esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders
## 5 Experiments and Results
### 1-Manifold Alignment  
The dataset stemmed from the Protein Data Bank at Brookhaven National Laboratories [44]. It contains one-dimensional manifolds representing the structure of the glutaredoxin protein PDB-1G7O. The protein structure can be described in three-dimensional space as a chain of amino acids, that is the structures are 1-manifolds in three-dimensional space. The dataset comprises 21 models of the glutaredoxin protein and provides for each model 215 points in 3D. Models 1 and 21 were previously used for method evaluation by articles on manifold alignment using Procrustes analysis [23] and manifold alignment preserving local geometry [39]. We followed their example and used the same models to test our PDAE. To examine the robustness of the methods, the dataset generated from Model 21 was scaled by a factor of four because previous publications on manifold alignment did the same when testing the robustness of their methods [22; 23; 39; 45; 46]. Figure 3a shows the three-dimensional graphs of Models 1 and 21 where the \(x,y,\) and \(z\) coordinates of each model were the columns of the input data matrices \(X\) and \(Y\), respectively.  
Datasets \(X\) and \(Y\) were aligned by MAPA, MALG, MAGG, and PDAE. PDAE had nine fully-connected layers, where the architecture of the encoder network was 3-4-5-4-, the code layer had three neurons, and the decoder architecture was -4-5-4-3, that is it mirrored the encoder. The \(tanh\) function was applied to the output of each layer as the activation function. The network was trained for 10,000 epochs using the Adam optimiser with a learning rate of 0.001 and stopping condition \(E\leq 0.0008\).  
The graphs of the resulting aligned manifolds shown in Figure 3b indicated that the outcome of MAPA-feat was not as robust as that of the other methods in Figure 3c-h. The manifolds aligned by the parallel autoencoders in Figure 3h were as well-aligned and as accurate as the MALG and MAGG methods from Approach 2 in Figure 3d-f.  
We calculated \(\Delta\) and \(\sigma\) of the aligned manifolds for each method. The results in Table 1 show that PDAE was the best performer. PDAEs were executed 10 times with 10 different sets of randomly-initialised weights. The standard deviation of \(\Delta\) for the basic autoencoder was 0.009 and for the deep autoencoder was 0.003. This showed that the selection of the initial weights did not have much impact on the autoencoder results.
## 5 Conclusions  
In this paper, we evaluated the effects of receive/transmit parameters on the image quality provided by a linear array in which elements are mutually interconnected, which mimics the elevation direction of our prototype 2D matrix transducer. The parameter selection procedure was based on the effective covered aperture size, level of grating lobes, and resolution/sidelobes. To achieve 1000 volumes/second, only 20 transmit-receive shots were available, considering a pulse repetition frequency of 20 kHz and imaging depth of 37.5 mm. To suppress the effects of grating lobes, an angular weighting technique was used. The proposed imaging sequence consisted of a full-aperture transmission (all 80 elements), receive aperture size of 5, and aperture shifting distance of three elements. For depths of 10-20 mm, the results show that, on average, about two times wider point spread function at \(-\)6 dB and marginally (4 dB) higher clutter level were obtained, compared to the ground truth, and grating lobes level of about \(-\)27 dB lower than the main lobe. Having a proper imaging sequence based on 1000 V/s could be beneficial to generate 3D maps of carotid artery stiffness, providing valuable information regarding the cardiovascular risk.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/15/5300/s1](http://www.mdpi.com/2076-3417/10/15/5300/s1)]([http://www.mdpi.com/2076-3417/10/15/5300/s1](http://www.mdpi.com/2076-3417/10/15/5300/s1)), The results obtained with multi-scatterer and multi-cyst phantoms are provided in the supplementary document.  
Conceptualization, M.M. and M.S.; methodology, M.M., M.S. and F.F.; formal analysis, M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J.; investigation, M.M., M.S., J.G.B. and N.d.J.; resources, M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J.; data curation, M.M., M.S. and F.F.; writing--original draft preparation, M.M. and M.S.; writing--review and editing, F.F., M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J.; visualization, M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J.J.; supervision, M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J.; project administration, M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J.J.; funding acquisition, M.A.P.P., H.J.V., M.D.V., J.G.B. and N.d.J. All authors have read and agreed to the published version of the manuscript.  
This research is a part of the 3D-ICE, PUMA and UltraXtreme projects (project numbers 14297, 13154 and P17-32, respectively), which are partly financed by the Netherlands Organisation for Scientific Research (NWO). Moreover, the authors acknowledge a joint grant from the NWO/ the Netherlands Organisation for Health Research and Development (ZonMw) and the Department of Biotechnology (Government of India) under the program Medical Devices for Affordable Health (MDAH) as Project Imaging Needles (Grant Number 116310008).  
The authors declare no conflict of interest.
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 5 Mechanical Properties of Foam Concrete
### Models Predicting Strength
It was also reported that with the development of simulation tools, experiments can be supported by simulations, used to verify performance and determine optimum design parameters in a relatively short time. Some equations for density, compressive strength and elastic modulus developed by different researchers are demonstrated in Tables 9-11, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Remarks** & **Equations** \\ \hline
[107] & P\({}_{\text{cr}}\) = the critical porosity corresponding to zero strength, K\({}_{\text{s}}\) = a constant, & \(\text{f}_{\text{cc}}\) = K\({}_{\text{s}}\)ln(P\({}_{\text{cr}}\)/p) \\  & “Schiller’s equation” & \(\text{f}_{\text{cc}}\) = 1.048 f\({}_{\text{c}}\)(1 - A)\({}^{2}\).[793] \\ \hline
[95] & f\({}_{\text{c}}\) = compressive strength (MPa); c, w, a = absolute volumetric ratios of cement, water & \(\text{f}_{\text{c}}\) = K\({}_{\text{cc}}\) + a\({}^{2}\) + w \\  & and air; K = is used as a constant. & \(\text{f}_{\text{c}}\) = 1.172 f\({}_{\text{c}}\).[37] \\ \hline
[43] & f\({}_{\text{c}}\) = cube compressive strength (MPa), t = time since casting (days), w/c = effective water/cement ratio, f\({}_{\text{cc}}\) = compressive strength of foamed concrete, \(\text{a}_{\text{b}}\) = binder ratio & \(\text{f}_{\text{cc}}\) = 1.172 f\({}_{\text{c}}\).[37] \\ \hline
[96] & \(\sigma_{y}\) = compressive strength; \(\sigma_{0}\) = theoretical strength of paste with zero porosity; b = power exponent; d\({}_{\text{c}}\) = concrete density; k = w/c (by weight); \(p_{\text{c}}\) = specific gravity of cement; and \(\text{y}_{\text{w}}\) = unit weight of water. & \(\sigma_{y}\) = \(\sigma_{0}\) (\(\frac{\text{d}}{1+\text{d}}\)) \(\left(\frac{1+\text{d}}{\text{d}\text{e}_{\text{f}}\text{y}_{\text{w}}}\right)^ {\text{b}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Equations for compressive strength. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Equations** & **Remarks** \\ \hline
[103] & \(\gamma_{\text{dry}}\) = 0.86\(\gamma_{\text{Cast}}\) \(-\) 55.07 & Fly ash–cement ratio (F/C = 0–4), casting density range of 700–1500 kg/m\({}^{3}\) \\ \hline
[104] & Dry density = (Wc + 0.2 Wc)/V\({}_{\text{batch}}\) & Wc = weight of cement V\({}_{\text{batch}}\) = volume of batch \\ \hline
[105] & Dry density = 1.2 C + A & C and A are weight of cement and aggregate in kg per cubic meter of concrete \\ \hline
[106] & D = (Mc \(-\) Mm)/Vm & D is the dry bulk density of concrete (fb/h\({}^{3}\)), Mc is the weight of the measure holding the concrete, Mm is the weight of the empty concrete measure, Vm is the volume of the measure, which is usually about 0.25 ft\({}^{3}\) for a pressure meter base.
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 5 Spoofing Detection Performance  
The proposed spoofing detection performance is evaluated by comparing the reduced-inertial and odometer (RIO) trajectory to that of the GPS-based trajectory. An open loop structure is used herein and results in three solutions computed in parallel, namely GPS, RIO and GPS/RIO as discussed in the sequel. In the GPS/RIO solution, the RIO errors at each update interval are corrected using actual GPS measurements. Hence, in the presence of a spoofing attack, the GPS/RIO integration can be easily spoofed. Comparing the GPS solutions with the RIO solutions over an extended interval is not practical due to RIO initial errors and accumulated sensor drifts. Therefore, for GPS authenticity verification purpose, the RIO correction update rate with GPS is adjusted as a function of the application. Between RIO updates, the RIO position errors are therefore bounded and are not contaminated by spoofed GPS measurements. To detect GPS signal spoofing, the trajectory provided by the receiver is compared to that of the RIO trajectory over the intervals between updates. Spoofing is detected if the difference is higher than a predefined threshold. Two data sets (Set 1 and 2) collected in suburban and dense urban environments to test the above approach are analysed below to demonstrate the performance of the approach.  
\begin{table}
\begin{tabular}{c c c} \hline
**IMU** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Tactical NovAtel SPAN IMU-LCI} & Accelerometer bias & 0.5 mg \\  & Accelerometer white noise & 40 \(\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro} \mathrm{\SIUnitSymbolMicro}\mathrm{
# A Strong Noise Reduction Network for Seismic Records
###### Abstract  
Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network's capability to distinguish between signal and noise in the time-frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DrnRDB models, using signals with SNRs ranging from \(-14\) dB to \(0\) dB, demonstrated our model's superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the \"Di Ting\" dataset and real noisy signals confirmed the model's generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications.  
denoise; low SNR; deep learning; U-Net; inception; channel attention; seismic records +
Footnote †: journal: Applied sciences
# Energetic and Structural Studies of Two Biomass-Derived Compounds: 6- and 7-hydroxy-1-indanones
## 3 Results and Discussion
### Experimental and Computational Enthalpies of Formation in the Gaseous Phase
To obtain the intramolecular hydrogen bond energy, \(\Delta E_{\text{intra-HB}}\), we used the energy values obtained at the G3(MP2)/B3 LYP level (see Table S5). The \(\Delta E_{\text{intra-HB}}\) value calculated with the _cis-trans_ method is 33.8 kJ\(\cdot\)mol\({}^{-1}\). This result is expected to be slightly overestimated, as seen with other studied compounds [29], in which the results with the same method provided overestimated values. Comparable results were also found by [PERSON] et al. [30] and [PERSON] et al. [31], which could be associated with the additional steric or electronic interactions, or both \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline
**Conbound** & **Atom.** & **Iso. (8)** & **Iso. (9)** & **Iso. (10)** & **Iso. (11)** & **Iso. (12)** & **Iso. (13)** & **Calculated Mean-Value** & **Experimental** \\ \hline
**6 HI** & \(-241.5\) & \(-242.0\) & \(-239.8\) & \(-242.9\) & \(-240.1\) & \(-241.7\) & \(-237.9\) & \(-240.9\pm 1.3\) & \(-240.2\pm 3.1\) \\
**7 HI** & \(-267.6\) & \(-268.1\) & \(-265.9\) & \(-269.0\) & \(-266.2\) & \(-267.8\) & \(-264.0\) & \(-266.9\pm 1.3\) & \(-265.6\pm 3.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the calculated and the experimental gas-phase enthalpies of formation of the compounds studied, at \(T=298.15\) K, through atomization and isodesemic reactions (8)–(13); all values are in kJ-mol\({}^{-1}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Reaction** & & & \(\Delta_{\text{H}}H^{{}^{\ast}}_{\text{m}}\)/**kJ-mol\({}^{-1}\)** & \(\Delta_{\text{H}}H^{{}^{\ast}}_{\text{m}}\)/**kJ-mol\({}^{-1}\)** \\ \hline  & & (7) & **6 HI** & 8933.89 & \(-241.5\) \\ \cline{3-5}  & & **7 HI** & 8959.97 & \(-267.6\) \\ \hline  & & (8) & **6 HI** & \(-0.96\) & \(-242.0\) \\ \cline{3-5}  & & **7 HI** & 25.12 & \(-268.1\) \\ \cline{3-5}  & & **6 HI** & 35.08 & \(-239.8\) \\ \cline{3-5}  & & **7 HI** & 61.16 & \(-265.9\) \\ \cline{3-5}  & & **6 HI** & \(  
Finally, the intramolecular proton transfer process of **7 HI** was evaluated (Equation (8)), using its G3(MP2)//B3 LYP calculated tautomerization enthalpy (absolute enthalpies of 7 HI and its tautomer are calculated as \(-497.486145\) hartrees and \(-497.462180\) hartrees, respectively). The G3(MP2)/B3 LYP calculated tautomerization enthalpy of \(62.9\) kJ-mol\({}^{-1}\) clearly shows that the tautomeric equilibrium is not energetically favorable for its tautomer conversion.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 4 Experiments
### Implementation and Hyperparameter Setting  
First, our dataset underwent the preprocessing steps previously discussed in Section 4.3. This dataset was saved in a file as clean data ready to be used in the experiments. Then, it was prepared for entry into the embedding layer by sequentially applying the tokenization, indexing, and padding processes. Then, each sequence in the dataset went through the embedding layer to extract the features. As previously explained in this paper, this layer requires the defining of three parameters: the input dimension, the input length, and the output dimension. The input dimension refers to the vocabulary size, which, in our dataset, equaled 15,816 words. The input length or the maximum length of each sequence in our dataset equaled a value of 55. We set the value of the output dimension for the embedding vector to a value of 150. The vector that exited the embedding layer passed through three layers: the fuzzify layer, the BiLSTM layer, and the defuzzify layer. Generally, there are several parameters that need to be tuned to compile a model. We set these parameters empirically through trial and error. Table 6 shows the optimal values of the parameters. The number of neurons in the BiLSTM layer was 32. We selected the optimizer ADAM, and we set the learning rate to 0.0001. We chose binary_crossentropy as a loss function. We applied dropout to regularize the neural networks and to avoid the overfitting problem. Finally, the output layer generated the predicted result using the sigmoid activation function.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 3 Results and Discussion
### Total Polyphenol Content and Antioxidant Activity  
The highest number of phenolic compounds was found in allspices berries (Figure 7). This raw material contained between 7017 and 12,595 mg GAE/100 g d.m. of phenolics, depending on the treatment parameters. In comparison, total polyphenol content measured for black pepper seeds and juniper berries stayed in a range of 1304-3102 mg GAE/100 g d.m. The plasma effect on phenolics was ambiguous but, in most cases, it changed significantly (\(p<0.05\)) the total phenol concentrations of allspices and black pepper seeds. No relevant changes were observed for all plasma treated juniper berries and black pepper seeds treated by 60 s. In the case of allspices, the shortest plasma irradiation led to a significant increment of the phenolics whereas, in the case of other materials, total polyphenol content did not change or decreased. Such results may be related to modification of the superficial structure of the allspices berry, which allowed the phenols to be extracted more efficiently, and most probably the shortest action of the reactive chemical species, which can cause oxidation. Such an explanation may be controversial, especially when considering the results for the longest treatment time, which should have led to the highest concentration of reactive species. However, the extractability of phenolics is a superposition of, for instance, structure modification that can improve mass transfer, and other factors, such as the presence of oxidative agents or elevated temperature, that can decrease the content. Therefore, it can be assumed that moderate treatment times were related to higher degradation due to oxidation of phenolics, which was not compensated by structure modification or formation of new compounds that can react with Folin-Ciocalteu (F-C) reagent. Therefore, it is recommended for future research to study the phenolics profile changes after plasma treatment of spices. It is also worth emphasizing that similar findings were reported previously for other nonthermal processing methods such as a pulsed electric field [49]. The minimal effect of plasma treatment on phenolics content has been reported by [PERSON] et al. [50] for fresh-cut apples. Other reports indicate that plasma radiation can increase the concentration of phenolic compounds. Such results may be related either to formation of new compounds that can react with F-C reagent or to
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 2 Definition of a Coordinate System
### Geographic Coordinate System \((O_{n}-X_{n}Y_{n}Z_{n})\)  
The north-east Earth coordinate system is used, with the center of mass of the UAV represented as the origin, the \(X\)-axis pointing to the north, the \(Y\)-axis pointing to the east, and the \(Z\)-axis pointing down perpendicular to the surface of Earth, forming a right-handed coordinate system, as shown in Figure 2. The geographic coordinates of a point are written as \((X_{n},Y_{n},Z_{n})\).
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 5 Experimental Results
### Experiment 4: Specimen 2, Specific Configuration  
In the previous section, we showed and discussed the issue with the real specimen and demonstrated how this problem can be overcome by painting the specimen surface. However, we think in a practical setup, it might be addressed by other means as well. For instance, if the temperature of the other side of the specimen is sufficiently colder than the temperature of the air inside the pipe which can be possibly true in real scenarios, the emissivity and other problems of the real specimen will not be an issue of concern. To simulate such scenarios, some ice was placed between the original specimen (without any painting) and the soil, then the thermal radiations from the specimen surface using the same thermographic camera were recorded. In this setup, no other thermal stimuli such as halogen lamps were used. The result for a sample frame is given in Figure 14. The black/blue regions in the gray/color image, inscribed in a dashed oval, represent lower temperatures and show the location which was exposed to the ice.
## 3 Modeling
### Strain Rate Sensitivity  
Equation (7) correlates stress and strain in the grain assuming viscoplastic behavior, such that grains subjected to lower stresses are deformed at lower speed than grains under higher loads. When the \(n_{s}\) value is high enough, (7) approximates the Schmid law [50], such that there will be no deformation in the planes subjected to a shear stress lower than the critical value. In practice, (7) implies a strain rate hardening effect, described by (1). In [8], (1) is actually used for the simulation of strain rate hardening from quasi-static up to high strain rates. However, the exponent \(n_{s}\) also affects the yielding behavior, and, if it is used to account for the macroscopic effect of strain rate as in [8], the required value of \(n_{s}\) will make the tensile curves much sharper in the yielding region than observed in steel. Therefore, a different approach is needed to include strain rate hardening in the model.  
As shown in [21], the strain rate sensitivity of many high-strength steels can be captured by the multiplicative relationship used in the Johnson-Cook model (2). However, this expression cannot be directly applied, since it was derived on the basis of macroscopic properties and, moreover, (7) already affects the strain rate hardening behavior. The \(\tau_{c}^{s}\) value calculated for quasi-static conditions, at a reference strain rate \(\dot{\epsilon}_{0}\), is first multiplied by a correction factor that cancels the strain rate hardening effect of (7), and second by the strain rate sensitivity term of the Johnson-Cook model. The value of \(C\) in (2) is calculated directly by applying the factor in (3), derived from literature data in [21] to correlate strain rate sensitivity with yield stress in multiphase steels, to the CRSS in a phase by phase basis. Combining Equations (1) and (2) with (3):  
\[\tau_{c}^{s}(\dot{\epsilon},\gamma^{s})=\left(1+C_{0}\,\tau_{c}^{s}(\dot{ \epsilon}_{0},0)^{-n_{C}}\,\text{ln}\!\left(\frac{\dot{\epsilon}}{\dot{ \epsilon}_{0}}\right)\right)\left(\frac{\dot{\epsilon}}{\dot{\epsilon}_{0}} \right)^{-\frac{1}{h}}\tau_{c}^{s}(\dot{\epsilon}_{0},\gamma^{s}) \tag{14}\]  
where \(\tau_{c}^{s}(\dot{\epsilon},\gamma)\) is the CRSS of slip system \(s\) at strain rate \(\dot{\epsilon}\) for an accumulated shear \(\gamma^{s}\), and \(\dot{\epsilon}_{0}\) is the reference strain rate for quasi-static conditions. The values of \(C_{0}\) and \(n_{C}\) are taken from (3), and \(n\) is taken as the average of all the \(n_{s}\) exponents. The CRSS at the reference strain rate, \(\tau_{c}^{s}(\dot{\epsilon}_{0},\gamma)\), is calculated using the hardening law described in the next section.