esa-sceva/satcom-chunk-collection huggingface dataset:
# Neighborhood Granule Classifiers
## 4 Design of a Neighborhood Granule Classifier
### Granulation and Classification of Neighborhood Granules  
The neighborhood granule classifier has only the granulation and the classification steps, and it has no training process. The granulation process includes: data preprocessing, partitioning of a training set and a test set, the training set granulated into a granular rule base, and the test set granulated into a set of test granules. The classification process includes: searching and matching in the granular rule base for test granules and determining the labels of test granules. The detailed granulation and classification processes are presented as follows:  
1. Data preprocessing: deleting the data with missing values and normalizing the data with the interval 0 1.
2. Divided with a training set and a test set: 80% is the training set, and 20% is the test set.
3. The neighborhood granulation of the training set: a granular rule base is constructed according to the Euclidean distance and neighborhood parameters.
4. The neighborhood granulation of the test set: for a test sample, computing the Euclidean distance between the test sample and each training sample to form a test granule, and all the test samples are granulated into the test granular set.
5. Searching and matching for the test granule: setting the value of a similarity parameter, for the test granule, computing the similarity between the test granule and the condition granule of a rule in the granular rule base, getting all the granular rules whose similarities are larger than the similarity threshold, and collecting the decision granules of matched granular rules to form a set of decision granules. Note:the elements of a decision granular set may be identical.
6. Judging the label of the test granule: The label of the test granule is the most numerous granule label in the decision granule set.  
From the above analysis, it can be known that the neighborhood granule classifier is similar to the KNN classifier, and it is a lazy learning algorithm.
## 2 Evolution of AI in GPR Interpretation
### Hybrid and Physics-Informed Approaches  
Hybrid models represent the next frontier in AI-driven GPR interpretation. These approaches combine the strengths of data-driven learning with domain-specific knowledge, such as electromagnetic theory and subsurface physics [28; 29; 30]. Figure 3 shows the hybrid pipeline combining physics-based simulation with deep learning.  
Physics-informed neural networks (PINNs) and model-guided DL frameworks are gaining traction. For example, instead of learning subsurface features purely from data, models are constrained by known relationships, such as Maxwell's equations or soil permittivity profiles. This improves generalization and enhances trust in the model's output, especially when deployed in unfamiliar terrains [31; 32].  
Another strand of the hybrid approach involves data fusion, combining GPR with complementary sensing technologies such as LiDAR, InSAR, or optical imaging. Multimodal fusion helps overcome the limitations of individual sensors and enhances the accuracy of detection and classification tasks. For example, combining InSAR deformation data with GPR time slices has been proposed for the structural health monitoring of bridges and tunnels.  
Ensemble methods that combine multiple AI models (e.g., SVM + CNN) or fuse hand-crafted and learned features have also shown promise, particularly in handling imbalanced or noisy datasets.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles  
Textiles can be given conductive features by using three types of materials: (a) conductive polymers, (b) conductive metals, and (c) conductive non-metals. These are explored in detail in the following subsections.
## 3 Results and Discussion
### Compression Tests
#### 3.2.1 Unreinforced Pieces  
Figure 4a shows an idealized strength-strain graph of sawn wood elements subjected to compression testing [70; 71; 72]. In this graph, four phases can be clearly differentiated between the start of testing and breakage. Phase (1) is associated with an elastic and linear phase in which the modulus of elasticity of all of the pieces is very similar. Phase (2) is associated with a non-linear phase triggered by the formation of an incipient cutting plane of the fiber until the maximum test strength is reached (\(\sigma_{\text{max}}\)). In phase (3), the cutting plane of the fibers experiences a significant rapid drop in strength levels. In phase (4), the development of the cutting plane is now stable and the strength levels remain at the same value until breakage occurs.  
Figure 3: Strength–strain graphs for all of the pieces subjected to compression testing.  
Figure 4b shows the strength-strain graphs for the unreinforced pieces of wood subjected to compression testing. It can be concluded that all of the pieces tested demonstrate very similar behavior and perfectly reflect the idealized graph of Figure 4a. The two specimens classified as Rejects in the visual classification, due to drying cracks, show no significant variation compared to those classified as C-22, which indicates that the drying cracks do not represent a defect that determines the behavior of the wood on compression [73].  
Usually, the term ductility refers to the ability of a material to undergo plastic deformation before breaking. For all materials in general, and for wood in particular, there is no single definition for this term [74; 75]. However, all of the technical and scientific literature is in agreement with regard to the beneficial effects of ductility on structures. Basically, all of the definitions of ductility can be grouped as one of two types--those based on the strains achieved up to breaking point and those based on strain energy densities [76]. Both of these types of ductility measurements have been used in this study, using for this purpose the values associated with breakage and the maximum load. Almost identical results were obtained for both procedures, very close to 12, which indicates the extraordinary ductility of these pieces when they are subjected to compression testing.
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 5 Results and Discussion
### Edge Monitoring
The camera successfully recorded high-quality video footage of bike trips and critical events leading up to accidents, ensuring comprehensive data collection for later analysis. The software program running on the edge computing unit was thoroughly tested through unit, integration, and performance testing. The program demonstrated reliable data processing and communication with the MQTT server, effectively sending real-time warnings to the mobile application. The mobile application for warnings and accident notifications was extensively tested, ensuring effective connection and message publishing to the MQTT broker. It successfully received and displayed warnings, triggered impact warnings when required, and promptly initiated emergency procedures. Testing included simulations of different emergency scenarios to verify the functionality of emergency contact retrieval and SMS sending. In conclusion, the edge monitoring system passed comprehensive testing for both hardware and software components, ensuring its ability to monitor the bike's environment, detect risky instances, and issue real-time warnings.
## 2 Numerical Methods and Target Model
### Vibro-Acoustics Model
#### 2.3.1 Finite Element Method
At the fluid-structure interface, the following relation should be satisfied:  
\[n_{f}\cdot\ddot{u}_{f}+n_{s}\cdot\ddot{u}_{s}=0. \tag{33}\]  
Equation (24) then becomes  
\[\Big{[}M_{f}\Big{]}\big{\{}\ddot{p}\big{\}}+\Big{[}B_{f}\Big{]}\big{\{}\dot{p} \big{\}}+\Big{[}K_{f}\Big{]}\big{\{}p\big{\}}-\Big{[}A^{T}\Big{]}\big{\{}\ddot{u }_{s}\big{\}}=\Big{\{}P_{f}\Big{\}}. \tag{34}\]  
The combined fluid-structure equations then become  
\[ \begin{bmatrix}M_{s}&0\\ -A^{T}&M_{f}\end{bmatrix} \bigg{\{}\ddot{u}_{s}\bigg{\}}+ \begin{bmatrix}B_{s}&0 \\ 0&B_{f}\end{bmatrix} \bigg{\{}\dot{u}_{s}\bigg{\}}+ \begin{bmatrix}K_{s}&0\\ 0&K_{f}\end{bmatrix} \bigg{\{} \begin{matrix}u_{s}\\ p\end{matrix} =\bigg{\{}P_{s}\bigg{\}}. \tag{35}\]  
The FEM was numerically realized using the commercial software Simcenter3D (version 20.0).
## 4 Experiments
### Experiment Analysis
#### 4.3.1 Ablation Study  
In order to study the effect of HGNN-based feature-semantic interaction and attentional factorization machine, we compare model variants of HGAFMN. HGAFMN-HG is HGAFMN without the HGNN-based feature-semantic interaction module. HGAFMN  
Figure 8: The loss plot, accuracy plot, and AUROC plot of the proposed HGAFMN. (**a**) loss plot, (**b**) accuracy plot, (**c**) AUROC plot.  
AFM is HGAFMN without the attentional factorization machine, and HGAFMN-HA is HGAFMN without the HGNN-based feature-semantic interaction module nor the attentional factorization machine. The experimental results are shown in Figure 9. The prediction performance of HGAFMN is significantly better than the model variant on both the F1-score and AUROC.  
From Figure 9, we can see that the model HGAFMN-HG without the HGNN-based feature-semantic interaction module, the model HGAFMN-AFM without the attentional factorization machine, and the model HGAFMN-HA are worse than HGAFMN. The differences between the structure graphs of different molecules are subtle and complex, and the structural complexity of different molecules varies greatly. The HGNN-based feature-semantic interaction of the molecule-odor descriptor can play an important role in olfactory perception prediction. In addition, the deep neural network with attentional factorization machine considers the importance of each feature interaction, and can learn both high-order combination features and low-order combination features, which improves the performance of olfactory perception prediction. It shows that HGNN-based feature-semantic interaction and attentional factorization machines are effective at improving the prediction accuracy of olfactory perception.
#### 1.1.1 Design and Shape  
Based on design and shape, greenhouses can be categorized into four models, illustrated in Figure 2. The explanations of these models are listed as follows:  
* _Gable roof greenhouses_: These have a traditional peaked roof, resembling a house. Gable roof greenhouses have a classic triangular roof shape with ridge vents for superior ventilation and good structural integrity. This design works exceptionally well in colder locations because of the high roof, which makes it easy for snow to slide off and avoid building up. Furthermore, because of their adequate water drainage, gable roof greenhouses are perfect for locations with a lot of rainfall. They can also endure strong winds, which makes them a reliable option for areas with bad weather.
* _Quonset (hoop) greenhouses_: These have a rounded or curved roof made from metal or plastic pipes. Quonset or hoop greenhouses, renowned for their ease of use and low-cost construction, have a semi-circular or hoop-shaped roof. Because of their compact size, these greenhouses work well in urban and suburban settings and are appropriate for moderate climates without harsh weather. Because they are simple to install and flexible, they are also perfect for short-term or seasonal use. They are a well-liked option for producers on a tight budget because of their affordability.
* _Gothic arch greenhouses_: These have a pointed or gothic-style roof design, providing better snow-shedding capabilities. Compared to Quonset designs, gothic arch greenhouses have better snow shedding due to their pointed arch roof shape. This greenhouse style blends classic design elements with practicality, making it visually pleasing. It has superior wind resistance compared to Quonset designs, making it appropriate for cold locations where snow accumulation is an issue. Growers who respect performance and aesthetics and live in areas with moderate wind loads should consider gothic arch greenhouses.
* _Sawtooth greenhouses_: These have a multi-ridged roof resembling a saw blade, with varying heights to optimize light distribution. Because of their open sawtooth structure and asymmetrical roof with one side taller than the other, sawtooth greenhouses provide outstanding natural ventilation. This design works well in hot, humid conditions like tropical and subtropical regions, where controlling indoor temperature and humidity is essential. Because sawtooth greenhouses may be angled to improve ventilation by taking advantage of prevailing breezes, they are also appropriate for areas with changeable wind directions. Therefore, they are perfect for farmers who want effective temperature control without relying too heavily on mechanical equipment.  
Figure 1: Different types of greenhouses [1,2].
# Status of BIPV and BAPV System for Less Energy-Hungry Building in India--A Review
## 6 Conclusions  
The following conclusion can be drawn from this review article:  
* Building integrated photovoltaic (BIPV) replaces traditional building envelop, such as window, wall, roof and most often they are thin film, or third-generation based transparent or semi-transparent in nature.
* Building attached/applied photovoltaic (BAPV) indicates when PV systems are attached to a building without replacing its traditional envelops.
* India's solar mission which geared up from 2015, accelerated the rooftop PV integration. For building, the rooftop application's majority are BAPV types and capacity higher than kW level. India's total installed solar capacity reached only 33.8 GW by the end of September 2019, which is way behind to achieve India's 40 GW rooftop PV power generation by 2022.
* New technologies, such as PV tiles, foil and windows, as part of BIPV or BPAV, are not popular in India.  
Figure 9: Schematic of a semitransparent BIPV window (**left**) and Sankey diagram while BIPV window is integrated into a building (**right**).  
* Indian electricity market needs a complete reform to allow smooth penetration of BIPV/BAPV in building. Also, due to rapid urbanization and development of zero and sustainable building industry, BIPV will keep pace in India soon.
* Single and double glass BIPV window systems have been identified as one of the most potential candidates for India.  
Conceptualization, A.G.; methodology, A.G.; investigation, P.R., M.V.N.S.G., A.K., S.N., A.G.; resources, P.R., S.N., A.G.; writing--original draft preparation, P.R., A.G.; writing--review and editing, S.N., A.G.; supervision, A.G.; project administration, A.G.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
This work didn't receive any specific grants.  
The authors would like to thank the anonymous reviewers for their valuable comments which were useful to enhance the quality of paper.  
The authors declare no conflict of interest.
# Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
## 1 Introduction
This avoids the need to force rotations or attitudes into a linear space, thereby preventing the introduction of approximation errors and enhancing overall prediction accuracy. 3. Reduction of Cumulative Approximation Errors: The proposed method addresses and overcomes the limitations of conventional DR methods that suffer from cumulative approximation errors in integration operations. These errors are typically caused by inaccuracies in linear and angular velocity measurements, motion prediction time intervals, and computational limitations. By performing accurate computations in nonlinear spaces, the proposed method effectively reduces these cumulative errors, enhancing the overall estimation accuracy. 4. Application in Pose Estimation Algorithms: The method can be seamlessly integrated as a process model in pose estimation algorithms, such as the KF. It provides highly accurate predictions of the current state during the prediction phase, thereby reducing estimation uncertainty and improving the robustness and reliability of the estimation process. The significance of the results can be highlighted as follows:  
1. The results demonstrate a substantial improvement in the accuracy and reliability of vehicle pose prediction. 2. The proposed method's ability to mitigate cumulative errors and operate effectively in nonlinear spaces highlights its potential for practical applications in autonomous driving and other related fields. 3. The findings suggest that adopting this method can lead to more precise and dependable navigation systems, ultimately contributing to the advancement of autonomous vehicle technologies. 4. This novel approach not only enhances the theoretical foundation of vehicle pose estimation but also provides a practical solution that can be directly implemented in real-world autonomous systems. 5. The method offers significant benefits in terms of performance and reliability, making it a valuable contribution to the field of autonomous driving. In Section 2, we provide an overview of the previous DR methods commonly used and discuss their limitations. Section 3 presents an overview of the theoretical background, Lie theory, and describes the Lie theory-based DR method. Section 4 illustrates how the proposed method outperforms previously used methods using experimental results obtained from real-world data. Finally, in Section 5, we summarize the research findings and present future research directions.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 2 System Model
### Communication Signal Model
Assume that \(s_{k,n}(t)\) has unit power, then the received SNR for the \(k\)-th vehicle at the \(n\)-th slot is expressed as  
\[\mathrm{SNR}_{k,n}=\frac{P_{k,n}\alpha_{k,n}\left|\mathbf{w}_{k,n}^{\mathrm{ H}}\mathbf{H}_{k,n}\mathbf{f}_{kn}\right|^{2}}{\rho_{C}^{2}} \tag{20}\]  
Correspondingly, the sum rate of \(K\) vehicles is given by  
\[\mathrm{R}_{n}=\sum\limits_{k=1}^{K}\mathrm{log}_{2}(1+\mathrm{SNR}_{k,n}) \tag{21}\]
# Forecasting Multivariate Time Series
### Data Cleaning
#### 2.4.5 Multiple Imputation  
Multiple imputations (MIs) offer a comprehensive approach to handling missing values by utilizing the complete set of features to approximate the missing values. MI generates multiple datasets by imputing missing values, allowing for the replacement of missing values with multiple possible values. These imputed values are estimated from each dataset and then aggregated into a final dataset. MI is applicable in cases where the data are missing completely at random (MCAR), when they are missing at random (MAR), and even when the data are missing not at random (MNAR).  
Iterative imputation, on the other hand, involves modeling each feature as a function of the other features in a sequential process. Past imputed values are used to predict succeeding features until all missing values are filled. Pretimize utilizes the IterativeImputer library in Python, which offers various estimators such as the Bayesian Ridge regressor, random forest regressor, K-neighbors regressor, decision tree regressor, and Extra Trees regressor.  
Additionally, the KNNImputer technique, provided by the same Python library, employs the k-nearest neighbors approach to impute missing values by considering values of observations for neighboring data points.  
DataWig, another method used for imputation, has shown effectiveness in the literature for imputing missing values. DataWig [40] is a deep learning library that leverages _MXNet_ as a backend to generate predictions for missing data.  
Furthermore, the hot deck [41] method, known for its computational efficiency, is utilized for imputation. This method imputes missing values by selecting a randomly similar record within a data matrix and using existing values from the donor object. The hot deck operates on one attribute at a time and serves as a single imputation technique.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 4 Proposed Observer and Control Structure
### Estimation of the Grid-Side Currents Using Luenberger Observer
Accordingly, the observer of (24) is replaced by  
In summary, first, a constant observer matrix \(L\) is designed, based on the system model of (15) in middle operating point \(P_{mid}\), and then it is used in the compensating term beside the exact nonlinear model of the system to complete the observer dynamic, as in (25). Figure 3 shows the block diagram of the overall system, including the controller, observer and the GFL-inverter power circuit. It can be seen that only the grid voltages and the DC-link voltage are measured, and using the proposed scheme, the estimated values of the grid currents (\(\hat{i_{dd}}\), \(\hat{i_{iq}}\)) are used as feedback signals in the control loop, instead of measuring (\(i_{dd}\), \(i_{iq}\)). In this structure, the three current sensors of the GFL-inverter are eliminated, and the terminal voltages description of the inverter will be as shown by (26):  
\[v_{d} =L_{f}K_{c}\Big{(}i_{d}^{ref}-\hat{i_{d}}\Big{)}+v_{gd}+R_{f}\hat{i_{d}}+ L_{f}w\hat{i_{q}} \tag{26}\] \[v_{q} =L_{f}K_{c}\Big{(}i_{q}^{ref}-\hat{i_{q}}\Big{)}+v_{gq}+R_{f}\hat{ i_{q}}-L_{f}w\hat{i_{d}}\]
## 5 Conclusions and Future Research  
### Conclusions  
TCPs represent a novel sustainable material for building envelopes because of their energy efficiency. TCPs with various VRs of OFs were constructed with LWM and NWM materials.  
Figure 16: Experimental VLT.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TCP Type** & \multicolumn{3}{c}{**NWM**} & \multicolumn{3}{c}{**LWM**} \\ \hline
**VR** & **1.5** & **3.0** & **6.0** & 1.5 & 3.0 & 6.0 \\ \hline Test 1 & 1.1 & 2.3 & 6.9 & 1.1 & 2.1 & 6.9 \\
2 & 1.2 & 2.3 & 7.0 & 1.0 & 2.1 & 6.9 \\
3 & 1.0 & 2.3 & 7.0 & 1.1 & 1.9 & 6.9 \\
4 & 1.1 & 2.3 & 6.9 & 1.0 & 2.0 & 6.9 \\
5 & 1.1 & 2.3 & 6.9 & 1.1 & 1.9 & 6.9 \\ Average & 1.1 & 2.3 & 6.9 & 1.1 & 2.0 & 6.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Visible light transmittance (VLT) of the TCPs (%).  
To understand their mechanical, thermal and light transmittance properties, tests were carried out. Some concluding remarks are given below:  
1. The mechanical properties of the NWM and LWM mortars determine the load-bearing capacity of the two materials. As a result of the proper mixing ratio of the materials, the compressive strength of the LWM was higher than that of the NWM, while the LWM was more brittle than the NWM.
2. The U-values of the plain panel were 4.25 and 5.45 W/(m\({}^{2}\) K) for TCPs with LWM and NWM, respectively. The existence of the OFs improved the thermal insulation property. The K-values of the LWM TCP were smaller than that of the common facade, which proved its excellent energy efficient performance. This potentiality of replacement of the traditional glass curtain wall system was predictable in the locations where the lighting requirement was not particularly high ([PERSON], 2020) [48].
3. The SHGCs (G-values) of the two tested TCP types--LWM and NWM--were 0.198 and 0.242, respectively.
4. The VLT tests showed that the light transmitted by the TCP was proportional to the density of the OFs in a matrix of concrete. The experimental light acceptance angle of the OF was measured to be 35 \({}^{\circ}\)C, which was close to the computational value shown by Equations (6) and (7).