esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Learning Approaches
### Labs in Education and VR-Labs Potential
[7] studied students performing chemistry experiments in a virtual world: Second Life (SL) and traditional lab, at a public university. Using their students' grades, surveys, and feedback, it was concluded that students performed better and held favourable views of their experiences in SL. Furthermore, [PERSON], et al. [7] conclude that \"SL setting minimized distractions and made certain aspects of the experiments easier to perform\" [7] (p. 1) and add no noticeable difference between genders in terms of performance and equivalent attitudes. Finally, [PERSON], et al. [8] designed a virtual earthquake engineering lab that aims to reinforce the research and academic expertise of students. The lab under the name of SUSMULAB showed a significant positive feedback for its effectiveness in visualizing and conveying engineering earthquake concepts, as well as interpretation of the results. Hence, the students are able to understand and apply different fundamental concepts in earthquake engineering. On the other hand, a neutral response was concluded in [PERSON], et al. [10]'s research studying the use of the virtual world Second Life (SL) to conduct an experiment for Biotechnology Masters students at the University of East London. They found that the students required less assistance in performing the experiment in the traditional lab after completing it in the virtual lab, and that the students showed a positive feedback and requested more virtual lab experiments. However, they conclude \"no difference in gains between the two groups\" [10] (p. 1) and \"both groups showed a significant increase in learning gain\" [10] (p. 1). Whereas, a negative response was concluded in [PERSON] and [PERSON]'s paper [51] where they have designed a virtual lab for second-year students at the University of Hong Kong, which was used in combination with traditional lab sessions at a summer term. A total of 50 engineering students participated in the survey and the responses show that the students prefer traditional laboratory to the virtual one, due to the ease of operation, flexibility, and satisfaction. The experiment, however, was done at the very early stages of VR technology on virtual labs and the responses are from a small sample group.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 2 Data and Preprocessing  
### Eddy Covariance Flux Tower Data  
The flux site data were obtained from the American flux network ([[https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)]([https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)), accessed on 17 April 2024), comprising eddy covariance (EC) observations that capture exchanges of carbon, water, and energy between ecosystem surfaces and the atmosphere at high temporal frequencies [30]. GPP_VUT_NT is derived using the nighttime partitioning method and is frequently utilized as a benchmark for GPP estimates in recent research [31; 32; 33]. The study selected data from 2015 to 2020. To be included in the dataset, daily site observations were required to have related remote sensing data and meet acceptable quality criteria. Observations were excluded from the dataset if the value of the quality label was less than 0.75. The reference values for estimating GPP are derived from GPP_VUT_NT_REF. To align the temporal resolution of most GPP products, high-quality flux data were extracted every eight days using quality flags, and any data with negative GPP values were removed. The final dataset includes 13,298 site records collected from 114 sites across the United States. Data from 11,222 records across 72 sites were used for model training and validation, with the remaining data reserved for accuracy comparison with other products. Figure 1 presents detailed information on vegetation types and site distribution in the study area. We computed a Moran's 1 index of 0.557 for the original site observation data, with a \(p\)-value of less than 0.01, indicating significant spatial autocorrelation among the sites, necessitating the consideration of spatial effects in further research.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 2 GNSS and IMU Integrated Filter Design
### Extended Kalman Filter  
The extended Kalman filter (EKF) is also designed for comparison, using the sensor measurements directly. The EKF procedure is described briefly.  
Figure 1: Flow chart of the designed ESKF\(-\)RTS algorithm.
# RF Energy Harvesting Wireless Communications:
## 7 Potential Applications
### Wearable Devices  
In recent years, wearable devices, such as the smart watch and the smart bracelet, have become very popular for health monitoring and physical activity tracking. Commercial wearable devices are often designed as a stand-alone ecosystem that integrates the information collection, data processing, wireless communication, and message display in a single device. The high energy consumption makes current wearable electronics heavily depend on high capacity batteries.  
If we separate the data processing and information display modules and integrate only sensing and data transmission into wearable devices, the energy consumption of the wearable devices will be greatly reduced so that they can be powered by renewable energy sources. In [96], a researcher  
Figure 20: A 3-tier architecture of an RF-EHWC network for the healthcare of animals [95].  
conducted an experiment to charge a wearable devices with a dedicated energy source held in the right hand. The transmission power and the frequency band of the energy source were \(2\,\mathrm{W}\) and \(464\)-\(468\,\mathrm{MHz}\), respectively. According to the experiment results, the device attached on the back received the lowest power, which was \(-12\,\mathrm{d}\mathrm{B}\mathrm{m}\) on average; the wearable device attached on the front of the head can harvest the highest power, which can reach an average of \(6.9\,\mathrm{d}\mathrm{B}\mathrm{m}\).  
To obtain more energy for the reliable operation, the wearable device can choose a hybrid energy harvester. In [97], the recommended energy sources are the thermal energy and RF energy. A small body-worn thermoelectric generator (TEG) collects thermal energy from the human body and typically provides output voltages lower than \(<100\,\mathrm{mV}\). It requires an ultra-low voltage multiplier to boost the low output voltage from \(<100\,\mathrm{mV}\) to \(1.35\,\mathrm{V}\). However, the applied voltage multiplier requires a \(600\,\mathrm{mV}\) voltage for startup. Therefore, an RF rectifier of \(-10\,\mathrm{d}\mathrm{B}\mathrm{m}\) sensitivity is used to harvest radio energy, and provide the required startup voltage. The implementation of other energy harvesters for wearable devices can refer to [98].
## 5 Discussion
### SVM Classification Results  
In this paper, the grid search method was used to search the optimal parameters of the HOG and SVM parameters, and the mean value of the identification accuracies of the 5-fold cross-validation was employed to evaluate these parameters. In terms of the HOG parameters, the cell size was set as 4, 6, 8, 10, and 12, and block sizes were 1, 2, 3, and 4. The used kernel functions of SVM were linear, polynomial, sigmoid, and radial basis functions (RBF). The log2(C) and log2(\(\gamma\)) were set in the range of \([-10,10]\) with the interval of 0.5. The best experimental results are summarized in Table 3. For the listed results, it should be noted when the same identification accuracy was obtained for the different combinations of HOG parameters, those parameters leading to the feature dataset with the smallest size were regarded as the best combination. According to the results, it seems that RBF performs best among all the kernel functions except on the dataset _I-ca_, for which the sigmoid function obtained the highest mean accuracy of 94.2%. It can also be seen that the proposed approach could achieve a satisfactory identification accuracy for different categories of adulterants, which is up to 98.3%. The identification accuracy of different adulterant content is slightly lower than that of category identification, but all the mean values of these were higher than 93%.  
Figure 10 shows the mean identification accuracies of the SVM identification mode based on the original response data, image, and HOG features. The results also displayed the similar trend that the three types of features obtain higher accuracy for the identification of the adulterant categories than the content identification. The identification accuracies based on the original response data are 86.6%, 84.2%, 83.6%, and 81.6% corresponding to the adulterants categories, CA-content, CB-content, and CT-content respectively, which are relatively better compared with the results for original features (8.3%, 72.5%, 73.3%, and 70.0%, respectively). It should be noted that the identification accuracies for all the HOG features on both category and content identification are significantly higher than the other two types of feature datasets. It can therefore be concluded that although the identification result of the response image is lower than its corresponding initial response data, the proposed HOG feature extraction algorithm can mine odor features that contribute more to adulterant identification. This is because HOG could extract the key image features that are beneficial for identification and discard the weaker features and the noise information.  
Figure 10: SVM identification accuracies based on different feature datasets.
## 4 GNSS-Based Location Context Profiling
### Applications of Location Context Profiling  
In this section, we demonstrate two applications with profiling: (i) a localization error estimator, and (ii) a semantic context search.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 3 Ergonomic Techniques in Human-Machine Interactions
### Principles of Ergonomics Applied to Human-Machine Interactions
#### 3.1.3 Organizational Ergonomics: Workplace Design and Collaboration  
As manufacturing advances toward Industry 5.0, the human-machine interaction is no longer limited to task automation but involves collaborative intelligence between humans and adaptive systems. This evolution places increased emphasis on organizational ergonomics--the optimization of structures, workflows, and policies to improve both system performance and worker well-being [9; 48].  
Workplace design significantly influences the quality of the HMI. Ergonomically optimized environments reduce musculoskeletal risks and mental strain, while promoting productivity and satisfaction. Elements such as task variation, job rotation, and clear communication channels contribute to lowering the incidence of fatigue and repetitive stress injuries [49; 50].  
Cyclic job rotation schemes, especially when optimized through algorithmic planning, have shown promise in reducing the ergonomic load and supporting sustainable work rhythms [51]. Moreover, motion capture technologies allow for a detailed analysis of posture and movement, offering objective data for ergonomic improvements [52].  
In physically demanding sectors, passive exoskeletons can alleviate biomechanical stress, though their effectiveness is conditioned by task fit and worker perception. Field studies show mixed acceptance, emphasizing the need for participatory design approaches [53].  
In collaborative workspaces with cobots, trust and system transparency are critical. Overtrust or distrust can impair performance and safety. Human-centered HMI design--featuring adjustable autonomy and intuitive feedback--helps calibrate user trust and enhances acceptance of intelligent systems [54; 55].  
Virtual reality (VR) has emerged as a valuable tool for prototyping collaborative stations. By simulating human-robot interactions and assessing ergonomic comfort in early design stages, VR supports more efficient implementation and reduces design errors [56].  
Beyond workstation design, organizational ergonomics involves participatory strategies and knowledge-based tools for layout optimization. Simulation environments, digital twins, and AI-supported design platforms are increasingly integrated in ergonomic planning to balance efficiency with human comfort [50]. Flexible, human-centered layouts--those that can adapt to anthropometric diversity and cognitive needs--are fundamental in Industry 5.0 environments. Here, the human is viewed not as an operator, but as a creative problem-solver, supported by intelligent machines [9; 48].
## 2 Materials and Methods
### Positive Matrix Factorization (PMF)  
#### 2.3.1 Model Background  
Receptor modeling aims at reconstructing the contribution of emissions from various sources of air pollutants (e.g., particulate matter) based on measurement data (i.e., particles' chemical compositions) monitored at a specific receptor site. Positive matrix factorization (PMF) is one of the most commonly used receptor models. This model uses a weighting scheme, taking into account errors of the data points, which are used as point-by-point weights. In addition, non-negative constraints are applied so as to obtain physically meaningful factors. The model version available by USEPA (PMF v.5.0) was applied in the present study. The PMF analysis background was described in detail by [PERSON] [55]. In a few words, the model can be written as  
\[X=\text{GF}+\text{E}, \tag{1}\]  
where \(X\) is the known \(n\times m\) matrix of the \(m\) measured chemical species in \(n\) samples; \(G\) is an \(n\times p\) matrix of the \(p\) sources contributions to the samples (time variations); and \(F\) is a \(p\times m\) matrix of source compositions (source profiles). Both \(G\) and \(F\) are factor matrices to be determined. \(E\) is defined as a residual matrix, i.e., the difference between the measurement \(X\) and the model \(Y\) as a function of factors \(G\) and \(F\).  
\[e_{ij}=x_{ij}-y_{ij}=x_{ij}-\sum_{k=1}^{p}g_{ik}f_{kj}(i=1,\ldots,n;j=1,\ldots,m;k=1,\ldots,p) \tag{2}\]  
The scope of PMF is to minimize the sum of the squares of the residuals weighted inversely with error estimates of the data points. Furthermore, PMF constrains all of the elements of \(G\) and \(F\) to be non-negative, meaning that sources cannot have negative species concentrations (\(f_{kj}\geq 0\)) and samples cannot have a negative source contributions (\(g_{ik}\geq 0\)). The task of PMF analysis can thus be described as to minimize \(Q\), which is defined as  
\[Q(E)=\sum_{i=1}^{n}\sum_{j=1}^{m}\left(e_{ij}/s_{ij}\right)^{2} \tag{3}\]  
where \(f_{kj}\geq 0\); \(g_{ik}\geq 0\); and \(s_{ij}\) is the error estimate for \(x_{ij}\).  
Occasionally, other auxiliary equations can be added in order to include information regarding, e.g., chemical profiles for specific sources [56]. These auxiliary equations are applied to the selected solution as constraints, which can lead to a free rotation of the solution with better physical meaning than the original one. Further, a number of rotations blocking zero values can be introduced to the matrix, increasing the rotational stability of the solution.
## 4 Performance Validation
### Results and Discussion
#### 4.2.2 Case 2  
Let us assume four processes arrived at time t = 0. P1 = [20, 5], P2 = [40, 5], P3 = [60, 5], P4 = [80, 5]. All the processes have the same priority. Results are provided in Table 2.  
In the second case, all four processes have the same priority. The results of the Round Robin with a fixed time slice are the same as the results as the algorithm that does not consider the process's priority, but simply assigns a specified time slice to each process, regardless of priority. This results in nine context switches, a total turnaround time of 111, and a waiting time of 65. In this case, the performance of the Round Robin with dynamic time slice and RRAPS algorithms is the same, as all the processes have the same priority and will be executed in a first-come-first-served manner. The main difference between the two algorithms is that RRAPS uses adaptive priority scheduling.  
In contrast, in Round Robin with a dynamic time slice, RRAPS and the AN algorithm adjust the time slice dynamically based on the process's burst time. In this scenario, the order of process execution is not impacted by the time slice. As a result, the frequency of context switches and turnaround time remain constant. However, the AN algorithm [3] outperforms other algorithms in terms of waiting time.
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
## 2 Experimental Setup
### The Description of Experimental Setup  
The seed was a diode end-pumped passively Q-switched monolithic microchip laser. The pump source was a single emitter semiconductor laser (LD1) with an 808 nm wavelength and a maximum CW of 10 W at 10 A current, and the electro-optical efficiency was about 50%. The emitting wavelength of the pump source was 808 nm at 25 \({}^{\circ}\)C. The fast-axis divergence and low-axis divergence were all 8\({}^{\circ}\) with fast-axis collimation (FAC). The heat sink of the pump source was attached to the TEC to help stabilize the pump wavelength. A gradient-index (GRIN) lens of 5 mm long was fixed between the LD1 and the composite crystal. L1 was a plane-convex mirror with antireflective coating at 1064 nm (<0.2%). M1 and M2, which were tilted slightly, were coated to be highly reflective of 1064 nm (>99.8%) at 45\({}^{\circ}\) angles. The seed beam was injected with a small incident angle to the power amplifier that consists of Nd: YVO4 (0.5 at. % Nd\({}^{3+}\)), the pump shaping optics, and a LD2 of CW 10 W. The three advantages of Nd: YVO\({}_{4}\) at 1064 nm are a large stimulated emission cross section, which is 5 times greater than that of Nd: YAG; a linearly polarized output, which can avoid thermally induced birefringence and be suitable for the SHG; and a strong broadband absorption at 809 nm. Hence, we used Nd: YVO\({}_{4}\) as an amplifier in this laser. The surface F3 of Nd: YVO4 was coated with antireflective layers at 1064 nm (<0.2%). The opposite surface F4 of the crystal had a highly reflective coating for 1064 nm and an antireflective coating for 808 nm. L3 and L4 were plane-convex mirrors with an antireflective coating at 808 nm (<0.2%), and they were used to collimate the pump light. L2 was a cylindrical lens for adjusting the horizontal direction distribution of the pumping beam. We used a 10 mm-long Type I LBO (\(\theta=90^{\circ}\), \(\beta=11.4^{\circ}\)) crystal (dual-band  
Figure 1: Schematic of the sub-nanosecond laser.  
antireflective coating at 532 nm and 1064 nm) for the SHG due to its high damage threshold and relatively large angular acceptance bandwidth. The temperature of the LBO was controlled by the TEC for better efficiency of the SHG. The 1064 nm beam was injected into the LBO through L5, which was a plane-convex mirror with an antireflective coating at 1064 nm (<0.2%) for focusing a 1064 nm beam. M3 was coated to be highly reflective of 1064 nm (>99.8%) at 45\({}^{\circ}\) angles for folding the beam path. M4 and M5 were beam splitters with an antireflective coating at 1064 nm (<0.2%) and a partially reflective coating at 532 nm for beam measurements.
## 3 Results
### Flip Angle Calibration and In Vivo Hyperpolarized \({}^{13}\)C MRS/MRSI Experiments  
In the prescan step, the flip angle was calibrated to 90\({}^{\circ}\) using a PFT \({}^{13}\)C birdcage RF coil with distilled water and ethylene glycol phantoms in a 3.0 T MRI system, as discussed in Section 2.6. Figure 7a-c present the slab dynamic \({}^{13}\)C MRS results of the [1-\({}^{13}\)C] compounds. In slab dynamic \({}^{13}\)C MR spectra, [1-\({}^{13}\)C] pyruvate, [1-\({}^{13}\)C] lactate, [1-\({}^{13}\)C] bicarbonate, and [1-\({}^{13}\)C] pyruvate hydrate were demonstrated for 180 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Case 1** & **Case 2** \\ \hline SNR & 33.80 & 35.76 \\ Standard deviation of SNR & 5.55 & 10.64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SNR and standard deviation of \({}^{13}\)C spectral grid in the corn-oil phantom. There was a 5.5% drop of overall SNR around the corn-oil phantom. This result can verify that the noise added by 40 CPSCs can be negligible.  
Figure 6: \({}^{13}\)C MR Spectra of the corn-oil phantom using 2D CSI sequence. \({}^{1}\)H reference image was acquired using high-performance \({}^{1}\)H RF coil with FIESTA cine sequence: (**a**). Each \({}^{13}\)C MR spectra of the corn-oil phantom was acquired using 16-leg low-pass PFT \({}^{13}\)C birdcage RF coil with 40 CPSCs: (**b**) and 16-leg low-pass \({}^{13}\)C birdcage RF coil without CPSCs: (**c**). For the SNR comparison between (**b**,**c**), a total of 27 \({}^{13}\)C spectral grids were selected, which are indicated by the red broken line in (**a**). The overall SNR at the selected \({}^{13}\)C spectral grid of the corn-oil phantom in (**b**,**c**) was 33.80 and 35.76, respectively. There was a slight SNR drop of 5.5% at the selected \({}^{13}\)C spectral grid in (**b**) compared with (**c**).  
Figure 7: Slab dynamic \({}^{13}\)C MR spectra of swine cardiac with respect to time. Stacked [1-\({}^{13}\)C] spectra in (**a**) are acquired 60 times every 3 s. The spectra of [1-\({}^{13}\)C] pyruvate, [1-\({}^{13}\)C] lactate [1-\({}^{13}\)C] bicarbonate, and [1-\({}^{13}\)C] pyruvate hydrate are also acquired. Reconstructed summed [1-\({}^{13}\)C] spectra in (**b**) and signal intensity graphs in (**c**) demonstrate the time course of conversion of [1-\({}^{13}\)C] pyruvate, [1-\({}^{13}\)C] lactate [1-\({}^{13}\)C] bicarbonate, [1-\({}^{13}\)C] pyruvate hydrate, and [1-\({}^{13}\)C] alanine.  
Figure 8a-c present the 3D swine cardiac \({}^{13}\)C MRSI results. Throughout the 3D swine cardiac \({}^{13}\)C MRSI, [1-\({}^{13}\)C] pyruvate and [1-\({}^{13}\)C] lactate signals were acquired and co-registered with swine cardiac \({}^{1}\)H MR images to investigate the [1-\({}^{13}\)C] metabolism.