esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Functional Characterisation by Fluorescence  
#### 2.4.1 Fluorescence Calibration  
A calibration protocol was developed to relate the observed fluorescent intensity to the number of Alexa 488 and FITC dextran molecules on the sample surface. The intensity was determined for both a dry and a wet sample environment in order to investigate possible differences. The test solutions consisted of 3 \(\upmu\)L MOPS buffer containing 0-4 \(\upmu\)g mL\({}^{-1}\) Alexa 488 dextran or 0-8 \(\upmu\)g mL\({}^{-1}\) FITC dextran pipetted on 10 \(\times\) 10 mm\({}^{2}\) large substrates cut from one of the 3-inch quartz wafers. A glass coverslip of identical size was placed on top of the quartz substrate to permit the drop to be equally spread over the area. This also allowed for an estimation of the height of the liquid film that was sandwiched between the substrate and the coverslip. The fluorescent intensity was then recorded with the fluorophore evenly distributed in the solution (wet state). In the dry state, the samples were permitted to dry for 2 h prior to use to ensure that the solvent had been evaporated. The coverslip (acting as a lid) prevented the fluorophore from being concentrated in the centre during evaporation. Any optical diffraction from the coverslip would be minimal due to the measurements being performed with an inverted microscope with a 10x objective.  
Fluorescence imaging was analysed using Cell'B software (Olympus) and Image J software (National Institutes of Health, Bethesda, MD, USA). The Image J software quantifies the intensity at a resolution of 8 bit (intensity scale from 0 to 255 a.u.). A constant exposure time (\(t_{e}\) = 2500 ms) was used throughout to optimize the range without triggering saturation of the intensity. The correlation between the concentration of fluorescent labelled dextran and the observed fluorescence intensity was based on the average intensity in the region of interest (ROI) which measured the size of the picture frame (1.3 \(\times\) 1.7 mm\({}^{2}\)), and was averaged over 10 images. These 10 images were captured at four positions at the edges and six positions within the centre region of each 10 \(\times\) 10 mm\({}^{2}\) sample. The number of recordings \(n\) were scaled up by repeating the measurement on three different samples for each fluorophore concentration (\(n\) = 30).
###### Abstract  
The Zr-2.5%Nb alloy used to manufacture the pressure tubes for the CANDU 600 power plant experiences continual corrosion when the reactor's fuel channels are functioning normally. Analysis of the hydrogen accumulation circumstances, the creation of zirconium hydride platelets, evaluation of the platelets' reorientation in the thermo-mechanical field at volumetric flaws, and determination of the hydrogen morphology are therefore significant goals of the worldwide research being conducted in this field. In this paper, pressure tube alloy samples with artificial V flaws were hybridized by the technique of electrolytic deposition of a hydrogen film layer, followed by a specific thermal treatment. Micrographs of the Zr-2.5%Nb alloy samples in the radial cross-section were taken to examine the hydride reorientation phenomenon that occurred when the samples were heated under mechanical stress. The complex stress field around a tip flaw promoted the formation of some circles from hydride filaments. To characterize the zirconium hydride morphology in the reorientation zone at the volumetric flaw tip, image analysis and processing techniques were applied. To better characterize the reorientation zone from the point of view of embrittlement and mechanical behavior, this paper proposes a novel metric for the morphology of hydrides located in the reorientation zone. This work's findings are beneficial for the investigation of CANDU fuel channels' structural integrity.  
CANDU pressure tube; hydride cracking; reorientation hydrides; novel metric
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 2 Related Works  
### Lightweight Natural Image SR  
In recent years, the continuous improvement in the performance of super-resolution (SR) networks has often come with increased parameters and computational overhead, posing challenges for practical deployment. Consequently, there is a growing demand for lightweight SR networks. FSRCNN [8] reduced the model parameters and computational load by reconstructing the SRCNN [7] architecture while maintaining performance. [PERSON] et al. [50] proposed a Cascading Residual Network (CARN), which uses multi-level representation and shortcut connections for more efficient information transfer. [PERSON] et al. [51] developed an Information Distillation Network (IDN), which extracts significant information by merging different features. Building on this, IMDN [15] enhanced IDN by using information multi-distillation blocks and channel splitting operations. [PERSON] et al. [44] designed a lightweight and precise Residual Feature Distillation Network (RFDN) using Feature Distillation Connections (FDC) and Shallow Residual Blocks (SRB). LatticeNet [16] achieved excellent performance while significantly reducing parameters by utilizing lattice filters based on a butterfly structure and a backward fusion strategy. ShuffleMixer [52] explored a method to reduce parameters and computational load by employing large convolution kernels, channel splitting, and shuffling techniques, achieving higher efficiency while maintaining performance. Due to the significant similarity between feature maps of multiple channels in the same CNN layer, Feature-Refined Networks (FRNs) [43] designed shadow modules to generate such similar feature maps, thereby reducing model complexity. [PERSON] et al. [53] proposed the Super Token Interaction Network (SPIN), which clusters locally similar pixels using superpixels and facilitates local information interaction through intra-superpixel attention. Omni-SR [19] introduced a full self-attention paradigm and a full-scale aggregation scheme to address issues related to limited effective receptive fields due to one-dimensional self-attention modeling and homogeneous aggregation schemes. [PERSON] et al. [49] proposed a Two-branch Adaptive Residual Network (TARN), which effectively utilizes residual features through a two-branch adaptive residual block (TARB) based on a lattice structure. [PERSON] et al. [54] proposed an Omni-Stage Feature Fusion Network (OSFFNet), which effectively integrates features from different levels and fully utilizes their complementarity. With full consideration of structural priors, [PERSON] et al. [55] proposed an Interactive Feature Inference Network (IFIN), which progressively extracts more specialized features to enhance the reconstruction of high-frequency details in images. The advancements in lightweight natural image SR have provided valuable insights for lightweight RSISR research, offering beneficial guidance for our work.
## 3 Memristive Analog Design
### Memristive RF Circuits
#### 3.2.3 CMOS Amplifier
An OTA-C filter is designed and implemented, and a high-pass filter is demonstrated to showcase the effectiveness of the OTA in filtering out low-frequency signals. The use of OTA-C filters also allows for the easy adjustment of filter parameters, making them highly versatile for various signal processing applications. A novel memristor-based compensation technique for an ultra-low-power single-output two-stage CMOS operational amplifier (Op-Amp) is presented in [54]. This technique utilizes the unique properties of memristors to enhance the performance of the Op-Amp, particularly in terms of power consumption and output stability. The Op-Amp uses a memristor programmer to regulate its compensation block's memristance, improving the stability and phase margin. The simulation results show a 94 dB DC gain, a 1 MHz unity gain bandwidth, and 2.7 \(\upmu\)W of consumption. The Op-Amp is designed in 0.18 \(\upmu\)  
Figure 6: Proposed three-stage true-class AB amplifier: (**a**) schematic and (**b**) simulated gain [51]. CMOS technology, with a phase margin of 47 degrees and a unity gain bandwidth of 1 MHz. Furthermore, the proposed compensation technique effectively addresses the trade-off between performance and power consumption, making it suitable for low-power applications. The utilization of a memristor programmer in the regulation of the compensation block's memristance not only enhances the stability and phase margin but also ensures efficient power utilization. These simulation results demonstrate the effectiveness of the Op-Amp design in achieving high performance with minimal power consumption in low-power applications. Memristors offer a non-volatile memory element that can adjust resistance levels, providing more flexibility in amplifier performance. Reported studies have shown that incorporating memristors into CMOS amplifier designs can improve both gain and frequency range tunability. This advancement could potentially transform the field of analog circuit design, offering increased flexibility and efficiency over conventional techniques. However, the presented studies employ a CMOS memristor emulator or a memristor model created with Verilog_A or LTSpice to verify design performance, which may not accurately represent the behavior of real memristors. Therefore, further research is needed to validate the performance improvements of CMOS amplifiers with actual memristor components. Additionally, exploring different methods of integrating memristors into amplifier designs could lead to even more significant advancements in analog circuit design.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 2 Orbit Dynamics
### Orbit Perturbations
The term \(U_{0}\) is responsible for the Keplerian gravitational acceleration, while \(U_{p}\) produces the perturbing acceleration  
\[\mathbf{a}_{g}=-\
abla U_{p}\ \ \text{with}\ \ \
abla=\hat{r}\frac{\partial}{ \partial r}+\hat{E}\frac{1}{r\cos\phi}\frac{\partial}{\partial\lambda_{g}}+ \frac{\hat{N}}{r}\frac{\partial}{\partial\phi} \tag{11}\]  
The gravitational action of the Sun can be treated as a third body perturbation. If subscript \(1\) is associated with Mars and subscript \(2\) with the Sun, the perturbing acceleration can be expressed as [17]  
\[\mathbf{a}_{P3B}=-\mu_{2}\bigg{[}\frac{\mathbf{r_{12}}}{r_{12}^{3}}+\frac{ \mathbf{r_{s}}-\mathbf{r_{12}}}{[(\mathbf{r_{s}}-\mathbf{r_{12}})\cdot( \mathbf{r_{s}}-\mathbf{r_{12}})]^{\frac{3}{2}}}\bigg{]} \tag{12}\]  
where \(\mu_{2}\) is the gravitational parameter of the third body, whereas \(\mathbf{r_{12}}\) is the position of the Sun with respect to Mars. The spacecraft is subject to solar radiation pressure produced by the photons emitted by the Sun. The related perturbing acceleration can be evaluated using the following expression [20]:  
\[\mathbf{a}_{PSR}=-v\frac{P_{sr}A}{m}C_{R}\hat{r}_{SSun} \tag{13}\]  
In Equation (13) \(m\) is the vehicle mass and \(A\) its reference surface, \(\hat{r}_{SSun}\) is the unit vector pointing from the spacecraft toward the Sun, \(P_{sr}\) is the solar radiation pressure, and \(C_{R}\) is the solar radiation pressure coefficient. Finally, \(\
u\) is the shadow function, which can assume value \(1\), if the vehicle is illuminated, or \(0\) if it is shadowed. However, because the distance of Mars from the Sun is much greater than the typical radius of Martian orbits, the following approximation is adopted: \(\hat{r}_{SSun}\equiv\hat{r}_{12}\).
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 5 Discussion
### Uncertainty Caused by BRDF Model  
The BRDF model proposed in this paper is based on time series of MODIS TOA reflectance. In order to analyze the accuracy of BRDF model, a MODIS image of Golmud test site on 24 February 2014 is selected, and DN value and imaging geometry information of the site are extracted. Then, the official reflectance calibration coefficients of MODIS and BRDF model coefficients are used to calculate the measured TOA reflectance and simulated TOA reflectance, respectively, and the relative error between them is calculated. The results are summarized in Table 8. As can be seen from the table, the relative errors of the four bands are \(-0.60\%\), \(-1.27\%\), \(-1.16\%\) and \(0.97\%\), respectively, which illustrates that the BRDF model constructed in this paper has high accuracy.
## 5 Experiment Data Analysis  
### Determination of AR Model Order and Model Parameters of Faulty Bearing Vibration Signal  
The following analysis uses MATLAB software to analyze the data. First, zero averaging and smoothing processing are performed on the original data, and then the data is analyzed according to the FPE criterion to obtain the FPE criterion function curve, as shown in Figure 8. The number of data samples is \(3\times 10^{4}\).  
In Figure 8: As the order of the model increases, the overall trend of the FPE criterion curve is decreasing. When n is 5, the corresponding FPE criterion curve is at a lower value, and the curve decreases slowly. When n is 33, the FPE standard curve has the lowest value, the corresponding variance is the smallest, and the FPE standard curve is stable after this point.  
Next, the standard deviation (SD) and mean square error (MSE) are calculated for n = 5 and n = 33, as shown in Table 2. When n = 5 and n = 33, the SD is close and the MSE is same.  
In the AR model, the higher order of the model will improve the accuracy of the estimation, but it will also increase the computational burden, and false peaks or false details will be generated in the power spectrum estimation. Combining the data in Table 2, the model order of this article is chosen to be n = 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Order** & **SD** & **MSE** \\ \hline
5 & 0.0635 & 0.0355 \\ \hline
33 & 0.0558 & 0.0355 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SD and MSE when n = 5 and n = 33.  
Figure 8: FPE criterion function curve.  
Figure 7: Fault bearing inner ring.  
According to the RLS identification algorithm, the AR model parameters are determined. The parameters \(\varphi_{1},\ \varphi_{2},\ \varphi_{3},\varphi_{4},\ \varphi_{5}\) have convergence and the values are 1.4209, \(-\)0.2045, \(-\)0.1405, \(-\)0.0694, \(-\)0.0371. The identification result of parameters are shown in Figure 9.  
According to the established AR model, its autocorrelation function is obtained, and the autocorrelation function has tailing, as shown in Figure 10.
# Influence of the Sound Source's Position on the Stage on the Speech Perception in the Auditorium
## 3 Results
### Effect of Decoration on the Quality of Speech Perception  
This task investigated whether a change in decoration affects speech perception. Given the purpose of the room for both musical and spoken word performances, it was possible to change the decoration by:  
* unfolding the horizon separating the two sections of the stage and lowering the side backdrops (Decor #1);
* hiding the horizon and pinning down the side scenes at the bottom (Decor #2).  
Figure 9 shows a comparison of the MOS values obtained for the two types of decoration and the three positions of the sound source S at the surveyed measurement points R (based on Table 6). In the column graph, for each position of the listener R, the results are presented as S01 (#1), S01 (#2), S02 (#1), S02 (#2), S03(#1), and S03(#2).  
Analysis of the results obtained for the two stage decorations showed that for Decor #2, the MOS values are lower than those for Decor #1. This applies to all listening positions R in the audience and all sound source placements S on stage. For sound source position S01 and for Decor #1, regardless of the spectator's position in the audience, the MOS exceeded 4.0, while for source positions S02 and S03, the points lying on the opposite side of the source obtained MOS values \(\geq\) 4.0. For Decor #2 only, for single spectator locations, the MOS exceeded 4.0. The maximum difference in MOS values between sound source positions, e.g., for central position S01(#1)-S01(#2) was 0.59, and the minimum was 0.04.  
In a theatrical performance, the actors move across the stage. Therefore, it seems relevant to examine the overall MOS rating derived from all positions of the sound source. The results obtained for the two types of decoration, averaged after all three sound source settings S on stage and male and female voices are shown in Table 7 and Figure 10.  
Figure 9: Results of the subjective quality assessment (MOS) for two types of decoration and three different sound source locations.  
Averaging the MOS values by sound source position S shows that the greatest impact of the change of decorations on speech perception was observed based on rows away from the stage, starting from the middle of the audience, but surprisingly also for the first four rows. It can also be concluded that for Decor #1, the average MOS value for the entire auditorium equalled or exceeded 4.0, while for Decor #2, the MOS value was \(\geq\)4.0 for only the middle rows.
## 3 Simulations and Results  
In this section, we present the performance results of the simulation experiments. To accomplish the experiments, a parallel discrete event-based platform was developed in MATLAB. In the simulation, a free space radio propagation model is used. In order to verify the success of the proposed approach, an energy-efficient ant-based routing algorithm EEABR [17], a well known ACO based WSN routing algorithm, was used to make comparisons. The simulation is capable of running packet level experiments, and uses parameter values of a hardware named MICAz more [21], specifications of which are given in Table 1. Experimental results are obtained for the two algorithms which are the one proposed in the approach described in Section 2 and the EEABR algorithm in [17]. The ACO parameters and the specifics of hardware are set to the values specified in Section 2.1 and Table 1.  
Figure 4: Acknowledgement signal content.  
Figure 5: Transmission an acknowledgement signal.  
The results of the proposed approach and EEABR algorithm were obtained using the same settings for the simulation. Deployment of sensors are made by random distribution over 200 x 200 m\({}^{2}\) (10 nodes), 300 x 300 m\({}^{2}\) (20 nodes), 400 x 400 m\({}^{2}\) (30 nodes), 500 x 500 m\({}^{2}\) (40 nodes) and 600 x 600 m\({}^{2}\) when 50, 60, 70, 80 and 100 nodes are used to monitor a static phenomenon. The location of the phenomenon and the sink node are not known. In Figure 6, network densities are calculated by dividing the number of nodes by the total area. In the scenario, the nodes near the phenomenon send the relevant data through the neighbor nodes to the sink by consuming energy. As the number of packets received by the sink increases, the average residual energy of the nodes (that is used as the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Name** & **Value** \\ \hline Frequency & 2,400 MHz \\ Transmit(TX) data rate & 250 kb/s \\ RF power & -10 dBm \\ Receive (RX) Sensitivity & -94 dBm \\ Current Draw in Transmit Mode & 11 mA \\ Current Draw in Receive Mode & 19.7 mA \\ Battery & 2 x (1,250 mAH, 1.5V) \\ Packet Size & 1 Mb \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values of the hardware used in simulations.  
Figure 6: Average residual energy for different WSNs having various number of nodes.  
metric) decreases as shown in Figure 6. In the simulations, the proposed approach gives better results, providing more network lifetime and consuming less energy, particularly for the networks having high densities.  
Figure 7 shows normalized average residual energy after 256 packets are received by the sink node for different WSNs having various number of nodes. From the figure, it is seen that the difference in energy levels increases as the number of nodes in the network grows as much as up to 10 percent.
## 2 Materials and Methods
### Laminates  
Laminates made with selenium nanoparticles were prepared. Firstly, selenium nanoparticles were synthesized using a solution-phase approach based on the reduction of selenite with ascorbic acid in the presence of 7% of concentration of 2,4,7,9-tetramethyl-5 decyne-4,7-diol ethoxylate as stabilizer agent (at pH 6.5). The final concentration of selenium nanoparticles in the solution were 1000 ppm. SeNPs between 50 and 70 nm were obtained. Figure 1 shows the SEM analysis, where the size and identification of SeNPs in the solution can be seen. The detailed description of the synthesis can be read in [PERSON] et al. (2013).  
This nanoselenium solution was incorporated at 10% (_w/w_) into a water-based adhesive dispersion. The formula of adhesive was provided under a confidential agreement and cannot be disclosed here.  
Using the adhesive, the laminate structure [substrate 1-adhesive-substrate 2] was manufactured first at laboratory scale and later at industrial scale. Industrial conditions were optimized as well to provide a colorless multilayer, where nanoSe was homogeneously distributed. At laboratory scale, the laminates were prepared using an extender machine K control coater, RK printcoat instruments (RK PrintCoat Instruments Ltd, Ltilington, UK) on a 20 \(\times\) 30 cm of substrate 1 polyethylene terephthalate (PET, 12 \(\upmu\)m), forming a uniform layer. The grammage of adhesive placed was 3 g/m\({}^{2}\), which was gravimetrically measured. Afterwards, a second 20 \(\times\) 30 cm of 60 \(\upmu\)m thickness film of low density polyethylene (LDPE) was placed on top of it, and then the laminate was pressed at 80 \({}^{\circ}\)C at speed number 4 in a BiO 330 model Laminator (Laminator-BIO 330, Korea)  
Blank laminates were also prepared as follows: PET (12 \(\upmu\)m)/adhesive/PE (60 \(\upmu\)m) (laminate B) using the same formula without nanoSe.  
Figure 1: (a) SEM of 1500 ppm solution of nanoSe and (b) EDX analysis of SeNPs. Samples on Cu grid.
## 4 Simulation and Discussion
### Composite Scattering of Canyon and Valley Composite Rough Surface  
In this example, the bistatic RCS of the canyon and valley environment are investigated. The wind speed \(v_{19.5}=0.2\) m/s, the RMS height \(h_{\rm rms}=0.2\lambda\), and the slope angles \(\alpha_{1}=30^{\circ}\), \(\alpha_{3}=30^{\circ}\). The illustration of simulation geometry is shown in Figure 12.  
Figure 11: Bistatic RCS results for a missile-like target above plain and valley composite rough surface.  
Figure 10: Illustration of a missile-like target above plain and valley composite rough surface: (**a**) target above plain; (**b**) target above valley.  
In Figure 13, the incident angle is \((\theta_{i},\varphi_{i})=(-30^{\circ},90^{\circ})\), and the observation angles are \((\theta_{s},\varphi_{s})=(-90^{\circ}\sim 90^{\circ},90^{\circ})\). These RCS curves have the same varying discipline. They both have two peaks; one is the forward scattering's depiction, \(\theta_{s}=30^{\circ}\); and the other is the vertical reflection of the right slope \(\theta_{s}=-30^{\circ}\). However, there is still a noticeable difference in these figures: on two sides of \(\theta_{s}=30^{\circ}\), the valley's RCS is much higher than the canyon's. The bottom of the composite rough surfaces' difference could explain the phenomenon. The specular reflection from the bottom contributes the most of the forward scattering at \(\theta_{s}=30^{\circ}\). Generally, the specular reflection of a calm water environment is much higher than that of undulating land, which causes the specular reflection from the valley with a water bottom to be stronger than the terrestrial bottom. As for peaks at \(\theta_{i}=-30^{\circ}\), the same right slope environment makes a similar vertical reflection amplitude. Hence, canyon and valley have similar scattering characteristics, and the differences in the bottom produce the different details between these two types of composite environment. When the environment is detected, the canyon and valley can be distinguished in the forward scattering direction.