientation of the photogrammetric models (Figure 6a). In the period 1988-1994, three additional points in the middle unit were monitored with single distance variation measurements (Figure 6b). During 1994 and 1995, a \"triangulateration\" from new base benchmarks E1 and E2 (Figure 6c) was extended to 16 points spread out through the whole landslide. The angle measurements were carried out with a Wild T2 theodolite (Wild, Heerbrugg, Switzerland, 1975), and the EDM with a Wild DIOR 3002S (Wild, Heerbrugg, Switzerland, 1988).  
The rate of movement measured during the period 1987-1995 was strongly dependent on the rainfall and the target position within the landslide. Rates up to 4 m per year were observed at certain points near the toe in the rainy years, while almost no displacement occurred during periods of drought (Figure 7). In the middle landslide unit (Figure 3), the rate of displacement was significantly smaller, in the range of 1 to 30 cm/year.  
Figure 5: Terrestrial photogrammetry sample result: Map of differences between two campaigns (one-meter contour-line interval) at the landslide toe (Figure 4).  
In terms of the precision of the observations, the EDM measurements proved to be more reliable (typically 1 cm) than angle measurements with theodolites (around 4 cm at typical distances), at least with the setup, equipment and sighting distances in use in Vallcebre. This is due to the fact that the precise determination of angles needed greater experience and better environmental conditions (no mist or smog in the line of sight, proper target illumination, lack of air vibration due to strong insolation and so on), often not completely available in mountain areas at the time when the measurements have to be taken.  
Figure 6: Schemes for the triangulation (**a**), single distance variation (**b**) and triangulateration (**c**) methods as used in the Vallcebre landslide from 1987 to 1995.
## 3 Problem Description
### Analysis of PV Influence on Distribution Network
#### 3.2.2 The Impact of PV Penetration Rate  
Case a. Not connected to PV power supply.  
Case b. Connected to a PV power supply with a capacity of 72 kW and a power factor of 0.95 at node TS.  
Case c. Connected to a PV power supply with a capacity of 144 kW and a power factor of 0.95 at node TS.  
Figure 9 shows the voltage of the bus nodes when different capacities of PV are connected at the TS at the end of the system feeder. The data as shown in Table 3. Figure 9a-d demonstrate that the voltage-boosting effect of PV on the node voltage is proportional to the capacity of the PV units themselves, and there is an approximate linear relationship between the two. This relationship is further studied in Section 4.  
Figure 10 shows the voltage deviation of the bus node when connecting PV systems of different capacities at the TS point at the end of the system feeder. The data as shown in Table 4. Compared to Figure 8, it is more evident that the impact of PV on node voltage and voltage deviation is closely related to the connected capacity. In scenario b, the voltage deviation at node TS decreased by 0.5%. However, in case c, due to a significant increase in PV capacity, the system's voltage deviation increased by 3%. The voltage deviation in case b is smaller, but the connected PV capacity is 50% that of case c. From an economic cost perspective, this is clearly not suitable. The voltage deviation in case c is still within a reasonable range, and the PV capacity is larger, resulting in more considerable benefits. Therefore, the estimation method for voltage rise value has important value in practical engineering.  
Figure 8: The impact of PV access location on node voltage deviation: (**a**) nodes MS1 and MS2; (**b**) nodes SS3; (**c**) nodes TS; (**d**) comparison of node voltage deviation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Voltage** & \multicolumn{2}{c}{**MS1/MS2**} & \multicolumn{2}{c}{**SS3**} & \multicolumn{2}{c}{**TS**} \\
**(p.u.)** & **Theory** & **Simulation** & **Theory** & **Simulation** & **Theory** & **Simulation** \\ \hline a & 1.0 & 1.0 & 0.995 & 0.99 & 0.99 & 0.9875 \\ b & 1.0 & 1.0 & 1.0125 & 1.01 & 1.0075 & 1.0075 \\ c & 1.0 & 1.0 & 1.02 & 1.025 & 1.0375 & 1.045 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Node Voltage Data Sheet: The Impact of PV Penetration Rate.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Deviation** & \multicolumn{2}{c}{**MS1/MS2**} & \multicolumn{2}{c}{**SS3**} & \multicolumn{2}{c}{**TS**} \\
**(\%)** & **Theory** & **Simulation** & **Theory** & **Simulation** & **Theory** & **Simulation** \\ \hline a & 0.0 & 0.0 & 0.5 & 1 & 1 & 1.25 \\ b & 0.0 & 0.0 & 1.25 & 1 & 0.75 & 0.75 \\ c & 0.0 & 0.0 & 2 & 2.5 & 3.75 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Node Voltage Deviation Data Sheet.  
Figure 9: The impact of PV penetration rate on node voltage: (**a**) nodes MS1 and MS2; (**b**) nodes SS3; (**c**) nodes TS; (**d**) comparison of node voltage curves.
# Improved Functions of Fermented Coffee by Lactic Acid Bacteria
## 2 Materials and Methods
### Measurement of the Antioxidant Activity of the Fermented Coffee Extract  
To evaluate antioxidant activity utilizing the ABTS (2,2-azino-bis-3-ethylbenzothazoline-6-sulfonic acid) (Roche, Mannheim, Germany) radical scavenging test, a modified version of the ABTS cation decolorization assay [31] was used. The procedure involves preparing an ABTS stock solution by mixing 7 mM ABTS dissolved in 0.45 \(\upmu\)m filtered ddH\({}_{2}\)O with 2.45 mM potassium persulfate (Daejung chemicals, Siheung, Korea) dissolved in 0.45 \(\upmu\)m filtered ddH\({}_{2}\)O in a 1:1 ratio and then incubating the solution at room temperature for 24 h in a dark location to allow the reaction to complete. The stock solution was then diluted with 0.45 \(\upmu\)m filtered ddH\({}_{2}\)O to reach an absorbance of 0.70 \(\pm\) 0.02 at 734 nm, yielding the ABTS working solution.  
For the assay, 4 \(\upmu\)L of the previously prepared filtered supernatant of 20 brix coffee extract sample was mixed with 196 \(\upmu\)L of the ABTS working solution and incubated for 10 min in a dark location. The absorbance was subsequently determined at 734 nm. A standard curve was created by dissolving ascorbate in ddH\({}_{2}\)O and diluting it to various concentrations, and the absorbance values of the samples were calculated by comparing them with the standard curve.
## 5 Discussion
### Limitations and Future Work  
The TAM model was analyzed with one specific academic course in this study. While both platforms can be used to create a wide range of activities, they may not be suitable for more complex or specialized content. Including H5P in more courses in different colleges will be a future enhancement. Moreover, more student participants must be added from various colleges after integrating H5P into their learning. It is important to note that the gender composition of the sample was skewed towards females. In future work, efforts will be made to recruit a more balanced sample with equal representation of both genders.
## 7 Performance Evaluation
### Numerical Results and Discussion  
Several experiments were applied to the FWOS model to examine overlapping scenarios with higher- and lower-priority queues. Based on the overlapping experiments, a complete view of our model performance is presented with all overlapping situations together in one chart. Different values of window offsets between nodes are used with varying network scales to compare our results with previous related works in the non-overlapping scenario.
## 3 Challenges in Immersive Video Quality Assessment
### Global Color Offset Error  
In order to obtain a high-quality rendered view, in practical immersive video systems, it has to be rendered using at least one real view. Real views are often captured by physical cameras, which sometimes differ in capture parameters (exposure setting, color balance), especially when cameras force automatic mode. These sources lead to slight, global differences between the real and the rendered view, called global color offset error (GCOE). Similarly to CPSE, this type of distortion is imperceptible for the viewer while significantly changing metric value.  
An example of the abovementioned errors is presented in Figure 3. The image presented in Figure 3B is a result of virtual view synthesis from two real cameras. Those cameras significantly differ in color characteristics (when compared to each other and the reference camera--Figure 3A). It leads to distractive artifacts clearly visible in the background of the exemplary scene. The image in Figure 3C contains a result of virtual view synthesis with color correction applied during the rendering process. The addition of the color correction eliminates background color inconsistency. However, SSIM metric values (0.9216 for B and 0.9107 for C) do not match the subjective perception of image C, which is less distorted than image B. When the IV-SSIM metric is considered, the image quality of example C is evaluated as higher than example B. Such a metric behavior is consistent with subjective perception. Please note that the definition of IV-SSIM (cf. Section 4) implies that its value is usually significantly higher than for SSIM, thus IV-SSIM higher than 0.99 does not mean that the image has near-to-perfect quality (as it would be the case for SSIM). However, for objective quality metrics, the crucial aspect is the correlation with subjective quality perception.  
Figure 3: Fragment of a virtual view rendered using views captured by cameras with different color characteristics (**B**) and color-corrected views (**C**), vs. reference input view (**A**); brightness increased by 40% to emphasize differences. Sequence Carpark [39].  
Figure 2: Noticeability of pixel shift in a rendered view: (**A**) correct position of the dancer, (**B**) dancer shifted by two pixels to the right, (**C**) dancer shifted significantly. Sequence Choreo [38].
## 3 The Time Source Techniques
### Low Earth Orbit
#### 3.2.3 Flexibility  
Like GPS, LEO timing is easy to deploy and connect. The LEO signal has higher strength compared to GPS; with a small antenna and a receiver, the LEO signal can be accessed globally, and the receiver handles signal interception, performs error correction, and computes PNT data.  
The cost of implementing LEO-based timing can be similar to that of GPS timing, with the primary investment being hardware costs. However, since GPS is a mature technology with an already deployed satellite network, there are no additional service fees. In contrast, LEO-based timing operates in an open market, with services provided by different companies. Depending on their pricing strategies, users may incur service fees in addition to hardware costs. This makes LEO timing slightly more expensive than GPS, but the overall cost remains relatively low.
## 3 Results and Discussion
### Development of the Hierarchical Classification Strategy  
Following the strategy summarized in Figure 1, the classification approach consisted of two consecutive binary PLS-DAs: the first model discriminated EVOO from non-EVOO samples, while the second one classified the non-EVOO samples into VOO and LOO. As discussed in our previous work [14], setting an uncertainty range for each binary discriminant model resulted more appropriate than the establishment of a single threshold to classify the samples, since the purpose was to develop a screening tool to reduce panels' assessment workload. In this way, virgin olive oil samples outside these ranges would be directly assigned to a given category, whereas the sensory panel would only determine the commercial category of uncertain samples (Figure 1). In the present study, the uncertainty range was not set according to conventional values, as it was done in our previous work [14]; instead, the range thresholds were obtained from each classification models' performance through ROC curves. This allowed us to propose specific ranges for the models, maximizing the accuracy of the instrumental screening tool [43], given that thresholds were set at PV where sensitivity and specificity were maximum (equal to 1). Therefore, a full model (\(n=301\)) was developed and cross-validated; ROC analysis was performed for both PLS-DA conforming the classification strategy to obtain the uncertainty ranges (Table 1). In order to externally validate the classification approach, the sample set was split into (a) a training set with 80% of the samples from each class (\(n=241\)); (b) a validation set with the remaining 20% (\(n=60\)). The hierarchical classification was then developed seven times with the training sets: models and classification criteria based on ROC results were established. Table 1 shows the thresholds of the uncertainty ranges for each of the seven models based on the ROC curves and the percentage of samples that fell into them.  
The mean of uncertain samples was 13.8% and 2.5% for EVOO/non-EVOO and VOO/LOO models, respectively. The remaining samples in both models were all correctly classified into one of the given categories in cross-validation, since we had set the models' boundaries at their best classification performance (sensitivity and specificity equal to 1).  
In the EVOO/non-EVOO model, the lower thresholds were more similar to each other compared to the upper thresholds, reflecting that sensitivity was less variable than specificity. Only two out of seven VOO/LOO models presented an uncertainty range, given that the accuracy in most of the cases was 100%.