esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a Multimodal Hidden Markov Model (MHMM) framework specifically designed for real-time human proficiency assessment, integrating physiological (Heart Rate Variability (HRV)), behavioral (Task Completion Time (TCT)), and subjective (NASA Task Load Index (NASA-TLX)) data streams to infer latent human proficiency states in industrial settings. Using published empirical data from the surgical training literature, a comprehensive simulation study was conducted, with the MHMM (Trained) achieving 92.5% classification accuracy, significantly outperforming unimodal Hidden Markov Model (HMM) variants 61-63.9% and demonstrating competitive performance with advanced models such as Long Short-Term Memory (LSTM) networks 90%, and Conditional Random Field (CRF) 88.5%. The framework exhibited robustness across stress-test scenarios, including sensor noise, missing data, and imbalanced class distributions. A key advantage of the MHMM over black-box approaches is its interpretability by providing quantifiable transition probabilities that reveal learning rates, forgetting patterns, and contextual influences on proficiency dynamics. The model successfully captures context-dependent effects, including task complexity and cumulative fatigue, through dynamic transition matrices. When demonstrated through simulation, this framework establishes a foundation for developing adaptive operator-AI collaboration systems in Industry 5.0 environments. The MHMM's combination of high accuracy, robustness, and interpretability makes it a promising candidate for future empirical validation in real-world industrial, healthcare, and training applications in which it is critical to understand and support human proficiency development.  
Industry 5.0; human proficiency assessment; Multimodal Hidden Markov Model; real-time monitoring; human-AI collaboration; temporal modeling; cognitive workload ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract
[49], and this method can reduce running time and enhance solving efficiency by linearly decreasing the inertia factor and choosing the particle number with roulette wheel selection. An improved beetle swarm optimization algorithm was put forward by [PERSON] et al. [50] to calibrate an industrial robot, and this algorithm can avoid local optimal solutions and enhance searching reliability. [PERSON] et al. [51] proposed an improved whale swarm algorithm to calibrate a two-segment hydraulic leg, and the kinematic calibration method can improve the tracking accuracy. [PERSON] et al. [52] proposed a hybrid particle swarm-artificial bee colony optimization algorithm to identify structural model parameters, and this algorithm has stronger searching ability and higher optimizing accuracy. [PERSON] et al. [53] proposed a kinematic calibration for serial robots and parallel robots, and kinematic parameter errors were determined by using the quantum-behaved PSO algorithm. In some respects, the PSO algorithm is similar to the genetic algorithm, but the PSO algorithm requires no extra operations, such as crossover, and fewer adjustment parameters. The principle of the standard PSO algorithm can be summarized as finding the optimal solution through collaboration and information sharing among the group. The superiorities of the standard PSO algorithm lie in simple modeling, convenient implementation, rapid convergence, and achieving global optimal solutions. The PSO algorithm can overcome shortcomings of conventional kinematic parameter error identification methods in kinematic calibration, which need to depend on specific error models and complex solving procedures. Therefore, the PSO algorithm is suitable for kinematic parameter error identification of parallel robots in kinematic calibration. In this article, a kinematic parameter identification method based on forward kinematics is presented for a parallel robot in a docking motion simulation system. The kinematic parameter identification is represented as an optimal problem for a discrete objective function. The global Newton-Raphson algorithm is used to solve the forward kinematics of the parallel robot. Since there is no analytical solution to the forward kinematics, the PSO algorithm is suitable to solve the discrete objective function. In order to solve this problem, improving the PSO algorithm is necessary. The proposed kinematic parameter identification method is applied to determine the kinematic parameter errors of the parallel robot. The rest of this paper is organized as follows: The kinematic models of the parallel robot are established in Section 2. The identification model and kinematic parameter identification method with an improved particle swarm optimization algorithm are presented in Section 3. The identification simulations and calibration experiments are provided in Section 4. Some conclusions are given in Section 5.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
###### Abstract  
The High Energy Rapid Modular Ensemble of Satellites (HERMES) is a constellation of 3U nano-satellites for high energy astrophysics. The HERMES nano-satellites' components have been designed, verified, and tested to detect and localize energetic astrophysical transients, such as short gamma-ray bursts (GRBs), which are the electromagnetic counterparts of gravitational wave events, thanks to novel miniaturized detectors sensitive to X-rays and gamma-rays. The space segment is composed of a constellation of CubeSats in low-Earth orbit (LEO), ensuring an accurate transient localization in a field of view of several steradians exploiting the triangulation technique. To achieve this goal, guaranteeing a solid support to future multi-messenger astrophysics, HERMES shall determine its attitude and orbital states with stringent requirements. The scientific measurements bind the attitude knowledge within 1 deg (1\(\sigma_{a}\)) and the orbital position knowledge within 10 m (1\(\sigma_{o}\)). These performances shall be reached considering the mass, volume, power, and computation constraints of a 3U nano-satellite platform. Thus, an effective sensor architecture for full-attitude determination was developed for the HERMES nano-satellites. The paper describes the hardware typologies and specifications, the configuration on the spacecraft, and the software elements to process the sensors' data to estimate the full-attitude and orbital states in such a complex nano-satellite mission. The aim of this study was to fully characterize the proposed sensor architecture, highlighting the available attitude and orbit determination performance and discussing the calibration and determination functions to be implemented on-board. The presented results derived from model-in-the-loop (MIL) and hardware-in-the-loop (HIL) verification and testing activities and can serve as useful resources and a benchmark for future nano-satellite missions.  
Keywords:attitude determination; sensor architecture; nano-satellite; gyroscope; Sun sensor; magnetometer; GNSS receiver +
Footnote †: journal: Sensors
# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
###### Abstract  
The classification and extraction of street tree geometry information in road scenes is crucial in urban forest biomass statistics and road safety. To address the problem of 3D fine extraction of street trees in complex road scenes, this paper designs and investigates a method for extracting street tree geometry and forest parameters from vehicle-mounted LiDAR point clouds in road scenes based on a Gaussian distributed regional growth algorithm and Voronoi range constraints. Firstly, a large number of non-tree and other noise points, such as ground points, buildings, shrubs and vehicle points, are filtered by applying multi-geometric features; then, the main trunk of the street tree is extracted based on the vertical linear features of the tree and the region growth algorithm based on Gaussian distribution; secondly, a Voronoi polygon constraint is established to segment the single tree canopy region with the main trunk center of mass; finally, based on the extracted locations of the street trees and their 3D points, the tree growth parameters of individual trees are obtained for informative management and biomass estimation by combining geometric statistical methods. In this paper, the experimental data from vehicle-borne LiDAR point clouds of different typical areas were selected to verify that the proposed Gaussian-distributed regional growth algorithm can achieve fine classification and extraction of tree growth parameters for different types of roadside trees, with accuracy, recall and F1 values reaching 96.34%, 97.22% and 96.45%, respectively. This research method can be used for the extraction of 3D fine classification of street trees in complex road environments, which in turn can provide support for the safety management of traffic facilities and forest biomass estimation in urban environments.  
mobile laser scanning; tree census; regional growing; Gaussian distribution; point cloud segmentation +
Footnote †: journal: Applied Sciences
## Framework of ECPSs
### DC Power Flow  
The Direct Current (DC) power flow model [40] is used to calculate the distribution of power flows. The DC power flow model converts nonlinear problems into linear circuit problems. It clearly reflects the overload phenomenon but greatly reduces the calculation. Although the model has an error of generally \(5\%\), it is acceptable when we estimate the performance of large-scale ECPSs.  
Define \(B\in R^{|V|\times|V|}\) as the conductance matrix associated with a power grid. Let \(\theta\) and \(p\in R^{|V|}\) be the phases and power injection at the buses, respectively. Let \(F\in R^{|V|\times|V|}\) be the matrix of the power flows of the branches. Then a DC power flow model can be described as:  
\[B\theta=p, \tag{1}\]  
\[F(i,j)=B(i,j)\big{(}\theta_{i}-\theta_{j}\big{)}, \tag{2}\]  
where \(i\
eq j,i,j\in 1,2,\ldots,|V|\).  
As for communication networks, this paper is concerned more about the impacts of ECPSs under attack rather than the mechanism of different cyber attacks. So we define the relationships of communication networks to power grids to be the control function. A detailed solution approaching fault detections, protection procedures, and adjustment strategies will be introduced in the next section.  
Figure 2: The framework of a regional ECPS.
## 4 Comparative Experiment and Ablation Study
### Ablation Study  
There are two improvement measures in our model, including: replacing the backbone with LCANet and replacing Proto with a ProtoC1 module. To verify the effect of these measures on our model, an ablation experiment is undertaken in this paper. The results of the ablation study are shown in Table 4. It can be observed from the experimental data that, by replacing the Proto module of YOLOv8n-seg with ProtoC1, the detection accuracy is almost the same, while the model complexity and computation are reduced. By replacing the backbone of YOLOv8n-seg with LCANet, since LCANet uses 10 dblocks and reduces the channels of feature maps, the detection accuracy of the model is reduced by 3% and the layers of the model are increased by 15, while the GFLOPs of the model are reduced by 4.6, which is 37%, and the weights and parameters of the model are also greatly reduced. Compared with the baseline model YOLOv8n-seg, our model reduces the detection accuracy by 3% but greatly reduces the model volume, which is friendly to low-performance devices. Furthermore, the introduction of transfer learning (TL) reduces the model training cost and enhances its robustness, resulting in a slight increase in model detection accuracy.  
Figure 16: Comparison of crack detection mask of YOLOv8n-seg model using different Proto modules. (**a**,**b**) are crack pictures with different crack shape.
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 5 Results
### Using 3 GPP Propagation Loss Model  
A scenario that implements the transmission of URLLC traffic using the 3 GPP propagation loss model was used during the experiment. The result of the distribution is presented in Table 2.  
Based on the obtained results, it can be concluded that at the initial time, when 10 subscribers are distributed around two base stations at a distance of 100 to 150 m, the SINR value varies from 22 to 50 dB. This spread is due to the influence of interference. Additionally, there is no packet loss when data is transmitted since the BLER value remains unchanged. The obtained data were compared with simulation experiments and data from real-world equipment reviewed in [45]. Despite using different frequency bands, data obtained at the start time are similar in appearance in both works.  
The Table 3 contains the results from one among 10 existing users obtained after running the previous simulation scenario where was added the ability of a subscriber to move away from the base station. Based on the obtained data, it can be seen that at a distance of less than 400 m, the SINR parameter value is sufficient for sending all available packets because the BLER indicator = 0. At a distance over 409 m, the SINR indicator  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BS Index** & **User Index** & **Distance** & **SINR** & **BLER** \\ \hline
1 & 1 & 131.22 & 30.043 & 0 \\
1 & 2 & 135.79 & 33.904 & 0 \\
1 & 3 & 141.28 & 30.813 & 0 \\
1 & 4 & 149.75 & 33.342 & 0 \\
1 & 5 & 128.71 & 40.246 & 0 \\
2 & 6 & 117.00 & 24.318 & 0 \\
2 & 7 & 139.74 & 29.138 & 0 \\
2 & 8 & 130.33 & 22.595 & 0 \\
2 & 9 & 142.81 & 23.107 & 0 \\
2 & 10 & 133.53 & 49.786 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical result of the distribution.  
Figure 4: Result of distribution.  
decreases, and the increase in the BLER value is inversely proportional. The distance from the base station exceeding 687.24 m brings the BLER indicator to one (100%) that indicates a complete loss of data.
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 1 Introduction
They found the shear stress distribution induced by coefficient of thermal expansion mismatch at the adhesive layer using analytical approaches, such as bonded joint and CBA (Composite Beam Analysis) method. Since numerous NCAs are formulated using various materials depending on the operating conditions, it is difficult to predict the behavior of NCA due to non-linearity and so characterization has relied on an experimental approach. Recently, machine learning (ML)-based studies have been reported to optimize and predict mechanical properties of adhesive joints. [PERSON] et al. [22] studied the prediction and optimization of the strength of epoxy adhesive joints. Only 32 data points with an epoxy resin and amine curing agent with four different molecular weights were considered. A gradient boosting machine learning model achieved a considerable accuracy with a coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 4.0 MPa, and 3.0 MPa, respectively. However, there are limitations such as the small datasets and consideration of only a few types of materials. [PERSON] et al. [23] showed the prediction of the lap-shear strength of epoxy adhesive using an ANN model. The optimized ANN models for lap-shear strength showed RMSEs of 0.053 and 0.590 and R\({}^{2}\) values of 0.642. However, types of resin, hardener, and catalyst were fixed; consequently, there was a limitation for a variety of materials. Therefore, research for prediction is needed for various material components. To overcome the current limitations of an experimental approach to characterize NCA, a ML framework considering various and extensive materials was investigated in this study. NCAs were formulated from 4 types of resins, 3 types of hardeners, 8 types of catalysts, and 1 type of coupling agent, and curing time and shear strength were measured by DEA and the die shear test, respectively. ANN models were trained using 65 data points, and the trained ANN models were verified by third-party experimental data from references. The prediction and the optimization for the curing time and shear strength of NCA were demonstrated according to the content of catalyst, hardener, and resin. From the results, a framework for prediction of the curing time/the shear strength of NCA and optimization of the NCA formulations using the ANN models was successfully exhibited.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 4 Results
### [PERSON] et al. Database  
With the [PERSON] et al. database (cf. Figure 12), \(J_{z}a_{2}b_{z}\) is the best color space for SSIM and MS-SSIM while the PU-encoding and the \(HDR\)-\(Lab_{100}\) are the best color spaces for FSIM. The best metrics for this database are \(\text{MS-SSIM}_{I_{2}a_{2}b_{z}}\), HDR-VDP2 and HDR-VQM. The good performances of HDR-VDP2 were expected for this database because it was part of the training set of this metric. For this database, the performances of the PSNR and the PSNR-HVS-M are relatively low compared to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quality Metric** & **All Images** & **Without the “Gamut Mismatch” Distortion** & **Compression Artifacts Only** \\ \hline \(\overline{\Delta\mathbf{E}}_{HDR-Lab_{100}}\) & 0.2578 & 0.3905 & 0.6190 \\ \(\overline{\Delta\mathbf{E}}^{*}_{HDR-Lab_{100}}\) & 0.2784 & 0.5687 & 0.6946 \\ \(\overline{\text{CSIM}}_{HDR-Lab_{100}}\) & 0.4065 & 0.6453 & 0.7714 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SROCC for the HDdtb database with and without the gamut mismatch artifact.  
Figure 11: SROCC performances for the [PERSON] et al. database for color-blind quality metrics (**a**) and for color quality metrics (**b**).  
Figure 10: SROCC performances for the HDdtb database for color-blind quality metrics (**a**) and for color quality metrics (**b**).  
the other databases. The fact that PSNR-HMA with the adequate color space significantly increases the performances of PSNR-HVS-M suggests that the backward compatible compression used by [PERSON] et al. (Section 3.1.1) creates distortions that impact the mean luminance and the contrast of the images. Indeed PSNR-HMA is an improvement of PSNR-HVS-M that takes into account these two kinds of artifacts [50].
## 2 Materials and Methods
### Sample Size Estimation  
A prior power analysis using G*Power software (Version 3.1; University of California, Los Angeles, CA, USA), based on pilot data, indicated that a sample of 14 participants was required to detect a 20% change in NRMSE (\(\alpha\) = 0.05, power = 0.80). To account for attrition, 16 individuals were enrolled. While SE was also considered a primary outcome, a separate power analysis for this variable was not conducted, and this is acknowledged as a limitation.
## 2 Materials and Methods
### Microbial Inoculum Preparation  
The _Salmonella_ strains used in this study were \(S\). Enteritidis (CCARM 8040), \(S\). Montevideo (CCARM 8052), and \(S\). Typhimurium DT 104. \(S\). Enteritidis and \(S\). Montevideo, obtained from Culture Collection of Antimicrobial Resistant Microbes (Seoul Women's University, Seoul, Korea), and \(S\). Typhimurium DT 104, obtained from Agricultural Biotechnology Culture Collection (Seoul National University, Seoul, Korea). Each _Salmonella_ strain was cultured in tryptic soy broth (TSB; BD, Franklin Lakes, NJ, USA) at 37 \({}^{\circ}\)C for 24 h; this process was repeated twice. Cultured strains were centrifuged (4000\(\times\)_g_ for 5 min) and washed twice with 0.1% (_w/v_) peptone water. After washing, the three strains were mixed in equal volume to prepare a _Salmonella_ cocktail, the concentration of which was checked by spreading on xylose-lysine-desoxycholate agar (BD).  
TV is used as a surrogate for human norovirus [9; 18; 19]. The TV inoculum was prepared, as described by [PERSON] et al. [8]. TV obtained from Cincinnati Children's Hospital Medical Center (Cincinnati, OH, USA), was propagated in a monkey kidney cell line (LLC-MKZ) from Korea Cancer Center Hospital (Seoul, Korea). The M199 medium (Gibco) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Gibco, NY, USA) and 1% antibiotic-antimycotic solution (Cellgro Mediatech Inc.; Herndon, VA, USA) was used as the cell growth medium. The cells were cultured in a humidified incubator at 37 \({}^{\circ}\)C under conditions of 95% air and 5% CO\({}_{2}\), infected with TV, and incubated for 2 d in a 5% CO\({}_{2}\) atmosphere, as described by [PERSON] et al. [9]. TV was harvested following incubation via three freeze-thaw cycles and centrifugation, following which the virus was used as an inoculum.  
In each time of inoculation, 20 and 3 CBCs were immersed in 600 mL of _Salmonella_ cocktail and 50 mL of TV inoculum, respectively, and stirred for 3-min for inoculation. Following inoculation, CBCs were dried in a biohazard hood for 2 h, and then used as samples. The concentrations of _Salmonella_ and TV on dried CBCs were \(5.1\pm 0.1\) log CFU/cube and \(3.6\pm 0.3\) log PFU/cube, respectively.
## The Living Building Blocks of MESs
### Shuttle-Mediated Extracellular Electron Transfer (SEET)  
The production and use of soluble electron shuttles by microorganisms allow the reduction of electrodes without the need for direct contact with them. While electron transfer of _Geobacter sulfurreducens_ to anodes is observed to occur within electroconductive biofilms, the transfer of electrons to anodes by _Shewanella oneidensis_ has been observed to occur via endogenously produced redox shuttles by mainly planktonic _S. oneidensis_[6]. Flavins produced by _S. oneidensis_ can be reduced by the microbe via c-type cytochromes and subsequently oxidized during interaction with a metal such as Fe\({}^{3+}\) or an electrode [69].
## 2 Reliability Analysis of Spatially Varied Soil Slopes
### Random Field Simulation of Spatially Varied Soil Properties  
The random field theory is often used to simulate the spatial variability of soil properties to consider its effect on slope reliability analysis [22; 32]. The steps of discretization of a random field are briefly introduced as follows: (1) Determine the statistical parameters, such as the mean, standard deviation, ACFs, and SOFs; (2) randomly sample a set of independent standard normal random variables; (3) generate the corresponding correlated standard random variables using some random field generation techniques, such as the Cholesky decomposition method [8], Karhunen-Loeve expansion method [17; 33], and local average subdivision method [34]; and (4) obtain the cross-correlated non-normal random fields through iso-probabilistic transformation. Interested readers can refer to [PERSON] [32] and [PERSON] [8] for details of the generalization of random fields. In this study, the Cholesky decomposition method was adopted as the technique for the realization of random fields because of its simplicity. In addition, extending the Cholesky decomposition method to layered soils problem is straightforward. One only needs to discretize each of the soil layers into different subdomains, with random field simulation for each subdomain following the general procedure described above. Details for simulating non-stationary random fields for layered slopes are given elsewhere [10].