esa-sceva/satcom-chunk-collection huggingface dataset:
# Hygroscopicity and Morphology of Bio-Based Boards--The Influence of the Formulation
## 2 Materials and Specimens
### Composite Samples Preparation  
The samples were produced as described by [PERSON] et al. [29; 30]. The same percentages of the bio-aggregates/adhesive and the same production process were considered. They were selected after several tests, maximizing the contents of the aggregates, minimizing the amount of the adhesive, and securing sufficient mechanical resistance. The selected percentages were 70% aggregates and 30% adhesive (by total volume).  
Table 1 shows the mix designs and the composites' designations. As for the latter, the first letters of the materials were considered (**A = A**. _donax_, **H** = hazelnut shells, and **AH = A**. _donax_ + hazelnut shells).  
The bio-aggregates and the sodium silicate solution were mechanically mixed for 10 min, placed into silicon molds without compaction, and leveled using a spatula to obtain a horizontal surface. The molds were closed and maintained at T = 60 \({}^{\circ}\)C for 3 h and then under laboratory conditions (air-dried), always being rotated to secure a homogeneous distribution of the sodium silicate solution [31]. After 2 days, the samples were de-molded and cured under laboratory conditions for 28 days. To secure complete drying, they were finally dried at T = 50 \({}^{\circ}\)C until reaching a constant mass (a variation in mass of less than 0.5% after 24 h) [46; 56]. The composite samples were analyzed after being dried under this condition.  
Cylindrical (diameter = 10 cm; thickness = 4 cm) and rectangular (10 cm \(\times\) 10 cm; thickness = 4 cm) samples were produced to be employed for different analyses (as described in the following section). Figure 2 shows the produced samples. Three replications for each composite were considered.  
As better detailed in the following section, the materials (bio-aggregates and adhesive, individually) were tested too. In this case, the bio-aggregates were employed after the preparation described in Section 2.1 (i.e., after shredding and drying at T = 60 \({}^{\circ}\)C until reaching a constant mass). For the designations, the repetition of the letters was considered: \(A\). _donax_ = **AA**; hazelnut shells = **HH**; and \(A\). _donax_ + hazelnut shells = **AAHH**. Again, three samples for each bio-aggregate were tested. As for the adhesive, the sodium silicate solution was placed in a rectangular silicon mold (10 cm \(\times\) 10 cm) and left to air-dry under laboratory conditions for 28 days. The employed designation was **S** (= sodium silicate solution), and four replications were considered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composite’s** & \multicolumn{3}{c}{**Materials’ Quantities (\% by Total Volume)**} \\ \cline{2-4}
**Designation** & _A. donax_ & **Hazelnut Shells** & **Sodium Silicate Solution** \\ \hline A & 70 & - & 30 \\ H & - & 70 & 30 \\ AH & 35 & 35 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix designs (quantities of aggregates and adhesives in percentages by the total volume) and the composites’ designations considered for the experimental analysis.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 2 XPCS Concepts
For spherical (nano-)particles dispersed in a liquid solvent the relaxation rate is then given by  
\[\Gamma(q)=D_{0}q^{2}=\frac{k_{\mathrm{B}}T}{6\pi\eta R}q^{2}, \tag{5}\]  
with the Stokes-Einstein diffusion constant \(D_{0}\) that depends on the particles radius \(R\), the solvent viscosity \(\eta\) at temperature \(T\), and Boltzmann's constant \(k_{\mathrm{B}}\). Deviations from the exponents indicate, e.g., hyper- or subdiffusive dynamics, examples are discussed below. For dense samples, the dynamics is slowed down due to particle-particle interactions and the effective diffusion constant becomes length-scale dependent:  
\[D(q)=\frac{D_{0}H(q)}{S(q)} \tag{6}\]  
with the static structure factor \(S(q)\). The function \(H(q)\) describes the hydrodynamic interactions, including electrostatic repulsion between particles and solvent fluctuations due to particle movement [41; 42; 43; 44]. Furthermore, multiple dynamical processes can be measured as well as a superposition of different decay of \(g_{2}\). Exemplary \(g_{2}\)-functions are shown in Figure 3b,c. When performing an XPCS experiment, typically a series of \(n\) speckle patterns is measured on a equidistant time scale. The exposure time of each pattern has to be slower than the characteristic time that needs to be measured while the total duration of the series should extend significantly longer. Ideally, the shortest time should at least be about \(0.1\tau\), while the length of the series should be longer than \(10\tau\). Afterwards, the \(g_{2}\) function is obtained by averaging over the whole series. This reveals several concerns: (a) What happens if the sample shows heterogeneous dynamics, i.e., the dynamics change during the exposure, (b) can dynamics faster than the minimum frame rate be detected, and (c) what is the role of the degree of coherence \(\beta\)? These questions will be addressed the following subsections.
## 4 Results and Discussion
### Spectrum Curves Seasonal Variation of Forest Canopy in the Footprint
#### 4.1.2 Reflectance First-Order Derivative Curves Characteristics in Different Seasons  
First-order derivatives of reflectance (FDR) illustrate the variability of wavelengths in the slope of the original reflectance spectrum. The FDR curves are illustrated in the spectrum of 350-950 nm in Figure 4. The first-order derivative spectrums in different seasons were not similar, especially in the red edge region. During the growing season, FDR peaks existed in the red edge region. In different DOYs, the peak value discrepancies illustrate an important feature of peaks in the visible and the red edgeregions. The first derivative of the red edge region was much higher in the growing season _vs._ the dormant season.  
Figure 4: Hyperion average reflectance first-order derivative spectrum of the Korean pine and broad-leaved mixed forest in 1 km\({}^{2}\) square region in different DOY of 2008 and 2009. Wavelength regions where significant plant absorptions occur are indicated. (**a**–**f**) show the FDR spectrum in different seasons in 2008 and 2009. The spectrum sample size of Figure 4a–f is 1089 pixels.  
Figure 3: Hyperion average reflectance spectrum of the Korean pine and broad-leaved mixed forest in 1 km\({}^{2}\) square region in different DOY of 2008 and 2009. Wavelength regions where significant plant absorptions occur are indicated. (**a**–**f**) show the reflectance spectrum in different seasons in 2008 and 2009. The spectrum sample size of Figure 3a–f is 1089 pixels.
This strategy is illustrated in the Figure 1. With detailed analysis of the overall closed-loop stability, Feed Forward Neural Network (FFNN) is designed to generate peak the desired voltage that is tracked by the proposed control law. In addition, the high gain differentiator is used to observe the system's states, which are utilized by the proposed control algorithm. Results validate the applicability of the developed model for control law in terms of maximum power extraction, steady-state error, and robustness against abrupt variations in atmospheric conditions compared to the conventional MPPT methods. The proposed control strategy finds potential in power applications of PV where one may have PV output from a system with unknown nonlinear dynamics. The proposed control strategy finds potential in power applications of PV where one may have PV output from a system with unknown nonlinear dynamics. The rest of the manuscript is presented in the following structure. In Section 2, an equivalent mathematical model of a PV system is given. Afterward, the detailed modeling of the DC-DC buck-boost converter is provided in Section 3. Section 4 derives the control algorithm for changing duty cycle of buck-boost topology of converter to ensure maximum power extraction. The simulations results are compared with the stan \begin{table}
\begin{tabular}{l l l} \hline
**Techniques** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{Conventional} & & Oscillatory voltage around \\  & & the point of maximum power. \\  & & Time to reach the MPP might \\  & & be slow under \\  & & changing conditions. \\ \hline \multirow{6}{*}{Bio-inspired} & They show low converging & \\  & time compared to & These techniques need many \\  & conventional techniques. To & parameters, such as \\  & resolve the MPPT problems, & crossover rate, mutation size \\  & they are effective in & and chromosome selection, \\  & controlling the system’s & whose estimation is difficult. \\  & non-linearities & \\ \hline Artificial & These techniques have fast & \(\varsubsetneq\) Require a large memory \\  & tracking speed and low & size and need more time of \\  & computation requirement. & training to track MPP. \\ \hline \multirow{6}{*}{Nonlinear Controllers} & \(\varsubsetneq\) These techniques are & \\  & efficient in tracking of MPP. & When implementing these \\  & They are robust in extracting & techniques, a significant \\  & maximum power under & overshoot and steady-state \\  & changing atmospheric & error has been observed. \\  & conditions. & \\ \hline \end{tabular}
\end{table} 
Table 1: Explanatory list of the existing control techniques. in Section 5, which endorses robustness of the developed controller. At last, concluding remarks of this work are presented in Section 6.
## 3 Simulation Results  
### BPMR Channel Model  
With the original data \(u[k]\)\(e\) {0,1}, it is modulated into the 2D data array \(a[j,k]\) e {-1,1}. The input data \(a[j,k]\) are taken into the BPMR channel, in which the signals are interfered by ISI and ITI. At the output of the channel, additive white Gaussian noise is added to present the noise model. In this research, we apply a 2D Gaussian function to represent the 2D island response of the BPMR channel as follows [12]:  
\[P(x,z)=A\exp\left(-\frac{1}{2c^{2}}\left[\left(\frac{x+\Delta_{x}}{PW_{x}} \right)^{2}+\left(\frac{z+\Delta_{z}}{PW_{z}}\right)^{2}\right]\right), \tag{12}\]  
where \(x\) and \(z\) are the down- and cross-track directions, respectively; \(\Delta_{x}\) and \(\Delta_{z}\) are the down- and cross-track bit location fluctuations, respectively; \(c\) is 1/2.3548, which represents the relationship between the standard deviation of a Gaussian function and PW\({}_{50}\), which is a parameter of the pulse width at half of the peak amplitude; and PW\({}_{x}\) and PW\({}_{z}\) are the PW\({}_{50}\) components of the down- and cross-track pulses, respectively.  
The BPMR channel pulse response is expressed as  
\[h[j,k]=P\left(jT_{x},KT_{z}-\Delta_{off}\right), \tag{13}\]  
where \(j\) and \(k\) are the discrete indices in the down- and cross-track directions, respectively; \(T_{x}\) and \(T_{z}\) are the bit period and track pitch, respectively, and \(\Delta_{off}\) is the read-head offsets for the cross-track. TMR is defined as the ratio between the head offset size and the magnetic-island period, as follows:  
\[\text{TMR}(\%)=\frac{\Delta_{off}}{T_{z}}. \tag{14}\]  
The readback signal \(y[j,k]\) for BPMR is given by  
\[y[j,k]=a[j,k]*h[j,k]+n[j,k], \tag{15}\]  
where \(a[j,k]\), \(h[j,k]\), and \(n[j,k]\) are the 2D discrete input data, 2D channel response, and electronic noise modeled as additive white Gaussian noise (AWGN) with variance \(\sigma^{2}\) and zero mean, respectively.
###### Abstract  
Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20-30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich(r)) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich(r) FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842-1), an AUC of 0.945 (0.833-1), and an F1 score of 0.947 (0.842-1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.  
Keywords:indeterminate thyroid nodules; fine-needle aspiration cytology; metabolomics; thyroid cancer; diagnostic biomarkers; machine learning; deep learning +
Footnote †: journal:
## 4 Fault Diagnosis Based on Traditional and Interval Analytical Redundancy Relations
### Diagnostic Thresholds and Data Acquisition
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Nominal Value** & **Absolute Value Type Uncertainty** & **Interval Type Uncertainty** \\ \hline \(Sp\) & \(2.0685\times 10^{7}\) Pa (3000 psi) & \(\pm 2.0685\times 10^{5}\) & \([-1.2000\times 10^{5},2.0685\times 10^{5}]\) \\ \(R_{Ex-l}\) & \(9.35\times 10^{6}\) N - s/m\({}^{5}\) & \(\pm 9.35\times 10^{4}\) & \([-7.50\times 10^{4},9.35\times 10^{4}]\) \\ \(R_{In-l}\) & \(3.40\times 10^{6}\) N - s/m\({}^{5}\) & \(\pm 3.40\times 10^{4}\) & \([-1.80\times 10^{4},3.40\times 10^{4}]\) \\ \(R_{PA}\) & \(2.0\times 10^{12}\) N - s/m\({}^{5}\) & \(\pm 2.0\times 10^{10}\) & \([-0.5\times 10^{10},2.0\times 10^{10}]\) \\ \(R_{PB}\) & \(2.0\times 10^{12}\) N - s/m\({}^{5}\) & \(\pm 2.0\times 10^{10}\) & \([-0.5\times 10^{10},2.0\times 10^{10}]\) \\ \(R_{AT}\) & \(7.8\times 10^{9}\) N - s/m\({}^{5}\) & \(\pm 7.8\times 10^{7}\) & \([-5.0\times 10^{7},7.8\times 10^{7}]\) \\ \(R_{BT}\) & \(7.8\times 10^{9}\) N - s/m\({}^{5}\) & \(\pm 7.8\times 10^{7}\) & \([-5.0\times 10^{7},7.8\times 10^{7}]\) \\ \(p_{T}\) & \(2500\) Pa & \(\pm 25\) & \([-5.25]\) \\ \(C_{Radl}\) & \(8.30\times 10^{-13}\) m\({}^{5}\)/N & \(\pm 8.30\times 10^{-15}\) & \([-3.60\times 10^{-15},8.30\times 10^{-15}]\) \\ \(C_{Ralless}\) & \(2.55\times 10^{-13}\) m\({}^{5}\)/N & \(\pm 2.55\times 10^{-15}\) & \([-1.50\times 10^{-15},2.55\times 10^{-15}]\) \\ \(A_{Rad}\) & \(0.0075\) m\({}^{2}\) & \(\pm 7.50\times 10^{-5}\) & \([-4.50\times 10^{-5},7.50\times 10^{-5}]\) \\ \(A_{Rodless}\) & \(0.0125\) m\({}^{2}\) & \(\pm 1.25\times 10^{-4}\) & \([-0.50\times 10^{-4},1.25\times 10^{-4}]\) \\ \(R_{Eq-f}\) & \(5.5\times 10^{14}\) N - s/m\({}^{5}\) & \(\pm 5.5\times 10^{12}\) & \([-3.0\times 10^{12},5.5\times 10^{12}]\) \\ \(F_{ex}(x)\) & \(2.22\times 10^{5}\) Pa & \(\pm 2.22\times 10^{3}\) & \([-1.00\times 10^{3},2.22\times 10^{3}]\) \\ \hline \end{tabular}
\end{table} 
Table 6: The defined uncertainty value. Figure 14: Diagnostic simulation model of the system. respectively. MSE represents the error that cannot be explained by the model, so the larger the MSE, the worse the matching effect of the model. According to this principle, the larger \(R^{2}\) is, the better the matching effect of the model. The value of \(R^{2}\) can only be 0-1. Generally, if \(R^{2}\) is lower than 0.5, the model will have poor effect. If \(R^{2}\) is greater than 0.75, the model will perform well. In Figure 15a and b, the average \(R^{2}\) is 0.7676, so the model matching is satisfactory.
## 4 Results and Discussion
### Comparison Between the Training and Synthetic Data
#### 3.2.2 Case Study 2: Generation of Synthetic Power Consumption Data for a Pig Farm
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & \begin{tabular}{c} **Synthetic Data** \\ **with 12 States** \\ \end{tabular} & \begin{tabular}{c} **Synthetic Data** \\ **for 17 States** \\ \end{tabular} & \
\begin{tabular}{c} **Synthetic Data** \\ **for 23 States** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Average Frobenius distance of the \\ hourly matrices (Favg h) \\ Average Frobenius distance of the \\ hour-change matrices (Favg h-ch) \\ Average \(R^{2}\) of the hourly matrices \\ (R^{2}\).avg h) \\ Average \(R^{2}\) of the hour-change \\ matrices (\(R^{2}\).avg h-ch) \\ Relative error of the variance \\ (\(\sigma^{2}\).rel.diff) \\ Relative error of the standard \\ deviation (\(\sigma\).rel.diff) \\ \end{tabular} & 0.18 & 0.26 & 0.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the metrics of the three generated datasets of the agricultural consumer with 12, 17, and 23 states.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results
### Effect of Type of Spreading Codes on Robustness  
Researchers used many types of codes for spreading the watermark bits. Gold code, PN code, Hadamard codes are the commonly used spreading codes. The effect of the different types of spreading codes on the performance of the proposed FWHT based algorithm is studied in detail. We have used Gold code, Hadamard code and MATLAB generated PN codes for spreading. Figure 11 shows that Hadamard codes have better performance against compression (JPEG) and PN codes are the worst. Detailed analysis of the robustness of the algorithm against different attacks reveals that Hadamard and Gold code sequences show almost similar robustness. As shown in Figure 12, only in the case of blurring attack PN codes outperforms Hadamard sequences.
## 3 Finite Element Model of STACER
### Finite Element Model of STACER
The validation of the numerical model correctness was conducted by both the simulation phenomenon on the state of the STACER and the tests. For the simulation validation, it is focused on the storage characteristics in the early stage of modelling process. Whether the top of STACER can be moved down smoothly and whether the bottom of STACER can enter the storage bucket are standards for the correct establishment of the model. It is a simulation result validation on a physical structure configuration of the STACER. In the tests, the force which has been registered for a top rod can be compared with the simulation results on both the value and tendency. The force on the top bar can represent the force to the spring reel during the gathering process. In the simulation process, the top rod is defined as a rigid body and a reference point for the modelling establishment, for which it is easy to achieve a data output. It also indicates the materials properties of the STACER under load. Moreover, in the test process, the force on the top rod is easier to measure. Therefore, the force analysis on the top rod is selected for both the simulation and test. \begin{table}
\begin{tabular}{c c c c c} \hline
**Name** & **Mesh Type** & **Number of Meshes** & **Parameters** & **Numerical Value** \\ \hline Top rod & R3D4 & 4000 & Strip thickness & 0.15 mm \\ STACER & S3R & 88 & [PERSON]’s modulus & \(2.05\times 10^{5}\) MPa \\ STACER & S4R & 38842 & Density & \(7.80\times 10^{3}\) kg/m\({}^{3}\) \\ Storage barrel & R3D4 & 9419 & Poisson’s ratio & 0.31 \\ Spring reel & R3D4 + S3R + S4R & 52349 & Yield Strength & \(1.29\times 10^{3}\) MPa \\ \hline \end{tabular}
\end{table} 
Table 1: Specification of the STACER numerical model.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 2 Inter-SubNet
The output cIRM, \(\mathbf{M}^{r}+j\mathbf{M}^{i}\), is element-wise multiplied with the input complex-valued spectrogram \(\mathbf{X}=\mathbf{X}^{r}+j\mathbf{X}^{i}\) as \[\mathbf{\hat{X}}=(\mathbf{M}^{r}+j\mathbf{M}^{i})\odot\mathbf{X}=(\mathbf{M} ^{r}+j\mathbf{M}^{i})\odot(\mathbf{X}^{r}+j\mathbf{X}^{i}),\] (5) where \"\(\odot\)\" is the element-wise multiplication operator. The resulting spectrogram \(\mathbf{\hat{X}}\) is then converted back to a time-domain signal \(\hat{x}[n]\) using the inverse STFT, providing an enhanced version of the input signal \(x[n]\). The **mean-squared error (MSE)** between the estimated cIRM, \(\mathbf{M}^{r}+j\mathbf{M}^{i}\), and the ground-truth cIRM (denoted by \(\mathbf{\bar{M}}^{r}+j\mathbf{\bar{M}}^{i}\)) is employed as the loss function to train the Inter-SubNet framework. The MSE loss is expressed by \[ \begin{split}\mathcal{L}_{cIRM,MSE}&=||((\mathbf{M} ^{r}+j\mathbf{M}^{i})-(\mathbf{\bar{M}}^{r}+j\mathbf{\bar{M}}^{i})||_{F}^{2}\\ &=\sum_{f}\sum_{t}\Big{(}(\mathbf{M}^{r}_{f,t}-\mathbf{\bar{M}}^{ r}_{f,t})^{2}+(\mathbf{M}^{i}_{f,t}-\mathbf{\bar{M}}^{i}_{f,t})^{2}\Big{)},\end{split} \] (6) where \(||.||_{F}\) is the Frobenius norm.
## 4 Results
Furthermore, phase shift, also known as phase offset, may arise in real implementations, resulting in a degradation of BER as the phase shift increases, as shown in Figure 17.  
Figure 14: BER performance comparison with inter-band interference-Rx filter:third-order Butterworth; \(p(t)\): raised cosine filter (\(\beta=1\)) and the matched Butterworth filter (Simulations 7 and 8). Figure 16: Received QAM constellation for the first 1000 symbols in the first band, showing constellation rotation in the presence of a frequency shift in the receiver Local Oscillator (LO), of 5 Hz @ \(f_{\mathrm{c}}^{\mathrm{l}}\)= 50 kHz: red circles, received symbols without frequency shift; blue points, received symbols. Figure 15: BER performance using guard bands, by suppressing the even bands (Simulations 9 to 12). Both problems can be solved with system synchronization, which can be accomplished using the carrier frequency recovered from the suppressed carrier modulation, using for instance a Costas loop [54]. The synchronization will often actuate in a Voltage Controlled Oscillator (VCO) in a feedback loop, in order to match the LO frequency and phase with the carrier in the received signal. Hence, the resulting LO frequency will depend on the synchronism loop characteristic, resulting in voltage error and noise in the voltage at the VCO input, considered to be AWGN with zero mean and variance \(\sigma\). The noise at the VCO input will cause a fast switch in VCO synthesized frequency, introducing phase noise in the demodulation. For simulation purposes, a VCO with a quiescent frequency \(f_{\mathrm{c}}\) Hz and a sensitivity of \(f_{\mathrm{c}}\) Hz/V was considered. Figure 18 presents the BER results of the considered scenario, varying the \(\sigma\) of the input voltage AWGN. Figure 17: BER performance considering constant phase shift in the receiver LO for \(\{0,\pi/50,\pi/25,\pi/10\}\) rad/s. Figure 10:0 BER performance considering phase noise. As observed in Figure 18, even small values of \(\sigma\) led to a degradation in the BER, imposing a lower bound. Note that the small values of \(\sigma\) need to be analyzed taking into account the high VCO sensitivity, which normalizes the value of \(\sigma\). Therefore, considering \(\sigma=10^{-6}\) and \(f_{c}=50\) kHz, about 68.3% of the frequency values had a frequency error lower than 0.05 Hz. In this scenario, the BER performance was severely degraded for \(Eb/No<9\) dB.