esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion  
The fundamental and second harmonic wave fields generated by different types of transducers are calculated in this section to obtain the received average fields. In numerical simulations, the propagation medium is chosen as Al6061 with the wave velocity of \(c=6430\) m/s and the nonlinear parameter of \(\beta=5.5\). To investigate the pulse-echo nonlinear behavior in a relatively thin sample, the sample thickness is set to \(z_{0}=15\) mm.  
Figure 3: Illustration of phase shift.  
The attenuation was ignored in this study. Each transducer element emits a fundamental wave at a frequency of 5 MHz with the source displacement of \(u_{0}=1\times 10^{-9}\) m.  
The radius of the single-element transducer shown in Figure 2a was chosen to be 12.7 mm. For the four-element array transducer shown in Figure 2b, the inner radii of these elements are \(r_{0}=0\) mm, \(r_{2}=3.175\) mm, \(r_{4}=6.35\) mm and \(r_{6}=9.525\) mm, respectively. The outer radii are \(r_{1}=3.175\) mm, \(r_{3}=6.35\) mm, \(r_{5}=9.525\) mm and \(r_{7}=12.7\) mm. It was assumed that there is no gap between the elements of the array transducer.  
The dual element transducer shown in Figure 2c is composed of an outer ring element and a central disk element. The inner and outer diameters of the ring element are 6.35 mm and 12.7 mm, respectively. There is no gap between the two elements, so the diameter of the inner element is 6.35 mm.
# Concept Justification of Future 3 DPVS and Novel Approach towards its Conceptual Development
## 3 Conceptual Design Approach for 3 DPVS
### Establishing Conceptual Design Process for 3 DPVS
#### 3.3.2 Design Initiation  
Design initiation aims at problem selection and requirement identification. This phase is mainly developed to collect requirements and speculations for the 3 DPVS design, and the acquired information can be used as the input for the concept design in the next phase. Requirement specification is documented to provide a detailed description of the 3 DPVS project, and the requirements identified to make it achievable.  
Figure 4: Illustration of the conceptual stage for 3 DPVS system design.  
Figure 5 illustrates the basic requirements specification list for the 3 DPVS conceptual design. There requirements need to be addressed in sequence. Some requirements can be easily understood while others need to be explained to help better perception. For instance, the main aim of 3 DPVS is to be set up in first place, for external cell culture studies or tissue-engineering purposes. Design objectives tend to have a hierarchy so basic and secondary function of 3 DPVS may vary from one case scenario to another. Base models refer to a set of 3D static scaffolds that can be possibly used as the starting point for 3 DPVS development. Then it comes to the requirements of newly introduced vibration mechanism, the difference between traditional vibration mechanisms and the proposed vibration-scaffold integration mechanism. Furthermore, the potential specifications of 3 DPVS will include four major aspects, namely geometrical control, mechanical properties, biochemical control and dynamic functions. For biochemical control, the basic requirements of 3D static cell culture regard the viability, proliferation, morphology and differentiation of cells cultured are concluded at this stage. The previous works on 3 DP fabrication techniques, scaffold architectures, materials, biochemical functions and mechanical properties for 3D scaffolds fabrication are to be reviewed and prepared. The final requirement is about the innovation roadmap, which could refer to the design methodology tailored for the 3 DPVS.
## 3 System Overall Process  
In recent years, most studies have classified defect detection as an object detection task, primarily due to the excellent performance of object detection models in multi-object localization and classification tasks. However, in industrial production, most parts do not have defects during inspection. Using traditional object detection methods to locate all parts one by one would significantly increase computational overhead and hinder the real-time and high-efficiency requirements of production lines. Additionally, for defect-free parts, complex localization operations result in unnecessary resource wastage. The original images used in this study are 4K high-resolution images. Directly scaling these images  
Figure 4: Lighting setup diagram.  
Figure 5: Comparative images under different light sources.  
for input into the detection network would not only reduce resolution but also risk losing defect details. Therefore, this study adopts a sliding window cropping approach to divide high-resolution images into smaller images suitable for network input. However, for most samples, only a small portion of the image requires inspection. Detecting all sub-images one by one would consume a large amount of computational resources. To address the computational overhead of traditional object detection methods in processing defect-free parts, this study designs an efficient detection network that redefines the detection task workflow. For the vast majority of defect-free images, the network performs a quick classification to determine whether they are qualified. For parts detected to have abnormalities, the network further performs precise localization to label defect areas.  
To achieve this goal, this study proposes an efficient single-encoder dual-decoder network structure. The network uses a shared encoder for feature extraction, quickly classifying and filtering out most defect-free sub-images, and performs precise localization only for sub-images containing defects. This method effectively reduces computational overhead, significantly improves processing efficiency while maintaining high detection accuracy, and better meets the industrial production demands for real-time performance and high efficiency.
## 3 Results  
### Accuracy of in-Domain Data Classification  
The accuracy of BNNs under various prior constraints proved to be higher compared to their deterministic ensemble counterparts for both architectures. This can be stated for most of the participants (Figures 4 and 5) for both architectures. These differences proved to be statistically insignificant for EEGNet architecture (Ensemble vs. IG: T = 10.5, \(p\) = 0.16, IG vs. IG + t: T = 15.0, \(p\) = 0.67, IG vs. CG + t: T = 16.5, \(p\) = 0.57), though, which is shown by [PERSON] signed rank test. However, for Shallow ConvNet architecture, there are some statistical differences, shown by the [PERSON] signed rank test (Ensemble vs. IG: T = 5.5, \(p\) = 0.04, IG vs. IG + t: T = 3.5, \(p\) = 0.07, IG vs. CG + t: T = 2.0, \(p\) = 0.01). Group average data are shown for EEGNet and Shallow ConvNet in Figures 6 and 7, respectively; noticeably, Shallow ConvNet accuracy seems to increase with prior complexity.  
Figure 5: Values of In-Domain 2-classes accuracy for all participants, using different prior constraints compared to the ensemble for Shallow ConvNet architecture. The cyan dashed line represents the 0.5 threshold of random guessing. See Figure 3 caption for other details.  
Figure 6: Values of In-Domain 2-classes accuracy averaged across all participants, using different prior constraints compared to the ensemble for EEGNet architecture. The error bar represents the 95% confidence interval. The cyan dashed line represents the 0.5 threshold of random guessing. See Figure 3 caption for other details.  
As for the experiment involving all four classes of data (Figures 8 and 9), the Bayesian networks outperformed their deterministic counterparts, according to the Wilcoxon signed-rank test (EEGNet vs. Bayesian EEGNet: T = 0.0, \(p\) = 0.004, Shallow ConvNet vs. Bayesian ShallowConvNet: T = 0.0, \(p\) = 0.01).  
Figure 8: The 4-classes accuracy for all participants in four classes. The cyan dashed line represents the 0.25 threshold of random guessing.  
Figure 7: Values of In-Domain 2-classes accuracy averaged across all participants, using different prior constraints compared to the ensemble for Shallow ConvNet architecture. The error bar represents the 95% confidence interval. The cyan dashed line represents the 0.5 threshold of random guessing. See Figure 3 caption for other details.
# Simplified High-Performance Cost Aggregation for Stereo Matching
## 4 Discussion
\begin{tabular}{l l} \(\mathbf{A}_{k}\) & a constant matrix used in deriving the proposed aggregation cost of the first step \\  & consisting of the parameter \(\lambda\). \\ \(\mathbf{A}_{v}\) & a constant matrix used in deriving the proposed aggregation cost of the second step \\  & consisting of the parameter \(\lambda\). \\ \(\underline{B}\) & a matrix consisting of elements of \(\overline{\mathbf{C}}_{1}\). \\ \(\overline{\mathbf{C}}\) & the general form of an aggregated matching cost. \\ \(\overline{\mathbf{C}}^{\prime}\) & an updated disparity cost volume to reduce the noise effects in images. \\ \(\overline{\mathbf{C}}^{\prime\prime}\) & the final aggregated matching cost that integrates the proposed algorithm with a cross-scale cost aggregation scheme described in [20; 30]. \\ \(\underline{C}_{0}\) & the primary matching cost used in this paper. \\ \(\overline{\mathbf{C}}_{1}\) & the proposed aggregation cost defined in the first step. \\ \(\overline{\mathbf{C}}_{2}\) & the proposed aggregation cost defined in the second step. \\ \(\underline{C}_{Consus}\) & a matching cost calculated by the Hamming distance of the census transform. \\ \(\overline{\mathbf{C}}_{k}\) & the matching cost of the \(k\)-th scale layer, each aggregated by the proposed scheme. \\ \(\hat{\mathbf{C}}_{k}\) & the estimated matching cost of the \(k\)-th scale layer, each aggregated by an integrated scheme consisting of the proposed scheme and a cross-scale cost aggregation algorithm described in [20; 30]. \\ \(\mathbf{C}_{TGD}\) & a matching cost calculated by the truncated absolute difference of the gradient. \\ \(D\) & a matrix consisting of elements of \(\mathbf{C}_{0}\). \\ \(d\) & the estimated disparity of a pixel. \\ \(\underline{D}\) & the disparity map based on an image pair. \\ Ham & a function to calculate the Hamming Distance. \\ \(H_{x}\) & an independent objective function of image rows. \\ \(\mathbf{I}_{L}\), \(\mathbf{I}_{R}\) & the left and right images, respectively. \\ \(J_{CS}\) & an objective function for the cross-scale scheme proposed in [20; 30]. \\ \(J_{h}\), \(J_{v}\) & objective functions representing the least squared difference between the proposed aggregation cost and the primary matching cost in the horizontal and vertical directions, respectively. \\ \(K\) & the number of pairs in the cross-scale cost aggregation. Each image pair is \\  & down-sampled \\  & from the original pair. \\ \(L\) & a path determined by the minimum spanning tree technique [17]. \\ \(M\) & the vertical resolution of \(\mathbf{I}_{L}\). \\ \(\min\) & a function to calculate the minimum value between several amounts. \\ \(N\) & the horizontal resolution of \(\mathbf{I}_{L}\). \\ \(\mathbf{N}_{k}\) & a set of 2 horizontal neighbors. \\ \(p\), \(q\) & the location of a pixel. The symbols are interchangeably used as parameters. \\ \(T_{Consus}\) & the Census transformation. \\ \(V_{y}\) & an independent objective function of image columns. \\ \end{tabular}
## 2 Materials and Methods
### Downscaling Process  
Experimentally, b1, b2, b3, b4, NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST were selected as the driving variables for constructing the downscaling model. During the model construction process, the resolution of the driving variables was first adjusted to match that of the eSIF data. Then, the corresponding image values were extracted according to the image points of the eSIF, to maintain data accuracy and consistency. The experiments were selected from October 2018 to May 2019 and from October 2019 to May 2020 for modeling, respectively. The downscaling model construction process is illustrated in Figure 2. 1 Data pre-preprocessing: The downscaled sample dataset was constructed by analyzing the causes of SIF resampling. The selected MODIS data were resampled to the same spatial resolution as the eSIF data, and the driving variables were extracted based on eSIF. 2 Data downscaling: The SIF downscaling model is constructed based on the CNN model, Stacking integrated learning model, Random Forest model, Extreme Random Tree model, Adaptive Enhanced Regression model, and Gradient Boosting Tree model, respectively, and R\({}^{2}\), RMSE, and MAE are selected as the accuracy evaluation indices, and the model with higher accuracy is selected as the SIF downscaling model. Then, each driving variable is resampled to 1 km, input into downscaling, and based on the assumption that the spatial relationship before and after downscaling is unchanged, the 1 km resolution eSIF inversion is realized. 3 Accuracy Verification: In order to determine the accuracy of 1 km SIF, a month-by-month verification was conducted based on a time series. Additionally, the downscaled 1 km SIF products were compared with other SIF satellite products, including TanSIF and GOSIF, to verify their consistency. The reliability of the 1 km SIF was further tested by performing correlation analysis with GPP data, based on the GPP-SIF mechanism. Finally, 1 km SIF was utilized to monitor the growth of wheat in Shandong Province, and its applicability was tested by comparing it with the vegetation index and its trend.
## 8 Summary and Future work
### Damage Prognosis  
As the extension of damage diagnosis and health monitoring, damage prognosis plays an important role in predicting the future loading environment and structural performance and eventually determining the remaining life of structures. The anticipated objective of damage prognosis is to provide a reliable and cost-effective approach to the structure operation, assessment, and maintenance, which consists of estimating the probability of mission completion, determining the optimal time for preventive maintenance, and developing the appropriate operation modifications preventing observed damage propagation [196]. The multidisciplinary nature of the damage prognosis problem makes it a grand challenge for researchers and engineers.  
1. The remaining useful life (RUL) distribution is sensitive to many aspects as discussed in the paper, and thus one challenge of prognosis is to minimize the uncertainty in the estimation of RUL distribution.  
2. The evolution of damage such as cracking, wear, and corrosion plays a critical role in determining the structure health. It is strongly essential to study damage mechanisms and develop accurate and comprehensive failure models. The prediction model of future loadings is another important component of a successful damage prognosis.
3. Since the damage prognosis is affected by many sources of uncertainties, the verification and validation of prognosis model is very essential to establish the confidence in the predictive capabilities. In particular, a significant challenge is to validate nonlinear models.
4. The probabilistic reliability analysis will be necessarily applied to quantify the confidence of the estimated RUL, which assists the decision makers for the proper action.
5. The future research of damage prognosis also includes the life cycle modeling considering the effect of deterioration processes, assessment of consequences associated with possible events, and modeling of preferences to cover the society performance [247].
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
###### Abstract
The ACE score is estimated using the centrality of the nodes relative to each layer, also taking the density of those layers into account. A high ACE score then indicates candidate anomalous nodes. We evaluated our proposed approach on a multi-layer synthetic network as well as real-world complex networks, demonstrating its efficacy. Specifically, for the synthetic network, we applied data generated via Erdos-Renyi random graphs with a set of different link probability (\(p\)) configuration settings in order to generate structurally different network layers. In addition, we applied four real-world multi-layer networks, including social interaction, trade, and social media networks. A key feature of our proposed approach is its interpretability and explainability, since we can directly assess anomalous nodes with respect to the network topology; for this, we present a human-centered process model for anomaly detection, enabling computational sensemaking [11] on complex network data. Our contributions are summarized as follows:  
1. This paper presents a novel approach for identifying a set of anomalous nodes using many-objective optimization on multi-layer networks. Optimization is based on minimizing network centrality, to find a set of less important nodes in the network. 2. We present a novel measure, i.e., the ACE score, for ranking and identifying anomaly candidates balancing the criteria of connectedness, centrality, and density. 3. In our experiments, we evaluated the proposed method using one synthetic and four real-world networks: Thus, we generated simple to interpret network data and demonstrated the effectiveness of our approach on this synthetic data as well as on real-world multi-layer network data. 4. One particularly appealing feature of our proposed approach is its interpretability and explainability: the results of many-objective optimization and the presented ACE score can directly be assessed in a human-centered process for computational sensemaking. For this, we can use simple and intuitive topological network features as well as layered visualizations; we show some examples in our case studies. The rest of this article is structured as follows: Section 2 discusses related work. Next, Section 3 describes our proposed method. After that, we discuss the experiments,case studies, and results in Section 4. Finally, Section 5 concludes with a summary and interesting directions for future work.
## 3 GNSS Data Collection with Android-Based Smartphones  
This section presents the setup configurations of the zero-baseline and short-baselines in upright and lying down positions, and describe the stochastic model settings. In the following sections, we will investigate the effects the setup has on the instantaneous RTK positioning performance and the corresponding DD carrier-phase and code residuals.
## 3 Modulation Scheme  
The level shift sinusoidal pulse width modulation (LS-SPWM) control strategy is executed. A continuous comparison is made between two carrier signals and a refereresignal \(U(t)\) at 50 Hz with a modulation index of \(M\) = 0.95. Carrier signals above the zero reference axis are labeled \(C_{1}(t)\), and those below it are labeled \(C_{2}(t)\) for each phase leg \(X\) (\(t\)). As \(U(t)\) becomes more significant than the carrier signal, it produces a \"1\" above the zero reference axis and a \"0\" below. Under the zero reference axis, a reading of \"0\" is obtained when the reference signal is more dominant than the carrier signal, while a reading of \"\(-\)1\" is obtained otherwise. All of the signals from the comparator are put together to form a single signal \(a(t)\). With the help of the lock-up Table 1, next, compare this aggregated signal to the fixed levels in order to obtained the gate signals. Figure 4a depicts the circuit design for the switching scheme for phase leg \"X\", and Figure 4b shows the modulated signal, carrier signal, and output voltage.
## 4 Results and Analysis
### Analysis
Under the condition of any sand layer coefficient, the fracture-mining ratio will increase with the increase in sand-mud ratio, and the increase in mining height will have a certain inhibitory effect on the increase in fracture-mining ratio. The results of the numerical simulation test show that the ratio of fracture to production is 16-44.7 under the condition of \"sand-mud ratio of 6:4 + mining height of 3 m\", and the ratio of fracture to production is 21.5-59.4 under the condition of \"sand-mud ratio of 8:2 + mining height of 3 m\", which is 10.37 higher than that of the former. Under the condition of \"sand-mud ratio of 6:4 + mining height of 5 m\", the crack-production ratio is 15.7-26.6, and under the condition of \"sand-mud ratio of 8:2 + mining height of 5 m\", the crack-production ratio is 27.6-35.6; the latter is 9.58 higher than the former on average. The theoretical calculation results of rock fragmentation expansion show that under the condition of \"sand-mud ratio of 6:4 + mining height of 3 m\", the fracture-mining ratio is 19-49.2, and under the condition of \"sand-mud ratio of 8:2 + mining height of 3 m\", the fracture-mining ratio is 30.2-61, which is an average increase of 12.23 compared with the former. Under the condition of \"sand-mud ratio of 6:4 + mining height of 5 m\", the fracture-mining ratio is 16.5-29.6. Under the condition of \"sand-mud ratio of 8:2 + mining height of 5 m\", the fracture-mining ratio is 27.1-36.6. The latter is 9.38 higher than the former on average. It can be seen that the results obtained by the two methods are very close, and together they show that after the mining height increases from 3 m to 5 m, the increase in the sand-mud ratio makes the fracture-mining ratio decrease by about 15.0% on average.
## 1 Introduction  
At present, over 150 different volatile compounds have been reported in raw and cooked milled rice, which are mainly from the classes, alkanes, aldehydes, alcohols, esters, ketones, phenols, fatty acids, benzyl derivatives, enones, furrans, tranrances, monoter-penoids, sesquiterpenoids, napthalenes, xylenes, pyridines, and pyrroles [1, 2, 3, 4]. However, only some of these volatiles are responsible for the overall perceived aroma of rice [5]. Among these, the 2-acetyl-1-pyrroline (2-AP), aldehydes, heterocyclics, and alcohols are the primary odorants [6].  
In cooked rice, the perceived aroma depends not only on the levels of primary odorant but also on their interactions [7]. For the past several decades, 2-AP has been considered a primary determinant of flavor in aromatic rice due to the loss of function of betaine aldehyde dehydrogenase 2 (_OsBADH_) and it has frequently been used as a marker compound to distinguish aromatic and non-aromatic rice varieties [8]. However, marker association of _OsBADH2_ is not tightly linked to the quantitative variation of 2-AP. Hence, the level of 2-AP in rice samples is usually assessed to predict aroma intensity. The genetic differences are primarily responsible for the variation in 2-AP contents. Besides this, the content of 2-AP is also found to be influenced by storage conditions, harvesting, and evaluation methods [6; 9]. In rice, 2-AP is enzymatically biosynthesized during the plant growth and may also be formed at the time of heating and cooking operations as part of the Maillard reaction [10]. However, the formation or release of 2-AP during cooking is not well understood, due to various reasons including the lack of a well-optimized analytical method [6].  
In the present study, multiple reaction monitoring (MRM) mode headspace solid phase microextraction with gas chromatography-tandem mass spectrometry (HS-SPME-GC-MS/MS) was optimized to estimate 2-AP contents in raw and cooked rice samples of traditional and improved rice varieties. Moreover, HS-SPME-GC-MS-based metabolite analysis was conducted to identify other critical odorants in raw and cooked rice samples of these varieties through the untargeted metabolomics approach. The interaction of volatiles and the proximities between volatile profiles were also investigated by computing [PERSON]'s correlation.