esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 4 Discussion
### Land Cover and Land Use Mapping over Long Time Series  
In the third setup, the application scenarios were related to the land cover and land use mapping using archives of SITS. Among the queries that we experimented with, we recalled the identification of extra-urban expansion, the demarcation of the urban area, the delimitation of forest areas, the discovery of agricultural areas, and the search for water bodies that remained unmodified over the years. However, due to the complexity of the SITS (i.e., containing 88 images captured over 27 years) and of the queries, the performance of the retrieval algorithm was assessed by visual inspection. The results shown in Figures 11 and 12 with Figure 6 show that the retrieval system was able to discover the queried spatio-temporal patterns for land cover and land use mapping even when the data was irregularly sampled and images were captured under different meteorological and illumination conditions.  
Figure 10: Delimitation of areas affected by floods in Dobrogea. (**a**) DTW distance image for a query in the flooded area. (**b**) Delimitation of the affected area using the proposed algorithm. (**c**) Ground truth marking the flooded areas. In the case of DTW distance image presented in (**a**), dark color represents similar evolutions and bright color represents non-similar evolutions, whereas in (**b**) white pixels correspond to spatio-temporal patterns that are similar to the query, which was selected from the region of interest.  
Figure 11: Land cover mapping in long Landsat SITS. (**a**) DTW distance image for a query made inside the forestry area. (**b**) Forestry area delimitation using the proposed query-by-example retrieval method. (**c**) DTW distance image for a query made in an area covered by water. (**d**) Water delimitation using the proposed query-by-example retrieval method. In the case of DTW distance images presented in (**a**,**c**), dark color represents similar evolutions and bright color represents non-similar evolutions, whereas in (**b**,**d**), white pixels correspond to spatio-temporal patterns that are similar to the query, which was selected from the region of interest.  
Figure 12: _Cont._
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 2 Methods
### Axle Load-Time-History Reconstruction  
In this paper, we estimated the axle load by reconstructing the load-time-history curve and calculating its peak value. This reconstruction task involved learning the relationship between the input vibration signal sequence and the output load sequence in a sequence-to-sequence manner. To achieve this, the input vibration signals and the output loads were synchronized in time, including the section before, during, and after the vehicle passes. In this study, the input sequence length was set to 700, which ensured that the complete vehicle signal was captured.  
Let \(\mathbf{X}=[\mathbf{x}^{1},\,\mathbf{x}^{2},\,\ldots,\,\mathbf{x}^{N}]\) denote a set of vibration signals, where xi is from the \(i\)th vibration sensor, and let \(\mathbf{Y}\) denote the corresponding load-time-history curve. During the forward propagation step, a subset of \(\mathbf{X}\) is fed into the TCN. The network then predicts the corresponding load-time-history curve, which is compared with the true load-time-history curve in the training dataset. The difference between them is computed as the error, which is then used to calculate the gradients. The weights of the TCN are updated using the gradients in a process called backpropagation. By repeatedly performing forward propagation followed by backpropagation, the weights of the network are adjusted to minimize the loss.  
The proposed TCN network is presented in Figure 3. To increase the channels of the signals, a \(1\times 1\) convolutional layer is applied. The signals are then passed through a series of non-causal temporal blocks. The output of the final temporal block is concatenated with the input signals, and the predicted load-time-history curve is generated using a linear layer similar to U-net. Each temporal block comprises two layers of non-causal dilated convolutions, with batch normalization, non-linearity, and a dropout layer placed between them. The residual skip connection adds the input to the output feature map, with the caveat that if the input and output depth are different, a \(1\times 1\) convolution is inserted. Figure 3 illustrates a temporal block and the stacking of three residual blocks together. The first convolutional block (CONV) has \(N_{T}\) filters. There are \(N_{B}\) TCN blocks that use the same number of filters \(N_{T}\), the same kernel length \(K_{T}\), and a variable dilation \(\mathbf{d}\in[1,2,4,\ldots,2^{N_{B}}]\). The default parameters are \(N_{T}\) = 32, \(K_{T}\) = 31, and \(N_{B}\) = 12.  
To capture both high- and low-frequency contents, it is necessary to experiment with different kernel sizes and the number of temporal blocks to determine the optimal model. A larger kernel size with more layers enables the network to capture the low-frequency trend, while a smaller kernel size with fewer layers allows the network to capture the high frequencies.  
Figure 2: Vibration signal detection algorithm.
## 5 Conclusions  
To improve the efficiency of detection of tractor faults as well as the quality of equipment before leaving the factory, the present study proposed a wireless vibration detection method based on the symmetrical characteristics of the tractor structure. A magnetic suction wireless vibration acceleration sensor was designed. Repeated tests were performed on newly assembled tractors to assess the effectiveness of the method. When the idle speed of the tractor was 800 r/min and 1000 r/min, the left and right sides of the front and rear axles of the tractor had similar symmetry. It was also found that the main vibration direction of the front axle was in the Y direction and the main vibration direction on the rear axle was in the Z direction. The experiment showed that the tractor wireless vibration detection method proposed in the present study had good accuracy and efficiency.  
During the experiment, some uncertain factors could have affected the results. Factors such as the consistency of the engine and gearbox batches, the experience of workers in the manual assembly process, and the shaking of the three-point suspension, may all haveexerted an uncertain impact on the test results. The present study could provide a new method for the rapid detection of tractor faults or tractor assembly faults before leaving the factory, improve the efficiency of tractor fault detection, and reduce the cost of off-line detection. Compared with manual test judgment, this method would improve the test efficiency and accuracy. In the future, wireless detection could be combined with cloud technology and large database technology to detect the vibration characteristics of each tractor, and could be expanded to include many functions, such as reminding the driver to maintain the tractor to reduce the incidence of tractor failure.  
Conceptualization, [PERSON], [PERSON], T.C. and L.H.; methodology, [PERSON], [PERSON], T.C. and L.H.; validation, [PERSON] and J.J.; literature search, [PERSON], J.J., [PERSON] and [PERSON]; figures, [PERSON]; formal analysis, [PERSON], [PERSON], T.C. and L.H.; investigation., [PERSON] and T.C.; resources, [PERSON]; data curation, [PERSON], [PERSON] and L.H.; writing--original draft, [PERSON] and T.C.; writing--review and editing, [PERSON], T.C. and L.H.; visualization, [PERSON] and L.H.; supervision, [PERSON] and L.H.; project administration, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the National Key R&D Program during the 13 th Five-year Plan Period, China, grant number 2017 YFD0700202.  
Not applicable.  
The data supporting the findings of this study are available in the article.  
On behalf of all authors, the corresponding author states that there are no conflicts of interest. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Special Issue on Smart Data and Semantics in a Sensor World
## 2 Topics of Interest for the Special Issue  
The goal of this Special Issue is to provide a venue to show the practical progress made in the area of data management for sensors, particularly regarding the use of semantic techniques to obtain and exploit smart data from the raw sensor data. The terms \"semantic web\" and \"sensor web\" were firstly used more than 15 years ago. Despite this, they are mature topics on which the research community is still very active and with significant research challenges to address.  
This Special Issue intends to provide insights on recent advances in these topics by soliciting original scientific contributions in the form of theoretical foundations, models, experimental research, and case studies for developing semantic Web-based applications. We aimed to bring together research related to several disciplines, such as Data Management, Knowledge Representation and Engineering, Web of Data, and Sensor Networks, among others. Original research contributions were invited on all aspects of the Semantic Web and Sensor Web, as well as their applications. Particularly, the following themes were within the scope of this Special Issue:  
1. Semantics and Sensor Data: real-time sensor data streams, data management for sensor data, obtention of smart data from sensors, analytics of sensor data streams, semantic modelling and annotation of sensor data, scaling semantic sensor systems, sensor data representation, acquisition, and cleaning, semantic integration of heterogeneous data sources, semantic data management technologies for sensor data, challenges with managing and integrating real-time and historical sensor data, provenance, and access control and privacy-preserving issues in semantic data and sensor data.
2. Linked Data: Linked Data applications and case studies, visualizations, and user interfaces for ontologies, sensor data and linked data, machine learning and data mining for the Web of Data.
3. Sensor-Based Applications: semantic modeling of data for smart cities, the mobile web, sensors and semantic streams, smart cities, urban and geospatial data, semantics and sensor data for smart cities, semantics and eGovernment, managing sensor data in transportation applications, collaborative sensing, and spatial crowdsourcing.  
We encouraged theoretical, methodological, empirical, and application papers. The submitted papers should describe original work, present significant results, and provide a rigorous, principled, and repeatable evaluation. In addition, the submission of papers incorporating links to data sets and other materials used for evaluation, as well as to live demos and software source code, was appreciated.
## 5 Performance Analysis
### Evaluation and Simulation Results
#### 5.3.2 Communication Efficiency  
Figure 3 shows the total amount of data transferred during the training process for different numbers of edge devices.  
EdgeGuard outperformed FedAvg in terms of communication efficiency, cutting the overall amount of data sent by as much as 30%, especially as the number of edge devices rose.
## 2 Electrochemistry of Nickel Electrodeposition
### Components of an Electroplating Setup  
The setup for the electrodeposition of nickel involves four main components, as shown in Figure 1. These components include a source of direct electric current, usually provided by a DC rectifier, the positively charged electrode known as the cathode, and the negatively charged electrode known as the anode [15]. The cathode is the part that is to be deposited on, known as the substrate, and the anode is usually a suitable form of nickel metal to facilitate the deposition process by replacing depleted ions in the electrolyte [26]. The final component is the medium of ion supply and transport known as electrolyte. Most nickel electroplating is carried out in an aqueous bath containing dissolved metal salts (ions). These solutions are favorable for plating on polymers because they operate well below the glass transition temperature of most plastics, thus avoiding thermal distortion of the substrate. Aqueous baths are further subdivided by pH into acidic, neutral, and alkaline systems [4]; the acidic (Watts type) formulation remains the most prevalent because it is simple to prepare and easy to control, and consistently produces high quality nickel deposits.  
For specialized applications--particularly when simultaneous deposition of multiple elements is required--ionic melts are employed instead of water-based baths. Operating between about 300 \({}^{\circ}\)C and 700 \({}^{\circ}\)C, these molten salts offer a much wider electrochemical window and higher metal ion solubility than aqueous solutions, making them ideal for alloy and composite coatings [27; 28]. Their elevated temperature precludes their use  
Figure 1: Schematic of an electroplating setup.  
on heat sensitive polymer substrates and demands more robust equipment and stricter safety measures.
# Weight Assignment Method and Application of Key Parameters in Shale Gas Resource Evaluation
## 5 Case Applications
According to the table, the critical value for the bilateral test for significance level \(\alpha\) = 0.001 is F\({}_{\alpha}\) = 4.893. Due to F \(>\) F\({}_{\alpha}\), the null hypothesis is rejected, indicating a correlation between the dependent variable y, shale gas resources, and the independent variables X\({}_{1}\), the effective shale thickness; X\({}_{2}\), the shale density; and X\({}_{3}\), the gas-bearing shale area. At the same time, the partial correlation coefficients of the parameters were obtained. The coefficient of influence for the gas content, q, was 0.219, the coefficient of influence for the effective shale thickness, h, was 0.205, the coefficient of influence for the shale density, \(\rho\), was 0.166, and the coefficient of influence for the gas bearing shale area, A, was 0.166 (Table 9). \begin{table}
\begin{tabular}{c c c c c} \hline
**Influence Factor** & **q (Gas Content)** & **h (Effective Shale Thickness)** & **\(\rho\) (Shale Density)** & **A (Gas-Bearing Shale Area)** \\
**m\({}^{3}\)/t** & **m** & **g/cm\({}^{3}\)** & **km\({}^{2}\)** \\ \hline Partial correlation & & & & \\ coefficient \(\beta\) value & 0.219 & 0.205 & 0.166 & 0.166 \\ \end{tabular}
\end{table} 
Table 9: Partial phase relation table. According to the weight conversion model, by substituting the partial correlation coefficients of the main parameters, it can be obtained that the M gas content is 0.29, the M thickness is 0.27, the M density is 0.22, and the M area is 0.22, respectively. Therefore, the main factor in the region is the gas content, followed by the effective shale thickness, shale density, and gas-bearing shale area, which are the parameters with lower influence. Their weight proportions are 29%, 27%, 22%, and 22%, respectively. According to the volumetric method for resource estimation, without assigning weights to the parameters, the calculated result is 782 \(\times\) 10\({}^{8}\) m\({}^{3}\). After using the method proposed in this article to perform parameter weight overlap addition, the estimated resource quantity is 759 \(\times\) 10\({}^{8}\) m\({}^{3}\). Due to the fact that these data were obtained in 2019, its value is closer to the cumulative production of 688.25 \(\times\) 10\({}^{8}\) m\({}^{3}\) as of 2020, which proves the high reliability of its predicted resource quantity [52]. By calculating the weight of key parameters in resource evaluation, the next step of analysis such as error transmission in the calculation process can be carried out. At the same time, data processing for the main parameters is also the key and core of the next research step. The method roadmap proposed in this article is as follows (Figure 1). Therefore, we can use this method to analyze the parameter weights (see Figure 2):
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Responsive Web-Based Data Acquisition Platform
#### 2.8.5 API REST  
The interface permits live monitoring, historical data queries, and the administration of the different Emergency Water Information Network devices. The REST (\"REpresentational State Transfer. It is an architectural style for distributed hypermedia systems\" [35]) carries out the following functions:  
1. The RiverCore real-time monitoring section displays the variables received from the deployed sensors along with the flow calculated from these data, and the model obtained from the previous topographic work. The interface displays the information on charts and graphs for easy interpretation. It also contains a map where every network device is shown. The icons representing the RiverCore nodes on the map individually change colors (green, yellow, or red), according to the current state of the river. The overflow alert is based on the flow analysis developed by the water team of the project. When an alert is triggered, a notification is displayed in the web browser and added to the notifications list in the navigation bar of the platform.
2. Device section: This section of RiverCore manages the fixed nodes and permits data queries. Only persons with administrator privileges can add, edit, and delete. When registering a fixed node, topographic information (allowing the platform to calculate flow and show alerts) and location must be included. The topographic information is unique to every location and the formula needed to display the correct information on the platform is also site-specific.
3. Report section: This section displays a personalized query of records, generates a report, and downloads it in several user-selectable formats (Excel, PDF, and CSV).
4. Drifter section: This section displays the real-time data related to the drifters. The data may come from the RiverCore or RiverDrone sniffers, which then are represented in tables and maps. It also allows users to upload the data retrieved from the drifter's SD card.
## 1 Introduction
### Motivation and Contributions  
The aim of this research is to design a privacy-preserving audiovisual emotion recognition model (AVER), capable of classifying emotions at the edge and supporting continuous model updates within a decentralized system. Unlike the model proposed in [20], we do not exploit an ensemble of classifiers for processing audio signals but rather implement a convolutional neural network on the audio modality as well. This way, post-processing could be simplified, as we do not need to train several ML classifiers, analyze their performance, and choose a subset of classifiers for decision-making. Furthermore, we exploit transfer learning and fine-tuning for detecting facial expressions, since training convolutional neural networks on a small set of data cannot guarantee satisfactory generalization. To validate the proposed approach, we perform experiments on the eNTERFACE'05 dataset and provide classification accuracy, F1-score, recall, and precision results obtained using multimodal data processing.  
One of the key aspects to consider in this context is the availability of training data and the composition of a set of speakers. There are two commonly discussed groups of emotion recognition models: speaker-independent and speaker-dependent [25]. These groups are related to different problems in the field. The aim of speaker-independent emotion recognition models is to generalize well and accurately predict the emotions of speakers that have not been seen before during the model training, i.e., to operate on the open-set of speakers. However, due to the lack of available datasets with accurate annotations, current models face challenges in achieving very high detection accuracy as a general solution. On the other hand, speaker-dependent emotion recognition systems require being trained for each user, maximizing the performance of the system for a closed set of speakers. This way, systems could be more robust and provide higher performance in an industrial environment compared to the general speaker-independent models. This approach is beneficial for applications designed for specific individuals, such as voice-based personal assistants or speech-based authentication systems. However, creating models for such scenarios requires recording individuals of interest, which can be time-consuming and consequently may involve acting emotions. This motivated us to make an effort to improve the overall accuracy of such models by incorporating federated learning in a closed-set scenario in order to leverage different data sources and minimize biases produced due to small training sets.  
The rest of this paper is organized as follows: In Section 2.1, we provide a description of the proposed audiovisual emotion recognition model. Section 2.2 provides an overview of federated averaging, which accompanies the model described in Section 2.1. The experiment is described in Section 2.3 while results are presented in Section 3. Comparison with other state-of-the art methods is provided in Section 4. Finally, the advantages and disadvantages of the proposed model are summarized and discussed in Section 5.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 3 Proposed Method
### Voxel Selection-Based Refining Segmentation (VS-RS)
#### 3.2.3 Refining Segmentation of Voxels  
Next, after the voxel selection is completed, we will refine segmentation. This requires calculating the final refined segmentation score \(F_{i}^{m}(p)\) for each projection plane as follows:  
\[F_{i}^{m}(p)=N_{i}^{m}(p)+\frac{\lambda}{M}\times\overline{S}^{\prime}_{i}(p), \tag{4}\]  
where \(M\) is the total number of neighboring voxels. There are no additional restrictions on neighboring voxels. \(\lambda\) defaults to 3.0, \(N_{i}^{m}(p)\) as the normal vector score of each projection plane in the voxel is represented as follows:  
\[N_{i}^{m}(p)=n_{i}^{m}\cdot c(p), \tag{5}\]  
where \(n_{i}^{m}\) is the normal vector of the m-th point in selected voxels, \(c(p)\) represents the constant unit vector of the projection plane \(p\). \(\overline{S}^{\prime}_{i}(p)\) represents the influence of the projection of points in neighboring voxels on \(F_{i}^{m}(p)\), as follows:  
\[\overline{S}^{\prime}_{i}(p)=\sum_{j=0}^{N-1}\omega_{j}\cdot S_{j}(p), \tag{6}\]  
Figure 7: An example of voxels for the final selection.  
where \(\omega_{j}\) is set considering that the influence of voxels with different distances should be different, the neighbor voxels which are closer to the refined segmented voxels have a larger weight, as follows:  
\[\omega_{j}=\alpha-\frac{\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j} \right)^{2}+\left(z_{i}-z_{j}\right)^{2}}}{R\times\beta}, \tag{7}\]  
where \(\left(x_{o},y_{o},z_{o}\right)\) represents the position coordinates of the current refined segmentation voxel, and \(\left(x_{i},y_{i},z_{i}\right)\) represents the position coordinates of the neighboring voxel. The default value of \(\alpha\) is 1.0, and \(\beta\) is 1.5. \(R\) represents the maximum distance between voxels.  
The final projection plane is determined by comparing the values of F, with the projection plane having the largest F value chosen. The above calculations are repeated until the maximum number of iterations is reached. The selected voxels for refined segmentation will adaptively terminate the change of the projection plane during the iteration process. The purpose of this is to avoid unnecessary computation. Specifically, it will record the change in the projection plane of each voxel. Once it is found that the plane is repeating changes, then we skip the voxel.