esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling Multiprocessor Tasks with Equal Processing Times as a Mixed Graph Coloring Problem
## 5 Discussion  
In Section 3.1, we defined a general shop scheduling problem \(\mathit{GcMPT}|t_{i}=1,[r_{i}]|\mathcal{C}_{\max}\) of finding a schedule with the minimum length for processing multiprocessor tasks with unit-time durations and integer release times \(r_{i}\) provided that two types of the precedence constraints may be given on the set of the multiprocessor tasks. Contrary to a classical shop-scheduling problem, several machines are required to process a multiprocessor task in the problem \(\mathit{GcMPT}|t_{i}=1,[r_{i}]|\mathcal{C}_{\max}\). Furthermore, it is required that specified subsets of the multiprocessor tasks must be processed simultaneously.  
We proved Theorem 6 showing that an optimal coloring \(c(G)\) of any colorable mixed graph \(G=(V,A,\,E)\) is equivalent to the general shop scheduling problem \(\mathit{GcMPT}|p_{i}=1\), \([r_{i}]|\mathcal{C}_{\max}\) on the mixed graph \(G=(V,A,\,E)\). Theorem 6 implies that most analytical and algorithmic results proven for optimal colorings of the mixed graph, have analogous results, which are valid for the newly defined scheduling problem \(\mathit{GcMPT}|p_{i}=1,[r_{i}]|\mathcal{C}_{\max}\), and vice versa. In particular, some results that have been proven in [10; 11; 27; 28; 29; 30; 31; 32; 33; 34] for the scheduling problem \(\mathit{GcMPT}|t_{i}=1,[r_{i}]|\mathcal{C}_{\max}\) and for its special cases may be interpreted as analogous results for optimal colorings \(c(G)\) of the corresponding mixed graphs \(G=(V,A,\,E)\). Similarly, some results that have been proven in [3; 4; 5; 8; 9; 17; 22; 23; 30; 31; 32; 33; 34] for optimal mixed graph colorings \(c(G)\) may be interpreted as analogous results for general shop scheduling problems \(\mathit{GcMPT}|t_{i}=1,[r_{i}]|\mathcal{C}_{\max}\). In the other words, there are articles [3; 4; 5; 8; 9; 11; 17; 18; 22; 23; 30; 31; 32; 33; 34] studied both of these problems without indicating that they are actually the same problems.  
Theorem 6 may be considered as a generalization of Theorem 2 proven in [4] and Theorems 3 and 4 proven in [23].  
In Section 3.2, we defined a new general shop scheduling problem \(\mathit{GcMPT}|t_{i}=1,[d_{i}]|L_{\max}\) to minimize the maximal lateness \(L_{\max}=\max\{C_{i}-d_{i}:I_{i}\in J\}\) with integer due dates \(d_{i}\geq 0\), two types of the precedence constraints, specified subset of tasks, which must be processed simultaneously, and unit-time durations of the multiprocessor tasks. We proved Theorems 7 showing that any solvable general shop scheduling problem \(\mathit{GcMPT}|t_{i}=1,[r_{i}]|\mathcal{C}_{\max}\) on the mixed graph \(G=(V,A,\,E)\) with integer release times is equivalent to a general shop scheduling problem \(\mathit{GcMPT}|t_{i}=1,[d_{i}]|L_{\max}\) with integer due dates, and vice versa. Corollary 1, Theorem 6, and Theorem 7 imply that any solvable general shop scheduling problem \(\mathit{GcMPT}|t_{i}=1,[d_{i}]|L_{\max}\) reduces to finding an optimal coloring \(c(G)\) of the specified mixed graph \(G=(V,A,\,E)\).
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 2 Materials and Methods
### Training Subset Selection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Feature** & **Details** \\ \hline
1 & Generalized norm (\(\mathbb{I}_{10}\)) & Order of norm, \(p=10\) \\
2 & Ratio of norms (\(\mathbb{I}_{20}/\mathbb{I}_{2}\)) & Ratio of high-order norm (\(p=20\)) to low-order norm (\(p=2\)) \\
3 & Kurtosis & Indicator for the tails of probability distribution \\
4 & BPFI feature & Median amplitude of 1–10 BPFI harmonics \\
5 & BPFO feature & Median amplitude of 1–10 BPFO harmonics \\
6 & BSF feature & Median amplitude of 1, 2, 4, 6] BSF harmonics \\
7 & BPFI sideband feature & Median amplitude of the nearest sidebands on both sides of BPFI harmonics \\  & & (20 sidebands altogether, spaced at shaft rotational frequency) \\
8 & BSF sideband feature & Median amplitude of the nearest sidebands on both sides of BSF harmonics \\  & & (8 sidebands altogether, spaced at FTF) \\
9 & GMF feature 1 & Median amplitude of 1–4 GMF harmonics and two nearest sidebands on both sides (20 frequency components altogether) \\
10 & GMF feature 2 & Median amplitude of 1 \(\times\) GMF and two nearest sidebands on both sides (5 frequency components altogether) \\
11 & GMF feature 3 & Median amplitude of 2 \(\times\) GMF and two nearest sidebands on both sides (5 frequency components altogether) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of features generated for condition monitoring. Figure 1: Flowchart for system identification with the position of ‘training subset selection’ highlighted (adapted from [6]).
## 4 Evaluation Methodology
### Evaluation Method
#### 4.3.3 ETS/CSI/PAG/ACC  
In class imbalance problems, there are methods for detection algorithm performance measurement: equitable threat score (ETS), critical success index (CSI), post agreement (PAG), and accuracy (ACC) [43; 44; 45; 46; 47]. There are common methods to verify the results of dichotomous (yes/no) forecasts on weather, detection, etc. Some indicators according to the confusion matrix are shown below (Table 2) (using indicators such as TP, TN, FP, and FN in F1-Measure)  
Figure 5: ROC curve.  
The equations of methods are as follows:  
\[\mathbf{ETS}=\frac{\mathbf{H}-\mathbf{\alpha_{r}}}{\mathbf{H}+\mathbf{M}+\mathbf{F}-\mathbf{\alpha_{r}}} \tag{2c}\]  
where  
\[\mathbf{\alpha_{r}}=\frac{(\mathbf{H}+\mathbf{M})(\mathbf{H}+\mathbf{F})}{\mathbf{H}+\mathbf{M}+\mathbf{F}+\mathbf{C}}\]  
\[\mathbf{CSI}=\frac{\mathbf{H}}{\mathbf{H}+\mathbf{M}+\mathbf{F}} \tag{2d}\]  
\[\mathbf{PAG}=\frac{\mathbf{H}}{\mathbf{H}+\mathbf{F}} \tag{2e}\]  
\[\mathbf{ACC}=\frac{\mathbf{H}+\mathbf{C}}{\mathbf{H}+\mathbf{M}+\mathbf{F}+\mathbf{C}} \tag{2f}\]
# Propagation of Optical Coherence Vortex Lattices in Turbulent Atmosphere
## 3 Numerical Results
### \(M^{2}\)-Factor  
In this subsection, we explore the evolution of the normalized \(M^{2}\)-factor of the OCVLs in turbulent atmosphere.  
Figure 5 displays the normalized \(M^{2}\)-factor of the OCVLs in turbulent atmosphere for several different topological charge \(l\) and beam array order \(M\) with \(\lambda=532.8\) nm, \(C_{n}^{2}=10^{-15}\) m\({}^{-2/3}\), \(d=1\) mm, \(\delta_{0}=5\) mm, _and_\(\omega_{0}=1\) cm. Figure 5a shows that as the normalized \(M^{2}\)-factor of the OCVLs remains invariant on propagation in free space (\(C_{n}^{2}=0\)), this property represents the best beam  
quality. However, for the turbulent atmosphere, the normalized \(M^{2}\)-factor of the OCVLs increases on propagation, and its value is smaller than that of the Gaussian Schell-model (GSM) array (\(l=0\)) when the propagation distance is fixed. Furthermore, the normalized \(M^{2}\)-factor increases with the decrease of the topological charge \(l\). From Figure 5b, one can see that the normalized \(M^{2}\)-factor of the OCVLs (\(M>1\)) is smaller than that of the GSM vortex beam (\(M=1\)), and the normalized \(M^{2}\)-factor increases as M decreases. Figure 5 indicates that the OCVLs will be less affected by the negative effects of the turbulence for larger topological charge and larger beam array order from the view point of the normalized \(M^{2}\)-factor.
# Identification of Rotundone as an Important Contributor to the Flavor of Oak-Aged Spirits
## 2 Results and Discussion
### Relative Potency of Rotundone in Bourbon Whiskey  
3.1 Identification of Potent Odorants in Bourbon Whiskeys of Increasing Age (4, 8, and 12 Years) by Aroma Extract Dilution Analysis (AEDA)  
A total of 40 odorants were identified by AEDA in solvent extracts of three bourbon whiskers varying in age (Table 3). Results show that the three bourbons analyzed had similar rankings, based on flavor dilution factors, for the majority of the compounds detected. The results are also in agreement with previous studies on the flavor analysis of whiskey [12; 30; 31; 32; 33; 34; 35; 36]. Almost all of the potent odorants identified were derived from the oak barrel during aging. The main exceptions were the branched short-chained alcohols, 2/3-methyl-1-butanol and phenethyl alcohol, which are products of fermentation.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 1 Introduction
### Contributions and Organization  
Figure 1 lists the problems and proposed resolutions for the NB-IoT uplink in LEO satellite communication. In this paper, we apply the underlined solution in Figure 1. Beam coverage reduction is proposed to address the residual Doppler shift. This study defines the relationship between the beam coverage size and the residual Doppler shift. Afterward, the maximum beam coverage size that the NB-IoT receiver can compensate for is calculated through 3D satellite orbit simulation. Even with the proposed beam coverage size, a residual Doppler shift exists, which degrades the link performance. In this study, we suggest adding _DMRS_ symbols to improve link performance. To the best of our knowledge, previous studies have not implemented simulations using actual satellite orbits in the NB-IoT NTN scenario.  
This study improved link performance by adding _DMRS_ symbols to reduce the minimum number of satellites required to service NB-IoT NTN. Because LEO satellites are fast-moving, the time to communicate with NB-IoT is short. Therefore, it is necessary to increase communication time by deploying many satellites. However, as the number of satellites increases, the service cost increases, so information on the minimum number of satellites is required. In this study, STARLINK satellites are implemented through MATLAB to find the minimum number of satellites. Satellite orbits are implemented with two-line element (TLE) files [29]. The performance of _DMRS_ symbol addition is verified through link-level simulation and link budget analysis. Link-level simulations show signal-to-noise ratio (SNR) and throughput results that satisfy _BLER_\(\leq\) 0.1 [30]. Link budget analysis analyzes the results in four metrics by calculating the link margin during the day. The proposed multiple DMRS symbol shows that the same performance can be achieved using fewer satellites than a single _DMRS_ symbol. This performance analysis method can be utilized as technical data for designing the LEO satellite communication system.  
The remainder of this paper is organized as follows. Section 2 describes the system model. The NTN architecture and NB-IoT uplink transmission method are explained, and the channel model is described. Section 3 describes the residual Doppler shift problem and the link performance problem. A solution to the residual Doppler shift problem is presented in Section 4. Section 5 describes the evaluation metrics for performance analysis. Section 6 provides link-level and link budget simulation implementation and the numerical results. In Section 7, the conclusion and future work directions are presented.
## 4 Experiments and Results
### Comparison between Fast ADMM and Plug-and-Play ADMM  
In order to verify the efficiency of the fast ADMM convergence, we adopted six images for comparison with the plug-and-play ADMM. The images were down-sampled by two-fold and white Gaussian noise was added to them. The standard deviation of the white Gaussian noise was set to 20.  
Figure 2: HR infrared images: (**a**) birds, (**b**) streets, (**c**) crossroads, (**d**) building, (**e**) passerby, and (**f**) figure.  
The convergence performance of the two algorithms is presented in Figure 3. As indicated by the curves in Figure 3, both the fast ADMM and plug-and-play ADMM algorithms are convergent. However, introducing the fast ADMM significantly reduces the number of iterations.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 4 Results  
### Experimental Setup  
To evaluate the performance of RD-SLAM, we conduct experiments on the Replica [34] (eight sequences), the TUM-RGBD [35] (three sequences), and the ScanNet [36] dataset (six sequences). The Replica dataset is a synthetic scene containing high-quality RGB depth images. The TUM-RGBD and ScanNet datasets are real-world images captured using an old, low-quality camera with significant noise and blurriness. In particular, the depth image could be more sparse, with many sections suffering from information loss. We validate the effectiveness of our method by evaluating it on both virtual synthetic datasets and real-world datasets.  
We compare RD-SLAM with the existing state-of-the-art (SOTA) NeRF-based and 3 DGS-based dense visual SLAM: NICE-SLAM, Point-SLAM, GS-SLAM, and SplaTAM. When comparing tracking accuracy on the TUM-RGBD dataset, we also compare our approach with three traditional SLAM systems: Kintinuous [37], ElasticFusion [38], and ORB-SLAM2.  
We evaluate the camera tracking accuracy using the average absolute trajectory error (ATE RMSE) and the map quality using the peak signal-to-noise ratio (PSNR), SSIM, and learned perceptual image patch similarity (LPIPS) [39]. For the experimental results, we use the average of three runs. The assessment tables mark the best results in bold, and the second best results are underlined. RD-SLAM is implemented in Python (v.3.9) using the PyTorch framework, incorporating CUDA code for 3 DGS. We run our SLAM on a laptop with Intel Core i7 10750H 2.60 GHz and NVIDIA Quadro RTX 3000.
###### Abstract  
An iterative fractional Doppler shift and channel joint estimation algorithm is proposed for orthogonal time frequency space (OTFS) satellite communication systems. In the algorithm, we search the strongest path and estimate its fractional Doppler offset, and compensate the Doppler shift to the nearest integer to estimate the coefficient of the path. Then signal of the path and its inter-Doppler interference are reconstructed and canceled from the received data with these two estimated parameters. The estimation and cancel process are iteratively conducted until the strongest path in the remained paths is less than the predetermined threshold. The channel information can be reconstructed by the estimated parameters of the paths. The normalized mean squared error (NMSE) of the proposed channel estimation algorithm is less than \(1/5\) of the available algorithms at a high signal-to-noise ratio (SNR) region, and its BER has about 4 dB SNR gain compared with those of the available algorithms when the bit error rate (BER) is \(10^{-3}\).  
LEO satellite communication; OTFS; channel estimation; fractional Doppler shift IEEEexample:BSTcontrol  
2020 IEEE 2020 IEEEexample:BSTcontrol
## 5 Discussion on Potential Applications of the Data Products  
With the complex waveform products and corresponding processing tools, different aspects of the GNSS-R technique can be further investigated, and new applications or approaches can also be envisaged. In this section, we preliminarily demonstrate some potential applications of these complex waveform products.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 3 Experiment and Result Analysis
### Apple Grading Experiments  
#### 3.3.1 Apple Grading Model Evaluation Metrics  
In the study of the apple grading model, we used a variety of evaluation metrics to comprehensively analyze the model's performance. These metrics included Precision (P), Recall (R), Jaccard Score, and F1 Score. The F1 Score and Jaccard Score were used to measure the overall performance and accuracy of the model across different categories. To provide a more comprehensive analysis of the model's performance and a better understanding of its effectiveness in multi-class problems, we introduced three different averaging methods: Micro, Macro, and Weighted. The Micro Average evaluated overall performance, reflecting the proportional impact of each sample; the Macro Average emphasized the importance of each category equally; and the Weighted Average addressed class imbalance issues by accounting for the size of each class. These metrics reflected the model's sensitivity, correctness, and applicability from different perspectives. The specific calculation formulas are shown in Table 6, where C represents the number of categories, \(N\) represents the total number of samples, and \(n_{i}\) represents the number of samples in the i-th category.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Categories** & **Equation** \\ \hline \multirow{4}{*}{Jaccard Score} & Micro & \(\frac{\sum_{i=1}^{N}\left[y_{i}\cap y_{i}\right]}{\sum_{i=1}^{N}\left[y_{i} \cup y_{i}\right]}\) \\  & Macro & \(\frac{1}{C}\sum\limits_{i=1}^{C}\left[\frac{y_{i}\cap y_{i}}{y_{i}\cup y_{i}}\right]\) \\  & Weighted & \(\sum\limits_{i=1}^{C}\frac{n_{i}}{N}\times\frac{\left[y_{i}\cap y_{i}\right]}{ \left[y_{i}\cup y_{i}\right]}\) \\ \hline \multirow{4}{*}{P} & Micro & \(\frac{\sum_{i=1}^{C}\frac{TP_{i}}{P_{i}+FP_{i}}}{\sum_{i=1}^{C}\left(TP_{i}+ FP_{i}\right)}\) \\  & Macro & \(\frac{1}{C}\sum\limits_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FP_{i}}\) \\  & Weighted & \(\sum\limits_{i=1}^{C}\frac{n_{i}}{N}\times\frac{TP_{i}}{TP_{i}+FP_{i}}\) \\ \hline \multirow{4}{*}{R} & Micro & \(\frac{2\times P\left(micro\right)\times R\left(micro\right)}{P\left(micro \right)+R\left(micro\right)}\) \\  & Macro & \(\frac{1}{C}\sum\limits_{i=1}^{C}F1\ Score_{i}\) \\ \cline{1-1}  & Weighted & \(\sum\limits_{i=1}^{C}\frac{n_{i}}{N}F1\ Score_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation metrics for the grading model.
# Biomimetic Liquid-Repellent Surfaces by Ultrafast Laser Processing
## 1 Introduction
### Laser-Irradiated Structures
#### 2.2.1 Periodic Nanostructures  
Laser-induced periodic surface structures (LIPSS, ripples) can be generated on different materials (metals, semiconductors, and dielectrics) by linearly polarized laser radiation at fluences slightly above the ablation threshold. By ultrafast laser radiation two different types of LIPSS can be fabricated: low-spatial-frequency LIPPS (LSFL) and high-spatial-frequency LIPSS (HSFL). LSFL on strong absorbing materials, such as metals and semiconductors are characterized by a spatial period close to or less than the wavelength of applied laser radiation and an orientation perpendicular to the polarization of laser beam. The formation of LSFL is explained by interference of the incident laser beam with surface electromagnetic waves generated at the surface roughness including excitation of surface plasmon polaritons. LSFL on dielectrics can be generated with orientation perpendicular or (for dielectrics with large band gap) parallel to the laser beam polarization. In case of dielectrics, the absorption of high intensity ultrafast laser radiation leads to a metal-like behavior of ionized material. Therefore, the ripple formation on dielectrics can be explained by analogy to metals. More detailed information can be found, for example, in [21]. LSFL on dielectrics are characterized by spatial periods near the wavelength of laser radiation (\(\lambda\)) or nearly \(\lambda/\)n, where n is the refractive index of the dielectric material. Detailed pictures of LSFL made by scanning electron microscopy show that LSFL generated by ultrafast laser radiation are densely covered by nanostructures [22; 23], in contrast to smooth LIPSS produced by long-pulsed lasers (Figure 5).  
Figure 4: Titanium surfaces after irradiation at 2.5 J/cm\({}^{2}\) with 100 laser pulses (stationary conditions): columnar structure (in the center of the ablation crater), periodic nanostructure (in the outer region of the ablation crater).  
High-spatial-frequency LIPSS (HSFL) have a significantly shorter spatial period compared to the irradiation wavelength (<\(\lambda\)/2), and orientation often perpendicular but occasionally parallel to the laser beam polarization. The formation of HSFL is still under discussion and explained by different mechanisms such as second-harmonic generation, self-organization, and interaction with specific types of plasmon modes. For the practical application of LIPPS in biomimetics, it is important to control their structural properties (spatial period). It can be realized by variation of laser wavelength (smaller wavelength leads to a smaller spatial period) [24], the angle of incidence (by changing the angle of incidence from 0\({}^{\circ}\) to 80\({}^{\circ}\) spatial period could be controllably increased, for example from 0.6 to 3.7 \(\upmu\)m for platinum target [25]), and processing environment (processing in liquid leads to a decrease in spatial period compared to processing under normal ambient conditions [26]). Detailed information can be found in the following key publications: [19; 27].
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 5 Conclusions  
1. The slip flow effect is most significant at the radius of 59.87 mm near the low-pressure side, and from this position, the closer to the inner and outer radii of the seal ring, the weaker the influence of the slip flow effect.
2. The slip flow effect reduces the gas film opening force and viscous friction power consumption, increasing the mass leakage rate. As the thickness of the gas film increases and the ambient pressure decreases, the role of the slip flow effect will gradually come to the fore.
3. The opening force of the compliant end face gas film seal will be strengthened with an increase in the inlet and outlet pressure ratio, wave foil chord length, and pitch and weakened with an increase in the wave foil thickness, and the trend is nearly linear, in which the inlet and outlet pressure ratio has the greatest influence.
4. The thickness of wave foil increases, the stiffness of the compliant end face increases, and it is not easy to deform in the axial direction. The decreasing trend of the mass leakage and the increasing trend of the stiffness leakage ratio show strong nonlinear characteristics, indicating that the increasing thickness of wave foil makes the compliant end face evolve gradually to the stiff end face.  
Figure 11: Influence law of wave foil pitch. (**a**) Gas film opening force and mass leakage rate; (**b**) Viscous friction power consumption and Stiffness Leakage Ratio.  
**Author Contributions:** Conceptualization, H.J. and S.Y.; methodology, H.J.; validation, H.J., S.Y. and X.D.; formal analysis, H.J.; investigation, H.J.; resources, H.J.; data curation, X.D.; writing--original draft preparation, H.J.; writing--review and editing, H.J.; visualization, H.J.; supervision, S.Y.; project administration, S.Y. and X.D.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Gansu Basic Research Program--Outstanding Doctoral Student Project (granted no. 23 JRRA782). The current research has been supported by National Key Research and Development Program (NKRDP) projects (granted 2020 YFB2010001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Effect of Multiple Reclosing Time Intervals on Axial Vibration of Winding
## 4 Evaluation of the Effect of Reclosing Time Interval on Short-Circuit Vibration  
The key factors that contribute to axial vibration in power transformers include the short-circuit electromagnetic force, pretightening force, winding mode, the change of insulating pad structure inside the winding, and the reclosing time interval. When there is a permanent short circuit outside the transformer, the vibration amplitude of the transformer winding also changes with the change in the reclosing time interval. To analyze the corresponding relationship between the two, the following analysis was conducted.