esa-sceva/satcom-chunk-collection huggingface dataset:
## Application of AI in Orthodontics
### AI Models Designed for Palatal Shape Analysis  
The palate has a complex structure, and its shape varies among individuals. The palate's shape is related to the facial pattern and a wide range of factors such as breathing pattern and occlusion [78; 79; 80]. The shape of the palate is of great interest to orthodontists as it is a potential area for evaluation and assessing the outcome of orthodontic procedures like maxillary expansion [81; 82].  
Palatal measurements usually include palatal surface area, volume, and depth [78]. These measurements often require greater experience and are often subjected to observer errors. The recent technological advancements have resulted in the development of AI based models designed for palatal shape analysis. [PERSON] et al. [44] reported on a deep learning model designed for analyzing the palatal shape. This automatic model demonstrated excellent repeatability with promising accuracy. [PERSON] et al. [45] reported on the application of an AI based deep learning model for palatal shape analysis, and this model achieved results similar to conventional approaches.
## 3 Results and Discussion
### Comparison with State-of-the-Art Works  
Table 3 presents the comparison of the proposed antenna with state-of-the-art works for similar applications. It could be observed clearly that the proposed antenna overperforms the existing antennas by providing the best combination of compact size, multi-mode operation, wide operational region, relatively high gain, and simple geometrical structure.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 2 CNNs and Normalization Layers
### 1D Convolutional Layer  
1D convolutional layers consist of filters computing cross-correlations over local areas of the input. Each filter consisting of \(N\) kernels with \(M\) weights computes an output value for a local area that consists of \(N\times M\) values, where \(N\) denotes the number of channels e.g., different vibration sensors at the first layer and \(M\) denotes the length of the local area e.g., time steps. The filters process the local areas subsequently along the length axis with the same set of weights. Equation (1) shows this cross-correlation computation for a given local area:  
\[y_{j}^{J,i}=\sum_{n=0}^{N}\mathbf{k}_{j,n}^{l}*\mathbf{x}_{n}^{l,i}+b_{j,n}^{l} \tag{1}\]  
where \(y_{j}^{J,i}\) denotes the \(i\)-th local area output value of \(j\)-th filter on the layer \(l\), \(\mathbf{k}_{j,n}^{l}\) is the kernel for \(n\)-th channel of \(j\)-th filter on the layer \(l\), \(\mathbf{x}_{n}^{l,i}\) is the \(n\)-th channel of the \(i\)-th local area, \(*\) denotes the dot product and \(b_{j,n}^{l}\) is the bias term of the kernel \(n\) of filter \(j\) on layer \(l\). To conclude, the output value \(y_{j}^{J,i}\) is the sum of the dot-products between the kernels and the local areas computed at each corresponding input channel \(n\). This computation is commonly referred to as the depth-wise convolution.  
The computation at a given convolutional layer can be formulated as matrix multiplication \(\mathbf{Xw}\). With one dimensional data and a single input channel, for example, time series data from a single sensor at the first layer, the rows in matrix \(\mathbf{X}\) would correspond to each local area \(\mathbf{x}_{1}^{1,i}\) and \(\mathbf{w}\) corresponds to a column vector of the weights in the only kernel \(\mathbf{k}_{j,1}^{1}\) of the filter \(j\). Typically, there are multiple input channels to each convolutional layer, and the number of kernels in a convolutional layer filter equals the number of input channels to that layer. The local areas \(\mathbf{x}_{n}^{l,i}\) of the latter input channels are concatenated to the transformed data matrix \(\mathbf{X}\), so that each local area \(\mathbf{x}_{n}^{i}\), corresponding to the \(i\)-th convolution, are on the same row. Similarly, the weights of each kernel \(\mathbf{k}_{n}\) for all channels \(n\) are vertically concatenated to the column vector \(\mathbf{w}\). This transformation of input data to matrix \(\mathbf{X}\) is similar to the commonly known im2 col transformation with the difference that time series data is one-dimensional. The transformation is visualised in Figure 1.
## 2 Methodology  
### Traditional TS-InSAR Data Processing  
To cover the entire Jinchuan mining area from October 2024 to April 2025, a total of 15 SAR scenes acquired by the descending Sentinel-1A satellite along path 33, frame 462, with an average temporal baseline of 12 days, were downloaded from the Alaska Satellite Facility (ASF). Additionally, from November 2024 to April 2025, we collected 15 scenes from the \"Fucheng-1\" satellite and 5 scenes from the \"Shenqi\" satellite, both of which were provided by the Tianyi Research Institute. These strip-mode datasets offer spatial resolutions up to 3 m and revisit intervals as short as 4 days. Routine interferometric processing steps, including geometric co-registration, flattening, topographic phase computation based on TanDEM-X DEM, interferogram generation, topographic phase removal, and geocoding, were carried out using Doris 2.5.0 software [19; 20]. Due to the characteristics of the Sentinel-1 TOPS imaging mode, a sub-pixel co-registration accuracy in the azimuth direction better than 0.001 pixels is required to avoid phase errors exceeding 3\({}^{\circ}\). Therefore, an enhanced spectral diversity method based on image intensity information was employed to refine the co-registration results based on precise orbit and correlation-based alignment. Subsequently, the persistent scatter processing implemented in StaMPS/MTI [21] was used to derive the spatiotemporal evolution of surface deformation. This included the selection of persistent scatterer (PS) targets, the estimation of the spatial uncorrelated/correlated look angle (SULA/SCLA), 3D phase unwrapping, and master atmosphere and orbit error estimation (AOE) [22]. Ultimately, 14 single-master differential interferograms from Sentinel-1A and 19 from the Tianyi satellite constellation were obtained after the removal of various artifacts. These interferograms were used to derive the average deformation rate and cumulative displacement time-series results across the study area during the observation period using a least-squares inversion approach.
## 2 Materials and Methods
### Data Pre-Processing  
Due to the large amount of data in the audio file, to facilitate the analysis of the data and feature extraction, it was necessary to preprocess the data, and the steps included frame splitting, normalization, and adding window functions.  
The first step was to split the frames. The audio file was intercepted with a duration of 1s, which is called frame \(x_{i}\), \(i\) is the current number of frames, and the amount of data per frame \(N\) is equal to the sampling frequency per second.  
The second step was normalization. The size of the audio data varied depending on the acquisition process and the external environment. To avoid attributes in the larger value range dominating attributes in the smaller value range, the data were mapped to the \([-1,1]\) interval. The normalized \(x_{i}\) is  
\[x_{i}(n)=\frac{x_{i}(n)-x_{mean}}{x_{max}-x_{min}},1\leq n\leq N \tag{4}\]  
\(x_{max}\) is the maximum value in each frame, \(x_{min}\) is the minimum value in each frame, and \(x_{mean}\) is the average value in each frame.  
The third step was to add window functions. To reduce the effect of spectral leakage of the Fourier transform, a Hamming window was added to smooth a frame of data, and the window function was  
\[w(n)=0.54-0.46\times\cos(\frac{2\pi n}{N}) \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Audio Type** & **Audio File Number** & **Sound Source** \\ \hline Background noise & A1 & Human speech \\  & A2 & Metallic clang \\  & A3 & Birds chirping \\  & A4 & Vehicle passing sounds \\  & B1 & Switchgear 1: Normal vibration \\  & B2 & GIS: CT abnormal vibration \\ Mechanical vibration & B3 & Switchgear 1: Abnormal vibration of sheet metal parts \\  & B4 & Switchgear 2: Abnormal vibration of side plate \\  & C1 & Switchgear 3: Discharge of the pressure relief flap \\  & C2 & Switchgear 4: Discharge of two metal tips \\ PD & C3 & Switchgear 5: Discharge of busbar contact box \\  & C4 & Switchgear 6: Discharge of single metal tip \\ \hline \end{tabular}
\end{table}
Table 1: Sound source of audio files A1–C4.  
Then, the audio data \(y_{i}\) after adding the window were  
\[y_{i}(n)=x_{i}(n)\times w(n) \tag{6}\]
## 3 Preliminaries
### Convolutional Neural Network (CNN)  
Convolutional neural networks (CNNs) represent a specialized type of artificial neural network (ANN) that closely mimics the structure and functionality of the mammalian visual cortex [37]. These networks, particularly 2D-CNNs, have found extensive applications in computer vision tasks, such as image categorization and object recognition, owing to their ability to extract image features hierarchically. However, when it comes to classifying time-series data, 2D-CNNs are not the most suitable choice. In contrast, the effectiveness of 1D-CNNs has been demonstrated in various time-series-related domains, including electrocardiogram (ECG) analysis [38], structural damage identification through vibration analysis [39], and motor fault detection [40]. 1D-CNNs excel in time-series regression and classification tasks by automatically extracting features from one-dimensional time-series data. One notable advantage of 1D-CNNs is their capability to perform feature extraction directly on single-dimensional time-series data. This attribute makes them highly suitable for regression and classification in the time-series domain. Additionally, training one-dimensional convolutional neural networks for time-series regression can benefit from utilizing large labeled datasets of time-series signals without requiring prior feature extraction [41].  
The 1D forward propagation (1D-FP) is expressed in each CNN layer as follows [42]:  
\[y_{k}^{l}=f(x_{k}^{l})=f\Bigg{(}\sum_{i=1}^{N_{l-1}}\text{conv}\text{1D}(w_{ ik}^{l-1},s_{i}^{l-1})+b_{k}^{l}\Bigg{)} \tag{8}\]  
where \(y_{k}^{l}\) is the intermediate output obtained by feeding the input \(x_{k}^{l}\) through an activation function \(f(\cdot)\). \(s_{i}^{l-1}\) is the \(i^{th}\) neuron output at layer \(l-1\) and \(b_{k}^{l}\) is the bias.
# Unsupervised Deep Learning-Based RGB-D Visual Odometry
## 4 Experiments
### Training  
We implemented our code using Google's open source machine learning software library, TensorFlow and completed training on a DELL desktop with an Intel Core i7-4790K @4.0 GHz CPU and an Nvidia GeForce GTX Titan X 12 GB Memory GPU (Xiamen, China).  
We used KITTI [18] Visual Odometry Datasets for training and testing. KITTI Odometry consists of 22 video sequences captured by driving around a midsize city, in rural areas, and on highways. Sequence 00-10 are provided with ground truth for quantitative analysis, whereas sequence 11-21 are used for qualitative analysis. In order to compare with SfMLearner and UnMono, all images are resized to \(416\times 128\times 3\) before feeding into the neural network. Because the training data set is relatively small, we have deployed online data augmentation. Through the following steps, we artificially created more training images.  
1. We randomly adjusted the RGB image brightness by gamma adjustment, and the value range of the encoding/decoding gamma value is \(\gamma\in[0.7,1.3]\). Depth images were not processed.
2. We randomly adjusted the size of image pairs. The scale factors of the x and y axes are \(s_{x},s_{y}\in[1.0,1.2]\).
3. Based on the center of an image, the image pairs were randomly rotated, and the rotation angle \(r\in[-5,5]\) degrees.
4. Based on the image center, the excess part of image was removed and the missing part was interpolated. Finally the image size were adjusted to \(416\times 128\).  
After data preparation, RGB-D images were fed into the network for training. Thanks to the framework of unsupervised deep learning, the ground truth provided by sequence 00-10 were not used for training, but for performance evaluation. The batch size was 32, and the number of RGB-D images in each batch (sequence) was 5. In order to better complete the training, we deployed the Adam adaptive learning rate optimizer [40] and set the exponential decay rates for the first and second moment estimates \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\), respectively. The RCNN iterated for 40 epochs, and the total loss usually stabilized after 20 epochs. The subsequent iteration epochs were applied to fine-tune the network. Thus we decreased the learning rate from the initial value 0.0002 to 0.0001 after half training epochs.
## 4 Discussion
### Listening Positions and Head Orientations  
The clustering showed an interesting pattern in the perception of different positions in a concert hall. Figure 7 provides a visual representation of the clustering. Conditions P1_0\({}^{\circ}\), P1_90\({}^{\circ}\) and P1_180\({}^{\circ}\) are tightly clustered, meaning a high similarity between them. At this position, it can be suggested that the head orientation does not influence the perception of spatial impression greatly. This might be caused by the level of the direct sound mostly, overpowering many of the other perceptual attributes. The second cluster is formed by P2_0\({}^{\circ}\) and P2_180\({}^{\circ}\). Both of these positions are in-line with the central axis of the room, meaning that the overall spatial impression in not greatly changed by a 180\({}^{\circ}\) rotation. However, it was observed that differences in the direction of arrival of the early reflections and reverberation (front-back) were elicited. In the third cluster, conditions P2_90\({}^{\circ}\), P3_90\({}^{\circ}\) and P4_90\({}^{\circ}\), which are facing 90\({}^{\circ}\) from the source can be seen. This is an interesting result, as it suggests that the perception of spatial impression for a 90\({}^{\circ}\) head orientation is different from one facing directly towards the source. P2_90\({}^{\circ}\)--which is on the central axis--falls in the same cluster with P3_90\({}^{\circ}\) and P4_90\({}^{\circ}\)--positioned laterally--suggesting that changes in perceived spatial impression evoked by the head orientation are independent of the lateral positioning of the listener. This opens a new research path in studying the way in which different head orientations influence the perception of spatial impression attributes. Finally, the fourth cluster presents conditions P3_0\({}^{\circ}\) and P4_0\({}^{\circ}\), facing towards the source from a lateral listening position. The perceived differences in spatial impression are thought to be due to the asymmetrical arrival of early reflections and reverberant tail, as suggested by the elicited constructs. These results are encouraging for further research, looking into the influence of lateral movement on the perception of spatial impression attributes as well as on preference.  
Figure 7: Visual representation of the position clusters.
## 5 Performance Evaluation
### Comparison Algorithms  
This paper selects three comparison algorithms as the baseline, i.e., PSO-LSTM algorithms, ARIMA and the Support Vector Machine model (SVM). ARIMA relies on traditional mathematical and physical methods, while SVM and PSO-LSTM concentrate on neural networks or deep learning technologies. PSO-LSTM, specifically, employs the PSO algorithm to optimize hyperparameters for the LSTM method. Our approach shares similarities with PSO-LSTM as we also leverage similar techniques to enhance the performance of the LSTM model. SVM predicts by constructing an optimal hyperplane for classification or regression, while ARIMA predicts by modeling the autocorrelation and trend in time series data to forecast future values. PSO-LSTM predicts by combining the optimization capabilities of Particle Swarm Optimization (PSO) with the LSTM neural network to improve prediction accuracy.
# A Distributed Indexing Method for Timeline Similarity Query
## 3 Distributed Triangle Increment Tree (DTI-Tree)  
As mentioned above, a timeline query can be transformed into the computation of interval sets. When the timeline data become larger, the size of interval data will also increase rapidly. We intend to manage the big interval data on a Distributed Triangle Increment Tree, called DTI-Tree, to share the computation capacity between the machines on a Spark cluster shown in Figure 6. The DTI-Tree is an interval-based index supporting a data partition strategy named triangle increment tree method. It composes of two parts. Part one is a global index, called T-Tree, running on the master node. The master node is the computer on which the Spark master, the Hadoop Name Node and the driver programs execute. The global index is the external user interface that only managers some triangles.  
Figure 5: An example of two timelines similarity computation process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Function** & **Description** \\ \hline cartesian & returns the cartesian product of this timeline and another one, that is, the result of all pairs of elements (a, b) where a is in this and b is in other. \\ \hline count & returns the number of elements in the set. \\ \hline filter & returns a new set containing only the elements that satisfy a predicate. \\ \hline flatMap & returns a new set by first applying a function to all elements of this set, and then flattening the results. \\ \hline foreach & applies a function \(f\) to all elements of this set. \\ \hline groupBy & returns a set of grouped elements. \\ \hline intervalize & convert a timeline into a set of intervals \\ \hline min & returns the minimum element from this set as defined by the specified comparator. \\ \hline map & returns a new set by applying a function to all elements of this set. \\ \hline max & returns the maximum element from this set as defined by the specified comparator. \\ \hline reduce & reduces the elements of this set using the specified commutative and associative binary operator. \\ \hline similarity & calculate the similarity of two timelines. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Common functions.  
Part two is a lot of Triangle Increment Tree (TI-Tree) running on a slave node. A TI-Tree is a local index structure indexing all the interval points located in the triangle corresponding to one of the triangles in the global index. We employ the Hadoop Distributed File System (HDFS) to store the index data.
# Utilization of a Mobile Application for Motor Skill Evaluation in Children +
## 2 Materials and Methods
### Study Participants  
The participants were randomly selected from students of a school in the area of A Coruna (CEIP Francisco Vales Villamarin, Betanzos, A Coruna, Spain) involved in the detection and promotion of motor coordination. Children from primary education (from first grade to sixth grade, ages from 6 to 12) voluntarily participated in this study. Each grade group had 7 or 8 children, for a total of 45 children (24 males, 21 females, mean age 9 \(\pm\) SD 2.2) enrolled in the study.  
Physical Education teachers evaluated the motor coordination state of the children through the administration of the Movement Assessment Battery for Children Second Version (MABC-2) [16] during the normal course of the class sessions.  
The aim of this study was to obtain reference results from the general population of early school years using our app, so the enrolment criteria included only those children without problems at the motor level, according to the previous assessments.  
A short test (writing their names) was carried out to check the dominance of the children. All the children were right-handed.  
The Clinical Research Ethics Committee of Galicia (CEIC) approved this study and the ethical principles agreed in the Declaration of Helsinki have been respected for all study procedures. Parents and legal guardians signed the free informed consent to collect the clinical data used in the study.  
Figure 3: Example of an exercise done with the app: (**a**) before the exercise; (**b**) during the exercise; (**c**) after finishing the exercise, showing the results.
## 8 Conclusions and Future Perspectives
While lab-scale studies demonstrate the high potential of SDBs for pollutant removal, practical deployment requires careful consideration. In the field trials, there are some challenges compared to the ideal lab-scale. For instance, variability in sludge composition can result in inconsistent biochar quality and contaminant removal performance. Furthermore, variable influent composition, fluctuating pH, flow rates, and biochar aging under continuous operation also impact the performance of SDB. Developments of flow-through system applications and standardized multi-cycle durability testing for used SDB materials should be prioritized. Bridging this lab-to-field gap through more pilot-scale, long-duration, and real wastewater studies is essential for accelerating the adoption of SDB in real wastewater treatment systems. Finally, regulatory acceptance is another crucial factor. SDB's application, especially in agriculture or open environments, should not introduce any harmful leachates or by-products. Conceptualization, L.W. and H.G. ; methodology, L.W. ; formal analysis, L.W., N.L. and L.H. ; writing--review and editing, L.W., L.L., G.C., N.L. and L.H. ; writing--original draft, H.G. and L.L. ; supervision, N.L. and H.G. ; Funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Young Scientific and Technological Talents (Level Two) in Tianjin (QN20230214), the Tianjin Natural Science Foundation (24)CYBJC01290), the Climbing Program of Tianjin University (2023 XPD-0006), and the National Key R&D Program (2024 YFC3908903). The authors declare no conflicts of interest.