esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 3 Results and Discussions  
### Redarge Estimation  
The SWAT model performance for monthly streamflow simulation was evaluated based on basic statistics, such as Nash-Sutcliff (NSE), percent bias (PBIAS), Correlation coefficient (R\({}^{2}\)), and root mean square error divided by the standard deviation (RSR). The \(p\) and the r-factors measures the fraction of observed data contained in the 95 PPU and the ratio of the average width of the 95 PPU band, respectively (Table 1).  
Using the best parameter values from the calibration process, monthly water balance components were simulated using the SWAT model for the year 1988 to 2004 (Figure 4). Average monthly variations of basin values for the various water balance elements estimated during the simulation period display average gain and losses on the monthly base from the watershed with the change in soil water storage (Figure 5). Actual evapotranspiration (AET) from these components contributed a more considerable amount of water loss from the watershed.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 7 Suppression Method of Photodarkening
### Eliminate the Related Defects  
In Yb-doped aluminosilicate fibers, the nonbridging oxygen hole centers (NBOHC) and the aluminum oxygen hole centers (AlOHCs) were considered to be important defects for causing PD [107; 108]. By eliminating these defects, the PD effect can be reduced. In a high-power YDF amplifier, H\({}_{2}\) load can fundamentally suppress the PD effect and greatly improve laser performance [14; 37]. The reaction formula is as follows  
\[\mathrm{Si-O\cdots O-Si+H_{2}\to 2 Si-OH} \tag{17}\]  
Although the matrix structure of quartz glass is compact, H\({}_{2}\) molecules move freely in quartz fiber. They can react with the dangling bonds of NBOHC to form a more stable covalent bond of -OH which is stable and transparent from the ultraviolet to the visible band. Furthermore, this reaction can release electrons that could be bound with the hole-related defects, so as to eliminate the related defects [109; 110].
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 4 Subsystems Analysis
### Propulsion System
#### 4.2.2 Electric Micro-Propulsion
However, its maximum thrust level of 0.35 mN is not sufficient, since all the analyzed NEAs require peak thrust magnitude of at least 0.8 mN, as reported in Table 4. So, without further analysis, optimizing the trajectory with a limit on the maximum thrust, IFM Nano thruster is not suitable for this application. The low-thrust trajectory analysis showed that, considering these targets, electric propulsion systems are less performant than chemical ones for a 3U CubeSat. Moreover, it has to be remarked that it has been assumed Earth's position and velocity as initial conditions, which in a patched conics approach translates into a zero escape velocity from the SOI. So, to these results, the low-thrust escape trajectory has to be added, resulting in even larger propellant consumption. However, electric propulsion applicability cannot be excluded in general, since some optimization techniques may show its feasibility with other NEA targets, but for the purposes of this work, chemical propulsion will be chosen. Additionally, electric micro-propulsion systems usually require larger power consumption than chemical ones, which for a 3U CubeSat imposes several challenges for the power subsystem design. Moreover, for larger CubeSat architectures, where heavier and larger propulsion systems can be integrated, together with larger allowable solar panels delivering higher powers to the subsystems, electric propulsion system overcomes all the problems related to this compact architecture. This is the case of ESA M-Argo, which is aimed to rendezvous a NEA using low-thrust trajectory based on electric propulsion. Figure 5: Target asteroid: 65679 (**a**) mass profiles in function of the \(\mathrm{Isp}\), (**b**) Required thrust norm. Finally, as the Navigation section showed, asteroid visibility times are short. Then, high-thrust chemical propulsion systems are preferred over low-thrust electric propulsion systems, as they allow impulsive maneuvers.
###### Abstract  
Adverse weather conditions at night are very fatal to drivers, causing serious traffic accidents. Road lighting is a facility that can alleviate these dangerous situations. Nevertheless, road lighting has only rarely been studied during adverse weather. The reason is that the current road lighting performance evaluation method is presented based on normal weather. The current road lighting performance evaluation method uses a luminance meter to measure the road surface, which is not suitable due to scattering during adverse weather such as rain and fog. Therefore, this study proposes obstacle recognition distance as a measure of effectiveness to evaluate the performance of road lighting during adverse weather. There is a lack of actual research on whether obstacle recognition distance can be used as a measure of effectiveness for road lighting during adverse weather. Therefore, in this study, 30 subjects were used to measure the subjects' obstacle recognition distance according to changes in weather conditions, road lighting grade, and road lighting color temperature. As a result, it was analyzed that there was a clear trend of change in obstacle recognition distance depending on the change in each condition. It was found that, under the same road lighting performance conditions, there was a difference of up to 72.86% by weather condition; under the same weather conditions, there was a difference of up to 22.75% by road lighting grade; and by color temperature, there was a difference of up to 21.87%. In addition, a statistical significance test was performed to support the existence of a difference, and the results were synthesized to suggest that obstacle recognition distance can be used as a performance measure of effectiveness of road lighting in adverse weather.  
road lighting; adverse weather; obstacle recognition distance; traffic accidents; measure of effectiveness +
Footnote †: journal: Applied Sciences
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 2 Background
### Radiation-Digital Count Mapping  
The method of estimating temperature by mapping radiation and digital count obtained from infrared camera was used in [29; 30; 31; 32]. This method of estimating the temperature first calculates the radiation of the temperature range (\(T_{min}-T_{max}\)) to be measured through Equation (5). In this case, the wavelength range (\(\lambda_{1}-\lambda_{2}\)) is applied to the range that the infrared camera used can measure. Thereafter, as shown in Figure 3, the digital count (\(G_{min}-G_{max}\)) for \(T_{min}\) and \(T_{max}\) obtained through the blackbody and infrared camera and the previously calculated radiation (\(L_{min}-L_{max}\)) mapping function is obtained. After that, when a specific digital count is obtained through measurement, the radiation is obtained through the mapping function, and the corresponding temperature is estimated by the radiation. For more information, see Figure 4 and the Equations (5)-(8). The Radiation-Digital Count mapping function is expressed in Equation (6) using the relationship curve between the radiation and digital count according to the temperature in Figure 4.  
\[G(L)=a[L-L(T_{min})]+G_{min} \tag{6}\]  
where \(a\) is the slope of the mapping function, obtained by Equation (7):  
\[a=\frac{G_{max}-G_{min}}{L(T_{max})-L(T_{min})} \tag{7}\]  
Therefore, you can get Equation (6) to obtain a specific \(L(G_{obj})\) through Equation (8):  
\[L(G_{obj})=\frac{G_{obj}-G_{min}}{a}+L(T_{min}) \tag{8}\]  
When the digital count is measured, the radiation is obtained through Equation (8), and the temperature of the object \((T_{obj})\) is determined through the value of \(T\) that makes \(L(G_{obj})=L(T)\) from \(L(T)\) previously calculated by [PERSON]'s law.  
Figure 2: [PERSON]’s curve: (**a**) from 2000 K to 7000 K, (**b**) at room temperature.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 2 Materials
### Orfeo ToolBox (OTB)  
OTB is an open-source project for processing remotely sensed imagery, developed in France by CNES (www.orfeo-toolbox.org, accessed 10 April 2019) [(39)]. It is a toolkit of libraries for image analysis and is freely accessible and manageable by other open-source software packages as well as command-line [(34; 33; 40)]. The algorithms implemented in OTB were applied in this work through the QGIS software (www.qgis.org, accessed 10 April 2019). The OTB version used was 6.4.0; it interfaced with QGIS 2.18 Las Palmas, which was installed on an outdated workstation (Table 10). This is an important detail that highlights the fluidity of OTB because it works even with underperforming hardware. In OTB, the algorithms used in the phase of image object analysis were Large-Scale Mean-Shift (LSMS) segmentation for the image segmentation, and SVM and RF for the object-based supervised classification.
## 1 Introduction  
Tomographic reconstruction of projection fields has been used in many areas for decades. It is an established technique for 3D imaging and measurement in medicine [1, 2, 3, 4, 5], geoscience [6], for studying combustion [7] or materials science [8]. Another important field is in acoustic research, where it is becoming increasingly important with modern technology to make complex acoustic phenomena visible for the first time [9].  
In this field, especially the noninvasive volumetric measurement of sound pressure fields, there is a suitable application for tomographic reconstructions [10]. For investigations of local phenomena, for instance in sound dampers such as the bias-flow-liners in aircraft turbines, it is necessary to perform measurements within a flow channel in order to mimic the conditions of real applications [11, 12]. However, in a flow channel, it is not possible to measure parallel to the flow direction without disturbing the flow. This results in a limitation of the angular scan range available for the measurement. Often, the angular scan range is limited from 180\({}^{\circ}\) at ideal conditions to about 100\({}^{\circ}\) due to technical facilities [13].  
Tomographic reconstruction algorithms such as filtered back projection require a measurement from different scan angle positions in the range of 180\({}^{\circ}\)[9]. Limitations of the angular scanning range result in artifacts such as diagonal lines in the local reconstruction field and disappearance of sharp edges outside the scan angle range occurs [1, 2, 3, 14, 15]. Furthermore, an unknown systematic measurement error occurs, i.e., the absolute values of the reconstruction are strongly distorted [16, 17]. To improve the reconstruction result, additional prior knowledge about the measurement object must be integrated into the reconstruction process. This could be iterative approaches such as algebraic reconstruction technique [18] or total variation [18]. They need a lot of computing power but can use estimations as initial value for the reconstruction. Errors in initial estimation can distort the complete reconstruction.  
The hypothesis in this publication is that neural networks can be used to extrapolate data from missing scan angles to significantly improve reconstruction results. This enables the use of standard algorithms for the tomographic reconstruction. There are several existing deep learning based approaches employing neural networks to directly reconstruct local data. They replace the established standard reconstruction methods, such as filtered back projection, and achieve good results, for instance with sparse angle tomography [19; 20; 21; 22; 23]. Synthetic data are used for training and validation. Finally, reconstruction improvements of real limited angle sound pressure measurements are presented.
## 4 The Proposed Scheme HFECS
### Cluster Formation
```
1:if(CH_Election_Set message received),then
2: Start preparing CH_Announce message in order to advertise your role as potential CH
3: Send CH_Announce message to neighboring sensor nodes \(S_{j}\) which are in working mode
4:endif
5: Wait till the Con_Req message is received from neighboring \(S_{j}\)
6:if(Con_Req message is received from neighboring \(S_{j}\)),then
7: CH creates a cluster member set, i.e., CM_set
8: Add the neighboring \(S_{j}\) in CM_set
9:endif
10:if(all working \(S_{j}\) in a cluster are part of CM_set of their new CH),then
11: Each new CH transmits the TDMA schedule to all CMs keeping in view the working-sleeping cycle of CMs
12:endif
13:if(integrated data received from \(S_{j}|S_{j}\in\) CM_set),then
14: CH performs aggregation on multiple data packets received from \(S_{j}\) by concatenating into a single file of specified length
15: CH standardizes the data received from \(S_{j}\), calculates the hesitant fuzzy entropy and weights
16: CH synthesize the attribute values of sensor nodes to determine threshold attribute values
17: CH compares the original attribute value with that of threshold attribute values
18:if(original attribute values are greater than threshold attribute values after T),then
19: CH periodically transmits the aggregated data to BS
20:elseif(original attribute values are less than threshold attribute values after T),then
21: CH will not transmit the aggregated data to BS
22:endif
23:endif
24: Calculate Link connectivity of CH with its cluster members using Equation (14)
25:for each cluster member \(V_{CM(i)}\) in CM_set,do
26:if(\(L_{V_{CM(i)}}\)\(\geq\)\(L_{V_{CM(i)}}\)\(V_{CM(i)}\)),then
27: Add \(V_{CH}\) to CH_Election_Procedure set \(\zeta\)
28:elseif(\(L_{V_{CM(i)}}\)\(\leq\)\(L_{V_{CM(i)}}\)\(V_{CM(i)}\)),then
29: Add \(V_{CM(i)}\) to CH_Election_Procedure set \(\zeta\)
30:endif
31:endfor
32:if(\(\zeta=1\)) and \(V_{CH}\)\(\in\)\(\zeta\),then
33:\(V_{CH}\) will be new \(V_{CH}\) for next round C\({}_{R+1}\)
34:elseif(\(\zeta=1\)) and \(V_{CH}\)\(\
otin\)\(\zeta\)
35:\(V_{CM}\) will be new \(V_{CH}\) for next round C\({}_{R+1}\)
36:elseif(\(\zeta>1\))
37:\(V_{CH}\) executes the CH election procedure and selects new CH with highest (\(\alpha_{x(i)}>D_{f_{i}}\))
38:\(V_{CH}\) sends CH_Election_Set message to new CH
39:endif
```  
**Algorithm 4** HFECS (CH Side)
## 5 Proposed Approach
### Data Reduction with Clustering  
Data of similar characteristics can be grouped using a clustering technique to handle each group of instances in a similar way. The aim is to provide a mechanism to maintain a useful dataset but with reduced number of instances.  
Figure 4 shows the steps of applying the data reduction with clustering stage. First, the 2/3 split of data forming the training data is clustered using the \(k\)-means++ clustering algorithm to produce a set of clusters. Second, each cluster is reduced by 10% to form an updated set of clusters, which are then aggregated to form a reduced but comprehensive dataset in the last step.  
Figure 3: The proposed SLFN-SVM-SMOTE technique including the three stages reduction with clustering, oversampling, and classification.  
The updated dataset is then passed to the oversampling stage discussed in the following section. This provides another benefit of the reduction process as it is later enlarged at the oversampling stage, which results in minimizing the processing volume of the computation while maintaining a useful and comprehensive dataset.
# A Survey of Machine Learning Methods for Time Series Prediction
## 3 Tree-Based Machine Learning Architectures
### Gradient-Boosted Decision Trees
#### 3.2.2 LightGBM  
LightGBM [83], developed by Microsoft in 2017, shares many foundational principles with XGBoost with an even larger focus on the computational efficiency. LightGBM achieves a superior speed by employing histogram-based binning, which discretizes continuous features into bins, trading minor accuracy losses for dramatic speed gains. Additionally, LightGBM introduces Exclusive Feature Bundling, which is particularly effective for many real-world datasets with high-dimensional sparse features because it consolidates mutually exclusive features into a single representation. Another innovation is Gradient-Based One-Side Sampling, which prioritizes instances with large gradients while randomly sampling smaller gradients, optimizing the learning efficiency. LightGBM also utilizes a leaf-wise tree growth strategy, as opposed to the level-wise growth used in traditional GBDT and XGBoost. This approach selectively grows the leaf with the greatest potential to improve the model, enabling faster convergence and improved accuracy.
## 5 Benchmark Analysis
### U-Drawing Test Set-Up  
The U-drawing test is a commonly used benchmark test for springback simulations. In this test, a 90-degree square U channel is first drawn. When the material is released from the die, characteristic springback occurs [28]. The results of the test are primarily controlled by the die geometry, stroke and blank holding force (BHF). Figure 11 shows a schematic representation of the U-drawing test set-up used in this study.  
Figure 10: Anisotropic behaviour of the material in where: (**a**) normalized yield stresses using the Hill48 yielding function and (**b**) \(r\)-values. The markers show the experimental values, and the continuous line shows the model prediction.  
During the test, a strip 330 mm long and 110 mm wide was clamped between the blank holder and the die (radius of 8 mm and opening of 106 mm) and then drawn with the punch (radius of 5 mm and width of 100 mm). Details of the test set-up are shown in Table 8.  
Two different BHFs were used to try to generate different strain states during the drawing step: a low BHF (L-BHF) of 8 t and a high BHF (H-BHF) of 28 t.  
In the benchmark test, the pre-strain conditions were in accordance with industrial standards. In the AR test condition, the sheet was directly drawn to 70 mm of stroke. In contrast, in the PSTT condition, the sheet was first drawn to 35 mm of stroke. The resulting channel from that step was then annealed following intermediate annealing conditions. Finally, the annealed component was drawn up to 70 mm.  
The resulting channels were measured using a coordinate-measuring machine to obtain the final post-springback profile. In this step, the channels were clamped in a central line (a symmetry line), and the profile was then measured.  
Figure 12 shows the resultant profiles of the AR and PSTT materials under the different BHFs (L-BHF and H-BHF). Only a representative profile of three tested samples is shown.  
Figure 11: U-drawing test schematic.  
Figure 12: Experimental representation of the resultant channels after springback in the AR and PSTT materials under low blank holding forces (L-BHFs) and high blank holding forces (H-BHFs).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{W_{p}}\) (mm) & \(\mathbf{R_{p}}\) (mm) & \(\mathbf{R_{d}}\) (mm) & \(\mathbf{W_{d}}\) (mm) & \(\mathbf{L_{s}}\) (mm) & Stroke (mm) \\ \hline
100 & 5 & 8 & 106 & 330 & 35-70 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters of the U-drawing experimental test.  
As shown in Figure 12, the holding force did not have a strong impact on springback. Although there was a slight difference in springback of the PSTT material at L-BHFs and H-BHFs, the difference could be due to the positioning on the coordinate-measuring machine scatter. The intermediate TT had a marked impact on the final springback of the U channel.
## 2 Polymer Blend and Polymer Composite Materials
### Polymer Composite Materials  
The polymer composite materials are typically prepared by incorporating a filler into the polymer matrix. The process could be done either in the melt, solution, or emulsion condition [24; 33; 34]. The polymer composites regularly possess improved thermo-mechanical properties compared to their polymer matrix [35]. The polymer composite materials are easy to prepare, and they also need processing machines such as polymer blends. Table 4 displays the examples of polymer matrices, matrix types, fillers, processing conditions, and processing machines. Thermoplastic polymer composites can be prepared through melt, solution, and emulsion processing. However, for thermosetting polymer composites, they are generally prepared through solution processing before curing because they cannot be melted, emulsified, and reshaped after curing [36]. The chemical nature of thermosetting polymers has allowed them to be processed in such condition because they are bonded with irreversible covalent bonds. Figure 3 displays the chemical structures of PBE, DGEBA epoxy, and PU. On the other hand, biopolymer composites are ordinarily prepared via solution processing [37]. Figure 4 demonstrates the chemical structures of cellulose, chitin, chitosan, and agarose. The melt processing can be made by means of internal mixer, two-roll mill, or extruder. In contrast, the solution and emulsion processing can be operated by using mechanical stirrer, overhead stirrer, sonicator, or magnetic stirrer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Polymer Matrix** & **Matrix Type** & **Filler** & **Processing Condition** & **Processing Machine** & **References** \\ \hline HDPE/LDPE & Thermoplastic & Cellulose & Melt & Internal mixer & [16] \\ PE & Thermoplastic & Sawdust & Melt & Single-screw extruder & [38] \\ PBE & Thermosetting & BN & Solution & Mechanical stirrer & [18] \\ DGEBA epoxy & Thermosetting & chitin & Solution & Mechanical stirrer & [21] \\ PU & Thermosetting & SiO\({}_{2}\) & Solution & Overhead stirrer & [23] \\ Cellulose & Biopolymer & Chitosan & Solution & Sonicator & [39] \\ RC & Biopolymer & MMT & Solution & Magnetic stirrer & [25] \\ Agarose & Biopolymer & Tc & Solution & Magnetic stirrer & [19] \\ SBR & Elastomer & Bentonite & Melt & Two-roll mill & [20] \\ Nylon-6/INR & Blend & MMT & Emulsion & Mechanical stirrer & [24] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of polymer matrices, matrix types, fillers, processing conditions, and processing machines.  
Figure 2: Chemical structures of thermoplastic polymers and the abbreviations are explained in Table 1.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 4 Performance Evaluation
### Outlier Removal
#### 4.2.2 Impact of Outlier Removal  
Outlier removal is a common practice to improve data quality and enhance the performance of machine learning models. However, the impact of outlier removal can vary depending on the nature of the data and the specific application. Additionally, it may undermine the intrinsic characteristics of the data. In this study, we analyzed the effect of outlier removal on the accuracy of angle estimation. The model was initially trained on the original dataset, which included all data points, encompassing potential outliers. This approach allowed the model to benefit from the representation of the indoor environment's complexity, resulting in a certain level of accuracy. After outlier removal, the cleaned dataset, from which outliers identified by the Z-score method were removed, was used to retrain the model.  
As shown in Figure 9, there is an improvement in the accuracy of the PLAE model predictions after outlier removal. The model trained on the dataset with outliers removed shows a closer alignment with the actual angles, as indicated by the more consistent proximity of the predicted values to the ideal line in the scatter plot. This suggests that outlier removal helped the model to generalize better by focusing on the most relevant patterns in the data, leading to a noticeable enhancement in prediction accuracy.