esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Our Contributions  
Considering the reliance of deep learning on large volumes of meticulously annotated data and the inherently massive scale of point cloud data, the creation of such datasets becomes an exceedingly labor-intensive and time-consuming process. As a result, deep learning approaches were not adopted in this study for the investigation of surface defects in magnetic tiles. Instead, our research focused on leveraging point cloud data to achieve higher precision in detecting tiny defects. Nevertheless, we emphasize the significant potential of deep learning in this domain. Inspired by neuroscience, Hebbian learning [67], which forms the foundation of neural networks, represents a promising direction for our future research endeavors. Our contributions are as follows:  
* Combining the improved weighted covariance matrix and information entropy, the accuracy of defect detection is enhanced while reducing the need for extensive feature engineering.
* Leveraging luminance and point cloud mapping (LPM), the ability to identify potential defects is enhanced.
* Combining single-frame and multi-frame point cloud fitting reduces false detection rates, minimizes reliance on feature engineering, and aids in effective defect assessment.  
The remainder of this paper is structured as follows: Section 2 describes the materials required for the experiments, Section 3 presents the methodology, Section 4 reports and discusses the experimental results, and Section 5 provides the conclusion.
## 7 Online Monitoring and Automation  
One application of the PES is to provide instant feedback to operators regarding lasing performance and spectral characteristics during machine tuning. To make this possible, dedicated automation and monitoring software has been developed at European XFEL: a Karabo device which can be bridged to other environments such as the accelerator control system DOOCS. When the undulator gap changes, the retardation voltages are automatically adjusted to retain the photoelectron peak in a fixed TOF region where the resolving power is optimized. The presented spectral distribution is calibrated with Equations (1)-(7) that continuously update to adjust for changing applied voltages. In two color mode, this is performed for both colors independently. This software device has been deployed for both existing online gas-based spectrometers, the SASE1 PES and the SASE3 PES.  
The device also provides online polarimetry diagnostics by fitting the peak intensity from each of the detectors to Equation (8) and presenting the fitting parameters \(P_{L}\) and \(\psi\). In the case of polarimetry, s-orbitals (\(\beta=2\)) are preferable since higher anisotropy means more accurate fitting. In photon energy regions where s-orbitals are unavailable for reasons such as very poor \(\sigma\) or overlap with Auger-Meitner lines, p- or d-orbitals must be used where \(\beta\) varies with photon energy. This is in particular relevant below N\({}_{2}\) 1s at 410 eV. To perform polarimetery in those regions, while tuning the undulator, the monitoring software device must continuously update the anisotropy parameter from a library file by using feedback from the target gas and the calibrated photon energy. Figure 7 presents the DOOCS panel for online polarization monitoring averaged over 10 pulses for better statistics. The target gas is Krypton and the selected photoemission line is Kr 3d. \(\hbar\omega=402.5\) eV corresponds to \(\beta=1.079\)[12]. Pink bars indicate the integrated intensity over each of the 16 detectors.
## 4 Experimental Setup and Results
Since for IoT applications, a BER of \(10^{-3}\) is sufficient for communication, the BER of \(1.21\times 10^{-3}\) for 2880 bps at a distance of 200 cm is sufficient for IoT applications. The performance of the system deteriorated with an increase in Tx data rate and distance. Increasing the data rate adds more sampling errors, whereas increasing the distance adds frame drops due to the incorrect detection of LEDs from the group of interfering LEDs. The performance of the proposed system with respect to throughput and rotation support was compared with existing similar OCC techniques that used an \(8\times 8\) LED panel as Tx and a 30 FPS camera as Rx for their implementation, as shown in Figure 13b, where T is the angle of rotation by which Rx is rotated. RS-based OCC systems provide high throughput for a fixed short distance, but as the distance increases, the throughput drops. RS-based systems also offer limited support for rotation. The data matrix system provides a throughput of \(1.92\times 10^{3}\) bps for zero-degree rotation. QR code-inspired systems provide rotation support; however, with a throughput of \(1.44\times 10^{3}\) bps, it is lower than the data matrix system, as few LEDs are constantly used for representing anchor positions. The proposed QCD-OCC system uses 16 LEDs to represent anchor positions, and 3-bit colors to send data, thus achieving a throughput of \(4.32\times 10^{3}\) bps with rotation support. Figure 12: Evaluation of proposed prototype. (**a**) PSNR at distances 50, 100, 150, and 200 cm under different ambient lighting conditions. (**b**) Success of reception (%) at distances 50, 100, 150, and 200 cm under different ambient lighting conditions. Table 3 shows the comparison of the proposed system with existing systems based on parameters such as the type of Tx device, Rx camera FPS, maximal distance with tolerable BER, flickering, rotation support, type camera, MIMO support, BER, and throughput. The proposed QCD-OCC achieved support for rotation, compatibility with RS and GS cameras, allows for multiple links, and provides a combined throughput of 2160 bps. In a similar system, the multicolor QR code achieved better performance than that of the proposed QCD-OCC system, as it uses 4 LEDs for rotation compensation; however, the system uses an LCD that consumes more power compared to an 8 \(\times\) 8 LED panel and 60 fps camera at the Rx side.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 5 User Study 1: Fitts' Law
### Results
#### 5.5.2 Movement Time  
An ANOVA identified a significant effect of the device used on movement time (\(F_{2,22}=17.31\), \(p<0.0001\), \(\eta^{2}=0.03\)). The mouse exhibited the quickest performance, with a mean time of 1258 ms, followed by the controller at 1327 ms and the thimble at 1487 ms. The selection method also a significant effect (\(F_{1,11}=51.56\), \(p<0.0001\), \(\eta^{2}=0.1\)). The on-press selection method achieved the fastest performance, with a mean time of 1,185 ms, while the on-release method resulted in a slightly longer mean time of 1530 ms. A Tukey-Kramer test revealed the thimble required a significantly longer movement time compared to both the mouse and the controller. In addition, the test showed that all input methods performed significantly better when using the on-press selection method compared to the on-release method. Figure 6 illustrates the average movement time categorized by input device and selection method.  
Figure 5: Average throughput (bps) by input device and selection method. Error bars represent \(\pm 1\) standard deviation.
# Measuring the Inferential Values of Relations in Knowledge Graphs
## 4 The Proposed Approach
### Relation Entropy for Knowledge Inference  
In the [PERSON] information theory, the uncertainty of a discrete event \(x\) with probability \(p(x)\), called the _self-information_ of \(x\), can be measured by  
\[h(x)=-\log p(x). \tag{4}\]  
The average level of uncertainty of an information system including a group of discrete events \(X=\{x_{1},x_{2},\cdots,x_{n}\}\), called the _entropy_, is defined as  
\[H(X)=\sum_{i=1}^{n}(p(x_{i})*h(x_{i})). \tag{5}\]  
In this study, entropy is employed to measure the levels of the uncertainties of the relations on inference chains in knowledge graphs. The concept of _relation entropy_ is proposed. Relation entropy considers the inferential values of the relation in logical inference. If the entropy of a relation \(r\) is larger than others, it can eliminate more uncertainty in an inference chain; in other words, \(r\) has a greater impact on the inference than other relations. According to this idea, relation entropy is defined as follows.  
**Definition 4**.: _Relation Entropy. In a knowledge graph, the relation entropy of a knowledge structure \(\chi(r)\) on a relation \(r\) is defined as_  
\[\mathcal{H}(\chi(r))=-\sum_{X_{i}\in X(r)}\big{(}P(X_{i})\log P(X_{i})\big{)}, \tag{6}\]  
_where \(X_{i}\) is an equivalence class in \(\chi(r)\), and \(P(X_{i})=|X_{i}|/|U|\). \(P(X_{i})\) is the ratio of the number of samples in the equivalence class \(X_{i}\) to the total number of samples in the sample set \(U\)._  
**Remark 2**.: _The definition of relation entropy abides by the basic principle of Shannon information entropy. For a knowledge structure with \(n\) samples on a relation \(r\), when each sample has a different value on the relation \(r\), the number of equivalence classes in the knowledge structure equals the number of samples, \(n\). In this case, the value of the relation entropy is maximized at \(\log n\). Conversely, if all samples have the same value for this relation \(r\), the knowledge structure contains only one equivalence class. According to Definition 4, the entropy in this situation is minimized at \(0\). In most cases, the value of relation entropy is between these two extremes._
## 2 Description of Active Distribution Network Integration of Multi-RMGs  
With the fast development of rooftop PV system and EVs, the households have local generation sources, such as the rooftop PV generation and plug-in electric vehicle battery energy storage, and thus are able to provide local energy production to supply their domestic power demand. That is, household prosumers can internally generate and consume energy, therefore, they are looking for autonomous operation. The households are usually located geographically close to each other [25], such as in the residential district, so the group of household prosumers are often collectively regarded as an RMG. The conceptual schematic outline of RMG is shown in Figure 1. The RMG is capable of operating both in an interactive and independent way. If there is electricity deficiency in RMG, RMG owners purchase electricity from DSO. If there is surplus power, the RMG is able to feed electricity back to the power distribution grid [26].  
The hierarchical structure of ADN connected with multi-RMGs is illustrated in Figure 2. ADN is in the upper level and RMGs are in the lower level, two entities are linked together through tie-lines. In addition to a connection with the upstream grid, the ADN commonly consists of feeders, distributed generation (DG) (e.g., controllable DG, renewable DG, etc), and loads in a local region. As a new entity, the RMG has the ability to exchange power with the ADN and to communicate with the distribution system operator. It should be noted that the RMGs at the same level has no direct affiliation with each other.  
Figure 1: Conceptual schematic outline of the residential microgrid (RMG), where the solid black line with arrows represents power flow.
## 4 Experiments and Discussion
### Experimental Results  
The clockwise operator model and the PI model have the same function in this article; both are used to describe the hysteresis characteristics of the system and achieve precise control. However, the innovation of this article lies in the introduction of the consideration of NCD caused by speed changes, which has not been fully considered in previous studies. By incorporating NCD into the model design, this paper allows for more comprehensively capturing the nonlinear behavior of the system, thereby improving control accuracy and  
Figure 14: Inverse of NCD model in boost phase: **(a)** 0.25 Hz. **(b)** 0.5 Hz. **(c)** 1 Hz. **(d)** 2 Hz.  
Figure 15: Inverse of NCD model in back phase: **(a)** 0.25 Hz. **(b)** 0.5 Hz. **(c)** 1 Hz. **(d)** 2 Hz.  
robustness. In contrast, most existing studies rely solely on linearization assumptions or ignore the influence of nonlinear factors, which may result in limitations of the model in practical applications. This study not only fills this gap, but also provides new ideas and methods for further exploration in related fields. The output and error of the clockwise operator model are shown in Figure 16.  
In this paper, an appropriate threshold is selected, and the least-squares method is employed to optimize the weights [30]. The results of threshold and weight values are shown in Appendix B.  
Below are the relevant translations and explanations of the formulas and calculation results:  
Maximum Relative Error (MRE):  
\[e_{\text{max}}(\%)=\frac{\text{max}(|e|)}{\text{max}(x_{d})-\text{min}(x_{d}) }\times 100\% \tag{19}\]  
Maximum Mean Square Error(MMSE):  
\[e_{\text{max}}(\%)=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}e_{i}^{2}}}{\text{max }(x_{d})-\text{min}(x_{d})}\times 100\% \tag{20}\]  
The specific results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **0.25 Hz** & **0.5 Hz** & **1 Hz** & **2 Hz** \\ \hline MRE & 3.89\% & 4.89\% & 5.48\% & 4.78\% \\ MMSE & 0.11\% & 0.12\% & 0.15\% & 0.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error evaluation value.  
Figure 16: Clockwise operator model: (**a**) 0.25 Hz. (**b**) 0.5 Hz. (**c**) 1 Hz. (**d**) 2 Hz.
## 3 Image Processing Technology Based on CT  
The Brilliancei CT (128 rows, 256 layers) equipment produced by the Philips Company was used in this study (shown in Figure 4). The specimens of matrix asphalt mixture were scanned by the plane scanning method at a temperature of 20 \({}^{\circ}\)C (shown in Figure 5). For every specimen, there are nearly 100 scanning planes (the interval of each plane was 0.67 mm). All the specimens were scanned at settings of 120 KV, 100 mAs, 78 mA and 1282 ms.  
Figure 3: Gradation of test asphalt mixture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Content of BF (\%)** & **Content of NTC (\%)** & **Asphalt-Aggregate Ratio** \\ \hline Base matrix asphalt mixture & 0 & 0 & 5.00 \\ NTC modified asphalt mixture & 0 & 5.5 & 5.34 \\ BF modified asphalt mixture & 6.2 & 0 & 5.78 \\ NTC-BF composite modified asphalt mixture & 3.9 & 5.1 & 5.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental design of asphalt mixtures.  
The original scanning images of the test specimen were obtained and pre-processed by the \"Philips DICOM Viewer\" software designed for this scanner. It is essential to make an appropriate preset for further analysis. Under the guidance of scanner operators and multiple trials, the settings of W (window): 4095 and L (level): 600 were determined. The pre-processed images of the matrix asphalt mixture before freeze-thaw cycles and after five freeze-thaw cycles are given as an example in Figure 6.  
Figure 4: The Brilliancei CT scanning equipment.  
Figure 5: The process of plane scanning.  
Figure 6: Pre-processed scanning image of matrix asphalt mixture: (**a**) before freeze–thaw cycles; (**b**) after five freeze–thaw cycles.  
In this study, the specimens of base matrix asphalt mixture and three kinds of modified asphalt mixtures were scanned by CT after 5, 10 and 15 F-T cycles, respectively. The pre-processed images were further processed by ImageJ software to analyze the porosity and pore morphology of specimens under the influence of freeze-thaw cycles. This further processing technology of images is achieved by enhancement, denoising, threshold cutting and binarization., which are introduced as follows.
## 2 Disposal Scenario and Monte Carlo Model
### Canister Loading  
A bounding fuel case assessment in the context of criticality safety constraints for Swiss SNF loading in disposal canisters showed that loadings exclusively with UO2 FAs and a fuel enrichment of 4.94 \({}^{\text{w}}/_{0}\) 235U are the most critical ones [11]. The comparison covered loadings with all relevant PWR FA-designs: UO2 fuel, MOX as well as enriched reprocessed uranium (ERU), but also mixed loadings with FAs of different design. With UO2 FAs being the most critical type of loading, only this design was chosen for a more detailed ND-UQ in the present work.  
The canister is considered to be loaded with 4 PWR-FAs having the same nuclear and mechanical design. The FAs correspond to Gosgen nuclear power plant (KKG) fuel design. Thus, a specific KKG FA representing the batch of 4.94 \({}^{\text{w}}/_{0}\) 235U enriched fuel and operated up to five cycles and, therefore, reaching the highest burnups at the end of life, was selected for the analysis. The irradiation history was reconstructed based on real plant operational data, see Section 3.1. In consequence, this study is based on a best estimate modelling approach for the fuel operating history.  
The FA has a 15 \(\times\) 15 array of fuel pins containing UO2 enriched to 4.94 \({}^{\text{w}}/_{0}\) 235U and 20 guide tubes. The ND-UQ was performed for the fresh FA as well as for assembly averaged discharge burn-ups of 17.61, 33.82, 50.47, 61.92 and 72.75 GWd/tHM, corresponding to five operation cycles.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 2 Machine-Learning-Based Path Loss Prediction
### Feature Selection and Scaling  
In practice, the data used for machine learning may contain hundreds of features. Leaving out relevant features or retaining irrelevant features can both lead to poor quality of the predictor. The goal of feature selection is to select the optimal subset with the least number of features that most contribute to learning accuracy [25].  
According to the relationship between feature selection process and model design, there are usually three alternative feature selection approaches, including filter, wrapper and embedded. The filter approach is independent of the proposed model when evaluating feature importance. The wrapper approach takes the prediction performance into account when calculating the feature scores. The embedded approach combines feature selection and the accuracy of the prediction into its procedure [26]. For different algorithms, the stopping conditions are related to the selection of the search algorithm, the feature evaluation criteria, and the specific application requirements.  
Some machine-learning-based algorithms, such as ANN, SVR, and KNN, are sensitive to the scale of the input space. Thus, normalization process should be finished before the training begins. That is, all input features and path loss values should be changed in the range from \(-1\) to \(1\) or from \(0\) to \(1\). The normalization method chosen in this paper is the same as that in [7]. It can be expressed as  
\[x_{N}=\frac{2\left(x-x_{\text{min}}\right)}{x_{\text{max}}-x_{\text{min}}}-1 \tag{1}\]  
where \(x\) is the value that needs to be normalized, \(x_{\text{min}}\) represents the minimum value of the data range, \(x_{\text{max}}\) represents the maximum value of the data range, \(x_{N}\) is the value after normalization. The predicted values can be obtained by anti-normalized according to the normalization method.  
In contrast, the feature scaling is not required by decision-tree-based methods.
## 2 Methodology
### Pre-Processing
#### 2.1.1 Detailed Conditions and Cloning  
First, the detailed conditions were applied to Setup 1 using the values outlined in Table 2. This process involved defining the analysis, flow, wall, thermodynamic and velocity parameters. As shown (Table 2), the thermodynamic parameters were set to a standard environment of atmospheric pressure at 20 \({}^{\circ}\)C, whilst the velocity parameters were defined as a 3D vector to ensure airflow was only modelled in the \(Y\) axis. It is important to note that the results are dependent on these settings, and slight changes to pressure, temperature or flow direction will all affect the post-processing results. Low velocity (laminar flow) was utilised as the steady flow provides more repeatable and regular calculations, enabling analysis to concentrate on the impact of surface roughness and increased velocity. Once Setup 1 was fully complete (post-processing included), the clone tool was used to efficiently generate the remaining seven experiments [6]. The boundary conditions were then amended as per Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Settings** & **Setup 1** & **Setup 2** & **Setup 3** & **Setup 4** & **Setup 5** & **Setup 6** & **Setup 7** & **Setup 8** \\ \hline \hline \multicolumn{8}{c}{Analysis Type} \\ \hline Analysis Type & External & External & External & External & External & External & External & External & External \\ \hline Physical & Fluid & Fluid & Fluid & Fluid & Fluid & Fluid & Fluid & Fluid & Fluid \\ Feature & Flow & Flow & Flow & Flow & Flow & Flow & Flow & Flow \\ \hline \multicolumn{8}{c}{Fluids} \\ \hline Fluids & Air & Air & Air & Air & Air & Air & Air & Air & Air \\ \hline \multirow{3}{*}{Flow Type} & Laminar & Laminar & Laminar & Laminar & Laminar & Laminar & Laminar & Laminar & Laminar \\  & and & and & and & and & and & and & and & and \\  & Turbulent & Turbulent & Turbulent & Turbulent & Turbulent & Turbulent & Turbulent & Turbulent & Turbulent \\ \hline \multicolumn{8}{c}{Wall Condition} \\ \hline Wall & Adiabatic & Adiabatic & Adiabatic & Adiabatic & Adiabatic & Adiabatic & Adiabatic & Adiabatic \\ Conditions & Wall & Wall & Wall & Wall & Wall & Wall & Wall \\ \hline Roughness & 5 μm & 5 μm & 5 μm & 5 μm & 100 μm & 100 μm & 100 μm & 100 μm \\ \hline \multicolumn{8}{c}{Initial and Ambient Conditions} \\ \hline \multicolumn{8}{c}{Thermodynamic Parameters:} \\ \hline Pressure & 101,325 Pa & 101,325 Pa & 101,325 Pa & 101,325 Pa & 101,325 Pa & 101,325 Pa & 101,325 Pa \\ \hline Temperature & 293.2 K & 293.2 K & 293.2 K & 293.2 K & 293.2 K & 293.2 K & 293.2 K \\ \hline \hline \multicolumn{8}{c}{Velocity Parameters:} \\ \hline Definition & 3D Vector & 3D Vector & 3D Vector & 3D Vector & 3D Vector & 3D Vector & 3D Vector \\ \hline Velocity X & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Velocity Y & 0.01 m/s & 1.00 m/s & 0.02 m/s & 2.00 m/s & 0.01 m/s & 1.00 m/s & 0.02 m/s & 2.00 m/s \\ \hline Velocity Z & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Turbulence Parameters & N/A & N/A & N/A & N/A & N/A & N/A & N/A \\ \hline \end{tabular}
\end{table}
Table 2: Detailed conditions of simulation setups.