esa-sceva/satcom-chunk-collection huggingface dataset:
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
## 2 Methods
### Sparse Decomposition (SD)  
More recently, in the signal processing field, it was discovered that a better way to capture the structure of natural images, speech audio an other types of signals is to have available a large collection of prototype atoms \(\{\boldsymbol{\phi}_{i}\in\mathds{R}^{N}\}\) (\(i=1,2,\ldots,I\)) with \(I\geq N\) and use only few and distinctive coefficients to represent every signal \(\mathbf{x}\) in the space. This model is mathematically described by adding an sparsity constraint \(\|\boldsymbol{\alpha}\|_{0}\leq K\) to the model of Equation (2) leading to the following equation:  
\[\mathbf{x}\approx\boldsymbol{\Phi}\boldsymbol{\alpha},\text{ with }\| \boldsymbol{\alpha}_{i}\|_{0}\ \leq K\ll N, \tag{4}\]  
This approach is usually referred as \"sparse coding\" or \"sparse representation\" of signals and matrix \(\boldsymbol{\Phi}\in\mathds{R}^{N\times I}\) is called a \"dictionary\" ([32; 33] (see Figure 1c).  
For general overcomplete (\(I\geq N\)) dictionaries, the sparse vector of coefficients can be obtained by applying some of the available algorithms that were designed to solve the sparse coding problem, which includes greedy methods such as matching pursuit (MP) [34], orthogonal matching pursuit (OMP) [35], compressive sampling matching pursuit (CoSaMP) [36], \(\ell_{1}\) norm minimization methods such as basis pursuit [37] and many others (see [38] for a review of algorithms).  
Some theoretically derived dictionaries, e.g., those based on the Discrete Cosine Transform (DCT) or Wavelet Transform (WT), are excellent candidates for sparse coding. However, in some ML applications when large datasets are available, sometimes it is good idea to learn an optimal dictionary for an specific dataset. To that end, some dictionary learning algorithms are proposed in the literature [39; 40]. Sparse coding has to date a large record of successful applications in signal processing tasks like compressed sensing [41; 42], blind source separation [43], denoising [39], inpainting [44] and others.
## 2 Experimental Setup and Methods
### Numerical Simulation  
Due to the complicated structure of the flow field in the leeward side of wind-break walls, it is difficult to obtain flow information accurately with a Pitot tube as the only one-dimensional and unidirectional speed measuring device. Therefore, numerical simulation on computational fluid dynamics (CFD) was used to study the wind field information under the corresponding conditions of the wind-tunnel experiments (Figure 3). Meanwhile, the wind speed comparison between the wind-tunnel experiment and the numerical simulation was used to verify the reliability of the numerical simulation. Results show that the three-dimensional channel size of the numerical simulation was consistent with the working section of the wind tunnel. The wind speed profiles of wind-tunnel experiments were collected at the inlet with the velocity-inlet as the inlet boundary condition, the outflow as the outlet boundary, and the walls as other boundaries [27; 28; 29].
## 1 Introduction
### Motivation
#### 1.1.1 Cultural Tourism as the Application Scenario  
Virtual historic cityscapes are employed in various scenarios [8; 9; 10; 11], for example, to teach history and heritage in informal settings such as museum experiences, serious games, or television broadcasts [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. For example, interactive applications for city exploration [23; 24; 25] allow virtual visits and remote spatial learning [26], guide visitors through the city [27; 28; 25], provide access to additional information, and allow users to gain a virtual view of temporal changes, historical spaces, buildings and monuments, or covered parts [9; 28; 29; 30; 31; 32; 33]. Extended reality (XR) includes different levels of augmentation of reality with computer-generated content. This is mostly visual content, ranging from augmenting real-world views with information or graphical elements (augmented reality, AR) to fully computer-generated virtual worlds (virtual reality, VR) [34]. Several application scenarios are specifically relevant to cultural heritage (see recent works [8; 9; 31]). A frequently discussed scenario is the use of XR for 4D city exploration [23; 24]. Various sub-scenarios can be supported by XR applications [25]:  
* Before visiting the place of interest, XR applications can be used to obtain inspiration or an image of the place [35] or plan a visit [25].
* During a visit, XR applications can help guide visitors through the city [25], offer virtual city tours [27; 28], provide information about the city's history and also about amenities (e.g., restaurants) [36], allow users to obtain a virtual view of temporal changes, historical spaces, buildings, and monuments [9; 29; 30; 31; 32], and provide a visual impression of hidden or covered parts [28; 33].
* Following a visit, XR applications can assist users in recalling tours and visits, or provide access to places that visitors have not been able to visit [25].
# Efficient Non-Destructive Detection for External Defects of Kiwifruit
## 3 Experiments and Results
### Model Comparison  
AlexNet [31] is the first to introduce the Relu activation function to speed up model convergence and to use the Dropout mechanism to avoid the problem of model overfitting. AlexNet consists of a total of eight convolutional neural network layers, including five convolutional layers, two fully connected layers, and an output layer, which allows for deep feature extraction. Each convolutional layer in the VGG [32] uses a smaller 3 \(\times\) 3 convolutional kernel, and this small convolutional kernel can increase the sensory field of the convolutional layer, which can improve the network's extraction of features. Moreover, the shallow and deep convolutional structures of the VGG are basically the same, and they are easy to optimize. Inception [33] is a multi-scale convolutional neural network. Each module in Inception uses a parallel structure, i.e., it operates in parallel using convolutional kernels of different scales and then splices them. The network is realized to extract the features in the image at different scales to obtain the optimal result. As shown in Table 7, the experiment compares the recognition accuracy and recognition consumption time of AlexNet, VGG16, InceptionV3, ResNet34, and ResNet34 + CBAM for the kiwifruit samples test set, and it can be found that ResNet34 + CBAM has the highest recognition accuracy, which is higher than AlexNet, VGG16, InceptionV3, and ResNet34 by 7.9%, 12.7%, 11.8%, and 4.3%, respectively. The single-sample recognition time of ResNet34 + CBAM is 15.0 ms, 6.0 ms, and 3.0 ms higher than AlexNet, InceptionV3, and ResNet34, respectively, and it is 80.0 ms lower than VGG16. It can be concluded that AlexNet, VGG16, and InceptionV3 have lower recognition accuracies for kiwifruit in the test set than ResNet34 and ResNet34 + CBAM. By analyzing the differences between the models, it can be found that AlexNet has fewer layers than ResNet34 + CBAM, and it has limited ability to extract features, so the recognition accuracy is low, but the consumed time cost is also low. VGG16 has fewer layers than ResNet34 + CBAM, but the number of fully connected layers used is high, so it results in a large number of parameters and consumes high time cost. InceptionV3 uses multiple-sized convolutional kernels for feature information extraction; it is weakly adapted to the input image compared to ResNet34 + CBAM, and it has low recognition accuracy for kiwifruit. ResNet34 + CBAM has improved recognition accuracy for kiwifruit compared to ResNet34, but it has an increase in time cost.  
In summary, this study fuses ResNet34 with the CBAM to achieve highly accurate recognition of external defects in kiwifruit, which meets the online sorting requirements of grading lines, although it results in an increase in time cost.
# Experimental Study on Flexural Properties of Polyurethane-Cement Composites under Temperature Load
## 3 Synthesis Mechanism of Polyurethane Cement Composites
### Interaction between Polyurethane and Silicate Cement  
#### 3.3.1 Mutual Adaptation of Polyurethane and Silicate Cement  
By analyzing the polyurethane components' electrostatic potential and the silicate cement's energy band density, it can be known that the silicate cement has good adaptability to the polyurethane colloid. The electron excitation of CaO and SiO\({}_{2}\) in C\({}_{3}\)S and C\({}_{2}\)S, the main components of silicate cement, requires high energy and does not undergo a series of chemical reactions when mixed with polyurethane cement composites have good corrosion resistance and high durability.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 2 Materials and Methods
### Selection and Application of Classification Models  
Finally, each feature vector is labeled with the corresponding gesture (which appears in the data file name), and the data is prepared for classification. This data is then used to train a classification model, which identifies gestures based on the characteristics extracted from the signals [19].  
Using MATLAB's Classification Learner app, a systematic approach was implemented for the selection and application of classifiers. A strategy from the general to the particular was followed, allowing a comprehensive evaluation of the models. It was composed of four steps:  
1. Initial comparison of classifiers: In a first stage, a preliminary comparison of the thirty-three available classification models was made, using accuracy (or \"effectiveness\") as the main metric. At this stage, not only did accuracy continue to be considered as an evaluation metric, but additional metrics--the F-score and the area under the ROC curve (ROC-AUC)--were also introduced to obtain a more complete picture of the performance of the models;
2. Impact of features selection on the selected models: After selecting the best-performing classifiers, an in-depth study was conducted to understand the impact of feature selection on the performance of these models;
3. Hyperparameter adjustment: based on the best combinations of the classification method and percentile obtained in the previous step, the hyperparameters were optimized for these models. This step allowed us to further refine the selected models;
4. Performance comparison between the use of all data, only EMG, and only FMG: in parallel, a comparison of the performances of the models was made using all the characteristics and characteristics exclusively derived from EMG or FMG. This step is justified by the traditional practice of using only EMG signals in the control of myoelectric prostheses and by studies that evaluate the performance of systems with FMG only. The objective of this comparison was to evaluate the feasibility of each of these types of signals for the control of prostheses.
# A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
## 3 System under Consideration  
As shown in Figure 2, the system under consideration is a PMSG-based WECS integrated to a three-phase grid via two conversion stages. An AC/DC conversion stage at the machine side applying a diode rectifier-boost topology followed by a DC/AC conversion stage at the grid side featuring a three-phase voltage source inverter (VSI).
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 2 Related Work
### Fused Positioning Methods  
Since pure PDR or BLE-based methods cannot continuously offer reliable indoor positioning service, great efforts have been made by researchers to find solutions integrating these two technologies to mitigate the limitations of each individual technology. Studies have shown that the Kalman filter and particle filter can be used to promote GPS positioning, so they can also be used to improve the performance of indoor localization systems [26]. Filter theories such as the Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter have been widely used to combine sensory data from disparate sources to obtain better positioning accuracy [22]. For instance, researchers have applied the KF [19; 47], EKF [10; 48], and UKF [49] to indoor positioning and obtained satisfying performance. However, the KF and EKF are not suitable for systems with severe nonlinearity and non-Gaussian noise [50], whereas the UKF can be applied to the nonlinear system but cannot deal with highly non-Gaussian noise [22]. On the contrary, the particle filter, which is based on Bayesian statistical theory and a sequential Monte Carlo framework, can approximate the true posterior distribution of the state for the nonlinear/non-Gaussian system. Particle filtering has demonstrated its excellent performance in several studies integrating PDR and RSS-based positioning methods [8; 21; 22; 23].
## 2 Materials and Methods
### Analysis of Volatile Compounds  
Solid-phase microextraction (SPME) coupled to gas chromatography mass spectrometry (GC-MS) were used for volatile compounds determination. Analytical methods used for experiments have been described previously [7]. Briefly, a CombiPal autosampler (CTC Analytics AG, Zwingen, Switzerland) was used to condition/extract samples and to introduce them into a GC instrument. Five grams of soy sauce was placed in a 20 cm\({}^{3}\) glass autosampler vial closed with screw caps sealed with Teflon and conditioned for 10 min at 50 \({}^{\circ}\)C. Headspace was extracted for 180 min using SPME fibers coated with 50/30 \(\mu\)m thick Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) films. Before use, the fibers were conditioned for 60 min at 270 \({}^{\circ}\)C in the GC injector port. The fibers were desorbed for 10 min at the injector port operated at 260 \({}^{\circ}\)C in the split-less mode. An Agilent 5975 C/6890 GC/MS instrument (Santa Clara, CA, USA) was used for analysis. Chromatographic separation was performed on a ZB-WAX fused-silica capillary column 60 m \(\times\) 025 mm id \(\times\) 0.25 \(\mu\)m film thickness. Helium flowing at a 1.2 cm\({}^{3}\) min\({}^{-1}\) constant rate was used as the carrier gas. The oven was programmed for 6 min hold at 40 \({}^{\circ}\)C, ramped at 4 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, ramped at 20 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, and held for 5 min. Samples were analyzed in triplicate. Urethane was used as an internal standard. The results are expressed as the relative peak area of individual quantified peaks.
## 2 Materials and Methods
### Passive Remote Sensing of SIF and Reflectance
#### 2.3.1 Instrumental Setup and Retrieval of Fluorescence
In Appendix A, the retrieval errors were assessed on a simulated database of canopy and reference panel radiances with a known level of fluorescence. The mean retrieval error (\(\overline{SIF}_{bias}\)), as well as the root mean square error (\(\overline{SIF}_{RMSE}\)) computed from retrievals on a set of simulated top of canopy (TOC) radiances were found to be higher for SIF687 than for SIF60 (\(-0.038\) vs. \(0.015\)\(\mathrm{mW}\cdot\mathrm{m}^{-2}\cdot\mathrm{sr}^{-1}\cdot\mathrm{nm}^{-1}\) for \(\overline{SIF}_{bias}\), and \(0.061\) vs. \(0.022\)\(\mathrm{mW}\cdot\mathrm{m}^{-2}\cdot\mathrm{sr}^{-1}\cdot\mathrm{nm}^{-1}\) for \(\overline{SIF}_{RMSE}\), respectively, under a PAR irradiance of \(1400\)\(\mathrm{\SIUnitSymbolMicro m}\cdot\mathrm{m}^{-2}\cdot\mathrm{s}^{-1}\) (see Appendix A). These error values should be taken into account in the interpretation of the experimental results after PAR correction.
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
###### Abstract  
In this paper, nano SiO\({}_{2}\) particles modified organic silane coatings were successfully prepared to aim at the application of the self-cleaning coating on PMMA substrate for deep-sea optical windows. The chemicals, surface microstructure, wettability, hardness, adhesion, transparency, water scouring resistance as well as microorganism attachment rate of the coatings were investigated. The results showed that adding SiO\({}_{2}\) nanoparticles into the organic silicon coating can effectively improve the hydrophobicity due to generating a micro-nano structure surface. However, excessive addition would result in a decrease in hydrophobicity, adhesion, as well as transparency, due to the inorganic SiO\({}_{2}\) particle destroying the integrity of the organic coating. The optimal coating was obtained by adding 0.5 wt% nano SiO\({}_{2}\) particles, which possessed a water contact angle of 114.2\({}^{\circ}\), hardness of 4H, adhesion level of 0, and visible light transmittance of 0.886. After 40-h water scouring, the water contact angle decreased to 108.3\({}^{\circ}\) and the visible light transmittance decreased to 0.839, suggesting good water scouring resistance. The microorganism attachment rate of the S05 coating was 0.17% after a 6 h immersion test, which was about half that of the PMMA substrate.  
Keywords:self-cleaning coating; wettability; hardness; adhesion; transparency; water scouring +
Footnote †: journal: Applied
## 3 System Architecture
### Reactive-UCB  
Algorithm 1 presents the proposed MAB algorithm (also includes the default parametrisation used in the experiments), Reactive-UCB, which evaluates the equations presented in the previous sections (assuming constant input in Line **1**) to exploit the synergies of the following arm-sampling policies: opportunistic, complete, correlated, and optimistic.  
Opportunistic sampling occurs when the average reward is good enough, i.e., statistically above \(\xi\) (0.9 in our tests) and, thus, exploration is not required (Lines **7-8**). The three other sampling policies are only available when opportunistic sampling is not active.  
Complete sampling refers to the sequential execution of every arm \(k\in\mathcal{K}\), which occurs at \(t=1\) and every time a reset occurs, i.e., whenever \(R_{t}=1\) (Lines **4-5**). This ensures that all arms are fairly sampled at least once.  
Optimistic sampling (in the face of uncertainty), which occurs with probability \(1-\epsilon\) (\(\epsilon=0.1\) in our tests) when not performing complete sampling, deterministically selects the arm with the highest upper confidence bound (breaking ties randomly) (Lines **9-10**).  
Correlated sampling, which occurs with probability \(\epsilon\) when not performing complete sampling, selects the arm by sampling the probability mass function \(F_{Y_{t}}\) (Line **12**). This ensures that all arms are likely to be selected by exploiting their correlations.  
The adaptation of the algorithm to the SDN-based routing problem occurs at Line **15** by assuming that an arm represents a path and that the reward is defined in terms of the path's delay. Formally, the reward obtained from choosing a path \(i\in\mathcal{K}\), \(X_{t}(i)\), is defined as a function of the observed instantaneous path's delay, \(\delta_{t}(i)\), which ensures that: (i) the higher the delay, the lower the reward; (ii) the delay's changes are felt more strongly in the reward for lower delay values, helping the agent to be more accurate in its best-arm choices; and (iii) \(10\,\mathrm{ms}\) is the lowest path delay. Formally,  
\[X_{t}(i)=\max\Bigl{(}0,0.5\log\Bigl{(}\max(\delta_{t}(i),0.01)^{-1}\Bigr{)} \Bigr{)}. \tag{30}\]
# Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
## 3 Converter System Description and Analysis
### Switched System Representation  
#### 3.2.1 State Space Model  
Observing (33) and (34), it becomes clear that the dynamics of the system depend on the vector \(p\), and thus, on the switching state of the inverter. Due to the control of the switches by a PWM, the system exhibits both switching behavior and continuous dynamics. To cope with this intrinsic switching behavior of the phase potentials, the system in (33)and (34) is described as a switched system with subsystems of the form in (2) with the state space vector  
\[\underline{x}(t)=\left[\begin{array}{c}i^{\prime}_{\kappa}(t)\\ i^{\prime}_{\beta}(t)\end{array}\right]\!. \tag{35}\]  
Consequently, the system matrix \(\underline{\underline{A}}\) is given by  
\[\underline{\underline{A}}=\left[\begin{array}{cc}-\frac{1}{T_{ \rm s}}&0\\ 0&-\frac{1}{T_{\rm s}}\end{array}\right]\!, \tag{36}\]  
the subsystem equilibria \(\underline{x}_{{\rm eq},s}\) by  
\[\underline{x}_{{\rm eq},s}=\frac{u^{\prime}_{\rm DC}}{R^{\prime}}\left[ \begin{array}{c}p_{a}-\frac{p_{b}+p_{c}}{2}\\ \frac{\sqrt{3}\,(p_{b}-p_{c})}{2}\end{array}\right]\!, \tag{37}\]  
and, with \(\underline{\underline{B}}=\underline{\underline{A}}\), the input \(\underline{u}(t)\) is equal to the normalized current caused by the EMF. In case of a normal operation, where it can be assumed that the angular frequency \(\omega^{\prime}\) is constant, it follows  
\[\underline{u}(t)=i^{\prime}_{\rm EMF}(t)=c_{\rm EMF}\,\omega^{ \prime}\left[\begin{array}{c}-\sin(z_{p}\,\omega^{\prime}\omega_{\rm nom}\,t +\varepsilon_{0})\\ \cos(z_{p}\,\omega^{\prime}\omega_{\rm nom}\,t+\varepsilon_{0})\end{array} \right]\!, \tag{38}\]  
where it is assumed that the angular frequency \(\omega^{\prime}\) is constant.  
Within this system representation, each possible vector \(\underline{p}\) defines one subsystem. The assignment of a given vector \(\underline{p}\) to the respective subsystem is given by  
\[s=4\cdot p_{c}+2\cdot p_{b}+1\cdot p_{a}. \tag{39}\]  
Due to the structure of the VSC, the vectors \(\underline{p}=\left[\begin{array}{cc}0&0&0\end{array}\right]^{T}\) and \(\underline{p}=\left[\begin{array}{cc}1&1&1\end{array}\right]^{T}\) imply the same voltage to the load. Under the condition that each subsystem has a unique equilibrium, both vectors are treated as subsystem 0, i.e., \(s\in\mathcal{S}=\{0,1,\ldots,6\}\).