esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 3 Concept of Mixed Reality
### Algorithm
#### 3.2.5 Registration and Mapping  
Interaction among virtual and real objects requires mapping and registration of the virtual model in real space. Ref. [46] proposed the use of the ICP algorithm for overlaying the virtual model of a building in real space using user position. This model was implemented for heritage building inspections. This method handles the problem of mapping and registration for large data using the preferred key object. The key objects were selected by the user on a presented miniature virtual model. Ref. [47] proposed a markerless registration model for surgery to register a medical image on the patient. This method used facial features to find the transformation parameters for registration. The model contains steps such as generating a 3D model of the patient, detecting facial features, and finding the transformation for registration using these features based on the camera's intrinsic parameters and the headset position. This model was followed by the ray tracing algorithm to locate an object in a real scene. Ref. [48] proposed a block box approach to align the virtual cube to the real world using a mathematical transformation. To achieve this goal, different transformation models were studied, such as perspective transformation, affine transformation, and isometric transformation. The transformation model aims to match the coordinate system of the tracker and the virtual object.  
This model is a good solution to achieve good accuracy for HoloLens calibration. Ref. [49] employed tetrahedral meshes to register a virtual object in a real object in a virtual panel application.  
Despite the importance of the proposed method, there are many challenges involved in the use of these models. Using the ICP algorithm in MR application based on the use of HoloLens requires a device with good accuracy. This is important because this operation is based on matching between a two-point set data related to the HoloLens and the virtual model. Handling large data using user interaction during registration needs a well-designed interface. Such an interface facilitates user operation during key object selection [46]. Markerless registration requires the use of a high-resolution device to manage complex tasks such as the Web-based application of MR [47]. The alignment methods require the simultaneous use of sensor fusion, that handles limitations such as field of view, and occlusion, in addition to providing opportunities for tracking different objects [48].
## 3 Results and Analyses
### Analysis of Clustering Results
Next, the SSE value and SP value of the optimal clustering result obtained by the proposed method are compared with the k-means sound speed estimation method. The results show that the SSE and SP values of the optimal clustering result are better than the k-means sound speed estimation method. The optimal clustering results obtained by the proposed method have a higher degree of intra-cluster aggregation and a greater degree of inter-cluster dispersion. Whether observing and comparing the regional distribution of various tissues one by one or evaluating the overall clustering results through clustering indexes, the two comparison results show that the proposed method can significantly improve the regional distribution of each tissue in the sound speed field. Compared with the k-means sound speed estimation method, the optimal clustering result obtained by the proposed method is closer to the regional distribution of the real sound speed field, and its clustering performance is superior.
## Methods and Data
### Performance Evaluation Metrics  
Several statistical performance metrics are used to evaluate the predictive efficacy of established models. The root mean square error (RMSE), normalized root mean square error (nRMSE), R-squared (\(R^{2}\)), the mean absolute percentage error (MAPE), mean absolute error (MAE), and normalized mean absolute error (nMAE) are the six metrics that have been applied in this study. These metrics are mathematically represented in Equations (1)-(6).  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{t=1}^{N}(f_{t}-y_{t})^{2}} \tag{1}\]  
\[nRMSE=\sqrt{\frac{1}{N}\sum\limits_{t=1}^{N}\biggl{(}\frac{f_{t}-y_{t}}{(y_{ max}-y_{min})}\biggr{)}^{2}}\times 100\% \tag{2}\]  
\[R^{2}=1-\frac{\sum\limits_{t=1}^{N}(f_{t}-\bar{y})^{2}}{\sum\limits_{t=1}^{N}( f_{t}-\bar{y})^{2}} \tag{3}\]  
\[MAPE=\frac{1}{N}\sum\limits_{t=1}^{N}|\frac{f_{t}-y_{t}}{y_{t}}|\times 100\% \tag{4}\]  
\[MAE=\frac{1}{N}\sum\limits_{t=1}^{N}|f_{t}-y_{t}| \tag{5}\]\[nMAE=(\frac{MAE}{\bar{y}})\times 100\% \tag{6}\]
###### Abstract  
Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems (e.g., the dynamics of the wildland-urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20 th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values > 0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.  
urbanization; long-term settlement patterns; built-up land data; global human settlement layer; historical maps; topographic map processing; data integration 2021  
2
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 4 Construction of the FFNN-TabNet Determination Model
### Bayesian Optimization  
To further optimize the performance of the model, this study incorporated the Bayesian optimization algorithm (BOA), aimed at systematically adjusting the hyperparameter space of the FFNN-TabNet. Bayesian optimization, a probabilistic model-based optimization method, effectively tunes hyperparameters and predicts the performance of unexplored parameter configurations. Prior to the commencement of the algorithm, it is essential to define the search range for hyperparameters.  
The detailed steps of the BOA are as follows:  
1. Initialization: Based on the aforementioned search range, a set of initial hyperparameters is randomly selected, denoted as T(0).
2. Selection of Candidate Combination: Based on T(0), select a potential set of candidate hyperparameter combinations, denoted as R[t].
3. Construction of Probability Model: Build a Bayesian network based on the current distribution of hyperparameters to represent the probabilistic relationship between hyperparameters and the objective function.
4. Objective Function Fitting: Employ the constructed probability model to estimate the objective function, thereby generating a new set of hyperparameters, denoted as R\({}_{1}\)[t + 1].
5. Update of Parameter Combination: Substitute a part of R\({}_{1}\)[t + 1] into T[t] to form a new hyperparameter combination, T[t + 1].
6. Termination Condition Assessment: Evaluate whether the current candidate combination R[t] meets the predetermined termination criteria. If not, revert to Step 2 for further iteration; if satisfied, conclude the search.  
Figure 6: Pairwise relationships for \(T_{\text{eff}}\) (from LAMOST DR8), mass (from [PERSON] et al. [51]), and age (from SDSS DR17) of red giant stars.  
The Bayesian optimizer was initialized with parameters init_points = 15 and n_iter = 50, indicating that for each hyperparameter adjustment, TabNet undergoes 50 training epochs with a maximum of 65 iterations. After optimizing the hyperparameter space with the Bayesian optimizer, we identified four key parameters: N_d, N_a, N_steps, and LR, with their optimal settings presented in Table 2. Additionally, the table includes several other significant hyperparameters, which, although not derived through the Bayesian optimizer, also impact the model's performance.
# Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks
## 3 Improved Road Segment-Based Geographical Routing Protocol  
The ISR protocol uses segments to perform the information dissemination-centric routing in the urban VANETs environment. The proposed routing protocol is based on routing parameters including vehicular node position information, direction, traffic density, and link quality between the communicating nodes in the network. ISR divides the road map into segments of road and initiates routing via the physical location information of the head nodes in each segment. The routing decision depends on the segment information with a look-ahead of the next segment information. The next segment that is selected has a high traffic density. The main aim of ISR is to improve the success rate of the routing protocol and its dynamic assessment regarding the multi hop forwarding abilities and road segment formation, along with the connectivity for the vehicular network. The design and development of the routing framework is detailed below. The notations used in the routing framework design and development are precisely described in Table 2.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Dynamic Drone Deployment and Recall  
#### 4.2.1 Dynamic Deployment  
After the last drone to be dispatched has reached its destination and turned on its wireless access point, if the stress value (cf. Section 3.2, Equation (3)) of any node in the MANET exceeds a given threshold value (which, in our case, was set to 1.5 by default), then the release of an additional drone is triggered, which will head to a location in the vicinity of the unit experiencing the highest stress level. Once a drone is deployed, it will travel to its designated coordinates before turning itself into a network node and becoming an active member of the MANET.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)
#### 4.4.1 Unique Features of UAV  
UAV communications demonstrate the following significant characteristics as a prospective option to replace or supplement terrestrial cellular networks [153; 154]:  
1. LoS connections: UAVs flying in space without human pilots have a greater chance of connecting to ground users via LoS connections, which enables very reliable communications over long distances. Furthermore, UAVs can change their hovering places to preserve communication quality.
2. The capability of dynamic deployment: In comparison to the ground station's infrastructure, UAVs can be distributed dynamically based on real-time requirements,  
Figure 13: The General Scenario of UAV communication.  
making them more resistant to changes in the environment. Furthermore, UAVs as aerial BSs do not need the expense of site rental, eliminating the necessity for cables and towers.
3. Swarm networks based on UAVs: A swarm of UAVs can establish scalable multi-UAV networks and provide ubiquitous connections to ground users. A multi-UAV network is a good choice for quickly restoring and expanding connectivity because it has a high degree of flexibility and speed of service.
# Limitations of Using DAQ Cards for Absolute Vibration Measurement in Hydraulic Machines
## 3 Vibration Measurement--Measuring System  
The vibration measurement system can be divided functionally into three main elements:  
* sensor;
* signal conditioning;
* measurement, analysis recording, and visualization system.  
The block diagram of the vibration measurement system is shown in Figure 1.  
The sensor converts mechanical movement into an electrical signal. Piezoelectric accelerometers are usually used for measuring absolute vibrations, and the output signal is proportional to the vibration accelerations. Sometimes, vibration velocity transducers are also used. The electrical output signal can take various forms. It can be, for example, a charge signal or a signal in the IPE standard.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zone Boundary** & \multicolumn{3}{c}{**Bearing Housing Vibration v\({}_{\text{rms}}\) (mm/s)**} \\ \hline  & **Turbine** & **Generator DE** & **Generator NDE** \\ \hline  & \multicolumn{3}{c}{[PERSON] vertical} \\ \hline Action limit 1 (A–B/C) & 0.9 & 0.5 & 0.5 \\ Action limit 2 (C/D) & 1.4 & 0.8 & 0.8 \\ \hline  & \multicolumn{3}{c}{[PERSON] vertical} \\ \hline Action limit 1 (A–B/C) & 1.1 & 0.6 & 0.7 \\ Action limit 2 (C/D) & 1.8 & 1.0 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ISO 20816-5, zone boundaries for selected machines of group 3.  
Figure 1: Functional diagram of vibration measurement system.  
In the strict sense, the signal conditioning system ensures proper cooperation between the vibration sensor/transducer and the measurement system. Typical tasks of the conditioning system include powering the vibration sensor/transducer, matching signal levels, and filtering.  
Currently, a computer measurement system usually creates the measurement, recording, analysis, and visualization system. This term should also be understood as autonomous measuring devices built on the basis of microcontrollers. A common feature of computer measurement systems is the presence of an analog-to-digital converter in them. It does not matter whether it is in the form of a DAQ card or meter, or if it is built into the measurement transducer itself. An important role in this configuration is played by the software, which, on the one hand, controls the operation of the system, on the other, performs the appropriate analysis, and on the third, is responsible for deriving the measurement result, analysis, and interaction with the user.  
It is worth noting that some tasks have been transferred to the last of the elements discussed. Unlike before, today, some tasks such as signal integration or filtration (excluding anti-aliasing filters) can be performed by appropriate digital processing of the sampled signal.
## 2 Mathematical Formulation
### Minimizing \(Co_{2}\) Emissions (FO\({}_{3}\))  
The third objective function (\(FO_{3}\)) aims to reduce the MG's \(CO_{2}\) emissions associated with energy production by conventional generators. This is illustrated in Equation (3). Unlike the economic objectives, emissions are not monetized in the objective function. Instead, they are used as an environmental performance indicator to support decision-making, especially in environmentally sensitive areas like the city of Medellin, Colombia. This approach allows operators to evaluate trade-offs between economic, technical, and environmental goals based on changing priorities without assigning a fixed monetary value to emissions.  
\[FO_{3}=\min E_{CO_{2}}=\min\left(\sum\limits_{h\in\Omega_{R_{l}}}\sum\limits_{ i\in\Omega_{N}}CE_{l}P_{i,h}^{s}\Delta h\right) \tag{3}\]  
Most of the terms in this function have been explained in Equation (1), with the exception of \(CE_{i}\), which represents the emissions associated with the conventional generator installed at node \(i\). It is important to note that this function does not account for emissions from the WTs, as this technology, similar to PVs, does not produce emissions related to energy production [36, 37].
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 4 Results and Analysis of De-Icing Tests
### Anti-icing Tests
#### 4.1.1 Critical Anti-Icing Temperature
In addition, some ice layers generated by icing will cover the heating system surface, resulting in a reduced heat transfer surface and a slight increase in the temperature rise rate. After 10 min, as the temperature of the heating system surface rises, a large number of cold raindrops will take away the heat, resulting in a lower heating rate. In the five groups of anti-icing tests, the icing and melting phenomena on the surface of the heating system and temperature rise rules both showed relative consistency. At the time of 3 min, the surface of the heating system began to ice and saturation was reached at the time of 10 min. Therefore, this paper chose the temperature at the time of 10 min as the critical anti-icing temperature. The average temperature value at the time of 10 min in the five sets of tests was taken as the critical anti-icing temperature under a \(-15\)\({}^{\circ}\)C environment; this temperature was 1.12 \({}^{\circ}\)C.
## 4 Experiments and Analysis
### Performance Evaluation of Emotion Encoding Network  
As shown in Step 2 in Figure 1, the transformer-based emotion encoding network is utilized for emotion recognition experiments on audio from the IEMOCAP dataset and synthesized audio. The experiments covered both raw audio and audio that underwent specific augmentation processing, aiming to comprehensively assess the model's performance. Considering the specific application context of this research, which is students' English spoken language emotion evaluation, only spoken audio and their corresponding text were used as input data, and no image information was introduced. Furthermore, following the established method mentioned in the previous section, the 6-class problem of the dataset was adjusted to a 4-class problem to simplify the classification task. The experimental dataset includes 35,248 speech recordings. Detailed experimental results are as shown in Figure 4.  
In this research, the emotion recognition method proposed achieved an unweighted accuracy (UA) of 66.6%, surpassing the DialogueCRN algorithm for multimodal emotion recognition, which includes video, audio, and text, with an accuracy of 66.2% [30]. The F1 score also reached 64.1%, ensuring that the algorithm has robustness to accommodate various differences in student speech tones, with a focus on accuracy in spoken language emotion expression. The research method utilized only audio and text as inputs, relying solely on speech emotion features for feature extraction, achieving accuracy comparable to multimodal emotion recognition that includes video. This demonstrates the clear research significance of spoken language emotion recognition in the context of speech environments. The research objective in this paper was to extract emotion features for subsequent score calculation. The experimental results prove that the emotion encoding network excels in extracting audio emotion features and exhibits good robustness under data augmentation conditions.
# Morphometric Analysis and Three-Dimensional Computed Tomography Reconstruction of Thai Distal Femur
###### Abstract  
This study evaluates the distal femur morphology of the Thai population using a three-dimensional (3D) measurement method, measuring the distance between the triangular point of the femoral 3D model. The 3D model of 360 Thai femoral obtained from 180 volunteers (90 males, 90 females; range 20-50 years, average 32.8 years) was created using reverse engineering techniques from computed tomography imaging data. Using the 3D identified landmark method, the morphometric parameters evaluated included transepicondylar axis length (TEA), mediolateral length (ML), anteroposterior width (AP), medial anteroposterior width (MAP), lateral anteroposterior width (LAP), medial condyle width (MCW), lateral condyle width (LCW), intercondylar notch width (WIN), intercondylar notch depth (DIN), medial posterior condyle height (MPC), lateral posterior condyle height (LPC), femoral aspect ratio (ML/AP), lateral femoral aspect ratio (ML/LAP), and medial femoral aspect ratio (ML/MAP). The measured data were summarized for the analysis of an average value and standard deviation. Statistical analysis was performed using the independent samples \(t\)-test, unequal variances \(t\)-test, and linear regression. A \(p\)-value less than 0.05 (<0.05) was regarded as statistically significant and indicates strong evidence of the hypothesis. Additionally, the K-means clustering analysis of Thai distal femoral to the optimum size of the prosthesis with the correlation between ML length and AP width was performed. The results found that the morphometric parameters of the Thai male distal femur were significantly different and higher than those of Thai females, except for the ratio of ML/AP and ML/MAP. Comparatively, there was a significant difference between the specific size of Thai distal femur and that of the Korean population, which was also smaller than that for Caucasians. In addition, there was a mismatch between the distal femoral component sizing of knee prosthesis and what is available and commonly used in Thailand. At least six sizes of ML and/or AP should be recommended for the reasonable design of distal femoral prosthesis for covering the anatomy of Thais. These data are useful for predicting the morphometric parameters in forensic anthropology and provide basic data for the design of knee prostheses suitable for the Thai population.  
morphometric; distal femur; Thai; computed tomography; reverse engineering
# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 5 Results and Evaluation
### Quantitative Evaluation on Target Humanoid Robots  
In this experiment, we evaluate the overall performance of each target robot using the seven-stage regression-based hourglass network, identified as the optimal model. The pose estimation accuracies for each target robot, based on training with an average of only 347 images, are detailed in Tables 16 and 17.  
The results indicate that \(H1\) robot achieves the highest accuracy, while _FIGURE01_ robot shows the lowest. The poor performance of _FIGURE01_ is attributed to its metallic and shiny surfaces, which cause detection issues due to reflections and environmental interference. For joint detection, accuracy is generally higher for head- and torso-related joints (e.g., nose, neck, shoulders, and hips) compared to arm-related joints (e.g., elbows  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Input Size** & **Backbone** & **Parameters** & **GFLOPs** & **Inference (FPS)** \\ \hline RoboCup (NimbRo-Net2) [9] & 384 & ResNet18 & 12.8 M & 28.0 & 48 \\ Ours (4-stage) & 320 & Hourglass & 13.0 M & 48.66 & 63.64 \\ Ours (\(\gamma\)-stage) & 320 & Hourglass & 22.4 M & 78.98 & 42.81 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Details of the network architectures for the evaluated methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **mPCK** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline Human+Robot Telepresence [10] & 85.10 & 99.99 & 99.98 & 98.47 & 80.89 & 46.19 \\ Ours (\(\gamma\)-stage) & **85.76** & **100.0** & **100.0** & **98.73** & **82.56** & **47.52** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Performance Evaluation on on the HRP validation set [10]. Our method is evaluated using the Percentage of Correct Keypoints (PCK) as the evaluation metric. The best results are highlighted in bold.  
and wrists). The lower accuracy for arm joints is likely due to greater variability in their appearance, such as lack of consistent colors or shapes.  
These findings highlight that the effectiveness of full-body humanoid robot pose estimation is significantly influenced by the material composition of the robot's body covering.