esa-sceva/satcom-chunk-collection huggingface dataset:
# The Reproduction and Evaluation of Star Fields with the Milky Way in a Planetarium
## 3 Modeling
### Evaluation Trends of Observers  
In the analysis of the rating scores provided by observers, a specific tendency was found for each evaluation index. We classified the observers based on the rating scores of all non-outlier observers for each pattern using hierarchical clustering by [PERSON]'s method [20]. Observers were sorted into two clusters (Cluster 1 and Cluster 2). The numbers of observers in Cluster 1 and Cluster 2 were 28 and 4 for faithfulness (5 outliers), 29 and 3 for preference (5 outliers), and 19 and 8 for depth feeling (10 outliers), respectively. Figure 9 shows the normalized weights obtained from the application of our proposed equations for each cluster in the evaluation indices. For all evaluation indices, Cluster 1 focused on density. The weight results for Cluster 1 were stable across all evaluations; however, those for Cluster 2 changed depending on the evaluation indices, as shown in Figure 9d. These findings indicate that the most important physical factor in the reproduction of the Milky Way for the majority of observers is density control (Cluster 1). In Cluster 2, luminance reproduction by controlling the transmission filter of the preferred Milky Way is a more important factor than density reproduction. Size control is not very important for either cluster.  
Figure 8: Fitting results for each evaluation index by proposed estimation model. (**a**) Faithfulness; (**b**) Preference; (**c**) Depth feeling.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index \(\Phi\)** & **Mean \(\mu\)** & **Standard Deviation \(\sigma\)** \\ \hline Faithfulness & 4.00 & 0.37 \\ Preference & 4.03 & 0.35 \\ Depth feeling & 3.94 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean and standard deviation values of estimation model.  
We tried to find common characteristics within clusters using the answers to the questionnaires. However, we found no relationships between the rating score and personal data such as age, gender, or experience of astronomical observation. It is interesting that the faithfulness evaluation did not depend on the experiences of actual astronomical observations or planetarium observations. In other words, there is no difference in the evaluation of faithfulness between amateur and experienced observers. This result suggests that people can perceptually evaluate the Milky Way accurately, even if they have never seen the actual starry sky.
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 3 Overview of Data and Preprocessing
### Dataset Description and Analytical Tools  
In this study, we utilize two distinct datasets to investigate the relationship between soccer player performance and salary dynamics. The first, sourced from Understat [31], offers advanced performance metrics, such as expected goals (\(xG\)), assists, shots, and other key indicators essential for evaluating on-field contributions. The second dataset, obtained from Capology [32], provides detailed salary data, including weekly wages, contract values, and financial commitments across professional soccer. Both datasets are publicly curated and made accessible by an independent analyst, serving as a valuable resource for research in sports analytics [33]. The combined dataset spans the top five European leagues, covering six seasons from 2016-2017 to 2021-2022. It includes approximately 45,000 rows representing 5238 unique players across various positions, such as defenders, midfielders, forwards, goalkeepers, and specialized roles like attacking or defensive midfielders. This extensive scope enables a holistic analysis of performance and compensation trends across roles and leagues.  
For data processing and analysis, we employ Python (3.11.7) alongside libraries such as Pandas (2.1.4), NumPy (1.26.4), Matplotlib (3.7.5), Seaborn (0.12.2), Klib (1.3.2), and Scikit-learn (1.4.2). PyCaret (3.3.2) is used for the AutoML component of our modeling workflow. These tools facilitate data cleaning, the handling of missing values, feature engineering, and the construction of robust predictive models. Their integration ensures analytical rigor and enhances reproducibility throughout the study.
## 3 Mathematical Development
### Transient-Dynamic Formulation
To develop the algorithm, the structure equilibrium at an instant \(t+\Delta t\) can be formulated as follows:  
\[[\mathbf{M}]\ddot{\mathbf{U}}_{t+\Delta t}\ +[\mathbf{c}]\dot{\mathbf{U}}_{t+\Delta t}\ +[\mathbf{k}]\mathbf{U}_{t+ \Delta t}\ =\mathbf{F}_{t+\Delta t}\, \tag{24}\]  
where \(\ddot{\mathbf{U}}_{t+\Delta t}\), \(\ddot{\mathbf{U}}_{t+\Delta t}\), and \(\mathbf{U}_{t+\Delta t}\) are accordingly the acceleration, velocity, and displacement vectors of the structure, while \(\mathbf{F}_{t+\Delta t}\) is the external force vector, all at time step \(t+\Delta t\). If velocity and displacement vectors are considered from the previous instant value, \(t\), it is feasible to obtain the structural evolution in the next time step under dynamic loads. Thus, the constant acceleration concept and the trapezoidal rule in the integration of a step-function can provide the following equations:  
\[ \begin{split}\dot{\mathbf{U}}_{t+\Delta t}&=\ \dot{\mathbf{U}}_{t}+0.5\ \Delta t\ \Big{(}\ddot{\mathbf{U}}_{t+\Delta t}\ + \ddot{\mathbf{U}}_{t}\ \Big{)},\\ \mathbf{U}_{t+\Delta t}&=\ \dot{\mathbf{U}}_{t}+0.5\ \Delta t\ \Big{(}\dot{\mathbf{U}}_{t+\Delta t}\ + \dot{\mathbf{U}}_{t}\ \Big{)}.\end{split}  \tag{25}\]  
These equations are indeed applied to the plate rotations instead of translation. The evaluation of \(\ddot{\mathbf{U}}_{t+\Delta t}\) is the key solution of the algorithm; for that, previous expressions are substituted in the structure equilibrium equation at the time: \(t+\Delta t\):  
\[[\mathbf{M}]\ddot{\mathbf{U}}_{t+\Delta t}\ +[\mathbf{c}]\Big{[}\dot{\mathbf{U}}_{t}+0.5\ \Delta t\ \Big{(}\ddot{\mathbf{U}}_{t+ \Delta t}\ +\ddot{\mathbf{U}}_{t}\ \Big{)}\Big{]}+[\mathbf{k}]\Big{[}\mathbf{U}_{t}+0.5\ \Delta t\ \Big{(}\dot{\mathbf{U}}_{t+ \Delta t}\ +\dot{\mathbf{U}}_{t}\ \Big{)}\Big{]}=\mathbf{F}_{t+\Delta t}. \tag{26}\]  
On separating terms referred to time \(t+\Delta t\) and \(t\), respectively, at the left and right-hand sides of the equation, it is possible to solve it for the updated structure acceleration vector as:  
\[\Big{(}[\mathbf{M}]+[\mathbf{c}]\Delta t+0.25\Delta t^{2}[\mathbf{k}]\Big{)}\underbrace{ \ddot{\mathbf{U}}_{t+\Delta t}}_{at\ t+\Delta t}=\mathbf{F}_{t+\Delta t}-\underbrace{ [\mathbf{k}]\Big{(}\mathbf{U}_{t}+\dot{\mathbf{U}}_{t}\ \Delta t+0.25\Delta t^{2}\ddot{\mathbf{U}}_{t}\ \Big{)}-[\mathbf{c}]\Big{(}\dot{\mathbf{U}}_{t}\ +0.5 \Delta t\dot{\mathbf{U}}_{t}\ \Big{)}}_{at\ t}. \tag{27}\]  
Once solved for \(\ddot{\mathbf{U}}_{t+\Delta t}\), knowing the previous dynamic status at instant \(t\) (that is the _RHS_ of the equation), an updated vector for acceleration is attained. Thus, it is leading to new velocity and displacement vectors, as in Equation (25). The equations for each DOF are uncoupled. The iterative incremental solutions can take the following form:  
\[
## 2 Behavior of HDRBs  
HDRBs are composed by alternating layers of steel and rubber; thus, under horizontal loads, they inherit the shear behavior of the rubber compound adopted in their manufacturing. Figure 1 shows the shear behavior of a compound typically used for HDRBs (furnished by FIP MEC) at two different strain amplitudes, i.e., 100% and 150%. As can be observed from the hysteresis loops, the strain amplitude dependence and the repeated cycle effect are not negligible, even though they are limited. Differently, the strain rate dependence is generally negligible in the range of velocities relevant to seismic applications [7; 8].  
Figure 1: Hysteretic loops of a typical high damping rubber at 100% (red loops) and 150% of shear strain (blue loops).  
These behavioral effects can be accounted for in advanced numerical models [7; 8; 11; 35; 36]. Alternatively, simplified models can be adopted, such as linear models [37] (defined in terms of equivalent linear stiffness, \(G\), and damping, \(\mathcal{G}\)) or bi-linear equivalent models [38; 39] (defined in terms of strength at zero displacement, \(F_{0}\), and post-elastic stiffness, \(K_{p}\)), by assuming different sets of parameters properly calibrated to simulate these effects. Similarly, for the environmental effects of ageing and temperature, two simplified models can be calibrated to simulate the two extreme conditions (i.e., aged and not-aged conditions or the behaviors under maximum and minimum design temperatures).  
In Figure 2a, a general representation of UBDPs, LBDPs and NDPs is depicted, with reference to the bi-linear model. It is worth noting that the stiffness of the bearings directly influences the isolation period, \(T_{is}\), while their dissipation capacity influences the displacements of the bearings themselves. Moreover, both the parameters influence the base shear and the absolute acceleration experienced by the superstructure. Thus, LBDPs usually maximize the isolation system displacements, while UBDPs maximize the isolation system reactions, i.e., the base shear and floor accelerations of the superstructure.  
The following sections summarize the code recommendations in the most seismically active counties on the U/L bound approach to account for these sources of variability in the design and analysis of base-isolated structures. The main aim is to illustrate the limits of the current approach of the European codes, which are still based on outdated American standards and present some omissions and transcription errors, and to highlight and promote more modern approaches.
## 3 Results
### Assembly & Mass customisation
#### Mass Customisation Impacts Operators  
Fully automated assembly systems bring increased productivity for high-volume production but lack the flexibility and adaptability of human operators. People are better equipped for assembly tasks with small and frequent variations, but their potential for higher productivity is limited. In a context of market demand characterized by mass customization, which heads towards mass personalizedproduction, reconfigurable assembly systems that incorporate both machines and people can lead to cost-effective systems that are flexible and scalable [2]. Automation needs to consider both the physical and cognitive abilities of the human operators it supports [44].  
In order to improve the yield of assembly operations, providing support to human workers is necessary. Augmented Reality (AR) could be used, reducing the number of engineering/production management resources needed to provide assembly operators with cognitive support to perform their tasks [45,46]; as well as cognitive/handling skills transfer systems [47], self-adapting automatic quality control [48] or cognitive automation strategies [49]. Automation needs to ensure human safety, which led to research on Human-Robot Collaboration (HRC) plan recognition and trajectory prediction [50], and the concept of \"safety bubble\" [51]. When employing novel digital technologies for enhancing assembly systems performance, one cannot underestimate the strategic importance of IT/IS systems [52].  
**In conclusion**, in a context of market demand characterized by mass customization, which heads towards mass personalized production, reconfigurable assembly systems that incorporate both machines and people can lead to cost-effective systems that are flexible and scalable. Industry 4.0 digital technologies have a critical role to play in making possible mass customization assembly systems that do not compromise on quality and cost and that do not achieve increased performance by affecting human operators negatively.
# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
## 1 Introduction  
With the development of LiDAR, RGB-D cameras, and 3D structured light sensors, and the trend towards miniaturization, portability, and automation of acquisition devices becoming increasingly evident, the collection of point cloud data has become more convenient. Compared to two-dimensional data, point cloud data contain rich spatial structural information, making it a data type with a very high information density. Point cloud segmentation technology refers to the process of dividing a large amount of raw point cloud data into multiple subsets, each representing a separate entity or a relatively independent part within the depicted object. This technology is widely applied in fields such as autonomous driving [1; 2; 3; 4], robotic perception [5; 6; 7], and 3D environment understanding [8; 9; 10].  
Datasets serve as the foundation for deep learning. However, existing public datasets often focus on areas like autonomous driving and indoor/outdoor environments, with few datasets dedicated to the precision industrial component sector. Given that industrial parts often have complex shapes and small gears represent these components widely used across various sectors and that are irreplaceable in many aspects, studying suchrepresentative industrial components can facilitate the broad application of deep learning in industrial production.  
Based on this, the present study employs Metal Injection Molding [11; 12; 13; 14; 15; 16] (MIM) technology and designs experiments based on factorial design [17], producing gears and using a 3D structured light scanner to collect point cloud data of all gear shapes throughout the MIM process, thereby constructing a dataset containing 1101 point cloud datasets of small gears. Subsequently, the gears are manually annotated and segmented.  
Using the dataset created for this study, a multiscale feature fusion Point Transformer network is proposed, based on the PointTransformer [18] as the underlying network. This network incorporates multiple global feature extraction modules to extract features at multiple levels and employs an up-and-down sampling module [19] to integrate multi-scale contextual information, enhancing the modeling capability for complex structured point clouds.  
The structure of this paper is as follows. Section 2 introduces some commonly used public point cloud datasets and the MIM process along with the orthogonal experimental plan, followed by the point cloud data collection process, and finally the dataset's annotation and enhancement process. Section 3 introduces recent point cloud segmentation network models, details the network structure of PointTransformer, and elaborates on the Multilayer Feature Fusion Point Transformer (MFF-PT) network structure. Section 4 discusses experimental parameters, conducts ablation experiments to ensure the effectiveness of the model, compares it with other algorithms on the custom dataset, and presents the results. Finally, Section 5 summarizes the innovations and contributions of this research paper and points out its limitations.
# Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan
###### Abstract  
We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path survivability probability may drop, i.e., its risk may rise. The main objective is to reroute at-risk end-to-end flows with the minimum number of flow operation so that a fast flow recovery is guaranteed. We provide various formulations for optimizing network risk versus operational costs and examine the trade-offs in flow recovery and the connections between operational cost, path risk, and path survival probability. We present our suboptimal dynamic flow restoration methods and evaluate their effectiveness against the Lagrangian relaxation approach. Our results show a significant improvement in operational cost against a shortest-path approach.  
network optimization; network probabilistic failures; software-defined networks; Lagrangian relaxation method +
Footnote †: journal:  
0000-0001-149-  
the shortest path may no longer be the primary objective since rerouting a failed flow to the shortest path may not be efficient in the sense of minimizing the number of flow operations [10].  
The task of network recovery becomes further complicated in large-scale failure or disaster scenarios where multiple concurrent failures could affect any static protection scheme. In such scenarios, the main objective is to salvage as much traffic as possible while rerouting the traffic dynamically using the available routes as the condition of the post-failure network is dynamically evaluated. This problem highlights the need for proactive network protection approaches that would also be able to dynamically respond to network changes on the ground. For instance, a protection scheme that could evaluate and predict the risk of path failure and proactively reroute traffic from high-risk regions to low-risk regions could significantly reduce the number of service disruptions [11].  
In such scenarios, the problem of path restoration becomes a multi-objective optimization problem, i.e., finding a backup path with minimum delay and lowest risk while using a minimum number of flow operations in order to achieve scalability. In this paper, we intend to formulate and present optimal/near-optimal solutions for this problem. Our results also provide insights into the relationship between the number of flow operations and acceptable risk thresholds in a probabilistic failure scenario.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology  
### Proposed Framework  
To address the limitations identified in existing log anomaly detection methodologies, we propose a framework for log anomaly detection, emphasizing the real-time detection and categorization of anomalies. Central to this framework is the use of topic clustering, which organizes log messages into distinct topics, allowing users to better understand the structure and nature of anomalies as they emerge. By leveraging BERTopic [10], a powerful topic clustering technique that integrates BERT embeddings, the framework enables users to identify indicative keywords of anomalous behavior. This real-time categorization provides crucial insights into anomaly groups, empowering users to analyze and respond to potential issues quickly. The results generated by BERTopic are seamlessly integrated into an informative dashboard, which displays detailed information for each topic, including relevant words, classified log messages, and the probability of messages being anomalies. This unified presentation ensures that users can monitor, analyze, and act on anomalies in real time, providing an all-in-one framework for streamlined anomaly management. An overview of the proposed framework is presented in Figure 1.  
Figure 1: An overview of the proposed framework. Raw log messages optionally undergo preprocessing (dashed-line box) before topic modeling with BERTopic. The extracted topics are then utilized for anomaly classification with the results presented in an analytic dashboard.  
In the following sections, Sections 3.1.1-3.1.4, we provide a description of each process, ranging from data preprocessing and BERTopic to anomaly classification and the analytic dashboard.
## 4 Discussion and Conclusions  
In this paper, a low-power, low-noise, resistive-bridge microsensor readout circuit with chopper-stabilized, recycling folded cascode instrumentation amplifier is presented. The readout circuit consists of a chopper-stabilized RFC IA, AOCL, 2 nd-order LPF, ADC driving buffer, and 12-bit SAR ADC. The RFC structure was used to achieve low-power consumption. Additionally, low-noise was implemented by applying the chopper stabilization technique, and to realize high-input impedance for accurate signal amplifying, CFIA topology was implemented. The proposed readout circuit was implemented in a standard 0.18 \(\upmu\)m CMOS process, with an active area of 12.5 mm\({}^{2}\). The measured input-referred noise at 1 Hz was 86.6 nV/\(\upmu\)Hz and the noise efficiency factor (NEF) was 4.94, respectively. The total current consumption was 2.23 \(\upmu\)A, with a 1.8 V power supply. There were trade-offs between the input-referred noise and power consumption. The input-referred  
Figure 16: Measured input-referred noise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **This Work** & **[20]** & **[13]** & **[21]** & **[22]** \\ \hline Architecture & CFIA (with RFC) + chopping & CFIA + chopping + RRL & CCIA + chopping & CCIA + chopping + CDS + RRL & RC + chopping \\ \hline Technology (\(\upmu\)m) & 0.18 & 0.18 & 0.18 & 0.13 & 0.35 \\ \hline Supply voltage (V) & 1.8 & 3.3 & 1.8 & 3 & 5 \\ \hline Total current & 7.9 & 200 & 1200 & 326 & 860 \\ consumption (\(\upmu\)A) & 70-220 & 100 & 40 & 16-32 & - \\ \hline Gain of IA & 580 k & - & - & - & 2 k \\ \hline Current consumption & 2.23 & - & - & - & 700 \\ of IA (\(\upmu\)A) & & & & & \\ \hline Input-referred noise & & & & & \\ (nV/\(\upmu\)Hz) & & & & & \\ \hline NEF & 4.94 & 6.1 & 5.0 & 11.1 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance summary of proposed resistive-bridge ROIC and comparison with other studies.  
noise in this paper was higher; however, the power consumptions were lower than the previous research (in Table 3). This chip achieves relatively lower NEF, which shows the power-noise efficiency. The gain of the proposed IA can be adjusted from 70 to 220, via a 4-bit programmable resistor array _DIN<3:0>_, so that it can be expected to be available for various resistive-bridge sensor applications.  
Conceptualization and supervision, H.K. ([PERSON]); investigation and writing--original draft preparation, G.C.; writing--review and editing, H.H., D.Y., H.K. ([PERSON]), K.N., M.Y. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Practical Technology Development Medical Microrobot Program (R&D Center for Practical Medical Microrobot Platform, HI19C0642), funded by the Ministry of Health and Welfare (MOHW, Korea) and the Korea Health Industry Development Institute (KHIDI, Korea).  
Not applicable.  
The EDA tool was supported by the IC Design Education Center (IDEC), Korea.  
The authors declare no conflict of interest.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 4 Performance of fs-LIMS Instruments in the Element and Isotope Analysis
### Measurements of Elements and Isotopes
#### 4.4.1 Measurements of Elements
Multiplied charged ions and polyatomic species causing isobaric interferences can limit the quantification capabilities of the LIMS system. A DP ablation ion source significantly reduces abundances of polyatomic species and increases atomic ion yield, thus improving stoichiometry of the generated plasma produced near the ablation threshold. RSCs for the investigated NIST steel alloys SRM 661, 664, and 665 are much closer to 1 compared to SP results, indicating the quantitative character of these measurements. Further improvement in getting the RSCs closer to 1 is expected using deep UV DP laser ablation ion source owing to lesser dependence of ablation rate on the material. The influence of radiation wavelength on the quantitative performance was not explored systematically. Recent studies indicate, however, that matrix effects can be expected for special kinds of materials. The results from fs-LMS studies on silica chert performed with three different wavelengths show improvement of the ablation rate, ionisation efficiency, and quality of the ablated crater for UV fs-radiation. UV fs-laser ablation produces minimal heat-affected zones around the crater and material damage due to heat [160].