esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The main feature of the BNNs is prior constraints for the parameters, which leads to better regularization of the loss function, i.e., stricter restrictions on weights. More information on this matter was provided by [PERSON] et al. [13] and [PERSON] and [PERSON] [15]. Hence, BNNs are less prone to overfitting, a great advantage for a neural network when training datasets are small. Because of this improvement, the BNN may have better generalization properties. BNNs have been used in BCI for years, and in a few recent papers, they were successfully applied to enhance the performance of some existing deterministic neural networks for various types of data and experimental paradigms, such as EEG under motor imagery [12], the P300 potential [16], fNIRS data [17]. However, of special interest could be similar to the \"Bayesian\" enhancement of the most widely used, effective, and well-studied architectures, which we undertook in this study. Because of the BNN's ability to quantify the uncertainty of its predictions, we expect that the BNN-based OOD detection methods can also address the issue of OOD data in BCIs. The BNNs can be considered as an ensemble of an infinite number of neural networks [18]. Since the ensembles can provide one with an uncertainty metric [19], such as entropy [20] or variance of predictions across all of its nets, there is a perspective to use this type of algorithm for discovering OOD data. During BCI classifier application, including practical real-time scenarios, the BCI can be made to refrain from issuing a command when OOD is detected. The ability of BNNs to provide high-quality OOD detection was doubted by [PERSON] et al. [21]. However, they studied data with fewer dimensions than is typical for the EEG. In this paper, we:  
1. Assess performance enhancement in an EEG-based BCI task when two widely used deterministic neural network classifiers, EEGNet [22] and Shallow ConvNet [23], were turned into BNNs. 2. Assess the ability of these BNNs to enable OOD detection.
# LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set
## 2 Related Work
### Anchor-Free Object Detection  
In the field of object detection, although anchor-based methods are the mainstream, anchor-free detectors show strong vitality due to their efficient performance and anchor-free characteristics. Therefore, anchor-free methods have gradually become a research hotspot. The anchor-free detector can be traced back to YOLO-v1 [1], which outputs the bounding box directly, and does not depend on the prior box anchor. [PERSON] et al. [19] proposed the UnitBox, which uses the current pixel position and the distance between the upper left corner and the lower right corner to form the bounding box of the object. Recent efforts have pushed anchor-free detection to outperform their anchor-based counterparts. Some works, such as CornerNet [4], ExtremeNet [5] and CenterNet [20], reformulate the detection problem as locating several key points of the bounding boxes. To build a bounding box, CornerNet [4] estimates the two corners of bounding box. ExtremeNet [5] represents the bounding box by calculating the four extreme points of the heatmap (top, left, bottom and right) and a center point. CenterNet [20] uses the center point, center point offset, width and height to describe the bounding box. Others like FSAF [21], Guided Anchoring [22] and FCOS [23], encode and decode the bounding boxes as anchor points and point-to-boundary distances. The FSAF [21] network adopts feature selection module (FSAF) based on an anchor-free mechanism to make online feature selection in a feature pyramid. Guided Anchoring [22] can generate anchor automatically in the process of reference. The main contribution of FCOS [23] is to propose a new center-ness branch to reduce the weight of low-quality detection results. In addition, there are some methods exploring the new anchor-free form, such as RepPoints [6]. RepPoints transforms the problem of object detection into a regression problem of the point set, and the bounding box is represented by a flexible representation of the point set. These characterization point sets can describe the precise geometric and semantic features of the object.
# SEA and GATOR 10 Years Later
## 6 SEA and GATOR Functions beyond Nutrient Responding
### GATOR in Cell Division and Cell Cycle Regulation  
GATOR2 is important for both mitotic and meiotic division (Figure 5). Depletion of MIOS in HeLa cells resulted in mitotic defects, such as spindle assembly defects and delay or failure in cytokinesis [163]. MIOS regulates mitotic events through Aurora A kinase and Polo-like kinase 1 (Plk1), which control the localization and function of mitotic spindle. MIOS is important for spindle formation, subsequent chromosome segregation and proper concentration of active Plk1 and Aurora A at centrosomes and spindle poles. SEH1, which forms a complex with MIOS (see above), targets GATOR2 to mitotic chromosomes, required for the localization of chromosomal passenger complex and functions in chromosome alignment and segregation by regulating the centromeric localization of Aurora B [164]. This function of GATOR2 nevertheless seems to be related to its role in mTOR1 activation, because depletion of MIOS causes reduced mTOR1 activity at centromeres in mitotic cells [163].  
In _Drosophila_, Mio localizes to oocyte nucleus at the onset of prophase and meiosis I, and is required for the maintenance of the meiotic cycle during oocyte maturation [24]. _Drosophila_ Seh1 is also involved in the maintenance of meiotic cycle and regulation of microtubule dynamics in ovarian cysts [36]. Depletion of _iml1_ in the female germ line delays mitotic/meiotic transition and ovarian cysts undergo an extra mitotic division [18]. Thus, GATOR1 downregulates TORC1 activity to promote the mitotic/meiotic transition in ovarian cysts, while inhibition of GATOR1 by GATOR2 prevents the constitutive downregulation of TORC1 at the later stages of oogenesis.
## 3 Experimental Results and Discussion
### Electric Resistance  
The electrical resistance values, according to the measurement distance, are demonstrated in Figure 13. It was found that (1) as the addition ratio of the MWCNTs increased, the electrical resistance value decreased, and (2) the shorter the measurement distance, the smaller the electrical resistance value. Even though MWCNT agglomeration occurred in the cement composites, it spread throughout the entire structure, reducing the electrical resistance value. Furthermore, the electrical resistance decreased even in the liquid state specimen, denoting relatively good dispersion. This finding implies that the MWCNT-added cement composite exhibits electrical conductivity, regardless of homogeneous dispersion.
## 4 Experiments and Results
### Evaluation Metrics  
We first discuss the confusion matrix and our model's performance. The confusion matrix shows how well our model performs in supervised learning, specifically in classifying the geometrical objects; in this case, inside the confusion matrix, the number of columns (or rows) matches the number of types for classification. Each row of the matrix represents an instance of an actual class, while each column represents an instance of the predicted class. The darker the color in a grid, the more instances there are. The ideal performance of a model would be that the diagonal of the matrix would be very dark, meaning there are fewer false positive and false negative samples.  
To generate the confusion matrix on our dataset, we calculated the prediction on each label and normalized it by dividing the prediction by the total number of predictions. The confusion matrix looks like the one below in gray-scale.  
As seen in the confusion matrix diagram above Figure 4, there is a pattern consisting of self-explanatory dark-colored blocks on the diagonal, which means the model achieves a high true positive rate for most types. The color of blocks for other regions in the matrix is not as dark as the ones on the diagonal, which means our model also achieves a low false positive/false negative rate on most labels.  
Figure 4: Confusion matrix for our model. The rows and columns are categories of mechanical components. The scale of the similarity value in the table is from 0 to 1, with 0 being white and 1 being black. The darker the value, the larger the similarity value.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 4 Discussion
### Analysis of Spatialization in Yield Research  
Small-scale yield prediction for winter wheat is crucial for understanding planting structures and achieving optimal agricultural resource allocation. Previous studies typically used methods such as drone imagery and crop growth models for yield estimation at the field level [4; 56]. However, these methods often struggle to cover provincial scales simultaneously, and some crop growth models require numerous parameters. Due to spatial heterogeneity in soil, weather, and environmental factors, unifying some of these parameters can be challenging.  
In this study, the ensemble voting model was used to predict winter wheat yield during the heading stage, which offers the greatest yield prediction potential. By applying a spatial yield model, winter wheat yield estimates at the county level were downscaled to the pixel level. This approach not only meets the need for county-level yield prediction but also visually represents winter wheat productivity at a finer scale. The feasibility and practicality of this method make it a powerful tool for supporting agricultural decision-making and management, providing a scientific basis for precision agriculture.
# Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study
## 3 Analysis
### Model Development  
The energy model was created based on as-built drawings. EnergyPlus 8.9 [27; 28] was chosen for the simulation. Figure 6 shows the geometry and thermal zoning of the typical floor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & [PERSON] et al. [23] & Building A \\ \hline
**Num. of samples** & 8 & - \\
**25 th percentile** & 52.6 & - \\
**Median/Actual EUI (kWh/(m\({}^{2}\) a))** & 71.5 & 23.9 \\
**75 th percentile** & 80.3 & - \\
**SD (Standard Deviation)** & 28.0 & - \\
**Skewness** & 0.2 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: EUI comparison.  
Figure 5: Monthly building energy consumption.  
The operating conditions, i.e., the occupancy density and schedule, the lighting power density and schedule, the plug load density and schedule, and room indoor temperatures, were obtained from a building operator interview, onsite measurement, and observations. The room set point temperatures were read from the building automation system (BAS) directly. The building employed electrical submetering to monitor the HVAC system, the lighting, and the plug load power consumptions.  
The sensors used in the case building are all research-grade and the accuracy is much higher than the typical requirement in the building industry. The measurements concerned in this study are mainly power and temperature, and the sensor accuracies are listed in Table 5. As only direct measurements were involved, the propagation of uncertainty was not considered in the study.  
The hourly data on a typical weekday and weekend were used to calculate the lighting and plug load power densities and derive their schedules for weekdays and weekend days, respectively. The numbers of occupants were counted manually by the researchers every two hours from 6 a.m.  
The weather file was developed using the actual weather data from the National Climatic Data Center (NCDC) [29]. The data includes hourly observations of temperature, humidity, pressure, wind speed and direction, cloud cover, and precipitation intensity but misses the global horizontal solar irradiation, the diffuse horizontal solar irradiation, and the direct normal solar irradiation that are essential to the energy simulation. We adopted the models recommended by [PERSON] et al. [30; 31; 32; 33] to compute the solar irradiations:  
\[I_{h}=\left[I_{0}\cdot\sin h\cdot\left(C_{0}+C_{1}\cdot CC+C_{2}\cdot CC^{2}+C_ {3}(T_{n}-T_{n-3})+C_{4}\cdot\mathcal{D}\right)-C_{5}\right]/k \tag{1}\]  
\[I_{n}=K_{n}\cdot I_{0} \tag{2}\]  
\[K_{n}=A_{1}\cdot A_{2}{}^{-A_{3}A_{2}{}^{-A_{4}K_{t}}} \tag{3}\]  
\[I_{d}=I_{h}-I_{n}\cdot\sin h \tag{4}\]
## 3 Variable Deployment Modeling Method  
To give an overview of the idea of what Variable Deployment Models are, how they are created, and how they are used for deployment, this section presents the _Variable Deployment Modeling Method_, which consists of five steps. The method is split into the _Deployment Modeling Phase_ and the _Deployment Execution Phase_ as shown in the lower part of Figure 2. The two phases involve different user roles: while the modeling phase is typically conducted by deployment experts creating Variable Deployment Models, the execution phase  
Figure 1: Two different deployment variants for the motivating scenario: deployment model 1 for development (**left**) and deployment model 2 for production (**right**).  
can be conducted by experts as well as non-experts that want to deploy the application in a certain variant.  
During the modeling phase, the first step is (i) to identify deployment variants along with their commonalities and differences. Then (ii) a Variable Deployment Model is created that describes all these variants. During the execution phase, (iii) inputs are specified that are then used in step (iv) to automatically resolve the variability and to derive an executable deployment model, i.e., the inputs are used to evaluate conditional elements in the model that are then either present or not. Finally, in the last step, (v) the derived deployment model is executed to automatically deploy the respective variant. In the next subsections, we further explain each step in more detail.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Electrode Processing Techniques
#### 3.3.2 Solvent Selection  
The choice of solvent affects the dispersion stability of MXene flakes and consequently influences sticking. Solvents with high polarity, such as water or polar organic solvents, can promote strong interactions between MXene flakes, leading to increased agglomeration and sticking. Conversely, non-polar solvents or solvents with lower polarity can weaken inter-flake interactions and enhance dispersion stability, thereby reducing sticking. Thus, in solvent selection, one should consider the surface chemistry of MXene flakes and compatible solvents that minimize inter-particle adhesion.  
It has been demonstrated that Ti3C2 Tx can be dispersed in many polar organic solvents, but the best dispersions are achieved in dimethylformamide, propylene carbonate, and ethanol. By employing solvent analysis, various organic solvents can be identified as capable of dispersing Ti3C2 Tx [57]. This broadens the possibilities for processing techniques, including blending MXenes with other nanomaterials or polymers to create solution-processable composites, developing inks for printing, and facilitating deposition like spraying, for example. As well, the organic solvents prevent oxidation and enhance wetting ability. MXenes with hydrophilic surfaces have allowed for favorable dispersion in polar solvents [58]. However, MXenes show intensive aggregation in the highly polar organic solvents such as alcohols. Some authors have reported isolation of MXene flakes by intercalation with the solvent dimethyl sulfoxide (DMSO). As a result, Li-ion storage was achieved [59]. Figure 10 shows the formation of monolayers in few-layered non-stacked MXene nanosheets having a mean lateral size 1-2.1 mm, which is suitable for inkjet printing.  
Figure 10: MXene inks in different solvents and inkjet printing. Photograph and SEM picture of (a,b) MXene DMF ink, (**c,d**) ethanol ink, (**e,f**) MXene DMSO ink. Optical image of (**g**) NMP, (**h**) DMF,and (i) ethanol inks after 12 months and (j) DMSO ink after 6 months. Scale bar in the images of (**b,f**) 200 nm and (**d**) 100 nm. (**k**) Histogram of the MXene Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) nanolayer size from the statistics of transmission electron microscopy (TEM) of NMP ink (2.1 mm), DMF ink (1.3 mm), ethanol ink (1.0 mm), and DMSO ink (1.9 mm). (**l**) Schematic of organic MXene inks inkjet printing at fine resolution; here, green line = MXene nanolayers, blue arrow = ink droplets inward flow, red arrow = ink droplets outward flow. (**m**) The viscosity of organic MXene inks (NMP, DMSO, and ethanol) as a function of shear rate. (**n**) SEM of inkjet-printed pattern and images of (**o**) ethanol, (**p**) DMSO, (**q**) DMF, and (**r**) NMP. (**s**) Raman spectra of the inkjet-printed patterns after 24 months, confirming stability [60]. Reprinted from [PERSON] et al., Energy Sci. Eng., copyright (2019), Wiley.
## 3 Details of the Proposed Algorithm
### Update of Primal Variable: \(\mathbf{x}\)
2: Backtracking Line Search: find the minimum number of iterations \(i^{(k)}\) such that with \(t_{x}^{(k)}=t_{0}/\eta_{x}^{(k)}\) and \(\mathbf{w}_{x}^{(k)}=S_{\{t_{x}^{(k)}\}/\rho}\Big{(}\mathbf{u}^{(k)}-t_{x}^{( k)}/\rho\cdot\
abla f_{1}(\mathbf{u}^{(k)})\Big{)}\) \[\
abla f_{1}(\mathbf{x}^{(k)})=\rho\boldsymbol{\Phi}^{H}(\mathbf{z}^{(k)}+ \boldsymbol{\Phi}\mathbf{x}^{(k)}-\mathbf{y}+\gamma^{(k)}/\rho). \tag{22}\]1
3:if\(J(\mathbf{w}_{x}^{(k)})<\bar{J}^{(k-1)}\)then
4: set \(\mathbf{x}^{(k)}=\mathbf{w}_{x}^{(k)}\)
5:else
6: Compute the gradient \(\
abla f_{1}(\mathbf{x}^{(k-1)})\) by (22). 7: Backtracking Line Search: find the minimum number of iterations \(j^{(k)}\) such that with \(t_{x}^{(k)}=t_{0}/\eta_{x}^{(k)}\) and \(\tilde{\mathbf{w}}_{x}^{(k)}=S_{t_{x}^{(k)}/\rho}\Big{(}\mathbf{x}^{(k-1)}-t_ {x}^{(k)}/\rho\cdot\
abla f_{1}(\mathbf{x}^{(k-1)})\Big{)}\) \[\
abla f_{1}(\mathbf{x}^{(k)})=\rho\boldsymbol{\Phi}^{H}(\mathbf{z}^{(k)}+ \boldsymbol{\Phi}\mathbf{x}^{(k)}-\mathbf{y}+\gamma^{(k)}/\rho). \tag{22}\]2
8: Compute \(J(\tilde{\mathbf{w}}_{x}^{(k)})\) and \[\mathbf{x}^{(k)}=\left\{ \begin{array}{ll}\tilde{\mathbf{w}}_{x}^{(k)},& \text{if }J(\tilde{\mathbf{w}}_{x}^{(k)})<J(\tilde{\mathbf{w}}_{x}^{(k)}),\\ \mathbf{w}_{x}^{(k)},&\text{otherwise}.\end{array} \right.\]
9:endif
10: Update the extrapolation parameter \(\mu_{x}^{(k)}\) by (26), and \(c^{(k)}\) and \(\bar{J}^{(k)}\) by (29). ```  
**Algorithm 2** Update of Primary Variable \(\mathbf{x}\): Non-monotone Strategy
## 3 Methodology
### Metrics for Evaluating Membership Functions  
The primary objective of this stage is to evaluate the accuracy of various combinations of membership functions and defuzzification methods. Consequently, it delineates the different metrics available for assessing the system and the criteria for selecting the appropriate metric. Numerous methodologies for evaluating forecasting systems have been proposed throughout the years in this context. However, a concise literature review facilitates selecting one such approach from the many available. Relevant literature highlights metrics such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Relative Absolute Error (RAE), Median Relative Absolute Error (MdRAE), Median Absolute Percentage Error (MdAPE), and the Geometric Mean of the Relative Absolute Error (GMRAE) [30]. In this instance, the evaluation concentrated on identifying the minimum error in the forecast; therefore, the MAPE metric was deemed suitable for this objective, as in Equation (5).  
\[M=\frac{1}{n}\sum_{t=1}^{n}\left|\frac{A_{t}-F_{t}}{A_{t}}\right|, \tag{5}\]  
where  
* is the number of fitted points,
* is the actual value,
* is the forecast value,
* is the summation of the absolute value summed for every forecasted point in time.  
Consequently, the MAPE will exhibit diminished values for the optimal combination of the membership and defuzzification functions.
## 3 Methods for Assessing the Aesthetic Quality of Images
### Deep-Learning-Based Method for Assessing the Aesthetic Quality of Images
#### 3.2.1 Image Aesthetic-Assessment Method Based on Depth-Feature Extraction  
The use of deep neural networks to extract image features to achieve the assessment of image aesthetic quality was originally proposed by [PERSON] [51] in 2006, with 56 visual features for image aesthetic assessment. These extracted features include luminance, hue, saturation, etc. [PERSON] [74] proposed a hierarchical feature fusion model for aesthetic assessment in 2019. It is a two-stream convolutional neural network consisting of two branches with heterogeneous and complementary aesthetic-perception capabilities. The model is designed to learn the mapping from deep-image representations to their true aesthetic labels in an end-to-end manner. [PERSON] [75] designed a two-stream network to compute the aesthetic quality of an image, improving on SEReset-50, and proposed five traditional aesthetic-feature-extraction algorithms that are good at extracting luminance, color harmony, rule of thirds, etc. In 2021, [PERSON] [76] analyzed the learning features of deep models and models for aesthetic assessment from three different perspectives (image classification, aesthetic mean, and standard deviation classification) for different tasks.
## 2 Numerical Methodology
### Pebble Bed Physical Model  
After the DEM simulation, the pebble bed data, such as particle coordinates, particle diameters, pebble bed dimensions, and contact point, are transferred to the CFD software through the VB coupling interface program to regenerate the pebble bed geometry model. Due to the intricate geometry of the void flow channel in a randomly packed bed, a finer mesh is usually required to improve the accuracy of the CFD simulation. However, a finer mesh will inevitably consume a large number of computational resources. Therefore, a small-scale pebble bed or localized pebble bed is usually used in the particle-resolved CFD simulation in pebble beds. However, the fixed walls of small-sized pebble beds have a significant effect on the packing structure, which will inevitably affect the gas flow characteristics inside the pebble bed.  
In addition, in HCCB TBM, the breeder pebbles are packed in u-shaped cavities [2], as shown in Figure 3. The majority of the breeder pebble beds can be simplified as rectangular  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Symbols** & **Value**[35] \\ \hline Density (kg/m\({}^{3}\)) & \(\rho_{T}\) & 2323 \\ Young’s modulus (GPa) & \(E\) & 90 \\ Poisson ratio & \(v\) & 0.24 \\ Friction coefficient & \(\mu_{\text{trii}}\) & 0.1 \\ Restitution coefficient & \(\epsilon\) & 0.9 \\ Pebble diameter (mm) & d & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of pebble materials used in DEM.  
Figure 3: Pebble beds in (**a**) simplified cavities of HCCB TBM and three boundary conditions, (**b**) 4 fixed walls, (**c**) 2 fixed walls and 2 periodic boundaries, and (**d**) 4 periodic boundaries.  
pebble beds. However, the simplified full-scaled pebble bed is still huge for particle-resolved CFD simulation. It is necessary to use a localized pebble bed model for the simulation of purge gas flow behaviors. To investigate the impact of walls on flow behaviors in a localized pebble bed, three kinds of simplified bed boundary conditions are selected, as shown in Figure 3. Therefore, in this study, three different wall conditions of the pebble beds are used to establish the pebble bed models. The helium flow characteristics inside these three pebble beds are focused on and analyzed to assess the impact of the wall effect on the gas flow characteristics of beds. Figure 4 shows the pebble bed model that is randomly packed with various boundary conditions. In Figure 4a, four fixed walls are adopted in the small-scale pebble bed. Figure 4b shows the pebble bed with two fixed walls and two periodic boundaries, which can represent a pebble bed between two infinite parallel walls. Furthermore, the use of four periodic boundaries is demonstrated in Figure 4c, which eliminates the influence of fixed walls on pebble packing and approximates an infinitely sized pebble bed packed randomly.