esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 3 Results and Data Analysis
### The SNR for the Integrated Visible-SWIR Sensor System  
This section concerns with the imaging performance of an integrated (i.e., combined) visible and SWIR sensor system which employs a single focal plane array (FPA) as detector, together with the optics which involves a dual-angled blazed grating for spectral dispersion. The sensor for this integrated system was based on the Teledyne Chroma detector array [15] and to incorporate a dual-angle blaze grating, similar to that described by [PERSON] et al. [16]. The efficiency of the optics in the system had been adjusted to account for the broad-band losses which was originally designed by using a 3-mirror anastigmatic optics for the telescopic applications.  
Figure 8 shows the performances of the conventional and the multiplexed sensing HSI in terms of SNR, when the ground reflectance (albedo) are at 50% and 10% under the same irradiance geometry of 60\({}^{\circ}\) from zenith in these two scenarios. It is seen that the performances of the multiplexed sensing is independent of the target reflectance, i.e., the SNR is not dependent on the signal level at all! Although the conventional spectrograph sensing has better SNR when the signal level is high (i.e., at 50% ground albedo), the multiplexed sensing is more advantageous when the signal is weak (i.e., at 10% ground albedo) and especially at high required frame rate. It is also worth to mention that the sensing of low albedo targets at high frame rate by the multiplexed sensing, can be further enhanced by other means such as to increase the perceived radiance at the sensor, such as  
Figure 6: Depicts the variation of the SNR for the conventional and multiplexed SNRs as a function of wavelength (**left**) and SNR magnitudes (**right**), for frame rates at 35 Hz (**top**) and 200 Hz (**bottom**). The detector is a Teledyne Chroma array with 480 bands, at low illumination angle of 70\({}^{\circ}\) from zenith and \(f/2.5\) optics.  
to increase the angle of illumination, etc., would be able to further improve the relative performance of the multiplexed sensing system.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
## 2 Evaluation Tool and Methodology of Use
### The Evaluation Tool  
Figure 4 shows a schematic of the proposed evaluation tool using the Texas Instruments C2000-based board as the emulator. The cost of this board is close to USD 50, which is in the lower price range (Table 1). The board is a digital platform used in the field of motor control, power converters, and digital signal processing. The emulator generates the (i) emulated voltages of a three-phase system, \(v_{sa}(t)\), \(v_{sb}(t)\), and \(v_{sc}(t)\), (ii) the reference voltage \(v_{sa1,ref}(t)\), (iii) the synchronization signal \(sync(t)\) and (iv) the analog output signals \(out_{A}(t)\) and \(out_{B}(t)\).  
The digital input signal \(sw_{outAB}(t)\) allows for assigning to the outputs \(out_{1}(t)\) and \(out_{2}(t)\) the internal reference signals of (i) amplitude \(A_{1,ref}(t)\), (ii) phase \(\theta_{1,ref}(t)\), and (iii) frequency \(f_{1,ref}(t)\). Additionally, the digital input signals \(sw_{H}(t)\) and \(sw_{case}(t)\) allow us to modify the previously configured content and reference signals. Table 2 shows a summary of the signals in the emulator.  
The digital controller requires four ADCs, two DACs, and five digital inputs/outputs (DIOs). The digital controller specified in Figure 3 uses the input signal \(sw_{out01,ab}(t)\) to select the estimated signals and generate them at the outputs \(out_{0}(t)\) and \(out_{1}(t)\). The estimated signals correspond to the (i) grid voltage \(v_{sa,\rho}(t)\), (ii) amplitude \(A_{1,\rho}(t)\), (iii) phase \(\theta_{1,o}(t)\), and (iv) frequency \(f_{1,o}(t)\). A second digital input signal \(sw_{tc,ab}(t)\) allows the synchronization technique to be selected when evaluating multiple algorithms. On the other hand, the digital output enables the \(DT(t)\) signal to be obtained for the calculation of the execution time. Table 3 shows a summary of the signals in the digital controller.  
Figure 4: Diagram of the proposed evaluation tool based on the LAUNCHXL-F28379D board.
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 4 Experiments
### Results with Bicubic (BI) Degradation
#### 4.3.2 Visual Quality
Compared with the previous methods, SAN and NLSN show better detail recovery ability, but their reconstructed images are still  
Figure 9: Visual comparisons for SR tasks. (**a**) Visual comparison for \(\times\)4 SR task with BI model on Set14 and BSD100 datasets. (**b**) Visual comparison for \(\times\)8 SR task with BI model on Set14 and Urban100 datasets. unsatisfactory. Compared with the compared methods, the proposed GPSR shows better structure preservation and detail recovery performance, which is reflected in sharper and more complete edge details. From the image \"253027\" in the BSD100 dataset, the zebra stripes produced by most of the compared methods are blurred and confused, and there exist different degrees of information loss in the local regions. However, DRN, SAN, and NLSN retain more texture than LapSRN. However, they are still inferior to our GPSR. This is due to the fact that benefits from our GGS, the proposed GPSR, are able to effectively local gradient information to reconstruct high-frequency details while avoiding the problem of blindly restoring details that exist in most comparative models. Figure 8(b) presents visual comparisons of the \(\times\)8 SR task. For the image \"butterfly\" from Set5, we observe that most of the compared methods fail to restore the spots and suffer from blurred artifacts. In contrast, our GPSR is able to alleviate the blurred artifacts better, and reconstruct sharp edge and texture details. For the image \"img040\" from Urban100, the reconstructed images produced by most comparative methods have significant geometric distortions, such as significant errors in the extension directions of the edges. Although HAN, RDN, and DBPN are able to suppress geometric distortions to a certain extent effectively, this deficiency still exists in local regions caused by blind detail restoration. The optimal reconstructed image is generated by the proposed GPSR, which again proves the effectiveness of the proposed GGS.
## 2 Materials and Methods
### Measurements and Calculation  
The voltage output was measured once every 30 min, using a PISO-813 data acquisition system (ICP DAS Co., Ltd., Taiwan, China). Polarization curves were acquired by varying the external resistance from 200 \(\Omega\) to 2.5 \(\Omega\) (200, 150, 100, 50, 25, 20,15, 10, 5, and 2.5 \(\Omega\)). Data obtained at each resistor was recorded after a steady voltage was achieved, with a minimum running time of 1 h at each resistor. Before polarization curve measurement, the system was operated under open circuit mode for more than 12 h until a stable open circuit voltage was obtained. Power density (W m\({}^{-3}\)) and coulombic efficiency (CE) were obtained according to the previous method [28]. Chemical oxygen demand (COD), ammonium nitrogen, nitrate nitrogen and nitrite nitrogen were measured using the HACH DR/3900 Spectrophotometer (HACH Co., Loveland, CO, USA). Dissolved oxygen concentration was measured by a nonconsumptive dissolved oxygen probe (FOXY, Ocean Optics, Inc., Dunedin, FL, USA). The statistical significance of differences under different experiments was analyzed by Student's \(t\)-test. The levels \(p\)\(<\) 0.05 is considered significantly different.
## 3 Results and Discussion
### Comprehensive Driving Stability Analysis  
Based on the above analysis, summarized in Tables 12 and 13, the Type 1 and Type 4 configurations were found to exhibit superior driving stability in terms of SRS, impact hardness, and memory shake. Because the ride and roll analysis used in the driving stability evaluation was static, it was not possible to represent the acceleration at specific positions [8]. The final configuration was determined by considering the following four aspects:  
1. Higher positive SVSA angles lead to a decrease in memory shake performance.
2. Dramatic differences in driving stability may be less noticeable for short driving distances.
3. Taking into account the drivers' perception of lateral swaying may be more dramatic in cornering situations.
4. Even if the difference in SVSA angle is small, a difference in SVSA length may occur depending on the specifications of the vehicle to which the cross-member of the torsion beam suspension will be applied, which may ultimately result in a difference in tire wheel displacement.  
According to [PERSON] et al. [8], SRS sensitivity response characteristics can be improved as the SVSA angle decreases, and it was analyzed that the SRS sensitivity was 95% when the SVSA angle was \(-3\) degrees. In this study, Type 4 is analyzed to have an SRS sensitivity of 94.16%. Also, we believe that Type 4 will be better at improving driving stability than Type 1. Considering these four aspects, the Type 4 configuration was identified as the most favorable option for overall driving stability among the cross-sections of the torsion beam used in this study.
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Key Observations by Category: Methods, Diagnostic Applications, and Research Methodology
#### 3.5.1 Machine Learning Techniques  
In the period 2015-2019, classical methods dominated, such as support vector machines (SVMs), decision trees (DTs), or kNNs, which achieved high effectiveness with low data and computational requirements. For example, SVM achieved over 90% accuracy in bearing diagnostics [17], gearbox systems [39], and bridge structures [28]. In the analysis of compressor condition, the Lazy K Star classifier with autoregressive features achieved 99.67% accuracy [31].  
Since 2020, a clear increase in the use of deep learning methods has been observed--including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and autoencoders. CNNs allowed up to 100% accuracy in bridge diagnostics based on wavelet scalograms [29] and effectively classified acoustic images of gearboxes under noise conditions [38]. LSTM proved successful in stress prediction in bridge structures [14], and autoencoders enabled the reconstruction of missing acoustic emission data [12].  
Hybrid approaches are increasingly appearing in research, e.g., DCNNs + LSTM for dynamic load estimation [14], CNNs with classification using SVMs [37], or combinationsof autoencoders with PCA [30]. These models enable automatic feature extraction and classification accuracy improvement by 15-20% compared to single algorithms [9; 32].  
It should be emphasized that the future belongs to lightweight deep network architectures, transfer models, and energy-efficient solutions ready for integration with edge systems and embedded controllers (Edge AI). Wider use of generative models (e.g., GANs) is also to be expected for synthesizing training data in environments with limited labeled data.
## 4 Results
### Effect of Age on HRV Features
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Feature** & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **\(p\)-Value** \\  & **(18–29, \(N=696\))** & **(30–39, \(N=147\))** & **(40–49, \(N=100\))** & **(\(\geq\)50, \(N=150\))** & **\(p\)-Value** \\ \hline RRI & \(893.98\pm 129.38\) & \(909.82\pm 171.27\) & \(865.30\pm 128.90\) & \(914.53\pm 137.73\) & \(0.024\) * \\ SDNN & \(65.30\pm 34.65\) & \(68.56\pm 92.77\) & \(55.35\pm 55.12\) & \(55.17\pm 86.66\) & \(0.068\) \\ RMSSD & \(60.91\pm 46.33\) & \(65.86\pm 128.61\) & \(48.97\pm 78.41\) & \(54.48\pm 122.14\) & \(0.311\) \\ pNN50 & \(30.16\pm 21.87\) & \(23.65\pm 22.94\) & \(14.49\pm 19.23\) & \(11.29\pm 20.85\) & \(<\)0.001 * \\ TRI & \(13.64\pm 4.60\) & \(12.46\pm 4.98\) & \(10.39\pm 4.63\) & \(9.24\pm 5.43\) & \(<\)0.001 * \\ TINN & \(222.38\pm 88.63\) & \(220.03\pm 103.30\) & \(174.27\pm 87.14\) & \(175.65\pm 136.27\) & \(<\)0.001 * \\ logVLF & \(-5.18\pm 0.53\) & \(-5.11\pm 0.50\) & \(-5.15\pm 0.51\) & \(-5.00\pm 0.61\) & \(0.001\) * \\ logLF & \(-4.19\pm 0.54\) & \(-4.13\pm 0.57\) & \(-4.48\pm 0.68\) & \(-4.56\pm 0.86\) & \(<\)0.001 * \\ LFnu & \(0.50\pm 0.17\) & \(0.58\pm 0.20\) & \(0.61\pm 0.18\) & \(0.57\pm 0.19\) & \(<\)0.001 * \\ logHF & \(-4.21\pm 0.76\) & \(-4.50\pm 0.84\) & \(-5.00\pm 1.02\) & \(-4.88\pm 1.07\) & \(<\)0.001 * \\ HFnu & \(0.50\pm 0.17\) & \(0.42\pm 0.20\) & \(0.39\pm 0.18\) & \(0.43\pm 0.19\) & \(<\)0.001 * \\ LF/HF & \(1.50\pm 1.67\) & \(2.32\pm 2.30\) & \(2.57\pm 2.19\) & \(2.30\pm 2.48\) & \(<\)0.001 * \\ logTot & \(-3.22\pm 0.44\) & \(-3.26\pm 0.41\) & \(-3.59\pm 0.59\) & \(-3.51\pm 0.61\) & \(<\)0.001 * \\ ApEn & \(1.10\pm 0.08\) & \(1.07\pm 0.10\) & \(1.06\pm 0.12\) & \(1.05\pm 0.13\) & \(<\)0.001 * \\ SampEn & \(1.58\pm 0.25\) & \(1.48\pm 0.28\) & \(1.38\pm 0.31\) & \(1.39\pm 0.32\) & \(<\)0.001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean and standard deviation of HRV features calculated from each age group. One-way ANOVA was performed to compare the effect of age on HRV features (* \(p<0.05\)).
# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 3 Modeling of the SDM Problem Based on the Hypernetwork Model
### Initial Matching Model
#### 3.3.3 Objectives and Constraints  
The objective function is defined as maximising the utilities related to both network resilience and operational performance, making it essentially a multi-objective optimisation problem, as shown in Equation (29).  
\[max\ U=(U_{1},U_{2})^{T}\] \[=(\omega_{C}*U_{C}-\omega_{L}*U_{L},U_{S}+U_{T})^{T}. \tag{29}\]  
After defining the optimisation objective, we must consider some constraints to ensure the feasibility and effectiveness of the solution. First, each task can select only one service provider to complete its manufacturing task, as shown in Equation (30).  
\[\sum_{i=1}^{N\omega S}x_{ij}=1,j=1,...,N\omega T. \tag{30}\]  
Each service provider can accept multiple tasks within the limits of their resources, i.e., the total demand of all tasks allocated to each service provider must not exceed their capacity, as shown in Equation (31).  
\[\sum_{j=1}^{N\omega T}x_{ij}*B_{T}^{j}\leq B_{S}^{i},i=1,...,N\omega S. \tag{31}\]  
Furthermore, the cost incurred by the service provider matched to each task must not exceed the budget specified for each task, as shown in Equation (32).  
\[\sum_{i=1}^{N\omega S}x_{ij}*C_{S}^{i}\ast B_{T}^{j}\leq C_{T}^{j},j=1,...,N \omega T. \tag{32}\]  
Meanwhile, Equations (33) and (34) ensure that the service's available time meets the time constraint required by the task. Specifically, the service's earliest available time must precede the task's start time, and their latest available time must be later than the task's latest permissible completion time.  
\[\sum_{i=1}^{N\omega S}x_{ij}*Et_{S}^{i}\geq Et_{T^{\prime}}^{j},j=1,...,N\omega T, \tag{33}\] \[\sum_{i=1}^{N\omega S}x_{ij}*St_{S}^{i}\leq St_{T^{\prime}}^{j},j= 1,...,N\omega T. \tag{34}\]  
Finally, the service provider of the matching scheme is required to meet the quality requirements of the task, as outlined in Equation (35).  
\[\sum_{i=1}^{N\omega S}x_{ij}*Q_{S}^{i}\geq Q_{T^{\prime}}^{j},j=1,...,N\omega T. \tag{35}\]  
Therefore, the initial mathematical model for supply and demand matching is established as shown in Equation (36), where \(X=\{x_{ij}\}_{N\omega S\times N\omega T}\).  
\[\left\{\begin{array}{ll}\max&U(X)=(U_{1},U_{2})^{T}\\ \par s.t.&\sum_{i=1}^{NoS}x_{ij}=1,\hskip 28.452756 ptj=1,...,NoT\\ \sum_{j=1}^{NoT}x_{ij}*B_{T}^{j}\leq B_{S}^{i},\hskip 28.452756 pti=1,...,NoS\\ \sum_{i=1}^{NoS}x_{ij}*C_{S}^{i}*B_{T}^{j}\leq C_{T}^{j}j=1,...,NoT\\ \sum_{i=1}^{NoS}x_{ij}*E_{S}^{i}\geq E_{T}^{i},\hskip 28.452756 pti=1,...,NoT\\ \sum_{i=1}^{NoS}x_{ij}*S_{i}^{j}\leq St_{T}^{j},\hskip 28.452756 pti=1,...,NoT\\ \sum_{i=1}^{NoS}x_{ij}*Q_{S}^{i}\geq Q_{T}^{j},\hskip 28.452756 pti=1,...,NoT\\ x_{ij}=0\;or\;1,\hskip 28.452756 pti=1,...,NoS;j=1,...,NoT.\end{array}\right. \tag{36}\]
## 1 Introduction
The task-specific module is for the classification of emotion and language tasks. Specifically, the task-sharing module utilizes a long short-term memory network (LSTM) and attention mechanism from a new perspective, where LSTM uses the global feature dimensions as time steps to obtain long-term dependencies of features and the attention mechanism layer enables the model to better understand the important contribution of each feature in MSFs by assigning different weights. The weights of MSFs generated from the attention mechanism are essential to explain the reason for the improved validity and generalizability of the MSFL model and its MSF features. The main work and contributions of this paper are summarized below. 1. For the model generalization, our proposed model uses gradient normalization-based MTL to jointly learn the tasks of emotion recognition and language recognition, where the gradient normalization method is to adjust the gradient of each task dynamically. With the LSTM-attention structure in MSFL, two tasks can learn features from different perspectives to better regularize the model and uncover the high-level discriminative MSFs. 2. For the model interpretability, the validity and generalization of the model are explained in multilingual scenarios from the perspective of MSFs. The MSFs are ranked according to the weights from the attention mechanism in MSFL and we analyze the top features to compare with the monolingual features of three datasets. The differences and commonalities are found between the three monolingual features and MSFs. 3. The technical idea and model interpretability perspective are provided from model building to feature analysis in this study. In particular, the feature ranking and analysis lay the theoretical foundation for multi-language and multi-corpus data aggregation to alleviate the data sparsity, and promote the research prospects of multilingual SER. The rest of this paper is structured as follows. Section 2 presents the related literature on deep learning and MTL on SER. Section 3 describes the proposed MSFL model in detail. Section 4 presents the experimental setup, including the corpora employed to perform experiments, speech acoustic features extraction, and model configuration. Section 5 presents the experimental results and the significance of the observed findings. The conclusions and future research directions are provided in Section 6.
## 3 Construction of Knowledge Graph for Spectrum Sensing Data from LEO Satellite  
### LEO Satellite Sensing Scenario  
LEO satellite sensing, although highly dynamic, enables globally all-day, all-weather, seamless sensing. A snapshot model is used to represent the moment of LEO satellite over-top sensing. Each time a LEO satellite passes over an area, it receives sensing data from that area, and gradually forming sensing results for that area. On-satellite spectrum sensing data are data used to characterize terrestrial electromagnetic spectrum information. They are acquired by sensors or instruments carried on the satellite payload when the satellite is used as a spectrum sensing node. This paper focus on the construction of spectrum knowledge graph for the scenario of a single satellite over-top a certain area. A LEO satellite with an orbital altitude of 500 km makes up the system's space segment. The incline of its track is 86.4\({}^{\circ}\). The Earth station for the ground segment is situated in Nanjing. LEO satellite is employed as sources for spectrum sensing to gather spectrum data. The coordinates of an earth station are given in [latitude, longitude]. Nanjing Station is located at [32\({}^{\circ}\), 119\({}^{\circ}\)].
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 1 Introduction
### Timeline of Significant Advancements in Scaffold Development in Dentistry  
There were various challenges in optimizing scaffold design, material selection, and compatibility with the oral environment, which were gradually overcome. The following timeline, summarized in Table 1, shows the significant advances in scaffold development over the last three decades. The ability to create custom-designed scaffolds with tailored properties promises immense advances in regenerative dentistry and in the restoration of damaged dental tissue.  
Early 1990s  
Initially, scaffolds were primarily composed of natural materials, such as collagen, silk, and alginate. These materials were biocompatible and biodegradable, but they offered limited control over pore size and architecture [1; 5].  
Late 1990s-Early 2000s  
The introduction of synthetic polymers, such as polylactic acid (PLA) and polyglycolic acid (PGA) provided greater control over scaffold properties. These materials could be engineered with specific pore sizes and architectures to facilitate cell migration and tissue formation [15; 16; 17].  
Mid-2000s  
Hybrid scaffolds, which combined natural and synthetic materials, emerged as a promising approach. These scaffolds offered the biocompatibility and biodegradability of natural materials with the tunable properties of synthetic polymers [18; 19].  
Late 2000s-Early 2010s  
The advent of 3D revolutionized the scaffold development, enabling precise control over scaffold geometry and material composition [20; 21]. Bioprinting allowed for the creation of scaffolds with complex architectures that closely mimic native tissues, providing an optimal environment for tissue regeneration [21].  
Mid-2010s-Present  
Researchers are exploring the use of advanced materials, such as composites, hydrogels [22; 23; 24; 25], and bioactive materials [26], to further enhance the performance of scaffolds. These materials may incorporate growth factors, signaling molecules, or other bioactive agents to promote cell adhesion, proliferation, and differentiation [27; 28; 29].
# High-Harmonic and Terahertz Spectroscopy (HATS): Methods and Applications
###### Abstract
Particularly, HATS highlights the importance of the Coulomb potential on the rescattered electrons, as also discussed in Ref. [26] and references therein. It suggests that the strong field approximation (SFA) treatment [27; 28; 29] (as well as the SFA-based theories) may not be sufficient for dealing with the rescattered electrons, because typical SFA mainly considers the effect of the external field on the continuum state but neglects the influence of the Coulomb potential and the excited states. In the second example, we discuss that the joint measurement enables benchmarking HHG by using THz yield as a reference to calibrate the geometry phase of the driving pulse which is essential in HHG. Based on HATS, it is found that THz generation is related to the soft collision between the accelerated electrons and the ion, while HHG is related to hard collision. Therefore, it is possible to use HATS to fully characterize the electron wave packet, which is crucial in strong field physics, in particular when the driving pulse is shaping or gated. Thirdly, we review two examples of HATS detection from molecules. It is shown that THz yields help minimize the uncertainty and assumptions of the molecular frame photoionization cross sections, which can be estimated from HHG. The phase-delay dependence of THz generation helps resolve the optimal phases of aligned nitrogen molecules to maximize the yield of even-order high harmonics. Finally, we propose the probable extensions of the HATS method to polar molecules and other systems. We expect that HATS will be helpful in characterizing the interplay or competition between different ultrafast electronic processes in the near field regions for more complex molecules while detecting the corresponding emissions in the far field regions.