esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dough Farinographic Properties
#### 3.2.1 Water Absorption  
Flour hydration is essential because it affects the functional properties and the quality of bread [55; 56].  
The water absorption of the wheat flour and flour mixtures, corrected for 500 BU (Table 2), showed a linear (R\({}^{2}\) = 0.94) increase with the increase of the Rp addition to the flour, from 58.20 \(\pm\) 0.00% for the control to 61.90 \(\pm\) 0.00% for the 2.5% Rp addition. According to the ANOVA single-factor statistical analysis, the differences were significant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Water Absorption (500 BU), \%** & **Dough Development** & **Stability, min** & **Softening Degree (12 min** & **Faringgraph Quality** \\  & **(500 BU), \%** & **Time, min** & **after Maximum), BU** & **Number** \\ \hline WF & 58.20 \(\pm\) 0.00 a & 6.70 \(\pm\) 0.00 a & 11.00 \(\pm\) 0.00 a & 58.00 \(\pm\) 0.00 a & 116.00 \(\pm\) 0.00 a \\ WF–Rp 0.5\% & 59.70 \(\pm  
(\(p<0.05\)). The control sample with AA at 10 g/100 kg showed a water absorption identical to that of the control sample.  
The increase in water absorption is widely discussed in the literature. Thus, numerous papers mention the positive influence of protein quantity and quality on water absorption [55; 57; 58; 59]. Moreover, [PERSON] et al. [60] and [PERSON] et al. [61] reported increased water absorption with increased protein content with chickpeas in wheat flour.  
Since the addition of Rp to wheat flour decreased the protein content of the flour mixtures, the reason for the increase in water absorption must be sought somewhere else, for example, in the influence of fibre content. [PERSON] et al. [62] added purified fibres of various origins (oranges, peas, cocoa, coffee, wheat, and microcrystalline cellulose) to wheat flour bread. They obtained an increase in water absorption with an increase in the percentage of added fibres. For example, with the addition of 2% and 5% orange fibre, the water absorption was 63.00% and 65.40%, respectively, compared to 58.70% in the control sample (white wheat flour). The water absorption capacity increased with the addition of fibres due to the hydroxyl groups in the chemical structure of the fibres that allows the binding of water through hydrogen bonds [62]. Many other researchers have come to the same conclusion. Thus, introducing high-fibre wheat bran to white wheat flour increased the water absorption from 57.10 \(\pm\) 0.20% in the control sample to 68.20 \(\pm\) 0.60% for an addition of 30% wheat bran [44]. Similarly, [PERSON] et al. [63] obtained an increase in water absorption from 58.00 \(\pm\) 0.70% for the control sample to 75.30 \(\pm\) 0.70% for an addition of 15% hydrated apple powder. Similar results were reported by [PERSON] et al. [64] with the addition of carrot powder (water absorption increased from 60.67 \(\pm\) 0.35% for the control sample to 72.01 \(\pm\) 0.25% for a 15% addition of carrot powder) and [PERSON] et al. [65] with the addition of mango peel powder (water absorption increased from 60.00% for the control sample to 68.00% for a 10% addition of mango peel powder).
## 3 Results and Discussion
### TPPE Characteristics for Sample A and Sample C and Their Relation to the Properties of Liquids
Interestingly, the characteristics of the TPPE for sample A and sample C are correlated with \(AN\) and \(1/(\varepsilon/\varepsilon_{0})\) of the liquids, but take completely opposite trends. In Figures 10 and 11, the relation of \(N_{\rm Tmax}\)a vs. \(AN\), \(\Delta\)Eu\({}_{\rm Up1}\) vs. \(AN\), and \(\Delta\)Eu\({}_{\rm Up1}\) vs. \(1/\varepsilon/\varepsilon_{0}\) for sample A is completely opposite to that of \(N_{\rm Tmax}\)c vs. \(AN\), \(\Delta\)Eu\({}_{\rm Up1}\) vs. \(AN\), and \(\Delta\)Eu\({}_{\rm Up1}\) vs. \(1/\varepsilon/\varepsilon_{0}\) for sample C. However, in Figures 10 and 11, it is seen that the relation of \(N_{\rm Tmax}\)a vs. \(\Delta\)Eu\({}_{\rm Up1}\) is similar to that of \(N_{\rm Tmax}\)c vs. \(\Delta\)Eu\({}_{\rm Up1}\), although the order of the liquids at the data points is opposite. It remains unclear why \(N_{\rm Tmax}\) increases with increasing \(\Delta\)Eu\({}_{\rm Up1}\). Figure 11: [Sample C] TPPE characteristics of copper sheets subjected to 5-minute ultrasonic cleaning in the liquids and the property of the liquids: (**a**) relation between the maximum value of \(N_{\rm T}\)c (\(N_{\rm Tmax}\)c) and the acceptor number (\(AN\)) of the liquids; (**b**) relation between \(\Delta\)Eu\({}_{\rm Up1}\) and \(AN\) of the liquids; (**c**) relation between \(\Delta\)Eu\({}_{\rm Up1}\) and the reciprocal of dielectric constant (\(\varepsilon/\varepsilon_{0}\)) of the liquids; (**d**) relation between the value of \(N_{\rm Tmax}\)c during Up1 scan and \(\Delta\)Eu\({}_{\rm Up1}\). The data come from Table 2.  
In Refs. [4; 5], regarding contact killing, it is reported that a few general principles appear to be clear: higher copper content of alloys, higher temperature, and higher relative humidity increased the efficacy of contact killing. The temperature and water adsorption dependence of the photoelectron emission behavior observed in the TPPE experiment may be related to the efficacy of contact killing by metallic Cu.
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 3 Theory of Nano-TES and JES Bolometers
### JES Bolometer Performance  
The JES operates at \(T_{e}(I)\), that is fully in the superconducting state. Since the current injection does not change the energy gap of the active region (\(\Delta_{A}\sim\text{const}\)), only the effective critical temperature of \(A\) changes with \(I\), while the intrinsic values of critical temperature (\(T_{C}^{i}\)) is unaffected. As a consequence, all figures of merit of a JES are calculated deeply in the superconducting state, thus ensuring high sensitivity.  
In full analogy with the nano-TES, the thermal fluctuations are the limiting factor for the sensitivity of a JES bolometer. The related contribution to the NEP reads  
\[NEP_{TFN,JES}=\sqrt{4 YG_{th,JES}k_{B}{T_{e}}^{2}}, \tag{16}\]  
where \(G_{th,JES}\) is the thermal conductance in the superconducting state. The latter takes the form [50]  
\[G_{th,JES}\approx\frac{\Sigma_{A}\mathcal{V}_{A}T_{e}^{4}}{96\zeta(5)}\left[f_{ 1}\left(\frac{1}{\bar{\Delta}}\right)\cosh(\hbar)e^{-\bar{\Delta}}+\pi\bar{ \Delta}^{5}f_{2}(\frac{1}{\bar{\Delta}})e^{-2\bar{\Delta}}\right], \tag{17}\]  
where the first term refers to the electron-phonon scattering, while the second term stems from the recombination processes. In Equation (17), \(\zeta(5)\) is the Riemann zeta function, \(\bar{\Delta}=\Delta_{A}/k_{B}T\) is the normalized energy gap of \(A\), \(\hbar=h/k_{B}T\) represents exchange field (0 in this case), \(f_{1}(x)=\sum_{\bar{\Delta}=0}^{3}\Sigma_{n}x^{n}\) with \(C_{0}\approx 440,C_{1}\approx 500,C_{2}\approx 1400,C_{3}\approx 4700\)and \(f_{2}(x)=\sum_{n=0}^{2}B_{n}x^{n}\) with \(B_{0}=64,B_{1}=144,B_{2}=258\). We note that the thermal conductance for a JES is exponentially damped compared to the nano-TES, due to the operation in the superconducting state. Thus, we expect the JES to be extremely more sensitive than the nano-TES, that is \(NEP_{TFN,JES}\ll NEP_{TFN,nano-TES}\).  
The JES speed is given by the relaxation half-time (\(\tau_{1/2}\)), which reads [32]  
\[\tau_{1/2}=\tau_{JES}\ln 2, \tag{18}\]  
where \(\tau_{JES}\) is the JES intrinsic thermal time constant. The latter is calculated by substituting the JES parameters in Equation (13), thus considering \(C_{e,JES}\) and \(G_{th,JES}\) in deep superconducting operation. The electron heat capacitance needs to be calculated at the current-dependent escape temperature (\(T_{e}(I)\)), thus in the superconducting state, and takes the form  
\[C_{e,JES}=\gamma_{A}\mathcal{V}_{A}T_{e}\Theta_{Dump}=C_{e,nano-TES}\Theta_{ Damp}, \tag{19}\]  
where \(\Theta_{Damp}\) is the low temperature exponential suppression with respect to the normal metal value. The suppression is written [51]  
\[\Theta_{Damp}=\frac{C_{s}}{1.34\gamma_{A}T_{e}}. \tag{20}\]  
Finally, the electronic heat capacitance is given by  
\[C_{s}=1.34\gamma_{A}T_{C}^{i}\bigg{(}\frac{\Delta_{A}}{k_{B}T_{e}}\bigg{)}^{- 3/2}e^{\Delta_{A}/k_{B}T_{e}}. \tag{21}\]
## 1 Introduction  
The creation of smart wound dressings that work proactively with the human body to speed up the wound healing process and prevent infection has received more attention recently, particularly for individuals with chronic wounds. Due to the need for a new generations of smart wound dressings with the ability to accelerate the wound healing process and prevent infections, various kinds of dressings with novel materials and structures have been prepared and studied. In particular, biopolymers have attracted more interest due to their biocompatibility and low or non-toxicity.  
Chitosan is one of the most attractive biopolymers for wound dressing applications. It is a linear polysaccharide derived from chitin, which is the second most abundant biopolymer after cellulose. It has antimicrobial, anticoagulant, antibacterial, antifungal, anti-tumor, and hemostatic properties [1]. Due to these excellent characteristics, chitosan has long been a potential biomaterial for biomedical applications such as wound dressings [2], drug delivery systems, and tissue-engineering scaffolds [3]. Graphene oxide (GO) is one of the most important derivatives of graphene. In recent years, it has been considered as a suitable material for biomedical applications. Owing to its large surface area and functional groups, such as carboxyl and hydroxyl, GO is hydrophilic in nature, so it can be dispersed in aqueous solutions via electrostatic repulsion [4]. GO can be applied to reinforce natural polymers such as chitosan, and some reports have indicated that chitosan/GO composites can capitalize on the advantages of both chitosan and GO materials [5]. Therefore, there are several studies regarding the preparation and characterization of chitosan/GO composite films and their application in different biomedical fields, such as wound dressing and drug delivery systems. This review highlights the applications of chitosan/GO composites as wound dressings and drug delivery systems. First, the individual properties of chitosan and graphene oxide are described. Then, different hybrid chitosan/GO structures such as hydrogels and electrospun nanofibers are discussed.
## 2 Materials and Methods
### Problem Formulation  
First, the different signals presented in the manuscript are denoted. The far-end reference signal is denoted by \(x(n)\). The echo loudspeaker signal is denoted by \(s(n)\), and the near-end signal is denoted by \(d(n)\). The value \(v(n)\) denotes the background noise. The microphone signal is given by:  
\[m(n)=s(n)+d(n)+v(n)\,. \tag{1}\]  
The linear AEC receives as inputs, \(x(n)\) and \(m(n)\), and outputs two signals: \(a(n)\), the estimate of the echo signal \(s(n)\), and the estimate of the noisy near-end signal, i.e. the error signal \(e(n)=m(n)-a(n)\). The filter tap weights vector of length \(N\) is denoted by \(\mathbf{c}(n)=[c_{1}(n),...,c_{N}(n)]^{T}\), where \((\cdot)^{T}\) represents the transposed vector. Similarly, the far-end signal's vector at time \(n\) and length \(N\) is denoted by \(\mathbf{x}_{N}(n)=[x(n),x(n-1),...,x(n-N+1)]^{T}\).  
The error signal \(e(n)\) contains noise and residual echo components. The goal was to enhance \(e(n)\) by further suppressing the residual echo and possibly removing noise. This is done either by a speech denoising model, in which case it receives \(e(n)\) as a single input to be denoised, or by an RES model, in which case it also receives as inputs \(x(n)\), \(m(n)\), and \(a(n)\). The RES/denoiser block predicts \(\tilde{d}(n)\). The problem's setup and the related signals are depicted in Figure 1. When referring to the short-time Fourier transform (STFT) [20] domain transformations of the above signals, \(f\) denotes the frequency index, and \(k\) denotes the time index of the transformed signals. For example, \(E(f,k)\) is the STFT of \(e(n)\).  
Figure 1: Residual echo suppression setup.
# Raman 2D Peak Line Shape in Epigraphene on SiC
## 1 Introduction  
The Raman spectroscopy of graphene is a well-established technique [1; 2; 3] to determine number of graphene layers [4], strain [5; 6], charge density [7; 8; 9; 6], grain size [10; 11; 12; 13; 14], graphene functionalization [15], misorientation of graphene layers [16] or degree of hydrogen intercalation of epitaxial graphene on SiC [17; 18]. The graphene's most prominent Raman spectral features are the D peak, G peak and 2D peak. The D peak reflects the amount of defects, or the graphene grain size. The G peak is related to the in-plane bond-stretching optical vibrations of sp\({}^{2}\) hybridized carbon atoms in the graphene lattice [19; 20]. The 2D peak is recognized as a combination mode of lattice and electronic excitations. The predicted unique property of the 2D peak is its line shape. The 2D peak is predicted to have a Lorentzian line shape in the case of the single layer graphene (SLG) [21]. The 2D peak is predicted to have four components in the case of bilayer graphene. The single and four-component nature of the 2D peak was proved experimentally [22; 23]. It is also known that the spectral position of the 2D peak is determined by the uniaxial [24; 25; 26; 27] and biaxial [5; 6; 26] mechanical strain and charge density [7; 8] of the graphene layer. The scaling of the 2D peak position with charge and strain was studied extensively. The 2D peak line shape is influenced by the strain uniformity [28] and strain fluctuations on the nanometer scale [29]. Also, different line shape was identified for graphene on SiC terraces and step edges [30].  
However, beside the known spectral line shape and parameters determining the position of the 2D peak, there is little known about the combined effect of the homogeneous and inhomogeneous broadening. The knowledge of the mutual effects of the homogeneous and inhomogeneous broadening can provide deeper insight into the formation of the graphene layers and it can be used to further optimize graphene growth. Hence, we propose here to analyze in detail the spectral line shape of the 2D peak. Instead of describing the 2D peak line shape only by the Lorentzian broadening, we assumealso the contribution by the inhomogeneous broadening. The homogeneous and inhomogeneous broadening have to be taken into account simultaneously. As the inhomogeneous broadening describes the random nature of the parameters determining the position of 2D peak, it is described by the normal (Gaussian) distribution. The mutual effect of the homogeneous and inhomogeneous broadening thus leads to the convolution of the Lorentzian and Gaussian broadening, also called the Voigt broadening. We show here that the Voigt profile describes the 2D peak line shape better than the Lorentzian profile. We show that the Voigt broadening, though it has one more fitting parameter, it does not show any signatures of over-parametrized model. We test the better fit quality by the F-test and the results are compared for three different samples and four different locations on each sample.
## 5 Simulation Results  
### Simulation of Impact Resistance of the SDD  
Using the two-link space robot (\(n=2\)) and satellite systems in Figure 3 for simulation analysis. The initial position and velocity of space robot are \(\mathbf{q}(0)=[1.75,0.52,1.05]^{\rm T}\) rad, \(\mathbf{\dot{q}}(0)=[0,0,0]^{\rm T}\) rad/s. The actual parameters of space robot and satellite systems are as follow: \(m_{0}=100\) kg, \(m_{1}=m_{2}=10\) kg, \(m_{\rm s}=50\) kg, \(L_{0}=1\) m, \(L_{1}=L_{2}=2\) m, \(d_{1}=d_{2}=1\) m, \(d_{s}=0.5\) m, \(l_{0}=64\) kg\(\cdot\)m\({}^{2}\), \(l_{1}=I_{2}=3.5\) kg\(\cdot\)m\({}^{2}\), \(I_{\rm m1}=I_{\rm m2}=0.05\) kg\(\cdot\)m\({}^{2}\), \(I_{\rm s}=12.5\) kg\(\cdot\)m\({}^{2}\), \(k_{\rm s1}=k_{\rm s2}=1000\) N/rad, \(D_{\rm m1}=D_{\rm m2}=28.65\) N\(\cdot\)s/rad, \(D_{\rm t1}=D_{\rm t2}=1146\) N\(\cdot\)s/rad, \(D_{\rm t1}=D_{\rm t2}=28.65\) N\(\cdot\)s/rad.  
In order to verify the impact resistance of the SDD during the third stage, the space robot system with/without SDD was used to carry out acquisition simulation tests on spacecraft with different velocity. The simulation results are shown in Table 1.  
Figure 6: 2 st joint motor output torque.  
Figure 7: Joint angle trajectory of without the fast subsystem controller.  
It can be seen from Figure 8 that the joint motor enters the stable output state after four shutdowns. Figure 9 shows that the RL controller can continuously output reinforcement signals when the base attitude angle and joint angle do not reach the desired position and reinforcement signal will to zero when they reach the desired position. Figure 10 shows that the ACN and ASN are optimized through the interaction with the environment and finally make the hybrid system reach a stable state. Figure 11 shows that the joint impact is limited to the bear torque well, and the buffer compliance strategy can realize the compliance of capture operation well.  
Considering that the impact torque that the joint can bear will decrease with the increase of the space robot service years, the second group of simulation sets the shutdown torque threshold is \(80\) N\(\cdot\)m, and the startup torque threshold is \(10\) N\(\cdot\)m. The simulation results are shown in Figures 12-15.  
Figure 11: Joint impact torque (\(F_{\text{C1}}=120\) N\(\cdot\)m, \(F_{\text{O1}}=10\) N\(\cdot\)m).  
Figure 12 shows the joint motor enters the stable output state after seven shutdowns. As can be seen from Figures 12-15, even if the shutdown torque threshold is lowered, the joint impact torque can be limited well within a safe range, and the RL controller can still complete the calm control of the unstable hybrid system by outputting the reinforcement signal. This means that the SDD has excellent protection performance for space robot joints, and it plays an important role in prolonging the service life of space robot.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 5 The Methodology
### Post-Processing  
To enhance the quality of the flood extent layer and reduce the number of false positives, we used additional datasets and applied post-processing steps. First, we used the global surface water dataset (GSW) [40] to distinguish between permanent and semi-permanent water bodies. This dataset provides comprehensive maps of surface water location and temporal distribution and offers valuable statistics on the extent and changes in water surfaces over time. To identify areas with permanent or semi-permanent water bodies, we used the seasonality band within the dataset. This band provides information on seasonality, ranging from 0 to 12 months, allowing us to effectively mask out areas with consistent water presence throughout the year. By leveraging this dataset and incorporating seasonality information, we aimed to accurately delineate and analyze the distribution of permanent and semi-permanent water bodies, contributing to a better understanding of global surface water dynamics.  
We then employed topographic filtering techniques based on the elevation data from the DEM obtained from the HydroSHEDS dataset [39]. By setting a threshold slope percentage, we identified areas where the slope exceeded a certain value. These steep areas were considered unsuitable for flooding because water naturally flows away from them. By filtering these incorrectly flooded pixels, we aimed to improve the accuracy of our flood detection analysis. The inclusion of topographic information through the DEM allowed us to refine the flood mapping process and focus on areas where flooding is more likely to occur, enhancing the reliability of our results.  
Additionally, we used information provided by the MERIT hydro dataset [41]. To detect permanent water bodies, we focused on the \"wet\" band, where land areas are represented by 0 and permanent water bodies by 1. Furthermore, using this dataset, we  
Figure 3: Maps of multi-temporal (December 2014–2023) (**a**) temporal mean and (**b**) standard deviation, computed for the Zaragozza Ebro River event.  
implemented a 2 km buffer to specifically highlight rivers while eliminating any false positive water detections. This approach allowed us to accurately identify and visualize river networks while ensuring the exclusion of non-river water features.
## 3 Indoor Positioning Systems
However, other measures, such as response time, availability, and scalability, among others, also need to be considered. Considering that different applications require different types of IPSs in environments such as supermarkets and private homes, it is essential to know in detail each of the subcategories described in Figure 1. The next sections describe the systems, emphasizing their most typical applications and limitations.
## 2 Dynamic Power Management
### Dynamic Frequency and Voltage Scaling  
#### 2.2.1 Dynamic Frequency Scaling (DFS)  
Due to the large range of the performance requirements of different applications, the concept of dynamic frequency scaling (DFS), has been developed to optimize the frequency of operation and hence power (Equation 1) [8]. For example, the needs of a video codec are orders of magnitude more than that of an audio codec. Hence the frequency of operation is adjusted as required by the application, thus reducing dynamic power.  
A convenient way to adjust frequency is to use a divided version of the master clock. The advantage of this approach is that the new frequency can be obtained in just one clock cycle and hence is the fastest possible way to adjust frequency. However, this leads to large gaps in frequency values, especially between the fundamental and the first divided value.. An alternative is to adjust the divider ratio in the clock generation PLL, so that frequency can be adjusted in steps of the reference clock frequency, or even lower. However the PLL loop bandwidths are typically made small, in the MHz range, to obtain a low clock jitter. Thus the time taken to settle to a new frequency can be in the order of microseconds. Figure 3 shows another technique, using clock dividers and a state machine, to obtain much finer resolution frequency control, but with very low latency to change the frequency [8]. It is based on the observation that the clock does not have to be periodic. Hence the approach uses the next lowest frequency octave and selectively suppresses the required number of clock edges to obtain the desired frequency value. For example, the system generates clocks of frequency 128 MHz, 96 MHz, 64 MHz, 48 MHz, 32 MHz, 16 MHz and 8 MHz via a simple divider. Any frequency between 8 and 128 MHz in steps of 1 MHz can be generated with the help of the state machine. For example, to  
Figure 2: (**a**) IP block with power switch to support dynamic power switching; (**b**) SRAM with power switching and standby state retention support.  
generate 28 MHz, a 32 MHz base clock is chosen and the state machine suppresses 4 edges, in a sequence of 32 edges, to generate the desired frequency. In order to ensure a glitch free clock, the multiplexer select and enable signals are triggered at negative edges so that they are stable by the time of the positive clock edge. While this approach leads to a simple and quick way of changing frequency in small steps, it suffers from excess power dissipation compared to PLL adjustment approach as the supply voltage needs to be that for the highest base frequency used as input to the clock edge suppression state machine.