esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Band Medium-Frequency R-Mode Signal Quality Estimation +
## 6 Conclusions  
The paper presents a straightforward approach to define and compute a signal quality indicator, namely, the carrier to noise density (\(C/N_{0}\)) ratio, to predict the ranging performance of the MF R-Mode aiding carriers on the receiver side. The indicator is relevant to perform weighting of the measurements on the positioning algorithm as well as to monitor the signals and support the activity of the service operator. Due to the presence of the MSK and the nature of the system, which is based on FDMA, the \(C/N_{0}\) is estimated locally in the vicinity of the aiding carriers by using the output of the FFT to reduce the computational load of the receiver. Thought not claiming to be an exhaustive analysis of all possible approaches to estimate the received signal quality, the usability of the estimated \(C/N_{0}\) as defined in Equation (8) is validated with Monte Carlo simulation as well as with real data. Only the estimation with one set of parameters is presented in this paper, \(f_{\mathrm{s}}=1\,\mathrm{MS}/\mathrm{s}\), \(N=1\,\mathrm{MS}\), and \(L_{B}=25\), and in the future other possible values could be explored in order to optimize the estimation, assessing their impact on the \(C/N_{0}\) estimate and ranging performance prediction. Additionally, the work will be extended to consider larger data sets and include a better characterization of the error sources.  
All authors contributed equally to the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article.  
The authors express their gratitude to the Federal Waterways and Shipping Administration (WSV) and the The Federal Maritime and Hydrographic Agency (BSH) for their technical support during the data colletion.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
The novelty of this paper lies in the proposed combined control strategy that integrates VSG control with a simplified CVB method for grid-connected MMC systems. The paper presents a comprehensive analysis of the effectiveness of the proposed control strategy, comparing it with conventional approaches of decoupled PQ control, VSG control, and combined CVB-PQ control. The results highlight the suitability of CVB-VSG for the control of MMC-based grid-connected systems, highlighting its potential benefits in minimizing circulating currents, reducing DC-side disturbances, balancing capacitor voltages, eliminating the need for extensive sensor deployment, and reducing computational requirements. The rest of the paper is organized as follows: Section 2 presents the modeling of the MMC and its operating principle. The proposed control is presented in Section 3. The simulation and experimental results are discussed in Section 4. Finally, conclusions are drawn in the Section 5.
## 4 Experimental Results and Discussion
### Comparison with State-of-the-Art
#### 4.4.2 Quantitative Comparison  
To the best of our knowledge, there is no commonly used evaluation metric for methods preventing self-intersection of triangular mesh. In order to compare our method with other state-of-the-art approaches quantitatively, we adopt the per vertex errors as 3D pose estimation metric and we used the percentage of vertices in region of self-intersection computed by our proposed algorithm to evaluate the performance of our method and other state-of-the-art approaches. It should be noted that it is inappropriate to evaluate these methods only by per vertex error or percentage of vertices in self-intersection because an ideal approach of avoiding self-intersection should achieve both minimum per vertex error and minimum percentage of vertices in self-intersection region. Therefore these two evaluation metrics, per vertex error and percentage of vertices in self-intersection, were adopted to conduct a quantitative comparison.  
The quantitative results of the baseline method, the other state-of-the-art approaches and our proposed method is shown in Table 1. The Laplacian regularization method achieved a lower percentage of vertices in self-intersection but a higher per vertex error compared to the baseline method, which demonstrates that the Laplacian regularization does work in avoiding self-intersection  
Figure 11: 3D pose estimation results by four different methods. First column: Input images. Second column: Results obtained by minimizing reprojection (RE) error of 2D joints only. Third column: Results obtained by minimizing the sum of reprojection error and Laplacian regularization (LR). Fourth column through fifth column: Results obtained by minimizing the sun of reprojection error and the intersection between spheres. Sphere Approximation (SA). Sixth column: Results obtained by our proposed approach.  
but the side effect leads to loss of precision in 3D pose estimation. The approach of approximating body shape by a set of spheres outperforms the baseline method both in per vertex error and percentage of vertices in self-intersection. It is undeniable that this method may perform better with more carefully designed spheres, but it will be extremely tedious to implement this method. Our proposed approach outperforms the baseline method and two state-of-the-art methods and avoids the tedious procedure required for the sphere approximation.
## 3 Description of the Systematic Impact/Aptitude Land Planning Model
### Diagnosis and Territorial Analysis  
This part was split into three subsections. Initially, the study area was categorized into environmental land units (ELUs) according to the physical inventory. Next, we analyzed plant endemism to determine the present phytobiological value (PPV) of each environmental land unit based on their current condition. Next, we determined the extended phytobiological value (EPV) index by considering the anticipated endemism in the research area and used it to assess land capacity (LC) for various proposed activities (PAs) within each environmental land unit (Figure 3).  
The model compares the original model created by [PERSON] et al. [29], which assesses land capacity via the present phytobiological value, with the new model that presents an additional index named the extended phytobiological value for evaluation, according to the presence of endemism. It is reasonable to focus on the existence of species that should be considered as critical factors for land management.
## 1 Introduction
For the purpose of simulation, Landsat satellite images obtained from the Google Earth engine (GEE) open data access platform are utilized and classified using the random forest (RF) algorithm to establish a comprehensive LULC inventory spanning a fifteen-year period for the Mersin Metropolitan area. This inventory facilitates a detailed examination of LULC transitions over time, allowing for the measurement of the trajectory of LULC classes in terms of both growth and contraction. Utilizing these observed LULC transitions up to the year 2022, future LULC scenarios are computed through an urban growth modeling approach, employing the logistic regression algorithm. Subsequently, these computed LULC scenarios are juxtaposed with the current urban land use suitability map to discern which prospective urban growth patterns are indeed conducive to urban settlement. The incorporation of the urban land use suitability map as an input into the urban growth simulation represents a novel approach that has not been extensively explored in the existing literature. In summary, this study (1) generates a GIS-AHP-based urban land use suitability map, (2) compiles LULC maps covering a fifteen-year period using Landsat imagery from four different time points (2007, 2012, 2017, and 2022) and analyzes related LULC transitions, (3) formulates LULC scenario maps for 2027 through employing urban growth simulation techniques, and (4) evaluates the suitability of future urban land use by overlaying the urban land use suitability map with the simulated LULC map of 2027.
## 4 Materials and Methods
### Pipeline  
As mentioned above, the pipeline is composed of the trained segmentation model, trained classification mode, and some utility functions to adjust the input and output of the models appropriately to produce our final inferred visual magnitude assessment result. Therefore, to perform a magnitude assessment inference, the pipeline begins with entering the before-disaster image into the segmentation model to find the buildings, passing the segmentation mask (location of the buildings) with the corresponding after-disaster image to the classification model, and finally using the results to display our visual representation (Figure 1).  
The building segmentation process uses the trained segmentation model at its core. The before-disaster satellite image is entered as input into the model. The model will then perform a pixel-wise segmentation on the image to identify all the pixels in the image that belong to buildings. Once this is established, the segmentation process will generate corresponding polygons that will bound the identified building pixels so that we have an accurate representation of where all the buildings are located. These polygons will then be used in conjunction with the after-disaster images as input into the classification model. We will use the polygons to locate the buildings in the after-disaster images, and the classification model will provide a damage magnitude label to the buildings. The final output will be the mask with the magnitude of damage to buildings classified by lighter to darker shades of gray as the magnitude of damage increases (Figure 1).  
The classification process uses the trained classification model at its core. The polygons from the segmentation process and the corresponding after-disaster images are passed into the process. The polygons are used to extract the sub images of each building from the after-disaster images. Each extracted sub-image is then fed into our trained classification model and a semantic label is inferred from each sub-image from the set of ground truth labels that exist in the dataset from the joint damage scale. The inferred semantic label and polygon are then stored in a file. The labels and polygons are then read from the file and pieced back together into a new image so that they can be color-coded by magnitude to provide an overall representation of the inferred magnitude of damage to all buildings within the image.
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology
### Training Results  
The decision boundary (or hyperplane) generated from the SVM classifier is shown in Figure 5. In this figure, the red zone represents the healthy class and the blue zone represents the unbalance class. Note, the healthy class and unbalance class are close to each other. However, the SVM is able to create a hyperplane that allows the separation of both classes. In the SVM classifier, a semicircular hyperplane is generated through the radial basis function kernel, which describes the non-linearity between the classes. Linear models can be applied to describe this non-linearity. However, a lower accuracy can be achieved using this kind of model. Figure 6 shows the decision boundary generated by two linear models (logistic regression and simple perceptron). From this figure, it can be observed that although the unbalance fault can be inferred, samples of both classes are misclassified, achieving a lower accuracy in comparison with the proposed VTA model.  
Figure 3: Subset of texture features used for determining the better separability between the unbalance class and the healthy class, (**a**) Homogeneity and contrast texture features, (**b**) Mean and energy texture features, (**c**) Contrast and entropy texture features, (**d**) Contrast and variance texture features.  
Figure 4: Variation of the horizontal displacement \(d\), (**a**) Horizontal displacement \(d=1\), (**b**) Horizontal displacement \(d=2\), (**c**) Horizontal displacement \(d=3\), (**d**) Horizontal displacement \(d=4\).  
Figure 5: Decision boundary generated for the SVM in the training-validation stage.
# One-Dimensional Computation Method of Supercritical CO\({}_{2}\) Labyrinth Seal
## 2 1-D Computation Method
### Computational Process  
#### 2.2.1 Isentropic Expansion Process  
The isentropic expansion process needs to follow two governing equations in Section 2.1.2 and satisfy the isentropic expansion thermodynamic process equation at the same time. During computation of the mass flow at the sealing teeth, an estimation formula of the flow discharge coefficient \(C\) needs to be provided based on experimental or numerical simulation results of different sealing structures and working fluids. It is defined as follows:  
\[C=\frac{\overline{u}}{u} \tag{8}\]  
In Equation (8), \(\overline{u}\) is the average axial velocity at tooth, m/s; \(u\) is the axial velocity at tooth calculated by isentropic process, m/s.  
The governing equations of the isentropic expansion process are as follows:  
\[\left\{\begin{array}{l}s(n-1)=s(n-0.5)\\ h_{0}(n-1)=h(n-0.5)+k(n-0.5)=h_{00}\\ \dot{m}(n-0.5)=C(n-0.5)*\rho(n-0.5)*u(n-0.5)*A(n-0.5)=\dot{m}_{00}\end{array}\right. \tag{9}\]  
where \(h_{00}\) is the initial total enthalpy; J/kg; \(s\) is the entropy, kJ/(kg\(\cdot\)K); \(A\) is the cross-sectional area of labyrinth seal, m\({}^{2}\);  
The isentropic expansion process requires iterative computation. The purpose of iteration is to adjust the pressure difference of the seal, so that the computed seal leakage is equal to an expected leakage. Moreover, the specific iteration process is as follows:  
1. The expected mass flow \(\dot{m}_{00}\) is provided for computation of labyrinth seal performance;
2. For the computation site \(n-0.5\), \(P\) (\(n-1\)) has been obtained, and an initial static pressure difference \(\delta P\) is provided. In this case, \(P\) (\(n-0.5\)) is determined.
3. \(s(n-0.5)\) is obtained from the governing equation \(s(n-1)=s(n-0.5)\). Using (\(P,s\)) to query the National Institute of Standards and Technology (NIST) reference fluid thermodynamic and transport properties database (REFPROP) [19], the thermodynamic states (\(T,h,p,\dots\)) at \(n-0.5\) can be obtained.
4. Based on the total enthalpy conservation, the unit kinetic energy \(k(n-0.5)\) at \(n-0.5\) is computed, and then the flow velocity at \(n-0.5\) is obtained. The mass flow \(\dot{m}(n-0.5)\) is computed and compared with the expected mass flow \(\dot{m}_{00}\). If \(\dot{m}(n-0.5)\) is smaller, the static pressure is increased by the pressure difference \(\delta P\); or if \(\dot{m}(n-0.5)\) is larger, the provided static is reduced by the pressure difference \(\delta P\).
5. Iteration is performed under the error between \(\dot{m}(n-0.5)\) and \(\dot{m}_{00}\) is within the iterative error \(\delta\dot{m}(\delta m=0.002\) kg/s in this study). End the iteration process and enter the computation process of the computational site \(n-0.5\) to \(n\).
## 5 Qualitative Analysis
### Design and Implementation of Digital Twin Software Solutions for Collaborative Robots
#### 5.2.2 Integration of Artificial Intelligence and Machine Learning Algorithms  
The integration of artificial intelligence (AI) and machine learning (ML) algorithms is a key aspect in harnessing the full potential of digital twin software solutions for collaborative robots. By incorporating AI and ML capabilities, organizations can enhance decision-making, adaptive control, and overall performance in collaborative robotic systems [93]. AI and ML algorithms enable collaborative robots to learn from data, adapt to changing conditions, and make intelligent decisions. These algorithms can analyze large datasets generated by the digital twin and the physical robot, extracting patterns, identifying anomalies, and deriving insights that can optimize robot behavior and performance [94; 42].  
One area where AI and ML algorithms are particularly impactful is in predictive analysis and maintenance [95]. By analyzing historical data and real-time sensor information, these algorithms can predict potential faults or failures in the robotic system, allowing for proactive maintenance and minimizing unplanned downtime. This predictive capability improves the overall reliability and efficiency of collaborative robots.  
The coalescence of AI and ML algorithms also enables advanced capabilities such as computer vision, natural language processing, and speech recognition in collaborative robotic systems [96]. These capabilities enhance human-robot interaction, allowing for intuitive and efficient communication between humans and robots. Collaborative robots can understand and interpret commands, recognize objects or obstacles in their environment, and perform complex tasks with higher accuracy and precision.  
To successfully integrate AI and ML algorithms, organizations need to consider the availability and quality of data, the selection and training of appropriate algorithms, and the computational resources required for running these algorithms. Data pre-processing, feature engineering, and model training are important steps in preparing the data for AI and ML algorithms. Organizations should also ensure that the algorithms are continually updated and refined as new data becomes available, enabling the collaborative robot to improve its performance over time.
# Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges
## 3 Industrial Metavere's Architecture, Roadmap, and Core Technologies Overview
### Artificial Intelligence  
For the Metaverse to function based on the creator's principles, AI is a necessary technology [16]. AI has shown how crucial massive data processing is to improving immersion and enabling virtual agents to have an intellect comparable to that of humans [19]. With key functions including content production, security, personalization, maintaining digital identities, immersion, real-time translation, and intelligent Non-Player Characters (NPC), AI has become a crucial element of the Metaverse. AI can assist in the creation of 3D models, textures, and animations, among other types of content for the Metaverse. By automating the process of building and constructing virtual worlds, AI-powered solutions can cut down on the time and expense needed to create content:  
* Security: The Metaverse presents a number of security risks, including fraud, identity theft, and cyberattacks. AI can monitor user behavior and identify any questionable activities, such as identity theft or malevolent actions.
* Personalization: AI systems are capable of analyzing user data to provide each user with a customized experience. An AI system, for instance, can be trained to learn user preferences for virtual apparel, virtual accessories, and virtual activities and then provide tailor-made suggestions.
* Creating and managing digital entities: AI is utilized in the Metaverse to generate and manage diverse digital creatures, including chatbots, virtual assistants, and NPCs. These entities can interact with users, offering tailored experiences according to their tastes and actions.
* Immersion: Since AI makes realistic physics, lighting, and sound effects possible, it can aid in the creation of more immersive virtual environments. AI systems, for instance, can mimic the behavior of fire, water, and other natural elements, adding realism to the virtual world.
* Real-time translation: AI has the potential to facilitate real-time language translation in the Metaverse, facilitating international collaboration and communication. This could result in the ability of AI to translate spoken languages in the Metaverse in real time, facilitating international collaboration and communication. This has the potential for the establishment of an entirely worldwide virtual community.
* Intelligent NPCs: NPCs are virtual characters that can communicate with players in the virtual world and are managed by the AI of the game. AI algorithms can make it possible for NPCs to comprehend common language and respond appropriately, adding realism and interest to interactions [20].
## 3 Case Study
These design solutions are non-dominated with each other, making it difficult to distinguish the best one. The designer selects the optimum design, based on the client's preferences and specific requirements. Of the solutions, although the first one was non-dominated with the initial design, it was not further considered because of its larger LCE. The fifth design in Table 3 was selected as the optimum solution for the process that follows. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Decision Variable**} & \multicolumn{1}{c}{**Content**} & \begin{tabular}{c} **Unit Cost** \\ **(CNY/mq)** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Intensity (MJ/mq)** \\ \end{tabular} \\ \hline Window-to-wall ratio & \(\gamma\) & (0, 0.9) & & \\ \hline \multirow{4}{*}{Glazing type} & G1 & \begin{tabular}{c} 6 mm clear glass +12 mm argon gap \\ +6 mm clear glass \\ \end{tabular} & 400 & 1345.84 \\ \cline{2-5}  & G2 & \begin{tabular}{c} 6 mm low-e glass +12 mm argon gap \\ +6 mm clear glass \\ \end{tabular} & 700 & 1937.60 \\ \cline{2-5}  & G3 & \begin{tabular}{c} 6 mm tint glass +6 mm argon gap +6 mm \\ clear glass \\ \end{tabular} & 500 & 1549.60 \\ \hline \multirow{4}{*}{Wall type} & W1 & \begin{tabular}{c} 300 mm reinforced concrete wall +50 mm \\ Polystyrene (EPS) board +10 mm cement \\ and sand (1:3) screed +10 mm plaster \\ \end{tabular} & 143.67 & 564.50 \\ \cline{2-5}  & W2 & \
\begin{tabular}{c} 300 mm aerated concrete block wall \\ +50 mm EPS board +10 mm cement and \\ sand (1:3) screed +10 mm plaster \\ \end{tabular} & 125 & 370.40 \\ \cline{2-5}  & W3 &
\begin{tabular}{c} 300 mm lightweight concrete block wall \\ +50 mm EPS board +10 mm cement and \\ sand (1:3) screed +10 mm plaster \\ \end{tabular} & 114.68 & 370.70 \\ \hline \multirow{4}{*}{Artificial light type} & L1 & T8 fluorescent light, 30W & 50 & \\ \cline{2-5}  & L2 & T5 fluorescent light, 14W & 38 & \\ \cline{1-1} \cline{2-5}  & L3 & T8 LED light, 18W & 65 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information of decision variables. As introduced in Section 2.2, the LCEs and LCCs of the fifth optimal design were estimated, using the 3 lighting control modes. The annual energy consumption of the fifth design with ALS can be calculated using Ecotect Analysis. Considering that the MLS had high randomness, and Ecotect cannot provide precise values based on the ALD, the values for these 2 modes were determined based off statistical results from experiments and related studies [44,45]. Table 4 provides the performances of the 3 modes. When comparing the performances of the 3 lighting control modes, ALS had the lowest LCC and LCE. Consequently, the ultimate optimal design scheme was the fifth design with the automatic light switching with occupancy sensor.