esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 3 Experiments and Results
### Geomagnetic Positioning Experiment  
In this section, we apply one feature (total intensity of magnetic field), two features (horizontal and total components), three features (three-axis components), four features (three-axis components and horizontal intensity) and five features to geomagnetic positioning. Positioning errors are presented  
Figure 11: Geomagnetic characteristics interpolation map. (**a**) \(X\)-axis component interpolation map; (**b**) \(Y\)-axis component interpolation map; (**c**) \(Z\)-axis component interpolation map; (**d**) horizontal component interpolation map; (**e**) magnetic field strength interpolation map.  
in Figure 12. We also counted the positioning error of 50% and 80% of the test points, and compute the mean positioning error and root mean square error (RMSE) in Table 2.  
As shown in Table 2 and Figure 12, one-feature or two-features data can differentiate positions with lower accuracy. When five features data are used, the positioning accuracy and the RMSE are 2.98 m and 4.01 m, which are better than those of using four or fewer features. The positioning accuracy gradually increases with the number of features of data increasing. Based on the experimental results in Section 3.1, five-features data have different distributions in different areas and can better describe the magnetic field at different points. Therefore, we can conclude that five geomagnetic features could improve the geomagnetic positioning accuracy and geomagnetic fingerprint specificity.
## 3 Results
### SSAE vs. LDA  
Classification errors were computed for each day using five-fold cross validation, in which data of an individual day was divided randomly into five equal folds, ensuring that each fold had an equal number of all movements. Results were then averaged over seven days for each subject.  
For this analysis, EMG data were arranged in four sets including sEMG and iEMG data of both healthy and amputee subjects. All four sets of data were classified with SSAE and LDA separately and the results are shown in Figure 5 as the average of 10 healthy and six amputee subjects.  
Figure 4: Parameter selection for both layers. As a greedy layer-wise training strategy was adopted, layer two was trained independent of layer one. The same parameter values were varied for both layers and the best values were chosen to optimise the errors at (**a**) layer one and (**b**) two.  
SSAE achieved statistically less error rates than LDA for all cases (_p_ < 0.001) (Figure 5).
## 4 Results and Discussion
### Synthetic Dataset
In the t-SNE plot, each data point is represented as a colored dot, with the original data points shown in one color (e.g., blue) and the synthetic data points generated by the TGAN model shown in another color (e.g., orange). The proximity of the data points in the plot indicates their similarity in the high-dimensional feature space. The t-SNE plot shows that the synthetic data points generated by the TGAN model are well-distributed and intermixed with the original data points. This suggests that the TGAN model has successfully captured the underlying patterns and distributions present in the original data, and the generated synthetic data points share similar characteristics and relationships with the real data points. The ability of the TGAN model to generate high-quality synthetic data is crucial for data augmentation techniques, as it can help increase the size and diversity of the training dataset without introducing unrealistic or inconsistent data points. By generating 1000 additional data points using the TGAN model, the total training dataset size has been increased, potentially improving the performance and generalization capabilities of the ML models trained on this augmented dataset.
## 3 Proposed Identification Methods
### Excitation Signal  
The 2 MM system is excited using a swept-sine signal, also known as a periodic chirp, with a duration \(T_{f}\). The signal has a starting frequency \(f_{1}\), a final frequency \(f_{2}\), and an amplitude \(A\). The equation for the swept sine signal is given by  
\[x(t)=A\sin\left(2\pi\left(\frac{1}{2}(f_{2}-f_{1})\frac{t^{2}}{T_{f}}+f_{1}t \right)\right) \tag{7}\]  
where \(t\) ranges from \(T_{0}\) to \(T_{f}\).
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 5 Proposed Models
### Single-Modal ST-GCN for Skeleton Data  
Skeleton data consists of sequential time series data. Notably, the field of skeleton-based action detection has witnessed a surge in the adoption of graph convolutional networks (GCNs) [40; 41]. The ST-GCN-based model enhances expressive power and exhibits stronger generalization capabilities. Recently, pathological gait categorization has employed ST-GCN, incorporating attention mechanisms with 3D skeletal data [42]. This innovative technique introduces an attention mechanism for spatiotemporal GCNs, enabling a focus on crucial joints within the current gait. There are two types of edges, namely the spatial edges that conform to the natural connectivity of joints and the temporal edges that connect the same joints across consecutive time steps. Multiple layers of ST-GCN are constructed thereon, which allow information to be integrated along both the graph and the temporal dimension. This is achieved after initially extracting spatiotemporal features from 3D skeletal data through joint linkages and subsequently applying these features to GCNs. The ST-GCN attention mechanism facilitates the concentration on significant joints during pathological gait classification based on skeleton data (refer to Figure 3b for the block diagram illustrating a single-modal ST-GCN). Here, our previously proposed ST-GCN model from [43] was utilized, which uses an attention technique applied to pathological gait classification from the skeleton information. Our focus was twofold. The first objective was to extract spatiotemporal features from skeletal information presented by joint connections and to apply these features to graph convolutional neural networks. The second objective was to develop an attention mechanism for spatiotemporal graph convolutional neural networks to focus on important joints in the current gait. This model establishes a pathological gait classification system for diagnosing sarcopenia.  
As detailed in Section 4.3, Azure Kinect was employed to capture the 3D skeleton dataset, which encompasses 32 joints representing the human skeleton [44]. However, in this study, joints were selectively utilized as input data for the ST-GCN model. Specifically, joints such as the nose, left eye, right eye, left clavicle, and right clavicle were excluded. Instead, only 25 joints (as shown in Figure 4) sourced from Azure Kinect, which align with the same 25 joints found in Kinect v2, were used.
## 4 Transmission Line Fault Detection
As can be inferred from Figure 5c, the S-index pertaining to Phases A and B increases just after the fault event and continues to be near zero for the healthy Phase C. From Figure 5d, it is clear that the FI is zero at the moment of the pre-fault condition and increases just after the fault event for Phases A and B, which are the fault, but remains zero for Phase C, which is healthy. The FI pertaining to Phases A and B is above the pre-set threshold just after the occurrence of the fault with an LLG nature, whereas this FI continues to be zero for healthy Phase C. Therefore, the LL-G fault is efficiently recognized by the use of the technique introduced in this manuscript. All the phases are grounded at the same moment at the middle of the PTL to realize the fault with an LLL-G nature. The current measured at the sending end of the PTL, the W-index, the S-index, and the FI during the event of the LLL-G fault are illustrated in Figure 6a-d, respectively. From Figure 6b, it is clearly inferred that the magnitude of the W-index falls nearly to zero prior to the fault incidence related to all phases. This index associated with all phases rises immediately following the fault event. As pointed out in Figure 6c, the S-index related to all the phases rises immediately following the fault event. It can be concluded from Figure 6d that the FI is zero in the pre-fault condition and increases just after the fault event for all the phases. The FI related to all phases is high in comparison to the pre-set threshold just after the occurrence of the LLL-G fault. Hence, the LLL-G fault can be efficiently detected with the help of the proposed technique. Figure 5: Identification of the fault with an LL-G nature: (**a**) signals of the current (**b**) W-index (**c**) S-index, and (**d**) FI.
# 4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
## 2 Related Work  
Conventional research of generative models, including principal component analysis (PCA) [16], the Gaussian mixture model (GMM) [17], and independent component analysis (ICA) [18], assuming a simple formation of the data set. Later, restricted Boltzmann machines (RBMs) [19] and the Markov random field (MRF) [20] were hindered for the reason of lack of effective latent representations.  
Different from the present promising generative models, the GAN architecture consists of two opposing networks trying to outperform each other through a min-max game process. GAN is capable of modeling complicated and high-dimensional distributions, thus trains the learning model to generate data more similar to real samples. Nevertheless, the GAN model faces the converging problem in the training stage leading to the wide discrepancies between generated samples and the natural ones [21]. Meanwhile, the GAN cannot accurately represent the intrinsic characteristics of the normal samples because of the latent manifold providing little useful information [22]. To improve the quality of GAN-based detection, Mean and covariance feature matching GAN [23] was employed to provide mean and covariance feature matching, thus restricting the range of the parameters for reducing discrimination. Loss-sensitive GAN [24] tries to learn a loss function quantifying the quality of generated samples and uses this loss to generate high-quality data. In this paper, we manage to combine GAN and VAE, learning latent both spatial and temporal representations of real EEG. Due to imperfect measures such as the squared error and the injected noise, the generated signal samples are often distorted, viewed as a disadvantage of VAE [25], which can be made up nicely through repeated adjustment by the GAN model.  
Our model aimed to decode the MI-EEG by simultaneously training a conditional VAE and GAN network, enforcing the learning of the EEG data representations. Besides, we utilized statistic feature fusion to make the training converge more stable and fluent.
# Predictive Quantization and Symbolic Dynamics
## 4 Representation
### Combined Optimization  
The algorithm for combined predictive and representation optimization that accomplishes the information dynamics objective (7) is given in Algorithm 3. This algorithm  
Figure 7: Noisy channel between encoder and decoder.  
consists of an inner symbolization loop that finds the best quantization threshold for providing latent representation quality, and an outer loop that uses bit allocation to reduce the fidelity of a previously learned encoding. Although not a fully end-to-end representation and prediction method, we propose this algorithm as a way to search over lossy variants of previously learned high-rate representation encoding, taking into account the predictive properties of the encoded time series using symbolic dynamics.  
```
Given a time series \(X=(x_{1},x_{2},\ldots,x_{T})\), an element-wise encoder-decoder pair \(z=Enc(x),\hat{x}=Dec(z)\), with \(Z=(z_{1},z_{2},\ldots,z_{T})\), an error function \(Dist(x,\hat{x})\), a bit-allocation procedure with total rate parameter R, \(\hat{z}=Bitalloc(z,R)\), and a symbolization oracle with threshold \(\theta\), \(S=Oracle(Z,\theta)\)  Perform an encoding \(Z=Enc(X)=(z_{1}=Enc(x_{1}),z_{2}=Enc(x_{2}),\ldots,z_{T}=Enc(x_{T}))\) for rate \(R\) from \(R_{max}\) to \(R_{min}\) with decrements \(\delta\)do  Compute the reduced rate latent code \(\hat{Z}=Bitalloc(Z,R)\)  Compute the mean error \(D=E(Dist(X,Dec(\hat{Z}))\)  Normalize the sequence \(Z\) so that the maximal distance between samples of \(Z\) is 1 for threshold \(\theta\) from 0 to 1 with increments \(\epsilon\)do  Compute \(\theta^{*}=argmax(IR(\theta))\)  Store the result in a Loss Matrix \(L(R,\theta^{*})=IR(\theta^{*})-\lambda D\) endfor endfor  For best model select \(R^{*},\theta^{**}=argmin_{R,\theta^{*}}L(R,\theta^{*})\). return a symbolic encoding \(S\) of the original time series \(X\), \(S=Oracle(Bitalloc(Enc(X),R^{*}),\theta^{**})\)
```  
**Algorithm 3** Combined Rate-Prediction
## 1 Introduction  
While Optical Character Recognition (OCR) is regularly considered to be a solved problem [1], gathering the textual content of historical printings using OCR can still be a very challenging and cumbersome task [2], due to various reasons. Among the problems that need to be addressed for early printings is the often intricate layout containing images, ornaments, marginal notes, and swash capitals. Furthermore, the non-standardized typography represents a big challenge for OCR approaches. While modern fonts can be recognized with excellent accuracy by so-called omnifont or polyfont models, very early printings like incunabula (books printed before 1501), but also handwritten texts usually require book-specific training in order to reach Character Error Rates (CERs) well below 10% or even 5%, as shown by [PERSON] et al. [3] (printings) and [PERSON] et al. [4] (manuscripts). For a successful supervised training process, the Ground Truth (GT) in the form of line images and their corresponding transcriptions has to be manually prepared as training examples.  
In the last few years, some progress has been made in the area of historical OCR, especially concerning the character recognition problem. An important milestone was the introduction of recurrent neural networks with Long Short Term Memory (LSTM) [5] trained using a Connectionist Temporal Classification (CTC) [6] decoder, which [PERSON] et al. applied to the task of OCR [7]. The LSTM approach was later extended by deep Convolutional Neural Networks (CNN), pushing the recognition accuracy even further [8; 9].  
The present paper describes our efforts to collect these recent advances into an easy-to-use and platform independent software environment called OCR4 all that enables an interested party to obtain a textual digital representation of the contents of these printings. OCR4 all covers all steps of an OCR workflow from preprocessing, document analysis (segmentation of text and non-text regions on a page), model training, to character recognition of the text regions. Our focus is throughout on an easy-to-use and efficient method, employing automatic methods where feasible and resorting to manual intervention where necessary. A special feature of our process model is that manual interventions lead to the production of high quality GT being used as additional training data, thus enabling a spiral towards continuously higher automation. In the following, we give a short overview over the steps of a typical OCR workflow and how we address the challenges that arise for early printings.
## 4 Conclusions  
PIC coating layers consisting of CS and CHI on ALG-Ca fibers prepared by solution spinning was demonstrated. The thickness of the PIC layers on the resulting ALG-PIC fibers was controllable: it increased with increasing concentration of CHI solution used to form the PIC layer. The PIC coatings improved the mechanical strength of the fibers. In addition, the solubility in PBS of the fibers was greatly suppressed by the PIC coatings. These results indicate the existence of electrostatic interactions and hydrogen bonds among ALG-Ca and CS and CHI of PICs. Furthermore, FITC-BSA-loaded ALG-Ca fibers and FITC-BSA-loaded ALG-PIC fibers were successfully prepared. Thicker PIC layers slowed the release of FITC-BSA from the fibers and suppressed the initial burst release. Accordingly, the PIC coatings of the fibers improved the strength and durability of the core fibers and provided sustained drug release, and such fibers can be utilized as practical drug carriers. Because fibers can be used not only as the fiber morphology but also as materials in other forms created by bundling or weaving, they can be used as drug carriers in various internal and extracorporeal environments. The present approach can be utilized for other proteins such as real protein drugs. In such cases, the loading efficiency and the effect of the PIC coating layers on the release ability might be different depending on the size and surface charges of the proteins. A series of systematic investigations along these lines is one of the future tasks required to bring this system closer to practical use.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13148053/s1](https://www.mdpi.com/article/10.3390/app13148053/s1)]([https://www.mdpi.com/article/10.3390/app13148053/s1](https://www.mdpi.com/article/10.3390/app13148053/s1)), Figure S1: Diameters of ALG-Ca fibers and ALG-PIC fibers with each concentration of CHI solutions; Figure S2: Fluorescence microscopic images of ALG-PIC fibers and schematic image of irradiation and excitation light through the fibers.  
Conceptualization, M.H.; methodology, M.H., K.I. and Y.Y.; investigation, H.M.; data curation, T.S., H.M. and M.H.; writing--original draft preparation, T.S.; writing--review and editing, M.H.; visualization, T.S.; supervision, M.H.; project administration, M.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
Figure 10: (**A**) Time course for release behavior of FITC-BSA and (**B**) initial burst release ratio of FITC-BSA in FITC-BSA-loaded ALG-Ca fiber and FITC-BSA-loaded ALG-PIC fibers.  
**Funding:** This research was funded by JSPS KAKENHI, grant number JP25410178 and JP16K05799.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We also thank [PERSON] (Tokyo University of Science) for allowing us to conduct the analysis of the release ratio of FITC-BSA using a microplate reader.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Combining the monitoring results obtained by SBAS-InSAR technology with field investigations can accurately identify regional deformations, and its efficiency and accuracy are significantly improved compared to traditional methods. InSAR data have a wide coverage range and obtain a large amount of deformation information data. Direct analysis of these data during the identification of disasters is often time-consuming and laborious [13; 14]. Therefore, it is necessary to explore an effective method for rapid statistical analysis of data to achieve rapid and accurate identification of disasters. Kernel density analysis is a spatial analysis tool that can be used to calculate the unit density of point features within a specified neighborhood range. InSAR deformation results are usually distributed in a wide area of discrete monitoring points. Through kernel density analysis, these discrete deformation points can be smoothed to generate a spatial density distribution map to show the spatial distribution pattern of deformation data [15; 16; 17]. This method can quickly process a large amount of InSAR deformation data and transform complex deformation information into intuitive spatial distribution maps, which facilitatesthe rapid identification of potential geological hazards such as landslides and collapses [18], improving data processing, identification efficiency, and accuracy of geological hazard identification [18; 19; 20; 21; 22; 23]. In this way, the spatial distribution patterns of deformation can be revealed, the ability to identify tiny deformations can be enhanced, and potential geological hazards can be intuitively seen in which areas to ensure the safety of tower foundation site selection and the operation of power lines. In this study, in order to explore an effective method for the early identification of geological hazards in the Loess Plateau region, the transmission line in Weinan City and Xi'an City, Shaanxi Province, was selected as the study area, and SBAS-InSAR technology was used for the early identification of geological hazards. By combining kernel density analysis, the InSAR deformation rate, and field investigation, potential geological hazards around the line could be quickly and accurately identified at an early stage, providing suggestions for the site selection and smooth implementation of the transmission line project.
# An Improved Small Target Segmentation Model Based on Mask Dino
## 3 Materials and Methods
### Improvement of Swin Transformer
#### 3.1.3 Adjustment of the Swin Transformer Network Structure  
To extract more small object features and accommodate different tasks and various types of data, the flexibility of Post-Norm is more suitable for handling diverse inputs. Therefore, adjustments were made to the backbone's Swin Transformer structure by moving the Layer Norm layer from in front of the Attention layer to behind it. After computing the attention, normalization operations are performed on the output to stabilize the values. This helps the model generalize better on data outside the training set, allowing the learned feature representations to be more natural and less reliant on normalization constraints. It also enables the model to learn inter-layer feature fusion more effectively [29]. Since the normalization step is located at the module output, it enhances the efficiency with which subsequent layers utilize the outputs of previous layers, achieving better performance in various complex segmentation tasks [30].  
In Swin V2, the authors adopt a deep network model and introduce an additional layer normalization layer every 6 blocks on the main branch. Although layer normalization itself is not very complex, adding extra layer normalizations to each main branch of the model increases the overall computational burden, especially in the SwinV2-G model, which has 42 Transformer modules and is already quite deep. This can lead to increased training and inference times. Therefore, this paper uses a shallow Transformer network in the Swin Transformer backbone and introduces an additional layer normalization layer in the main branch. This normalization strategy can improve the feature extraction process, allowing each layer to learn the detailed features in the backbone more effectively, helping the model perform well in various segmentation tasks. Additionally, as a regularization method, it helps the model maintain generalization capabilities across different datasets and reduces the risk of overfitting. The improved structure is shown in Figure 2.