esa-sceva/satcom-chunk-collection huggingface dataset:
# Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
## 2 Materials and Methods
### Combining Inertial Flow and Flowcutter into InertialFlowCutter  
One drawback of Inertial Flow is the restriction to an a priori chosen imbalance, i.e., a value of \(\alpha\). We enhance FlowCutter by initializing \(S\) and \(T\) in the same way as Inertial Flow, though with a smaller parameter \(\alpha\) than proposed for Inertial Flow. Additionally, we pierce cuts with multiple nodes from the Inertial Flow order at once. We call this _bulk piercing_. This way, we enumerate multiple Inertial Flow cuts simultaneously, without having to restart the flow computations. Furthermore, we can skip some of the first, highly imbalanced cuts of FlowCutter that are irrelevant for our application.  
We introduce three additional parameters \(\gamma_{a},\gamma_{o}\in(0,0.5]\) and \(\delta\in(0,1)\) to formalize bulk piercing. Let \(L\) be a permutation of the nodes, ordered according to a direction. For the source side, we use bulk piercing as long as \(S\) contains at most \(\gamma_{a}\cdot n\) nodes. Furthermore, we limit ourselves to piercing the first \(\gamma_{o}\cdot n\) nodes of \(L\). Parameter \(\delta\) influences the step size. The idea is to decrease the step size as the cuts become more balanced. When we decide to apply bulk piercing, we settle the next \(\delta(\frac{1-\delta}{2}n-|S|)\) nodes to \(S\), when piercing the source side. To enforce the limit set by \(\gamma_{o}\), we pierce fewer nodes if necessary. For the target side, we apply this analogously starting from the end of the order. If bulk piercing can not be applied, we revert to the standard FlowCutter method of selecting single piercing nodes incident to the cut. Additionally, we always prioritize the avoid-augmenting-paths heuristic over bulk piercing. In our experiments, we conduct a parameter study which yields \(\alpha=0.05\), \(\gamma_{a}=0.4\), \(\gamma_{o}=0.25\) and \(\delta=0.05\) as reasonable choices. In Figure 2, we show an example for the InertialFlowCutter piercing step.  
Figure 1: Inertial Flow projection and initialization. Nodes are projected onto their closest point on the blue direction. \(S\) and \(T\) are highlighted.  
Figure 2: One piercing step of InertialFlowCutter. \(S\) in dark green, \(S_{r}\setminus S\) in bright green, \(T\) in bright blue, \(T_{r}\setminus T\) in dark blue. The vertical lines depict jumps in the Inertial Flow order. The cuts are depicted in red. The orange stripes in the left figure depict the piercing nodes for the source-side cut. In this case, they overlap with \(T_{r}\), which means that in the next iteration, the cut size increases. The right figure shows the piercing nodes settled to \(S\) as well as the newly computed cuts and \(S_{r},T_{r}\). The previous source-side cut is dashed.
## 3 Results and Discussion
### Effect of Grain Type on Biochemical Traits  
According to grain type, barley could be either hulless (naked) barley without a husk, or hulled (covered) barley with a husk [39]. The presence or absence of husk is thought to influence the composition and levels of biochemical traits in barley genotypes [40; 41]. As described before, the barley accessions in our study were also either hulless or hulled (Table 1), and the variations in biochemical traits between them were statistically analyzed. The result showed that hulless genotypes had significantly higher average \(\beta\)-glucan content (4.24 g/100 g) than the hulled genotypes (3.42 g/100 g) (\(p<0.05\)) (Table 3).  
Figure 2: Effect of origin on agronomical traits and biochemical traits in barley accessions grown in Korea. Different letters on boxplots indicate significantly different means based on [PERSON]’s multiple range test (\(p<0.05\)). ABTS: ABTS\({}^{\bullet+}\) scavenging activity, AL: Awn length, CL: Culm length, DH: Days to heading, DM: days to maturity, DHM: Days from heading to maturity, DPPH: DPPH\({}^{\bullet}\) scavenging activity, GPP: Number of grains per panicle, RP: Reducing power, SL: Spike length, TGW: One-thousand grains weight, TPC: Total phenolic content, \(\beta\)-GL: \(\beta\)-glucan content.  
\(\beta\)-glucans are thought to be found in the cell wall of barley endosperm [53]. The absence of husk in the hulless genotypes could have exposed the grain parts for better extraction of \(\beta\)-glucan. Although there are no comparative studies, some studies also showed a higher level of \(\beta\)-glucan content in hullless barley genotypes than in hulled genotypes [40]. In terms of the other biochemical traits, however, hulled accessions outweighed the hullless accessions. Specifically, the average TPC, DPPH\({}^{\bullet}\) scavenging activity, ABTS\({}^{\bullet+}\) scavenging activity, and RP were 3.02 mg GAE/g, 2.68 mg AAE/g, 5.57 mg AAE/100 g, and 2.71 mg AAE/g, respectively, in the hulled barley accessions, each being significantly different from the average values observed in the hullless accessions (\(p<0.05\)) (Table 3). Previous studies also indicated the importance of husk in barley as a source of health-promoting metabolites, including polyphenols [40; 41]. Overall, our research indicated that the content of metabolites differs among various types of barley grains. Consequently, hulled barley varieties have the potential to provide significant amounts of polyphenols and antioxidants, whereas hulls varieties may be rich sources of \(\beta\)-glucan.
## 2 Materials and Methods
### Experimental Measurement  
The experimental measurement was divided into two parts. In the first one, static loads were considered to verify the results obtained by numerical modelling using the finite element method. In the second part, the dynamic effects that can occur when the individual links of the manipulator accelerate or decelerate, or as a result of the vibration of the transferred object, were investigated.  
Part one: The strain gauge method was chosen for the experimental measurement. In order to consider the measured values as relevant and able to validate the results obtained by numerical modelling, it was necessary to design a loading mechanism to allow calibration measurements to be carried out. Figure 2 shows a test stand where the loading torque was induced by force acting on a known lever arm. The magnitude of the force was registered by a calibrated RSCC force sensor (HBM, Darmstadt, Germany). Radial bearings were used to eliminate the bending effect. A highly dynamic universal amplifier Quantum MX410 combined with the evaluation software CatmanEasy 3.5.1.48 (HBM, Darmstadt, Germany) was used to measure the analysed data. The available sampling frequency range is from 1 Hz to 19,200 Hz; thus, it is also suitable for dynamic measurements.  
The purpose of the experimental measurements was to verify the suitability of the flange design modification to increase the sensitivity of the measured data while ensuring the required stiffness. For the values of the strains, \(\epsilon_{a}\), registered by the strain gauge apparatus when the strain gauges are connected to the half-bridge, the following applies: \(\epsilon_{a}=2\epsilon_{45}\). Based on the evaluation of the increments in the measured values from the operating load, it can be concluded that the strain/stress states correspond to the results of the numerical modelling.  
Part two: The second part of the experimental measurements was focused on the case when the manipulator link may oscillate when handling the load. The above case was simulated by the vibration of a beam with a load attached to a rigid arm, as shown in Figure 3. Since a Quantum MX410 also allows the connection of acceleration sensors, the authors also carried out measurements using two uniaxial accelerometers, type Bruel & Kjaer 4507B. The accelerometers were installed on the beam at location A1 and on the flange at location A2, as shown in Figure 3. The measurements aimed to compare the amplitude spectra obtained from the samples recorded by the accelerometers and strain gauges.  
The experimental measurements show that the proposed methodology using the strain gauge method is so sensitive that even at low amplitudes of the measured strains and with a suitable sampling frequency, it allows estimating the natural frequencies of the assembled mechanical system, taking into account its real couplings. Measurements carried out on the flange already under laboratory conditions confirmed that they take into account not only manufacturing and assembly inaccuracies but also the accuracy of applying strain gauge sensors at the selected location.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 4 Experiments
### Stroke-Based Rendering  
Figure 5 presents stylized 3D synthetic scenes rendered using our method, with a variety of brushstroke shapes. In this example, we apply several brushstroke types, including moon, swirl, and flower shapes, among others. These results demonstrate the versatility and effectiveness of our method across different brushstroke shapes.  
A key hyperparameter in our method is the number of clusters used after the clustering process. In our experiments, we fixed the number of clusters to 150 for the Lego dataset, as all scenes in this dataset are relatively simple, typically containing a single object and lacking complex backgrounds. This value was chosen empirically, as we found, through extensive experiments, that 150 achieves a good balance between preserving semantic meaning and providing an abstract stylization effect.  
For more complex scenes such as those in the Tanks and Temples (TnT) dataset, which contain both foreground and background elements, we set the number of clusters to 10% of the total number of pretrained 3D Gaussian primitives. This setting was also determined empirically and offers a reasonable trade-off between preserving detailed structures and achieving abstract stylization. Furthermore, we allow users to adjust this hyperparameter: a smaller number of clusters leads to a more abstract rendering, while a larger number results in a more realistic and detailed appearance, giving users flexible control over the stylization level.  
Figure 5: Novel view synthesis results of various 3D brushstrokes. Our method produces artistic brushstroke representations while preserving strong semantic accuracy and geometric consistency.  
Figure 6 illustrates the transition from abstract to detailed painting results by varying the number of 3D Gaussian primitives used to reconstruct the scene. We observe that a lower number of strokes captures a strong shape-related geometric style, while increasing the number of strokes results in reconstructions with higher fidelity and finer details.
## 5 Discussion and Conclusions
### Influence of Design Parameters
#### 5.2.2 Influence of the Displacement Ratio  
The displacement ratio (\(\zeta\)) led to different displacements of the pump, thus resulting in a different mean outlet flowrate in Figure 15 when working under the same operating condition. Under the circumstance of the same mean outlet flowrate, there was a value of the displacement ratio which enabled the greatest decrease in the flow ripple, and the value varied with respect to different index angles, as shown in Figure 17.  
Referring to Figure 17, it can be seen that the index angle of \(13.85^{\circ}\) (\(\lambda/2\)) and the displacement ratio of 0.5 led to the greatest decrease in the outlet flow ripple, which was 10% lower than that of \(\gamma=13.85^{\circ}\) and \(\zeta=1\) (flow ripple: 1.57%), and 70% lower than that of \(\gamma=0\) and \(\zeta=1\) (flow ripple: 4.62%).
# Investigation of the Effects of Different Working Fluids on Compressor Cascade Performance
## 5 Conclusions  
The effect of working fluid physical properties on the performance of a subsonic compressor cascade at a wide span of Mach number, i.e., 0.5-0.7, is studied considering coefficient of total pressure loss and static pressure ratio using theoretical analysis and numerical validation in this paper. The objective is to analyze the relationships of the coefficient of total pressure loss and static pressure ratio in subsonic-speed linear compressor cascade with the physical properties of the alternate working fluids. Successively, theoretic study is validated using comparison of computational and theoretic results. The following conclusions are presented:  
1. Coefficient of total pressure loss is only linked to the specific heat ratio and Mach number of the working fluid at a matching Reynolds number. The greater specific heat ratio working fluid has a greater coefficient of total pressure loss.
2. Coefficient of total pressure loss increases by the increase of Mach number. Coefficient of total pressure loss of helium and carbon dioxide is calculated using Equation (13) and validated. The relative prediction error is less than 5%, which is enough to meet the engineering demand.
3. The relationship of compressor cascade static pressure ratio using dissimilar working fluid is governed by specific heat ratio and inlet relative Mach number. Higher specific heat ratio fluid has higher static pressure and variance rises with rise in Mach number.
4. Equation (15) accurately predicts the cascade static pressure ratio using other fluids. Predictive relative error is less than 8%, which is enough to meet the engineering demand.
5. When attack angle is fixed, flow separation in compressor cascade depends on adverse pressure gradient. Specific heat ratio and Mach number of working fluid affect flow separation in compressor cascade by influencing static pressure ratio in cascade.  
Conceptualization, Z.T. and Q.Z.; methodology, Z.T. and C.W.; software, Z.T. and Q.Z.; validation, Z.T.; formal analysis, Z.T.; investigation, Z.T.; resources, Z.T.; writing--original draft preparation, Z.T.; writing--review and editing, Q.Z. and C.W.; visualization, Q.Z.; project administration, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number51476039 and 52006021.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 13: Relative position of transition at different attack angles (Ma = 0.7, Re = 8.0 \(\times\) 105).
## 4 Added Value of the Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**HNMS Station**} & **Optim \(\psi\)’ (Degrees)** & **Optim \(\beta\)** & \(G_{e,\psi\prime\beta}\) \\  & **(Degrees)** & **(Degrees)** & **(kWhm\({}^{-2}\)year\({}^{-1}\))** \\ \hline \hline \multirow{2}{*}{Agrinio} & 166 & 29 & 1898.66 \\ \hline \hline \multirow{2}{*}{Alexandroupoli} & 164 & 29 & 1731.92 \\ \hline \hline \multirow{2}{*}{Anchios} & 169 & 29 & 1829.39 \\ \hline \hline \multirow{2}{*}{Andravida} & 174 & 29 & 1923.10 \\ \hline \hline \multirow{2}{*}{Aravos} & 168 & 29 & 1927.48 \\ \hline \hline \multirow{2}{*}{Chios} & 174 & 26 & 1820.86 \\ \hline \hline \multirow{2}{*}{Elliniko} & 172 & 27 & 1906.17 \\ \hline \hline \multirow{2}{*}{Ioannina} & 179 & 29 & 1745.51 \\ \hline \hline \multirow{2}{*}{Irakleio} & 168 & 24 & 1934.43 \\ \hline \hline \multirow{2}{*}{Kalamata} & 168 & 28 & 1916.94 \\ \hline \hline \multirow{2}{*}{Kasteli} & 167 & 26 & 1937.86 \\ \hline \hline \multirow{2}{*}{Kastoria} & 172 & 29 & 1794.31 \\ \hline \hline \multirow{2}{*}{Kerkyra} & 172 & 28 & 1761.94 \\ \hline \multirow{2}{*}{Kozani} & 153 & 33 & 1904.14 \\ \hline \hline \multirow{2}{*}{Kythira} & 163 & 26 & 1952.81 \\ \hline \hline \multirow{2}{*}{Lamia} & 169 & 27 & 1839.38 \\ \hline \hline \multirow{2}{*}{Larsa} & 171 & 28 & 1772.59 \\ \hline \hline \multirow{2}{*}{Lesvos} & 168 & 27 & 1878.96 \\ \hline \hline \multirow{2}{*}{Limnos} & 167 & 27 & 1819.81 \\ \hline \hline \multirow{2}{*}{Michoni} & 174 & 28 & 1892.05 \\ \hline \hline \multirow{2}{*}{Mikra} & 174 & 28 & 1735.11 \\ \hline \hline \multirow{2}{*}{Naxos} & 171 & 27 & 1914.38 \\ \hline \hline \multirow{2}{*}{Rodos} & 172 & 26 & 1951.10 \\ \hline \hline \multirow{2}{*}{Samos} & 171 & 26 & 1882.61 \\ \hline \multirow{2}{*}{Serres} & 175 & 29 & 1741.09 \\ \hline \hline \multirow{2}{*}{Stieia} & 169 & 25 & 1952.64 \\ \hline \hline \multirow{2}{*}{Skyros} & 170 & 27 & 1725.02 \\ \hline \hline \multirow{2}{*}{Souda} & 166 & 25 & 1945.01 \\ \hline \hline \multirow{2}{*}{Spata} & 166 & 26 & 1891.33 \\ \hline \hline \multirow{2}{*}{Tanagra} & 169 & 27 & 1855.87 \\ \hline \hline \multirow{2}{*}{Thira} & 171 & 25 & 1921.75 \\ \hline \hline \multirow{2}{*}{Tripoli} & 172 & 28 & 1987.37 \\ \hline \hline \multirow{2}{*}{Zakynthos} & 173 & 29 & 1917.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Maximum annual solar energy \(G_{e,\psi\prime\beta}\) received at the 33 sites in Greece (in alphabetical order) on inclined surfaces having specific azimuth \(\psi\)’ and tilt \(\beta\) angles.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Inter-Cell Interference Mitigation Techniques  
ICI is especially prominent at the edges of cells in dense deployments. 3 GPP has standardized several cooperative techniques to mitigate ICI.
## 3 Results
### Second Hypothesis  
Table 2 shows the correlation matrix between deltas of anthropometric and body composition and physical performance. Generally, variations in body mass, triceps, supraspinal, and supra-iliac skinfold thicknesses, such as the body fat percentage, and the \(\Delta\) endomorphic component were highly correlated with all motor tests. The lower limb power variation (\(\Delta\) CMJ) was strongly and positively correlated with \(\Delta\) stature and trunk height, \(\Delta\) humeral diameter, and \(\Delta\) FFM, whereas negative strong correlations appeared with \(\Delta\) APHV, \(\Delta\) medial calf skinfold thickness, \(\Delta\) UMA, UFA, and UFI. The maximal speed (15 m) change was correlated with \(\Delta\) medial calf thickness, \(\Delta\) UFA, UFI, and \(\Delta\) endomorphic. As regards RSA variation, it also shows a strong direct correlation with \(\Delta\) biceps skinfold thickness, \(\Delta\) CFI, \(\Delta\), and bio-electric resistance, whereas inverse strong correlations appear with \(\Delta\) FFM, \(\Delta\) trunk height, and \(\Delta\) body mass. \begin{table}
\begin{tabular}{l c c c c c c c  
Tables 3-5 show the multiple regression models for CMJ, the 15 m sprint, and RSA deltas, respectively. For the CMJ model, the average variance inflation factor (VIF) was 1.15, while the probabilities associated with the null hypothesis of homoskedasticity for each regressor were 0.79 and 0.30. One outlier was deleted from the final model (AIC model with 24 players = 110.05 vs. AIC model with 23 players = 101.2), which explained the 62% CMJ variability. As regards sprint, all of the subjects were included, and the average VIF = 1.03. Both of the regressors did not reject the null hypothesis of homoskedasticity (CFI \(\chi^{2}\) = 0.19, \(p\) = 0.67; medial calf SK \(\chi^{2}\) = 0.25, \(p\) = 0.62), and their adjusted goodness of fit was 0.501. Finally, the RSA regression model included two regressors (CFI and R) with 23 subjects (one outlier deleted, \(\Delta\) AIC = \(-\)40.89). The average VIF was 1.28, and neither of
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.1 Train on Different Historical Datasets  
Figure 6 illustrates the learning curves of our algorithm during training, based on datasets collected from the Lublin-256 SWF logs. The historical dataset (orange line) consists of 2500 trajectories gathered using a random scheduling algorithm. The replay dataset (green line) is composed of 2500 trajectories collected by the RLScheduler during training, with 50 trajectories generated per epoch over 50 epochs, until model convergence. The expert-guided dataset (red line) contains 2500 trajectories representing optimal scheduling performance, as guided by the best expert. The hybrid dataset (blue line) includes 80% expert-guided trajectories (2000 samples) and 20% early-stage trial trajectories (500 samples) generated by RLScheduler during its initial training phase.  
The results indicate that the model trained on the random scheduling dataset (orange line) failed to converge to an optimal solution, as it could not learn effective scheduling patterns. The replay dataset collected from RLScheduler (green line) also struggled, as RLScheduler's decisions were not always optimal, resulting in subpar performance in many scenarios. The dataset with expert-guided trajectories (red line), although based on optimal scheduling metrics from heuristic algorithms, presented significant complexity, making it harder for the model to learn effectively.  
The hybrid dataset (blue line), containing 80% expert-guided and 20% trial-and-error trajectories, yielded the best results. The model benefited from learning valuable scheduling decisions from early-stage trial trajectories while acquiring high-quality patterns from the expert-guided trajectories, ultimately achieving higher rewards and superior overall performance.
## 3 Background and Methods
### Data Acquisition and Processing
#### 3.1.2 Data Pre-Processing  
In the pre-processing, normalization was used for converting the values into the normal form without changing the differences in the value's range. This stage ensures the data quality and reliability of abnormal data with the help of the normalization process. In order to modify the lowest value to 0 and the highest value to 1, as well as to convert the remaining value to decimal, the min-max normalization [10] was taken into consideration. Equation (1) shows the process of min-max normalization.  
\[Input_{new}=\frac{Input_{old}-Input_{min}}{Input_{max}-Input_{min}} \tag{1}\]  
where input minimum and maximum values are indicated as \(Input_{min}\) and \(Input_{max}\), respectively; and input and preprocessed values are designated as \(Input_{old}\) and \(Input_{new}\), respectively. The pre-processed outcome is given as input to the LASSO feature selection to avoid redundant features.
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 1 Introduction
Previous studies have identified autonomous robot systems as key to reducing labor needs in agriculture and achieving automated agriculture. Therefore, the effectiveness of robots at each stage of the field must be evaluated. As a primary step, this study focuses on the ridge-forming stage, comparing the performances of conventional methods with a developed robotic system. The results of this comparison are used to discuss the feasibility of replacing human labor with robots for this specific operation.
## 2 Requirements Specifications and Measurements Gaps
### Sea Ice Type  
Sea ice type is a critical parameter of marine weather forecast to improve and to understand climate change. The requirement for this measurement is a 3-h revisit time with 10 km as the spatial resolution. Previous experiments demonstrated the sensitivity of microwave scatterometers to discriminate sea ice type [34]. According to the gap detected in the revisit time, this instrument will be evaluated in conjunction with other solutions such as microwave imagers, SAR and SAR altimeters [35].