esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fuzzy Kernel Nearest Feature Line Embedding (FKNFLE)  
According to the analyses above, a training DR scheme effectively extracts the discriminant features from the non-Euclidean and non-linear space. To this end, fuzzy kernel nearest feature line embedding (FKNFLE) is proposed for HSI classification. The idea of FKNFLE is to incorporate the fuzziness and kernelization into the manifold learning method. The kernel function not only generates a non-linear feature space for discriminant analysis, but also increases the robustness to noise during the training phase. Manifold learning methods preserve the local structure of samples in the Hilbert space. On the other hand, the fuzzy kernel nearest neighbor method extracts the non-Euclidean structures of training samples to enhance discriminative capability. NFLE has been successfully applied in HSI classification. Noise variations and high-degree non-linear data distributions limit the performance of manifold learning. A kernel trick is used to alleviate this problem as introduced in the following.
# WhistleGAN for Biomimetic Underwater Acoustic Covert Communication
## 2 Proposed Method  
### Dolphin's Call Signal  
The dolphin sounds used in in the biomimetic UWA covert communication can be classified into clicks and whistles. Figure 1 depicts an example of a real dolphin's call signal in which the click and whistle sounds are mixed. In Figure 1, the click sounds have a short period in time with a broad frequency band and the whistle sounds have a relatively longer time and various shapes in time frequency domain.  
In general, the clicks are used to localize, detect, and identify objects at close range, while the whistles are used for dolphin's social communication. The clicks are similar to pulse signals, and they have the disadvantage of having a short communication range. On the other hand, whistles have a relatively long communication range because of their long duration and variable bandwidth compared to clicks. Thus, the whistles have been mainly used in biomimetic UWA covert communication [8; 9; 10; 11; 12; 13; 14; 15; 16; 17].
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Sustainability and Environmental Planning
#### 2.6.3 Electric Vehicle Planning  
EVs are pivotal in steering modern power distribution systems towards reduced carbon emissions, supporting global sustainability initiatives. Their integration, particularly through vehicle-to-grid (V2G) systems, enhances grid technologies, bolstering stability but also introducing new challenges due to their increasing prevalence.  
Optimizing the placement of EV charging stations is crucial, with study [114] employing PSO to determine the optimal locations, leading to decreased power losses and better voltage profiles and highlighting the significant influence of infrastructure placement on grid efficiency.  
The advancements in optimization techniques for dynamic EV charging demand prediction and voltage stability are explored in [85; 115; 116], showcasing the sophistication of current methods in managing demand, ensuring system stability, and maintaining cost efficiency and emphasizing digital technologies' importance in EV infrastructure planning.  
Further discussions in [86; 117; 172] delve into optimal operational planning and the broader impacts of EV integration on distribution systems, providing comprehensive insights into EV infrastructure development and operational strategies.  
This narrative underscores EVs' critical role in transforming power distribution, spotlighting advanced planning and digital innovations in enhancing EV infrastructure's efficiency and sustainability. It presents a thorough synthesis of research on EV integration, from optimizing infrastructure to addressing grid stability and cost challenges, indicating the need for sophisticated forecasting models and regulatory adjustments to support EV integration's expanding demands.
###### Abstract  
Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of \(6.61\times 10^{-6}\), slightly lower than the desired level of \(10^{-5}\).  
GNSS; carrier phase; machine learning; regression; fault monitoring; over-bounding; DGNSS; noise modeling +
Footnote †: journal: _Mathematics_  
**MSC**: 62J05
## 2 Methods  
ArcGIS [17], a spatial analysis tool, was used for canopy coverage and distance from trail calculations. The set of sampled points representing changes in landscape was used to observe the continuity and variability of the data across a surface using interpolation tools. Deterministic and geo-statistical techniques were used for interpolation. Inverse Distance Weight (IDW) and Spline are deterministic techniques based on the extent of similarity of cells. Kriging is a geo-statistical interpolation technique.  
IDW is based on a linear weighted combination of a sample of points. The weight is assigned as a descending function when the distance of the input point to the cell location is growing.  
\[\hat{Z}(s_{0})=\sum_{i=1}^{N}\lambda_{i}Z(s_{i})\]  
\(\hat{Z}(s_{0})\) is the predicted value for given location \(s_{0}\). \(N\) is the number of measured points near the predicted location. \(\lambda_{i}\) are weights assigned to each measured point used. The weights value decreases as the distance increases. \(Z(s_{i})\) is the measured value at location \(s_{i}\).  
\[\lambda_{i}=d_{0}^{-p}/\sum_{i=1}^{N}d_{0}^{-p}\sum_{i=1}^{N}\lambda_{i}=1\]  
As distance increases, the weight is reduced by a factor of p. \(d_{i0}\) is the distance between prediction location \(s_{0}\) and each measured location \(s_{i}\). The optimal \(p\) value is determined by minimizing the root-mean square prediction error (RMSPE). The RMSPE is the statistic that is calculated from cross-validation where each measured point is removed and compared to the predicted value of the location. The IDW aims at identifying the value that produces the minimum RMSPE.  
Kriging performs statistical interpolation by fitting a function to a specified number of points with a specified radius to determine the output value for each location. Kriging provides information on the spatial autocorrelation of the dataset, the forest canopy coverage. We use the spatial resolution of a 30 m\({}^{2}\) cell covered by tree canopy. Fitting and loss of tree canopy coverage values were produced using a Random Forests regression. It assumes the distance between sample points reflects a spatial correlation that explains the variation in the surface.  
\[Z(s)=\upmu(s)+\varepsilon(s)\]  
where \(Z(s)\) is the value for location \(s\), \(\upmu(s)\) is the deterministic trend, and \(\varepsilon(s)\) are the random correlated errors.  
\[\upmu(s)=\beta_{0}+\beta_{1}x+\beta_{2}y+\beta_{3}x^{2}+\beta_{4}y^{2}+\beta_ {5}xy\]  
representing the second-order polynomial trend surface linear regression on x and y coordinates.  
Spline estimates values by minimizing the overall surface curvature. This is based on a smooth surface that passes through the input points.
## 3 The Application Architecture and Databases
### Using Relational MySQL  
Developing the application using relational MySQL database involves the structure presented in Figure 2, where the _car_announcement_ entity has a @OneToOne relationship with the _exterior_spec_, _interior_spec_ and _engine_detail_ entities and a @OneToMany relationship with the _sensor_detail_ and _car_image_ entities. Additionally, _exterior_spec_ and _interior_spec_ entities have a @OneToMany relationship with the _car_option_ entity, while _engine_detail_ has @OneToOne relationship with the _transmission_detail_ entity (Figure 2).  
Figure 2: Relational database structure.  
Most of the applications have a complex database structure, and by using the database representation of the tables shown in Figure 2, we wanted to analyze how this structure together with the relationships between the tables influence the performance of the application when the amount of data increases. Additionally, with this database structure we wanted to suggest when a non-relational approach is worth being used because, when the data structure is too simple, it could be possible for a non-relational database not to bring any performance improvement and only add extra processing time.  
The main disadvantage of this hierarchy of entities is that, with the increase of the database, the extraction of this information may require more time due to the necessary joins between the specified tables, and thus the performance of the application can decrease significantly, in contrast to the non-relational approach where extracting data could be faster because each document contains all the data necessary for each entity.  
Therefore, many applications also use non-relational databases, which contain relevant information from the relational database in a JSON format and involves the implementation of a mechanism that ensures that the data is correctly replicated from the relational database.
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 2 Materials and Methods  
### Construction of Rock Physical Prior Information  
Subsequently, the commonly employed prior information regarding wave velocity and formation density in seismic exploration, along with their acquisition channels, are expounded.
# Dataset Transformation System for Sign Language Recognition Based on Image Classification Network
## 5 Discussion  
In the research for motion recognition, a large amount of data and an RNN-based model with relatively high complexity were used. There also exists a motion recognition model using CNN, but it has a higher complexity than a model for classifying 2D images. In this paper, we proposed a dataset transformation system to improve this situation. Various attempts were made to solve these problems. Among them, there were approaches that added the pose estimation process, and it was used as an input for the RNN-based model. Although the size of the input was reduced by adding pose estimation process, it still required high complexity by using an RNN-based model. We conducted the experiment  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline Proposed method with ResNet & 84.5 \\ CNN-RNN [35] & 24.0 \\ GRU with OpenPose [27] & 52.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of training accuracy of the proposed method, CNN-RNN and GRU models.  
Figure 13: Confusion matrix of four words before and after data augmentation; (**a**) before data augmentation; (**b**) after data augmentation.  
by adding the posture estimation process as used in previous studies. In the previous study, the results of posture estimation were directly used as input to the RNN-based model, but we added the process of transforming it into a spatial-temporal map. By adding the transformation process, it was possible to use a CNN-based model rather than an RNN-based model with relatively high complexity. This enabled fast and accurate learning. In addition, by adding a transformation process, it was possible to reduce the capacity of the data used for learning. The proposed method was able to reduce the amount of dataset to about 1% and confirmed the relatively high accuracy compared to other studies using the same dataset. However, this study still has several problems to be solved. The first is that the size of the input layer of the image classification model is fixed, but the length of the video is different. In sign language, there are not only words expressed with one action, but also words that should be expressed with a combination of several actions. Simple motions relatively shorten the length of the video, and complex motions lengthen it. This problem is more noticeable in the case of sign language sentences. The second is that one datum is associated with one word or sentence. That is, in the case of a sentence including the trained word, it may be recognized as a word rather than a sentence. If these problems are resolved, this study is expected to develop into a model that can translate sign language beyond sign language recognition.
# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 3 Results and Discussion
### Multivariate Data Analysis Based on 22 VOCs  
As the PLS-DA score scatter plot in Figure 3 shows, some VOCs, namely terpenes and norisoprenoids, were of higher importance in the classification depicted in the correlation circle plot in Figure 3. Consequently, we focused on 22 terpenes and norisoprenoids, of which 3-carene, \(\beta\)-ionone, and trans-rose oxide were present below the limit of quantification, and therefore, they are not important. Additionally, these 22 VOCs could better represent the profiles of our samples, since four terpenes, i.e., \(\alpha\)-terpinene, \(\gamma\)-terpinene, 1,4-cineol, and 4-terpineol, as shown in Figure 2, are generally found at higher concentration in Grasevina wines in comparison to Chardonnay and Pinot Gris wines from the same producers.  
To uncover the patterns and trends in our dataset, we mapped and visualized the relationship between samples and variables (VOCs) using a heatmap, which revealed groups of correlated samples and variables. Additionally, we utilized Boxplots and Scattergrams as two impactful graphical tools to depict the distribution of our 22 VOCs, serving as predictor variables for samples from the Slavonia (S) and Podunavlje (P) sub-regions in the dataset. These visual representations are provided separately in Appendix D (Figures A8 and A9). In the heatmap analysis, we used 22 VOCs associated with 32 different samples from 2 different geographical regions to generate the heatmap.  
Based on the heatmap and hierarchical cluster analysis patterns, two major groups of samples were identified. Higher amounts of terpenes and norisoprenoids were present in the first group than in the second group, as shown in the heatmap (Figure 4). Interestingly, the wines represented in the first group with a higher content of terpenoids mainly originated from the Kutijevo district within the Slavonia sub-region, whereas most of the wines from Podunavlje sub-region were in the second group. Therefore, it appears that the heatmap and hierarchical cluster analysis managed to separate Grasevina wine samples according to their geographical origin to a certain extent. The dendrogram from the heatmap depicted in Figure 4 also shows two major clusters (or three minor clusters in total) of compounds, each cluster being composed of different groups of terpenoids and norisoprenoids without a clear trend.  
The sum of terpenes and norisoprenoids (but not individual compounds) showed a connection to the geographical region based on their concentration levels. These data indicate that norisoprenoids and even more terpenes can be used to a certain extent as markers of the geographical origin of Slavonian Grasevina wines.  
Figure 4: The heatmap corresponding to a total of 22 VOCs and 32 samples from 2 different geographical sub-regions, Podunavlje (P) and Slavonia (S), followed by their vintages.
## 5 Conclusions  
This paper reviews how the implementation of advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and Computer Aided Maintenance Management (CMMS) systems can transform warranty management, migrating from a reactive model to a predictive and proactive one. The integration of these digital enablers allows for the optimization of maintenance policies, anticipating failures and reducing operating costs in industries with highly critical assets [61, 63]. The introduction of servitization in warranty management also contributes to extending the relationship with the customer, offering a personalized and adapted aftersales service that not only optimizes the life cycle of assets, but also strengthens customer loyalty. The analysis shows that integrating advanced technologies such as AI and IoT into warranty management offers concrete benefits, including cost reduction, increased customer satisfaction, and asset life-cycle optimization.  
The goals presented in the introduction have been fulfilled through systematic analysis. Objective 1 (O1) was achieved by a thorough examination of the literature, identifying and assessing pertinent technologies, including AI, IoT, and CMMS, while demonstrating their use in predictive warranty management within different manufacturing settings. The analysis of case studies fulfilled Objective 2 (O2) by exhibiting how advanced technologies created operational cost reductions and process acceleration alongside increased customer satisfaction. Multiple case studies confirmed how businesses use AI and IoT technologies to drive better warranty management operations and enhance their customer experience. For Objective 3 (O3), the assessment focused on digital enabler integration within warranty management systems that follow ISO55000 and the standard practices of ISO 9001 and ISO 42001. The assessment evaluated real-world implementations across major companies in the aviation, automotive, and railway industries to demonstrate how these technologies result in sector-focused advantages, along with improved operational results. Future work will aim to validate the full framework through longitudinal studies or pilot programs within companies.  
Conceptualization, V.G.-P.; methodology, V.G.-P. and C.P.M.; software, C.P.M.; validation, V.G.-P., C.P.M. and P.V.G.; formal analysis, V.G.-P.; investigation, V.G.-P. and C.P.M.; resources, P.V.G. and F.K.R.; data curation, F.K.R. and A.C.M.; writing--original draft preparation, V.G.-P.; writing--review and editing, V.G.-P. and C.P.M.; visualization, P.V.G., F.K.R. and A.C.M.; supervision, C.P.M.; project administration, V.G.-P. All authors have read and agreed to the published version of the manuscript. This research was funded by Ministerio de Ciencia, Innovacion y Universidades of Spain Grant Ref. PID2022-137748 OB-C32, MCNN/AEI/10.13039/501100011033.  
The data are available upon request.  
The authors declare no conflicts of interest.
## 3 Implementation and Software
### Implementation of HCA to Estimate Cluster Probabilities  
For the implementation of HCA to estimate cluster probabilities related to MDS confidence ellipses, we followed the steps described in Section 2.2. Cluster Probabilities. Hierarchical clustering was performed using the _hclust()_ function from the base _stats_ package of R. For the cluster agglomeration method, _ward.D2_, that is, the ward minimum variance method, was selected.
# Evaluation of Shear Effect on Deflection of RC Beams
## 1 Introduction
In particular, it is challenging to calculate deflection analytically because RC beams deform significantly depending on the occurrence and propagation characteristics of cracks, and experimental research and verification are necessary. ACI 318-19 formula proposed by [PERSON] was derived from curvature relationships based on flexural theory and tests [18], and it is necessary to verify whether to underestimate the deflection of the beams when the effect of shear increases. However, there are few studies focusing on this aspect. In this study, flexural tests of RC beams were performed with the shear span-to-depth ratio, shear capacity ratio, and tension reinforcement ratio as test variables. Furthermore, the effect of shear on the effective moment of inertia and deflection of RC beams was experimentally evaluated in this study.
## 10 Future Research Directions
### Localization and Clock Synchronization API Support  
Considerable amount of work has been done in localizing the sensor node, as knowing the position of a sensor node at a given time has many applications. Similarly, many WSN applications (and someprotocols) benefit from synchronized clocks. Therefore, an OS needs to provide support for these additional network protocols on the sensor nodes. For example, recent versions of TinyOS provide an implementation of FTSP, one of many clock synchronization protocols. As clock accuracy requirements are very application-specific, a better approach may be to allow developers to design their own protocol, with the OS providing appropriate APIs to facilitate in accomplishing this task.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Routing Protocol
#### 4.1.2 RLSRP with PPMAC  
Reinforcement learning based self-learning routing protocol (RLSRP) with position-prediction-based directional MAC (PPMAC) is a hybrid communication protocol proposed in [49] wherein PPMAC resolves the directional deafness problem with directional antennas and RLSRP provides the routing path using RL.  
In [49], [PERSON] et al. predicted the positions of other nodes, controlled the communication and data transmission using the PPMAC scheme. The authors used self-learning RL to determine the shortest route with the shortest delay from the source to the destination. The partially observable Markov decision process (POMDP) is incorporated with the proposed RL algorithm, where the end-to-end data transmission delay is provided as a reward. Similar to QMR, RLSRP maintains a neighbor table to keep track of the changes in the network topology. The learning parameters, such as discount factor and learning rate, are fixed. Moreover, RLSRP uses a greedy policy and selects the route with the maximum value function, where the end-to-end delay is minimum.