esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Inner Speech Recognition via Cross-Perception Approach in EEG and fMRI
## 2 Related Work
### Unimodal Approaches  
Electroencephalography (EEG) has been widely used for inner speech recognition due to its high temporal resolution. Traditional machine learning methods have shown promising results in this domain. [PERSON] et al. [8] employed Support Vector Machines (SVMs) with Riemannian manifold features, achieving an accuracy of 68% in a four-class inner speech classification task. [PERSON] et al. [5] utilized Random Forests on time-frequency features, reporting an accuracy of 71% for a similar task. [PERSON] and [PERSON] [10] applied Linear Discriminant Analysis (LDA) to Common Spatial Patterns (CSPs) features, obtaining an accuracy of 73% in distinguishing between two inner speech classes. These studies demonstrate the potential of EEG for inner speech recognition, but they also highlight the challenges in achieving high accuracy due to the subtle nature of inner speech signals.  
Functional Magnetic Resonance Imaging (fMRI) offers superior spatial resolution for inner speech recognition. [PERSON] et al. [22] used multivoxel pattern analysis (MVPA) to decode visual imagery, achieving an accuracy of 78% in reconstructing visual patterns. [PERSON] et al. [23] employed Independent Component Analysis (ICA) combined with machine learning classifiers, reporting an accuracy of 82% in distinguishing between different semantic categories during inner speech. [PERSON] et al. [24] utilized Representational Similarity Analysis (RSA) to investigate the neural representations of inner speech, demonstrating significant correlations between predicted and observed neural patterns. These fMRI-based studies provide valuable insights into the spatial patterns of brain activity during inner speech, but are limited by the low temporal resolution of fMRI.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 3 Order Identification Based on EDM System
Therefore, in this study, the order of the EDM system was identified using the inverse M sequence that can fully stimulate various modes of the system. Combined with the characteristics of the EDM system and in view of the continuous erosion of the workpiece and the electrode in the actual processing, the amplitude of the reverse M sequence was set to 0 and 10 \(\upmu\)m after repeated trials. The input signal of the inverse M sequence in the identification process is shown in Figure 7. The data length \(L\) of the inverse M sequence was 100. Zero means that the electrode remains in the original place and 10 \(\upmu\)m means that the electrode moves 10 \(\upmu\)m toward the workpiece. The experiment was repeated three times, and \(DR(\hat{n})\) can be calculated by using the determinant ratio method. The results are shown in Figure 8. It can be seen from the figure that when \(\hat{n}\) changes from 1 to 2, \(DR(2)\) has a significant increase compared with \(DR(1)\), so the order of the EDM system model is \(n_{0}=\hat{n}=2\). Figure 7: Input inverse M sequence.
## 2 Materials and Methods
### Characterization of Nanoemulsions and Microcapsules  
#### 2.4.1 Size Distribution of Emulsions and Microcapsules  
Droplet size and size distribution of nanoemulsions were determined via dynamic light scattering (DLS) using a Zetasizer Nano-ZS90 (Malvern Instruments Ltd., Worcestershire, UK) at a fixed angle of 90\({}^{\circ}\). One mL of the emulsion was added into 10 mL of distilled water and homogenized in order to eliminate multiple scattering effects. After 90 s of equilibrium, the measurement was performed. The measurements were repeated three times and the average droplet size and average polydispersity index (PDI) were calculated.  
The diameter of dried powders was measured via low-angle laser light scattering using a Malvern Mastersizer 3000 instrument (Malvern Instruments Ltd., Worcestershire, UK). The samples were dispersed in absolute ethanol via vortexing before analyses. Particle size distributions were calculated from [PERSON]'s theory using the refractive indices of ethanol (1.361) and Maltodextrin (1.673). Mean size was expressed as the mean diameter D[4,3] of the particle size distribution. To give more information about the size distributions, the values of D\({}_{10}\), D\({}_{50}\), and D\({}_{90}\), which indicate diameters of 10%, 50%, and 90% of the volume of the particle group, were recorded. To calculate the width of the size distribution, Equation (1) was used. All measurements were performed at least in triplicate for each sample.  
\[\text{Span}=\frac{(\text{D}_{90}-\text{D}_{10})}{\text{D}_{50}} \tag{1}\]  
where D\({}_{90}\), D\({}_{10}\), and D\({}_{50}\) are the equivalent volume diameters at 90%, 10%, and 50% cumulative volume, respectively.
## 5 Conclusions  
The development of the communication systems for vehicles that exceed 500 km/h constitutes an important challenge that must be addressed by the new technology of transport by vacuum tubes. To achieve these objectives, it is necessary to follow a process similar to that carried out with GSM-R technology to adapt it to be used in high-speed railways in 2000. The starting point is the state-of-the-art radio technology available: WiFi 6 and 5G NR. Based on these technologies, it is necessary to solve the propagation problems in metallic tube environments, the very high-speed influence on handovers, Doppler, multipath, and other problems associated with both guided (leaky waveguide) and antenna propagation. In addition, systems like Hyperloop are demanding very high communication requirements compared to traditional rail systems. The confined environment, similar to subways, permits the development of these technologies with dedicated configurations. Moreover, today, there is a large experience in the deployment of communication systems in tunnel and HST environments, and there are advanced tools like propagation software simulator or hardware emulator that will allow for optimizing the communication design and testing it in the laboratory. Thus, the need for tests and measurements is minimized.  
In the future, the first step is, on the one hand, to characterize in detail the propagation at different frequencies in a test environment such as the one HyperloopTT has in Toulouse, and then it is necessary to develop a communications test environment that allows for emulating the operation of a communications system at mobile speeds of up to 1200 km/h. For this purpose, there is currently a multitude of channel emulator systems that allow propagation to be reproduced in any environment [36].  
A.T., C.B., D.C.-C. and R.B.A. have read and agreed to the published version of the manuscript.  
Hyperloop TT, company (Los Angeles, CA, USA).  
Not applicable.  
Hyperloop TT, ETSI Sistemas Telecomunicacion, Technical University of Madrid, Spain.  
The authors declare no conflict of interest.  
Figure 6: Proposal of design of a redundant network with strong overlap for critical communications and 5G for passengers.
## 4 Efficient FFT Algorithms
### Split-radix FFT Pruning  
[PERSON] _et al._[PERSON] et al. (2010) proposed Split-radix FFT (SRFFT) pruning algorithm to reduce the computational complexity in the OFDM based cognitive radio, where zero valued inputs/outputs outnumber the non-zero inputs/outputs. The SRFFT pruning algorithm was shown to be more efficient than other pruning algorithms and Transform decomposition method in computational complexity. In this method, pruning is applied to split-radix FFT algorithm proposed in [PERSON] et al. (2011) instead of normal radix 2 FFT.  
In split-radix FFT algorithm, an \(N\)-point DFT is decomposed into one \(N\)/2-point DFT and two \(N\)/4-point DFTs with twiddle factors. The decomposition is repeated for half- and quarter-length DFTs, until scalars result. The signal flow graph for SRFFT looks similar to that of radix-2 FFT except for the location of the twiddle factors. SRFFT offers reduction in complex multiplications compared to normal radix-2 FFT. Pruning the SRFFT corresponds to eliminating the computations that produce zero outputs. The general pruning proposed in [64], was applied in pruning the SRFFT with arbitrary length-\(L\) nonzero outputs.  
For an \(N\)-point DFT computation with \(N=2^{r}\), a matrix of size (\(N\times r\)) is generated with each element of the matrix corresponding to a node of the SRFFT flow graph. The elements determine whether a node value has to be computed or not. When the number of non-zero output is \(L\) and is arbitrarily distributed, then total number of complex multiplications using SRFFT pruning is given by,  
\[MUL_{SRFFTP} = \frac{L}{N}\times\left(\frac{N}{3}\times\ log_{2}(N)-\frac{8}{9} \times\ (N-1)\right) \tag{6}\]  
Whereas, the number of multiplications for \(N\)-point radix-2 FFT and Split Radix FFT are given by  
\[MUL_{Radix-2} = \frac{N}{2}\times\ log_{2}(N) \tag{7}\] \[MUL_{SR} = \frac{N}{3}\times\ log_{2}(N)-\frac{8}{9}\times\ (N-1) \tag{8}\]
# Advances in Wearable Smart Chemical Sensors for Health Monitoring
## 2 Wearable Chemical Sensors Based on Different Working Principles
### Wearable Chemical Sensors Based on Solar Cells
Researchers' efforts have been focused on creating flexible photovoltaic power sources for wearable electronics, which benefit from the advantages of both electronics and textiles. [PERSON] et al. [69] deposited polypyrrole as a catalytic material on nickel-plated cotton fabrics through the electrochemical polymerization of pyrrole, as shown in Figure 4j. Polypyrrole-coated counter electrodes were used to create dye-sensitized solar cells, achieving a power conversion efficiency of approximately 3.83% under AM1.5 lighting conditions, as illustrated in Figure 4k. Creating flexible solar panels that can withstand significant stretching and maintain durability through repeated cycles presents a challenge for electronic devices designed to be worn on the body or integrated into the skin. [PERSON] et al. [59] designed a chalcogenide solar cell using a paper-cut structure. The chalcogenide solar cell made from paper-cut material, featuring ideal geometric dimensions, demonstrated an exceptional flexibility and superior strength; for more details, refer to Figure 4i. Figure 4: Wearable chemical sensors based on solar cells. (**a**) Schematic of CPB-based chemical sensor. (**b**) Response time of the CPB sensor to pure O\({}_{2}\) gas under visible light irradiation. (**c**) Responsiveness of the dynamic CPB sensor to the injection of 1 ppm of acetone and ethanol (**d**) in air under visible light irradiation at room temperature. (**e**) Preparation process and mechanism of paper-based electrochemiluminescence-resonance energy transfer (ECL\(-\)RET) sensing platform. (**f**) Calibration plot of ECL intensity relative to miRNA-107 concentration. (**g**) Schematic diagram of the formation process of g\(-\)C\({}_{3}\)N\({}_{4}-\)Au\(-\)B\(-\)TiO\({}_{2}\) nanorods. (**h**) UV\(-\)Vis absorption spectra. (**i**) Schematic diagram of the self-powered humidity sensor and UV photodetector integrated with a fork-finger back-contact photovoltaic cell. (**j**) Photographs of nickel-coated (left) and polypyrrole/nickel-coated (right) fabrics. (**k**) Current–voltage (J-V) characteristics of dye-sensitized solar cells employing polypyrrole-coated fabric counter electrodes measured under AM 1.5 illumination. (**l**) Mechanical deformability and durability of the paper-cut structure of chalcogenide solar cells. Wearable chemical sensors powered by solar cells provide us with a new way to monitor and sense the world and the highly efficient transfer rate of solar energy conversion to chemical energy allows us to achieve comprehensive, real-time monitoring of ourselves and our surroundings without violating our privacy. The further development and application of this technology will bring more convenience and safety to our lives.
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 5 Experimental Evaluation
### Evaluation of Fireworks Dataset  
To the best of our knowledge, there is no other work that introduces the classification of firework types, so to monitor the robustness of our proposed model while validating the firework dataset, we conducted several control experiments using baseline models. As in our first work [19], we evaluated the performance of the fireworks dataset with different single-stream models. Furthermore, as in our project website [46], we implemented several two-stream baseline architectures using RGB and  
Figure 4: Sample RGB and optical flow frames. We colored the optical flow results with Hue value for better visualization. (**a**) RGB frames of three datasets; (**b**) Optical flow frames of relevant RGB frames.  
optical flow data and compared the results with our single-stream models. We noticed that our two-stream architectures outperformed the single-stream architectures. In practice, to classify dynamic patterns, it is required that we consider spatial and motion aspects separately as well as evaluating their relationship. However, prior studies did not address well the spatial-motion relationship between consecutive time steps.  
Inspired by this point, we implemented a deep neural network based on a two-stream architecture, using the SE-LSTM, to overcome the limitations of CNN and RNN. To the best of our knowledge, no previous studies assess the spatial-motion features while training to classify dynamic patterns. Performances comparisons of both single and two-stream models combining with proposed SE-LSTM concept is figuring out in Figure 5. Based on the results, it is demonstrated that the Dual-CNNSELSTM model achieved 81.76% accuracy while validating the fireworks dataset and, thus, we employ this result to make the dynamic patterns classification comparison in this work.
## 2 Materials and Methods
### Data Processing  
All parameters of Equation (1) were estimated by fitting the experimental data. The yield stress was considered as a deterministic parameter ranging in the interval (0-3.9 Pa) with steps of 0.05 Pa [36]. For each of these values, the coefficient of determination R\({}^{2}\) was estimated for the measured shear stress-shear rate values. The yield stress that maximizes R\({}^{2}\) has been assigned to the parameter \(\tau_{0}\) in Equation (1). The accuracy of the fitting of model was assessed through the standard estimation error. For each concentration (say \(i\)) of NVS, \(k\) and \(n\) parameters of the Herschel-Buckley model were related to the corresponding parameters of the unconditioned sludge.  
For the description of the thixotropic properties, the parameters of exponential models (Equations (2)-(4)) have been estimated with a non-linear least square constrained algorithm, as implemented by the _Isqcurvefit_ function of Matlab(r).  
The parameter \(\Delta\) (Equation (5)) was used to compare fittings of different exponential models in diverse conditions.  
\[\Delta=m_{line}-1 \tag{5}\]  
where \(m_{line}\) represents the coefficient of linear interpolation of model data as a function of experimental data. If \(\Delta\) is positive the model overestimates experimental data, and vice versa models were also compared using the Akaike's Information Criteria (AIC) index [39] (Equation (6)), which represents a method for evaluating and comparing statistical models. It is defined as:  
\[\text{AIC}=2p-2\text{ln}(L) \tag{6}\]  
where \(p\) represents the number of parameters in the statistical model and \(L\) is the maximized value of the likelihood function of the estimated model. In this study, given the Gaussian distribution of the error, \(L\) has been considered equal to the sum of the normalized residues squared. The AIC index provides a measure of the quality of the estimate considering both the goodness of fit and the mathematical complexity of the model. Generally, models with the lowest AIC are preferred.
## 3 Results
### Data Flow and Source Analysis  
Interviews revealed that communication gaps between warehouse operators and production line personnel were a primary source of inefficiency at Focchi S.p.A. The lack of real-time visibility into inventory levels and locations further hindered effective decision-making. Additionally, the reliance on manuals, paper-based systems, and siloed data management increased the risk of errors and delays. Analysis of Focchi's existing data infrastructure highlighted a complex landscape of interconnected systems useful to meet the DT's requirements (Table 2) and classified in a data inventory (Table 3). While the company's Enterprise Resource Planning (ERP) system includes a Warehouse Management System (WMS) (data set #1) module for material tracking, it remains underutilized due to perceived time constraints. Currently, information management relies heavily on the ERP system, which is customized for Focchi's manufacturing process and covers a timeframe of up to two months from material acceptance to production line utilization. Product information, including the traceability of raw materials, components, and finished products based on the bill for the materials, is managed within the ERP. In addition to the ERP, Focchi has developed proprietary tools and software, some of which are integrated with the ERP for data input. These tools support production planning (data set #2), inventory checks, and production order scheduling. They can be used in the design of the DT exploiting the IoT, and these new designed data sources can also be used with BIM information (data set #3) and its visualization (Figure 3). Data inventory revealed that real-time data are not essential for most warehouse operations due to the extended storage duration and assembly times. This is also understandable due to the sampling frequency of one every 12 h. However, safety measures, currently lacking data support, necessitate real-time (tenth of a second) interactivity to prevent collisions. This integration can be facilitated through the DT and IoT design and implementation. Data inventory rises are also issues related to challenges of data format, protocol variations, and closed systems (as is the case with ERP). These issues must be addressed by supporting raw data aggregation and analysis in the DT, leveraging existing data sources for their current scope, and simplifying the process for system integration and software development. The aggregation of these data will be part of the DT implementation with specific database definitions.  
Figure 3: BIM model of Focchi headquarters. The BIM model is used as a 3D model for the digital twin and its georeferenced data and warehouse locations will provide information to be linked to IoT network.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Dataset** & **Dataset** & **Data** & **Type of** & **Data Origin** & **Storage** & **Sampling** & **Storage** & **Storage** & **Read Frequency** & **Update** \\
# Prestack Seismic Inversion via Nonconvex L\({}_{1-2}\) Regularization
## 2 Method
### Resolving the Inversion Objective Function  
To find the solution of the above objective function, we apply the convex function difference algorithm in order to decompose Equation (19) into \(F(\mathbf{x})=G(\mathbf{x})-H(\mathbf{x})\), where  
\[\left\{\begin{array}{c}G(\mathbf{x})=\frac{1}{2}\|\mathbf{G}^{\prime} \mathbf{x}-\mathbf{b}\|_{2}^{2}+\lambda\|\mathbf{x}\|_{1}\\ H(\mathbf{x})=\lambda\|\mathbf{x  
where **y** is the Lagrange multiplier and \(\sigma\) is the penalty parameter. Equation (24) is solved by employing the alternating direction method of multipliers [23]; this method has an iterative recursive form that is expressed as  
\[\left\{\begin{array}{c}\textbf{z}_{k+1}=\arg\min_{z}L_{\delta}(\textbf{x}_{k}, \textbf{z},\textbf{y}_{k})\\ \textbf{x}_{k+1}=\arg\min_{x}L_{\delta}(\textbf{x},\textbf{z}_{k+1},\textbf{y }_{k})\\ \textbf{y}_{k+1}=\textbf{y}_{k}+\delta(\textbf{x}_{k+1}-\textbf{z}_{k+1}) \end{array}\right. \tag{25}\]  
where \(k\) is the number of iterations. The iterative formulas for **x** and **z** are  
\[\left\{\begin{array}{c}\textbf{x}_{k+1}=\left(\left((\textbf{G}^{\prime})^{ \mathsf{T}}\textbf{G}^{\prime}+\delta\textbf{I}\right)^{-1}\left((\textbf{G}^ {\prime})^{\mathsf{T}}\textbf{b}-\textbf{v}+\delta\textbf{z}_{k}-\textbf{y}_ {k}\right)\right)\\ \textbf{z}_{k+1}=\operatorname{shrin}\operatorname{k}(\textbf{x}_{k}+ \textbf{y}_{k}/\delta,\lambda/\delta)\end{array}\right. \tag{26}\]  
where \(\operatorname{shrink}(\cdot)\) is the soft threshold operator. To ensure fast convergence of the algorithm, we apply an adaptive penalty factor \(\delta_{k+1}:\)  
\[\delta_{k+1}=\min(\delta_{\max},\tau\delta_{k}), \tag{27}\]  
where \(\min(\cdot)\) is the minimum value function and \(\delta_{\max}\) is the maximum value of \(\delta_{k}\). We define the constraint weight \(\tau\) as  
\[\tau=\left\{\begin{array}{ll}\tau_{0},&\delta\frac{\|\textbf{x}_{k+1}- \textbf{z}_{k}\|_{2}}{\|\textbf{x}_{k}\|_{2}}<\varepsilon,\\ 1,&\delta\frac{\|\textbf{z}_{k+1}-\textbf{z}_{k}\|_{2}}{\|\textbf{x}_{k}\|_{2 }}\geqslant\varepsilon.\end{array}\right. \tag{28}\]  
where \(\tau_{0}\) and \(\varepsilon\) are the initial values for these parameters that are set to 0.001.  
Once we've obtained **r** from \(\textbf{r}^{\prime}\) in Equation (18), we can calculate the corresponding P impedance, S impedance, and density values:  
\[\left\{\begin{array}{l}\textbf{I}_{p}=\boldsymbol{\xi}_{p\_low}(1)\exp\! \left[2\sum_{i=1}^{N}\textbf{r}_{p}(i)\right],\\ \textbf{I}_{s}=\boldsymbol{\xi}_{s\_low}(1)\exp\!\left[2\sum_{i=1}^{N}\textbf{ r}_{s}(i)\right],\\ \rho=\boldsymbol{\xi}_{p\_low}(1)\exp\!\left[2\sum_{i=1}^{N}\textbf{r}_{p}(i) \right].\end{array}\right. \tag{29}\]
# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 3 Methods
### Texture Differential Image  
The texture-differential image is created from the NL-means-filtered image by using the median filter and sum-of-square operation. The image is used as one of the input layers. The texture differential image is created as follows:  
1. applying the NL-means filter to the sigma-naught image,
2. calculating the difference between the NL-means-filtered image and the sigma-naught image,
3. creating the texture image by applying the sum-of-square operation to the difference image,
4. applying the median filter to the texture image,
5. subtracting the median-filtered image from the texture image.  
The texture image is calculated by summing the squares of the pixel values of the difference image within a window kernel. The difference image is acquired by the difference between the sigma-naught image and NL-means-filtered image. The texture differential image is generated by subtracting the median-filtered image from the texture image. The kernel size of the NL-means filter in the texture-differential image generation may be same as, or smaller than, the intensity-differential image generation. The NL-means filter is utilized to enhance the object edges in the texture-image processing, unlike its use for noise reduction in the intensity-image processing. Thus, a little weak filtering can be imposed in the NL-means filtering. If the optimal processing is applied, most ship pixels in the texture image are still bright, except some pixels within large ships. Thus, the ship objects have higher pixel values through the sum-of-square calculation. The brightness values of background pixels are reduced by using the texture-differential image, which is created by subtracting the median-filtered image from the texture image. In this texture-differential image, the ship objects are highlighted, while the side-lobe and ghost effects, fish farms and offshore bars have low brightness values. Therefore, the texture-differential image can be efficiently used to reduce the misdetection rate in some areas.