esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 4 Experimental Results and Analysis of Feature Selection Based on Ensemble Learning
### Experimental Results and Analysis
Here, two LSTM layers were stacked together, each with its own hidden state. Learning_rate is the learning rate, controlling the step size of model parameter updates. A smaller learning rate promotes model stability. Batch_size indicates the number of samples input to the model in each update, with larger batch sizes enhancing training efficiency. Num_epochs specifies the number of iterations the model underwent over the entire training dataset. Seq_length is the sequence length, representing the temporal span of historical data considered at each time step. In this case, the model utilized data from the past 5 days to predict the closing price on the 6 th day; hence, this parameter was set to 5. The model employed the Adam optimizer and was implemented using the PyTorch framework. The final prediction results are presented in Table 6, with the best-performing metrics highlighted in bold. It is evident from the table that the gradient boosting decision tree method exhibited the best performance on one dataset, indicating that this method effectively identified the most influential features in that dataset. On the other hand, the recursive feature elimination and random forest methods achieved the best performance on two different datasets each, emphasizing their effectiveness in specific contexts. Moreover, ensemble learning methods demonstrated optimal performance on all three different datasets, underscoring their comprehensive applicability across various data types. These findings underscore the crucial role of ensemble learning-based feature selection methods in enhancing the accuracy of feature selection and optimizing the performance of time-series predictions. Employing features selected through ensemble learning for modeling demonstrated improved performance in terms of both the MAE and MSE metrics, effectively enhancing the effectiveness of time-series prediction models. This outcome emphasizes the potential of ensemble learning methods in improving the accuracy of feature selection and predictive outcomes, providing robust support for research and applications in the field of time-series prediction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Parameter Value** & **Parameter Description** \\ \hline input\_size & 5 & Input Feature Dimension \\ hidden\_size & 64 & Number of Hidden Units \\ num\_layers & 2 & Network Depth \\ learning\_rate & 0.001 & Learning Rate \\ batch\_size & 64 & Batch Size \\ num\_epochs & 100 & Epochs \\ seq\_length & 5 & Sequence Length \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Table of prediction model parameter settings.
# Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning
## 5 Conclusions  
In this paper, Field II, an acoustical simulation software well-established in the field of ultrasound medical imaging, has been applied to the simulation of the acoustic field in air produced by a circular transducer and to the evaluation of a ranging technique based on the measurement of this acoustic field.  
The original contribution of this work is to show that Field II can be profitably applied to the problem of ranging with ultrasound in the air. As the first significant result, numerical simulations have shown that it is not enough to guarantee a certain acoustic pressure in a spatial region to reach a certain low level of error. In fact, depending on the angle at which the emitter is seen, the received chirp undergoes a significant aberration in shape compared to that emitted. Shape aberration also occurs to its cross-correlation, so the usual peak detection technique cannot detect the true TOA, regardless of the signal level or SNR.  
Field II allows us to observe ranging errors greater than expected in the presence of signal shape aberrations, regardless of the SNR. This means that particular care must be taken in the acoustic design of an ultrasound positioning system and that the use of a numerical simulator such as Field II is necessary in the design phase. With such a tool, in fact, it is possible to evaluate effectively both the acoustic coverage and the accuracy of the ranging technique used.  
In particular, it was possible to observe the total coverage of a typical 4 m \(\times\) 4 m \(\times\) 3 m room by using a circular aperture of diameter D = 8.5 mm or less, a 30-50-kHz linear chirp signal, and cross-correlation-based peak detection. In this case, the maximum ranging error obtained across the entire volume was about 3.3 mm. Instead, for larger D, outside the favorable regions shown by the numerical simulations, the ranging error increases up to 14.6 mm.  
Many applications and services based on ultrasonic positioning systems can benefit from the presented simulation tool.  
Conceptualization, R.C.; data curation, R.C., M.M., and D.I.; formal analysis, R.C.; investigation, R.C., M.M., D.I., and F.G.D.C.; methodology, R.C.; software, R.C., M.M., and D.I.; supervision, R.C.; writing--original draft, R.C.; and writing--review and editing, R.C., M.M., D.I., and F.G.D.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** PAC Calabria 2014-2020, Asse Prioritario 12, Azione 10.5.12 is gratefully acknowledged by one of the authors (D.I.).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Baselines  
With the aim of demonstrating the effectiveness and superiority of the proposed unidirectional information interaction scheme, we implement the conventional single-branch network (SiNet) and bidirectional information interaction-based dual-branch network (BiInterNet) as the baselines.
###### Abstract  
Continuous control of blood glucose levels is important for the effective treatment of diabetes. The short-term use of enzymatic continuous monitoring systems involves expensive maintenance and is inconvenient, which limits their widespread use by diabetes patients. The fluorescent diboronic anthracene-embedded system has demonstrated in vivo continuous glucose monitoring for 12 times longer than enzymatic systems by protecting the dye from reactive oxygen species. However, its small Stokes shift and low excitation and emission wavelength should be heavily considered for easy fabrication. We successfully synthesized a derivative of bis-phenyl boronate with a large Stokes shift and long excitation wavelength by adding an acetyl moiety to the anthracene ring. This resulted in a -90-nm Stokes shift and 15-nm and 80-nm redshifts of the excitation and emission wavelengths, respectively. The fluorescence of the synthesized probe increased proportionally with the glucose concentration because the formation of the boronic acid-glucose complex prevented photoinduced electron transfer. The association constant and quantum yield for acetyl-substituted diboronic anthracene with glucose was 20% and 13% higher than that of the analog, respectively. While keeping resistance to the oxidation by reactive oxygen species, the improved optical properties and glucose-detecting performances of the newly synthesized dye will allow better pairing of the source and detecting unit for in vivo continuous glucose monitoring, leading to easy fabrication and then contributing more to utilization by diabetes patients.  
diboronic acids; stokes shift; photoinduced energy transfer; diabetes; glucose sensor Article  
Drug Manufacturing Center, Daegu-Cyengbuk Medical Innovation Foundation, 80 Chumbok-ro, Dong-gu, Daegu City 41061, Korea, [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (I.S.); [EMAIL_ADDRESS] (C.S.P)  
* Correspondence: [EMAIL_ADDRESS] (H.-s.Y.); [EMAIL_ADDRESS] (J.H.K.) + These authors contributed equally to this work.  
**Featured Application: Glucose detection such as in-vivo continuous glucose monitoring or point-of-care detection.**  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Activated trifluoromethyll Diboronic Acid Anthracene with a Large Stokes Shift and Long Excitation Wavelength as a Glucose-Selective Probe. _Appl. Sci._**2022**, 12, 2782. [[https://doi.org/10.3390/appl2062782](https://doi.org/10.3390/appl2062782)]([https://doi.org/10.3390/appl2062782](https://doi.org/10.3390/appl2062782))  
Academic Editor: [PERSON]  
Received: 8 February 2022  
Accepted: 4 March 2022  
Published: 8 March 2022  
**Publisher:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 4 Cell Adhesion Molecules
### Cell-ECM Adhesion Molecules  
Integrins are heterodimeric, cell-matrix adhesion receptors composed of \(\alpha\) and \(\beta\) subunits [56], that can bind ECM proteins such as fibronectin, collagens and laminins [76]. For example, \(\alpha\)2\(\beta\)1 can bind to certain members of the collagen family, \(\alpha\)5\(\beta\)1 binds to  
Figure 5: The four distinct families of cell adhesion molecules, which facilitate cell-to-cell binding or binding of cells to the ECM through adhesion. Adapted from [77] under Creative Commons CC BY license. Created with BioRender.com.  
fibronectin, and \(\alpha\
u\beta 3\) shows more promiscuous binding to a selection of ligands including fibronectin, vitronectin and fibrinogen [78]. Both the \(\alpha\) and \(\beta\) integrin subunits are membrane traversing glycoproteins with extracellular domains, single transmembrane helices and cytoplasmic regions, allowing the integrin molecule to interact successfully with the cytoskeleton. Integrins are normally sub-grouped depending on their ligand-binding affinities or on their subunit composition. Generally, the most common integrins are the \(\beta 1\) integrins, which are known to show a strong affinity for the major ECM proteins fibronectin, collagens and laminins [56].  
Following interaction of the integrin receptor with ECM proteins, the integrin itself undergoes a conformational change [56]. As a result, focal complex proteins such as focal adhesion kinase (FAK) and integrin-linked kinase (ILK) bind to newly exposed integrin cytoplasmic domains, forming a focal adhesion complex and initiating clustering of integrin molecules at the membrane site. This leads to activation of specific intracellular signalling cascades such as the mitogen-activated protein kinase (MAPK) pathway, leading to the regulation of essential cellular processes such as proliferation, differentiation, polarity and gene expression in what is known as an \"outside-in\" signalling process [56]. It is important to note that signals from intracellular proteins can also alter integrin activation and shape, changing their ligand-binding capacity in an \"inside-out\" signalling process. Thus, integrins are bi-directional signalling units that transmit information and allow connections between both intracellular and extracellular milieu [56].
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 3 Results  
### ATR-FTIR Spectral Characterization (Pre-Exposure)  
Figure 2 presents the ATR-FTIR absorbance spectrum of the unexposed thermochromic pigment masterbatch, showing distinct absorbance peaks characteristic of an ethylene-vinyl acetate (EVA) copolymer. These include C-H stretching near 2920 and 2850 cm\({}^{-1}\), carbonyl (C=O) stretching at \(\sim\)1740 cm\({}^{-1}\), and C-O stretching between 1240 and 1020 cm\({}^{-1}\), providing the initial evidence for EVA as the carrier matrix [70].  
In Figure 3, the ATR-FTIR absorbance spectrum of the thermochromic pigment masterbatch (ThermoBatch(tm)) is overlaid with the reference spectrum for the ethylene-vinyl acetate (EVA) copolymer (black), obtained from SpectraBase(tm)/Wiley (CAS No. 24937-78-8; entry A00641.Dx). An intense spectral match between the two indicates that the masterbatch carrier is EVA, a polymer frequently used for pigment dispersion due to its flexibility and compatibility [71]. The highest match (search score: 0.980245) was A00641.Dx, described as an \"Ethylene/Vinyl Acetate Copolymer\", followed closely by several additional EVA-based materials. The consistency of these matches and their high correlation scores (\(\geq\)0.90) with the acquired spectrum confirms the identity of the polymer as EVA.  
The sample spectrum shows prominent peaks at 2920 cm\({}^{-1}\) and 2850 cm\({}^{-1}\), attributed to the asymmetric and symmetric C-H stretching vibrations of methylene groups in the polyethylene backbone. A strong peak at 1740 cm\({}^{-1}\) corresponds to the C=O stretching vibration of the vinyl acetate moiety--this is the key distinguishing feature of EVA compared  
Figure 3: Overlay of the ATR-FTIR absorbance spectra for the ThermoBatch(tm) (magenta) and EVA reference spectra (black), confirming the material match.  
Figure 2: ATR-FTIR absorbance spectrum of the unexposed sample (magenta), showing characteristic peaks consistent with an ethylene–vinyl acetate (EVA) copolymer, including C–H stretching (\(\sim\)2920 and \(\sim\)2850 cm\({}^{-1}\)), carbonyl (C=O) stretching (\(\sim\)1740 cm\({}^{-1}\)), and C–O stretching (\(\sim\)1240–1020 cm\({}^{-1}\)).  
to pure polyethylene. A broad band between 1240 and 1020 cm\({}^{-1}\) represents C-O stretching vibrations, and the distinct peak at 720 cm\({}^{-1}\), typical of CH\({}_{2}\) rocking, supports the presence of semi-crystalline polyethylene segments in the copolymer [72]. Post-irradiation ATR-FTIR analysis was not conducted due to the technique's limited surface sensitivity relative to the shallow depth of UV-induced degradation, as well as the potential for spectral interference from thermochromic pigments. Given the more direct sensitivity of colorimetric and mechanical methods to degradation effects, FTIR was reserved for pre-exposure structural confirmation only.
## 1 Introduction  
The growing need for spectrum resources has driven the advancement of various technologies that enable radar and communication systems to coexist, thereby addressing spectrum congestion issues. A notable approach in this field is dual-functional radar-communication (DFRC), which integrates both radar sensing and communication capabilities within a single hardware platform. This integration takes advantage of a shared transmit/receive aperture and operates over the same bandwidth [1, 2, 3]. In recent developments, DFRC has been applied to multiple-input multiple-output (MIMO) systems, as illustrated in research such as [4, 5, 6]. These implementations have demonstrated significant potential in reducing multi-user interference (MUI), which is crucial for the effective detection and management of groups of unmanned aerial vehicles.  
Concurrently, large-scale antenna arrays have become integral for MIMO systems, particularly to counteract the significant path loss associated with high-frequency bands like millimeter-wave and terahertz frequencies [7, 8, 9]. While these extensive antenna arrays excel in beamforming capabilities, they also introduce challenges such as high energy consumption, complex circuitry, and increasing computation cost. To address these issues,some recent research has focused on low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) [10; 11; 12]. Based on these advancements, our study tries to explore the downlink precoding in MIMO-DFRC systems equipped with large-scale antennas and low-resolution DACs, also with consideration of the quantization constraints in DACs. Additionally, advanced radar sensing methodologies, such as the non-contact respiration and heartbeat detection technique proposed in [13], further underscore the growing importance of high-fidelity radar functionalities in such systems.
## 2 Experiments
### Measurements
#### 2.3.2 Fourier Transform Infrared Spectroscopy (FT-IR)  
Fourier transform infrared (FT-IR) spectroscopy measurement was carried out using a PerkinElmer Spectrum Two spectrometer (Waltham, MA, USA). The PBABI copolymers were measured with an average signal of 32 co-added scans, at a resolution of 4 cm\({}^{-1}\) in the wavenumber range 500-4000 cm\({}^{-1}\). The spectra were received from the tablets of the PBABI copolymers spread into KBr pellets, and the concentration was chosen to be 1% (150 mg).
## 2 Objectives and Experimental Scope  
The preparation and characterization of THFS modified asphalt binder were investigated in this study. The experimental scope of this study was the following:  
* The SK-90 was used as control asphalt binder. Five asphalt binders with different combinations of SK-90 and 0, 4%, 6%, 8%, 10% THFS by weight of SK-90 were prepared.
* The preparation conditions, including shearing temperature, shearing time and shearing rate, were selected and optimized using the orthogonal array test (OATS) and gray correlation analysis (GCA) methods.  
* The properties and rheological behaviors of the five asphalt binders were measured according to Penetration performance grade and Superpave performance grade specifications.
* The temperature step test was carried out in Dynamic Shear Rheometer (DSR). The complex modulus index (\(GTS\)) and stiffness modulus index (\(STS\)) were regressed using an indication to predict the temperature susceptibility of asphalt binder at high-and-low temperatures, respectively.
* The frequency sweep test in DSR was conducted. The master curves of complex modulus \(G^{*}\) and phase angle \(\delta\) with respect to frequency were plotted and the Christensen-Andersen-Marasteanu (CAM) models, which is a kind of rheological model, were regressed to predict the sensitivity to temperature and frequency.
* The effect of THFS on the rheological behaviors and temperature and frequency susceptibility of asphalt binder was explored through the integrated experimental-computational approach.
## 2 Materials and Methods
### Non-Negative Matrix Factorization  
Non-negative matrix factorization (NMF) was used to extract the muscle synergy from the selected ICs (ICA-synergy). NMF uses nonlinear optimization to iteratively find a potential nonunique solution [38]. NMF is constrained by non-negative constraints, where non-uniqueness is the result of non-convexity of the search space. The NMF was selected for the extraction of muscle synergies for two reasons. First, studies have shown that NMF can reconstruct muscle activation patterns better than other decomposition methods [39; 40]. Second, the muscle synergy extracted with NMF was limited to non-negative values, which is consistent with the characteristics of muscle activity. The IC \(U\) of the \(M\) channels, which is decomposed from the original EMG signal, is decomposed into:  
\[U=WC+error, \tag{8}\]  
where \(U\) denotes an \(M\times N\) time-point matrix; \(M\) is the number of calculated ICs; \(W\) is an \(M\times S\) matrix, where \(M\) and \(S\) are the numbers of ICs and synergies, respectively; and \(C\) is an \(S\times N\) matrix. Variance accounting for (VAF) \(\geq\) 90% is commonly used in studies on muscle synergy to determine the optimal amount of muscle synergy [41; 42]. The MSM simplifies the muscle groups used to control joint extension and flexion into two muscles and uses the motion signals of these two muscles as inputs for the model. To achieve a fair comparison with other regression models and fit the MSM, the number of synergies was limited to two, where the VAF of the muscle synergies was \(\geq\)80% [43].In other words, one synergy represents the extension activity and the other represents the flexion action. Based on the results of previous studies on the relationship between the number of synergies and single-joint motion, two synergies are sufficient to describe the extension and flexion motions of a single joint [44].
## 2 Motion-Planning Graph-Based Algorithm
### Reducing the Complexity of the Graph  
In this subsection, the graph-based motion-planning algorithm is built up for the whole prediction horizon. The complexity of the problem increases exponentially according to the number of the layers since every node connects to \(n\) nodes. In order to reduce the complexity of the algorithm, the nodes which have nearly the same parameters are merged. This means, the nodes, which are closer to each other than a specified distance, are replaced with one node. To solve this, the k-medoids clustering algorithm [15] is used, which is a clustering algorithm and divides the nodes in a given layer into \(k\) subsets. This plays an important role in calculating probabilities of the collision, using the density functions. The k-medoids algorithm minimizes the distance between the nodes and the computed center of the given set (\(\eta\)). During the determination of the number of the clusters, which is the input of the clustering algorithm, the following inequality must be satisfied:  
\[max(\eta_{i}-v_{j})\leq\frac{y_{b,p}}{2},\quad v_{j}\in V_{i}\quad V_{i}\subset V \quad j=1\ldots n, \tag{7}\]  
where \(\eta_{i}\) gives the center of the \(i^{th}\) set, and \(n\) is the number of the elements in the \(i^{th}\) subset and \(y_{b,p}\leq y_{b}\), where \(y_{b}\) is a bound, which is defined in Equation (1). Moreover, for the presented new subsets (\(k\)), new bounds should be defined, which reflect on the probability of the collisions.  
In the following example, a graph is built up, where the prediction horizon is set to \(T_{p}=6s\). One node connects to five other nodes. The number of layers was set to 6. This results in a large amount of possible points. Figure 4 shows the generated graph. As it is described in Section 2, the longitudinal positions of the nodes are set equidistantly. Since, the velocity of the ego vehicle (\(v_{x}\)) is known, the longitudinal positions of the nodes computed to time \(t_{i}=\frac{v_{i}}{v_{x}},\quad i=1\ldots N\). \(t_{i}\) gives the value when the vehicle reaches the given node.  
The total number of nodes, in this case, is 19,531. This graph cannot be used in the real-time implementation, because the processing of the graph requires high computational effort. In this case, the number of nodes is reduced from 19,531 to 240. The longitudinal position is changed to time which is necessary since the whole graph cannot be computed in real-time application. In this case, the calculated graph does not depend on velocity. Note that using the values of the curvatures and the velocity of the vehicle, the lateral acceleration can be computed. The result of the k-medoids algorithm is presented in Figure 5.