esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 5 Conclusions  
This study systematically evaluated the image quality of HoloLens 2 as a representative example of AR glasses with see-through displays for medical applications. To achieve this, evaluation methods suitable for conventional monitors were adapted for HoloLens 2. The results revealed deficiencies comparable to those observed in test methods specifically developed for AR glasses in the non-medical sector. Employing the selected methodologies facilitated the assessment of HoloLens 2 against the established criteria for medical display devices.  
Overall, this study reveals that HoloLens 2 surpasses the requirements for luminance and luminance ratio. Nonetheless, it shows shortcomings in rendering dark grayscales and in certain instances deviates significantly from the contrast response of the GSDF. Additionally, the color representation of HoloLens 2 lacks consistency. Therefore, HoloLens 2 is currently unsuitable as a primary diagnostic display device. Future investigations should consider software-based solutions to mitigate these limitations.  
These shortcomings are deemed less critical when the device functions as a secondary display. However, the requirements for each application must be assessed individually. The preliminary market approvals for HoloLens 2 in medical applications corroborate this evaluation [31; 32; 33].  
Conceptualization, S.K. and S.S.; methodology, S.K. and S.S.; investigation, S.K.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.S. and C.B.; visualization, S.K.; supervision, C.B.; project administration, S.S. All authors have read and agreed to the published version of the manuscript.  
This study was funded by apoQlar GmbH.  
The presented work does not include any studies on humans or animals. Due to the design of the study, a formal vote by an ethics committee was not required. The observations carried out by the participants do not pose any hazards that increase the general risk to the lives of the persons concerned. All participants gave informed consent to participate in the study.  
Informed Consent Statement: Informed consent was obtained from all participants involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 3 Results and Discussion  
### Brain Wave Dataset  
In this study, brain wave data of 25 regularly trained players were collected. Since one person's information was incomplete, it was removed, resulting in a total of 4433 records of data from all the subjects. Additionally, other attributes of stress were incorporated based on the stress classification in our experiment as indicators of high, medium (mid), and low stresses. In the end, each record of data included eight electrode positions (Fp1, Fp2, Fz, C3, C4, Pz, O1, and O2), each of which had the electrode data on eight frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma, and High Gamma) (in \(\upmu\)V/\(\upmu\)Hz); that was 64 attributes. An additional stress indicator made a total of 65 attributes. Of all the attributes, the brain wave data of the frequency bands were continuous data, while the stress levels and player groups were discrete data.  
The brain waves at a resting state prior to the test served as the data for low stress (1476 records), the SCWT as mid-stress data (1469 records), and MA as high-stress data (1488 records). The labeling method, when classifying rest, SCWT, and MA, was set to 0, 1, and 2, respectively; when classifying rest and SCWT, the label was set to 0 and 1, respectively; and when classifying rest and MA, the label was set, respectively, to 0 and 1.
# A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads
## 3 Stackelberg Game Coordination  
As stated in the previous section, EECC buys the total energy that all the TCL users demand from the wholesale market under the market price \(\mathsf{P}\) and broadcasts a selling price \(p\) to each TCL user. Then, based on the broadcast price \(p\), each TCL user determines the energy demand \(u_{i}\) i.e., setting its set-point temperature \(\widehat{\theta}_{i}\).  
Note that the decisions between EECC and the TCL users are actually interdependent. We establish a Stackelberg game to describe the interplay of TCL users and EECC in Section 3.1. Furthermore, the existence and uniqueness of the Stackelberg equilibrium are specified in Section 3.2.
## 3 Results
### The Micro-Solar CHP Plant Performance
In Tables 10 and 11, instead, the performances of the plant when integrated with a residential user load profile are reported in the case of a fuzzy logic controller. In this case, as can be noted by comparing the results reported in Table 10 with those in Table 6, the overall electrical and thermal energy production of the CHP plant is higher (6450 kWhe and 58,800 kWth, respectively, in the case of a baseline controller and 6740 kWhe and 61,680 kWth, respectively, in the case of a fuzzy logic controller), although the conversion efficiencies of the ORC unit are almost the same. According to the detailed results of the operating hours reported in Table 8, with the fuzzy logic controller, the operating time of the ORC is about 3450 h/year, while with the baseline controller, it is 3220 h/year (increase of about 7%). In particular, the use of the TES unit is significantly extended. Indeed, in the case of a fuzzy logic controller, the solar field charges the TES 443 h/year in OM3 and about 1320 h/year in OM4, while with the baseline controller, the operating hours in these OMs are about 262 h/year and 1270 h/year, respectively. As a consequence, in the case of a fuzzy logic controller, the operational time of the plant in OM5 is higher: more precisely, it is about 914 h/year versus 723 h/year with the baseline  
Figure 9: Daily trend of TES temperature and exchanged power when coupled with a school building load profile: (**a**) sunny winter day in the case of a baseline controller, (**b**) sunny winter day in the case of a fuzzy logic controller, (**c**) partially cloudy mid-season day in the case of a baseline controller, and (**d**) partially cloudy mid-season day in the case of a fuzzy logic controller. controller. Hence, the fuzzy logic controller is able to improve the contribution of the TES in performing load following. Finally, Figure 10a,b and Figure 11a,b report the daily trends of the most important performance parameters of the plant and of the TES unit when connected with a residential user for a typical winter day. In addition, in this case, the use of the TES is extended, although the overall benefit is a bit lower than in the case of the school load profile. In any case, the fuzzy controller is able to reduce the number of switches between the different OMs (as can be seen by comparing Figure 10a,b) and to better follow the peak demands in the time range of 14:00-17:00. Nevertheless, perfect following cannot be achieved, especially in the early hours of the day, since the temperature of the latent heat TES unit is not high enough to run the ORC (OM5) and the collected thermal energy used to heat up the diathermic oil. In any case, the absence of an additional TES unit between the ORC system and the users to store the thermal energy output from the condenser of the ORC unit does limit the capability of the plant in satisfying the thermal energy demand when the solar radiation is low. Nevertheless, the potential of the proposed fuzzy logic controller in increasing the electrical and thermal energy production of the plant together
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 5 Experimental Validation
### Crack Identification Using Freequncy Contour-Strip Method  
Three crack cases shown in Table 3 are arbitrarily chosen to demonstrate the effectiveness of the frequency contour-strip method in damage identification.
## Appendix A  
The detailed derivation of Equation (3) is presented as follow: Combining the Equations (1) and (2), there is  
\[\begin{array}{l}\theta_{n+1}=f(\theta_{n},t_{n},Q,C,R,d_{n},X_{n},\theta_{e,n})\\ =\big{[}(\theta_{e,n}+X_{n}QR-(\theta_{e,n}+X_{n}QR-\theta_{n})\exp[-(t_{n+1} -t_{n})/(RC)])(M-d_{n})+\theta_{e,n}d_{n}\big{]}/M\end{array} \tag{1}\]  
To make this expression simpler, we set that  
\[U_{heat,n}=X_{n}QR,\ \partial=\exp[-(t_{n+1}-t_{n})/(RC)] \tag{2}\]  
After bringing the simple expression into the Equation (1), there is  
\[\begin{array}{l}\theta_{n+1}=[(\theta_{e,n}+U_{heat,n}-\partial(\theta_{e,n}+U_{heat,n}-\theta_{n}))(M-d_{n})+\theta_{e,n}d_{n}]/M\\ =(1-\partial)(\theta_{e,n}+U_{heat,n})+\partial\theta_{n}-((1-\partial)U_{heat,n}+\partial(\theta_{n}-\theta_{e,n}))(d_{n}/M)\\ =\partial(1-d_{n}/M)\theta_{n}+(1-\partial(1-d_{n}/M))\theta_{e,n}+(1- \partial)(1-d_{n}/M)U_{heat,n}\end{array} \tag{3}\]  
Similarly, in order to make the Equation (3) shorter, we set that  
\[f_{n}=1-d_{n}/M \tag{4}\]  
After that, there is  
\[\begin{array}{l}\theta_{n+1}=\partial f_{n}\theta_{n}+(1-\partial f_{n}) \theta_{e,n}+(1-\partial)f_{n}U_{heat,n}\\ =\partial^{2}f_{n}f_{n-1}\theta_{n-1}+\partial f_{n}(1-\partial f_{n-1}) \theta_{e,n-1}+\partial f_{n}f_{n-1}(1-\partial)U_{heat,n-1}\\ +(1-\partial f_{n})\theta_{e,n}+(1-\partial)f_{n}U_{heat,n}\\ =\partial^{3}f_{n}f_{n-1}f_{n-2}\theta_{n-2}+\partial^{2}f_{n}f_{n-1}(1- \partial f_{n-2})\theta_{e,n-2}+\partial^{2}f_{n}f_{n-1}f_{n-2}(1-\partial)U_{ heat,n-2}+\\ \partial f_{n}(1-\partial f_{n-1})\theta_{e,n-1}+\partial f_{n}f_{n-1}(1- \partial)U_{heat,n-1}+(1-\partial f_{n})\theta_{e,n}+(1-\partial)f_{n}U_{heat,n}\\ \cdots\cdots=\left(\begin{subarray}{c}n\\ j=1\end{subarray}\partial f_{j}\right)\theta_{1}+\sum\limits_{i=1}^{n}\partial ^{n-i}\left(\begin{subarray}{c}n\\ j=i+1\end{subarray}f_{j}\right)(1-\partial f_{i})\theta_{e,j}+\sum\limits_{i=1}^ {n}\partial^{n-i}\left(\begin{subarray}{c}n\\ i=1\end{subarray}f_{j}\right)(1-\partial)U_{heat,i}\end{array} \tag{5}\]  
Hence, the Equation (3) holds.
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 4 Experiments
### Experiment Results
#### 4.2.3 Tunnel Centerline Extraction Results  
The extracted centerline of Tunnel d is shown in Figure 16, while the results for other tunnel centerlines can be found in Appendix A. From the figure, it can be observed that the algorithm in this paper has good noise resistance and reflects the undulating situation of the tunnel.  
To quantitatively evaluate the accuracy of the algorithm, the extracted centerline in Tunnel d was compared with other centerline calculation methods and the control points on the actual centerline of the tunnel, as shown in Figure 17. The control points were measured on-site using a total station, which provides millimeter-level accuracy. In tunnel environments, control points are typically placed where changes in the line of sight occur. In sections of the tunnel that are relatively straight with no significant undulations or deviations, control points may be spaced several tens or even hundreds of meters apart. In this specific scenario of Tunnel d, four control points were used.  
In Figure 17, the centroid of the convex hull (blue in the graph) shows much smoother behavior, with significantly less fluctuation compared to the purple line representing the mean of the convex hull and the green line representing the mean of points. This smoothness indicates greater stability in the calculated centerline, especially in areas where the tunnel shape changes.  
Figure 16: Centerline of Tunnel d. (**a**) Top view. (**b**) Perspective view.  
Figure 17: Comparison between the proposed algorithm with other methods and ground truth. (**a**) X-axis. (**b**) Z-axis.  
Based on the results shown in Table 3, the centroid of the convex hull method demonstrates the best performance in terms of both accuracy and consistency when compared to the other methods. The average error for the centroid of the convex hull is significantly lower on both the X-axis (\(-0.036\) m) and Z-axis (\(0.016\) m), indicating minimal deviation from the actual centerline. Furthermore, this method has the smallest standard deviation (X-axis: \(0.388\) m, Z-axis: \(0.495\) m), reflecting less fluctuation and more stable results. These findings confirm that the centroid of the convex hull is the most accurate and reliable method for determining the tunnel centerline, especially in complex environments where precision is critical.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 5 Adaptive Data Rate Algorithm Design
### ED Optional Parameter Acquisition Algorithm Based on Link State
When there is redundancy in the link resources, the SF is reduced to increase the data rate or the transmission power is lowered to reduce power consumption. When the link resources are insufficient, the transmission power is increased to improve the signal quality or the SF is increased to reduce the data rate. Additionally, when the theoretical link budget is large, the BW is increased to improve the data rate; when the theoretical link budget is small, the BW is reduced to lower the data rate. Based on the values of the SNR margin \(SNR_{margin}\) and \begin{table}
\begin{tabular}{l l} \hline \hline
**SF** & \(SNR_{min}\) **(dB)** \\ \hline
7 & \(-7.5\) \\
8 & \(-10\) \\
9 & \(-12.5\) \\
10 & \(-15\) \\
11 & \(-17.5\) \\
12 & \(-20\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Correspondence between SF and \(SNR_{min}\). theoretical link budget \(LM\), the algorithm outputs a selectable list of transmission power and data rate under the premise of maintaining the communication reachability of the ED.
# Contactless Ultrasonic Wavefield Imaging to Visualize Near-Surface Damage in Concrete Elements
###### Abstract  
We present work to detect and visualize near-surface damage in concrete using contactless ultrasonic wavefield imaging technology. A fully contactless ultrasonic scanning system that utilizes a micro-electro-mechanical systems (MEMS) ultrasonic microphone array is used to collect ultrasonic surface wave data from a concrete sample. The obtained wavefield data sets are processed with a frequency-wavenumber (_f-k_) domain wavefield filtering approach to extract non-propagating oscillatory fields set up by near-surface concrete cracking damage. The experimental results demonstrate that near-surface concrete damage can be detected and visualized using the proposed ultrasonic wavefield imaging approach.  
Keywords:air-coupled; concrete; cracking; damage visualization; MEMS ultrasonic microphone array; surface waves; ultrasonic wavefield imaging +
Footnote †: journal: Applied
# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 3 Data Characteristics
### Raw Data Preparation  
The first step of the data analysis process is to count occurrences of each message type in the captured communication. Surprisingly, the most used message type is NoMessage\({}_{11}\), which creates 99.98% of captured traffic and does not contain any useful data. This type of message must be filtered out from the learning data to avoid false positive results. The number of occurrences for each message type is shown in Table 1.  
Figure 1: GPON downstream frame structure.  
The remaining data are processed using several procedures resulting in a sequence of PLOAMd messages. These procedures remove the useless parts of the frame header and expand PLOAMd data fields into a suitable form.
## 2 Study Area and Database
### Data Base
#### 2.2.3 Digital Elevation Model  
A DEM was used as an ancillary dataset for the selection of the waveform portion. The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtains DEM data on a near-global scale from 56\({}^{\circ}\)S to 60\({}^{\circ}\)N [73]. The SRTM provides global data at two resolutions: 1 arc-second (\(\sim\)30 m) and 3 arc-seconds (\(\sim\)90 m). In our study, 1 arc-second global elevation data, which offer a worldwide coverage of void-filled data at a resolution of approximately 30 m, were used. The DEMs of water bodies are shown in Figure 2. The vertical accuracy of SRTM DEM is about \(\pm\)16 m (absolute) and \(\pm\)6 m (relative) [73; 74; 75; 76; 77]. However, vertical accuracy of the data decreases with the increase in slope and elevation due to presence of large outliers and voids [75].
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Feature Extraction from IMF-Based Features Obtained through EMD  
Each PPG signal segment was decomposed into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), from which time- and frequency-domain features were derived. Specifically, 20 features were computed for each IMF: mean (IMF\({}_{\text{mean}}\)), variance (IMF\({}_{\text{var}}\)), standard deviation (IMF\({}_{\text{std}}\)), peak-to-peak amplitude (PTP), skewness (IMF\({}_{\text{skew}}\)), kurtosis (IMF\({}_{\text{kurt}}\)), dominant frequency (F\({}_{\text{d}}\)), total power (P\({}_{\text{t}}\)) mean amplitude envelope (AE\({}_{\text{mean}}\)) mean instantaneous frequency (IF\({}_{\text{mean}}\)), zero crossing rate (ZCR), extrema (ext), mean power spectral density (PSD\({}_{\text{mean}}\)), PSD variance (PSD\({}_{\text{var}}\)), spectral centroid (SC), spectral entropy (SE), spectral flatness (SF), peak to spectral energy ratio (PSER), spectral band energy (SBE), spectral slope (SS). Basic statistical and frequency domain features such as mean, variance, standard deviation, peak-to-peak amplitude, skewness, kurtosis, dominant frequency, total power, and zero crossings are computed using standard definitions that are well-established in the literature. For features related to power spectral density (PSD), we have adhered to the procedure described in [9]. Descriptions and computational methods for the specific IMF-based features are detailed in Appendix A.  
Due to the varying number of IMFs across different wavelengths (red, green, and blue), zero padding was employed to equalize the feature sets. This ensured uniformity in cases where the number of IMFs observed in some signal segments did not reach the maximum of seven. For example, if six IMFs are generated from a red signal while seven IMFs are generated from a blue signal, the seventh IMF feature set of the red signal will be padded with zeros. Therefore, we have a total of 420 IMFs-based features for three wavelengths. From these extensive feature sets, we applied feature importance scores, and Figure 6 shows the top 30 important features out of 420 features for blood glucose estimation through EMD.  
Figure 5: Top 20 features extracted from the PPG waveform.  
The overall feature extraction and selection procedure is illustrated in the flowchart of Figure 7. Feature selection is primarily guided by the importance of each feature, evaluated using different methods depending on the model. For random forest, the Gini importance method was used, while for XGBoost and CatBoost, gain-based approaches were employed. The evaluation of LightGBM included both split- and gain-based methods. We then improved the model's performance by selecting the most significant features based on the feature importance.  
Figure 6: Top 30 important features extracted from IMFs.  
Figure 7: Flow chart of feature extraction and selection procedure.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 4 Evaluation
### Cluster Configuration  
The cluster was configured to evaluate the scenario provided in Figure 11. Ninety-eight coverage points were input into the OnRoad_Cluster_Data model. Of these, 98 corresponded to the evaluation scenario and a single registry corresponded to an undefined cluster, to which new registers unrelated to the already existing clusters will attach. Figure 16 provides an example of some of the registries corresponding to coverage points.  
The iteration time for the big data cluster to analyze the data stored was set at 5 min. This 5-minute interval was determined after simulations using different time intervals, concluding 5 min is sufficient time to obtain representative data of traffic events.  
Figure 15: Operation diagram of the RSU emulator.
## 5 Conclusions  
In SAR automatic target recognition technology, speckle noise can negatively impact the performance of the classification models. To address this issue, an MFN was proposed to preserve SAR local detail features and global semantic information using a parallelization method to enhance SAR texture detail features and to improve the problems caused by speckle noise, such as large intra-class differences and small inter-class distances. In addition, a Bayesian hyperparameter optimization algorithm was used to optimize MFN's hyperparameters. The experimental results showed that the proposed model had specific generalization and validity on the MSTAR dataset. Meanwhile, Bayes-MFN achieved excellent performance compared with other mainstream models under SOCs and EOCs.  
However, the FLOPs of the model proposed are suboptimal and need to be more lightweight. Moreover, we only tested the performance of Bayes-MFN on the MSTAR dataset, which may need to be more convincing. In future studies, we aim to improve the Bayes-MFN model and to reduce its computational complexity without degrading the classification performance. Furthermore, we plan to verify the robustness and generalization of the proposed model on SAR image datasets with more complex background conditions.  
Figure 10: Performance comparison of various models under EOCs.  
**Author Contributions:** Conceptualization, X.L., X.H. and C.G.; methodology, X.L., X.H. and C.G.; software, X.L. and X.H.; validation, X.L. and X.H.; formal analysis, C.G.; investigation, G.M. and Y.W.; resources, X.L. and C.G.; data curation, X.H., G.M. and Y.W.; writing--original draft preparation, X.L., X.H. and C.G; writing--review and editing, X.L., X.H. and C.G; visualization, X.H.; supervision, C.G., Y.W. and Y.G.; project administration, X.L. and C.G.; funding acquisition, X.L., W.G. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Beijing Natural Science Foundation (No. 6214034).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.