esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
## 6 Experimental Results
(**c**) Peak envelope spectrogram processed using GSF, (**d**) Unwrapped phase spectrogram processed using GSF. (**e**) Compressed 1D intensity image with marked plastic plates’ positions. (**f**) Compressed 1D phase image with marked plastic plates’ positions. linked to bandwidth \(B\), frequency step \(\delta f\) and integration time \(t_{i}\) and can be estimated as \(t_{px}=t_{i}\cdot B/\delta f\). The frequency step and integration time were kept the same throughout all four measurements, while the bandwidth was changed. First row of Table 3 shows acquisition parameters for the whole available spectrum. Image acquisition utilizing the whole spectrum would take more than 34 hours to complete. The second row of Table 3 shows acquisition parameters for the empirically determined frequency range. With narrower bandwidth, there is a big drop in image acquisition time. The optimised frequency ranges all have a lot shorter single-pixel acquisition times, as shown in fourth column of Table 3. Shorter single-pixel acquisition times will improve the overall image acquisition times, as shown in the last column of Table 3.
# Thermometric Characterization of Fluorescent Nanodiamonds Suitable for Biomedical Applications
## 4 Conclusions  
In general, the overall fluorescence intensity of the fluorescent nanodiamonds used for this work decreases as temperature rises within the range of biologically relevant temperatures (25 \({}^{\circ}\)C to 60 \({}^{\circ}\)C). We demonstrated how this fluorescence intensity mostly follows a linear dependence with the local nanodiamond temperature. Furthermore, there is a size dependency on the processes involved in the changes in the overall fluorescence spectrum of nanodiamonds which relates the smaller nanodiamonds with a broader and less intense signal, while larger nanodiamonds with a sharper and brighter spectrum. However, the ZPL position remains unaltered in all cases, and then the fitting process can discriminate and account for them successfully.  
The NV\({}^{0}\) and NV\({}^{-}\) centres' fluorescence allowed the construction of nanothermic scales. Additionally, this study unveils the possibility of using nanodiamonds as nanothermometers to distinguish delimitation of tumoral margins and several types of pathologies through the point measurement of the membrane's temperature (70 nm or 100 nm size nanodiamonds) or inner structures of malignant cells (with 35 nm or smaller nanodiamonds).  
Figure 3: **(a)** Fluorescence of 100 nm HPHT FNDs in HeLa cells. The fluorescence image was recorded around the membrane after 24 h of incubation at 37 \({}^{\circ}\)C. **(b)** MTT Cytotoxicity assay of different concentrations of 100 nm HPHT FNDs in HeLa cells, the red bar corresponds to the concentration used in **(a)**.  
Here, biomedical applications such as, the imaging and diagnosing instrumentation based on fluorescence became simple and could be implemented easily in biomedical facilities inside hospitals and health clinics.  
Conceptualization, D.M.-F., R.R. and M.P.-M.; methodology, K.S.-G., M.A.-E. and E.S.-C.; validation, S.N.-E.; formal analysis, F.P.-M., K.S.-G., M.A.-E., E.S.-C., O.A.-B. and M.P.-M.; investigation, F.P.-M.; data curation, F.P.-M., K.S.-G., M.A.-E., E.S.-C. and O.A.-B.; writing--original draft preparation, F.P.-M.; writing--review and editing, D.M.-F., D.S.-P., B.C., E.U.-B., S.N.-E. and R.R.; supervision, M.P.-M.; project administration, M.P.-M.; funding acquisition, D.M.-F. and M.P.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONACYT (Mexico) under Contract PDCPN2014-01/248982 and fellowship of [PERSON].  
For data acquiring please send an e-mail to [PERSON]'s institutional e-mail: [EMAIL_ADDRESS].  
Results of this paper are part of a Ph.D. thesis by [PERSON] under the advice of [PERSON].  
The authors declare no conflict of interest.
## 3 Mathematical Model  
### Governing Equations  
In order to simplify the mathematic model, the following assumptions are made:  
1. The liquid paraffin is regarded as an incompressible Newtonian fluid, and its density is subjected to the Boussinesq approximation.
2. The paraffin is assumed isotropic and homogeneous, and the flow of liquid paraffin is laminar flow. The volume change of paraffin during phase change is not taken into account.
3. The thermal properties of paraffin are constant in the solid or liquid region. In the phase change region, these parameters vary linearly with temperature.
4. Because the specimen is surrounded by thermal insulation tiles, the walls are regarded as adiabatic, except for the heating wall.  
Based on the above assumptions, the governing equations are written in Equations (1)-(4).  
Continuity equation:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0 \tag{1}\]  
Figure 5: The flowchart of experimental procedure.  
Momentum equations:  
\[\rho\frac{\partial u}{\partial\tau}+\rho\left[\frac{\partial(uu)}{\partial x}+ \frac{\partial(uv)}{\partial y}\right] =-\frac{\partial P}{\partial x}+\mu\
abla^{2}u+Au+S_{u} \tag{2}\]  
\[\rho\frac{\partial v}{\partial\tau}+\rho\left[\frac{\partial(vu)}{\partial x}+ \frac{\partial(vv)}{\partial y}\right] =-\frac{\partial P}{\partial y}+\mu\
abla^{2}v+Av+S_{v} \tag{3}\]  
in which \(S_{u}\) and \(S_{v}\) are regarded as the buoyancy source term, shown in Equations (4) and (5). \(A\) is defined as a porosity function based on [PERSON]'s equation, written in Equation (6).  
\[S_{u} =\rho g\beta(T-T_{m})\sin\alpha \tag{4}\] \[S_{v} =\rho g\beta(T-T_{m})\cos\alpha\] (5) \[A =A_{mush}\times\frac{(1-f)^{2}}{(f^{3}+\xi)} \tag{6}\]  
where \(A_{mush}\) is the mushy zone constant to control the damping amplitude, and \(\xi\) is a small constant number to avoid division by zero. In this paper, \(A_{mush}\) and \(\xi\) are \(10^{7}\) and \(10^{-3}\), respectively. \(f\) represents the liquid fraction, which is expressed in Equation (7).  
\[f=\begin{cases}0&T<T_{m1}\\ \frac{T-T_{m1}}{T_{m2}-T_{m1}}&T_{m1}\leq T<T_{m2}\\ 1&T\geq T_{m2}\end{cases} \tag{7}\]  
Energy equations:  
\[\frac{\partial\rho T}{\partial\tau}+\
abla\left(\rho\overset{\rightarrow}{U} T\right)=\frac{\lambda}{c_{p}}\
abla^{2}T+S_{h} \tag{8}\]  
in which the source item \(S_{h}\) is expressed in Equation (9) and the latent enthalpy \(\Delta H\) is shown in Equation (10).  
\[S_{h}=\frac{\rho}{c_{p}}\frac{\partial\Delta H}{\partial t} \tag{9}\]  
\[\Delta H=f\times L \tag{10}\]
## 5 Sensitivity Analysis  
### Sensitivity Factors Determination  
Through the above comparison of the four schemes, Scheme 3 is optimal in terms of traffic efficiency and environmental protection, and Scheme 2 is optimal regarding safety. However, the conclusions are based on measured data and are limited to a particular traffic volume and diverging and emerging ratios. It cannot reflect the advantages and disadvantages of the schemes for other traffic volume conditions, and the conclusions lack universality.  
Therefore, for the proposed different schemes to be reasonably applied in compound interchanges, it is necessary to use VISSIM to simulate various traffic conditions and obtain conclusive results.  
According to the service traffic volume of the basic section of freeway stipulated in China's \"Technical Standards of Highway Engineering\" [55], the traffic volume of a three-lane freeway is 2250 veh/h when the service level is grade I, 6600 veh/h when the service level is grade VI, and the measured traffic volume is 3758 veh/h. Therefore, the traffic volume range considered is from 0.2 to 1.0 V/C during the sensitivity analysis, and the traffic volume under the condition of 1.0 V/C is 6600 veh/h. Typically, the number of diverging vehicles is less than that of vehicles traveling straight through, but the measured diverging ratio is 51.44%. Therefore, the diverging and emerging 10-50% ratio was taken for sensitivity analysis. The traffic volume and diverging-emerging ratio combinations are listed in Table 9 for a total of 45 traffic combinations.
# Inverse Multiscale Discrete Radon Transform by Filtered Backprojection
## 1 Introduction
### Inverse DRT  
In contrast to the Fourier transform, the inversion of the algorithm does not invert the transform. In this case the algorithmic inversion leads to the adjoint transform, also known as backprojection.  
The mapping formula that gives rise to its algorithm being:  
\[\widetilde{f}^{m}(\boldsymbol{\sigma}\,|\,v_{m},\mathbf{v}\,|\,d)=\sum_{s_{n -m-1}}^{0,1}\widetilde{f}^{m+1}\Big{(}s_{n-m-1},\,\sigma|\,\mathbf{v}\,|\,d\, -\,v_{m}\cdot\big{(}s_{n-m-1}+\lambda(\boldsymbol{\sigma})\big{)}\Big{)}. \tag{6}\]  
Since the data were summed in the direct method, the algorithmic inversion is only able to return an averaged version of the measured values in the original domain. Bringing them back to the locations where they were taken from does not undo the addition. In any case it prepares the data to apply a filtering that will undo the averaging, the smearing. The inversion proposed by [PERSON] does not make use of these terms, but the terminology inherited from the iterative methods of improving a solution of a system of equations.  
In [PERSON] method, the exact inverse transform is unknown, but the adjoint transform is used as an approximation of it. In the loop that is formed by the direct and adjoint operators we know the application of the direct operator on the initial domain and the data in the transformed domain. By using backprojection as an approximated inverse operator, the error can be eliminated iteratively although the convergence is slow. Therefore, [PERSON] proposes to accelerate the convergence by using a multigrid method working in several scales. Even with that 'acceleration' a typical solution requires around 10 iterations to reach a 10e-3 normalized RMS error. Each iteration consists of \(\log_{2}(N)\) DRT pairs, i.e., a DRT pair at each scale below the original input size, \(N\times N\). We will look for a more straightforward approach.
# Carbon Dioxide Adsorption on Carbon Nanofibers with Different Porous Structures
## 3 Results and Discussion  
### FESEM Images  
In order to ensure that carbonization at 900 \({}^{\circ}\)C for 1 h was an appropriate process to cause the thermal decomposition of PMMA, the FESEM images were observed for the carbonized samples, as shown in Figure 1. These images clearly demonstrate the formation of hollow tubular structures for PANh15 (Figure 1a) and PANh12 (Figure 1b). Figure 1c is the cross-section of PANp, which displays several pores that were well distributed in the nanofibers. Since the pores were small and dispersed, a high magnification was needed to clearly identify the pores. These images show that the PMMA in the core or in the fibers experienced a variety of intrinsic thermal degradation processes, such as depolymerization, random scission, and side-group elimination, and finally formed volatile degradation products [19].  
The surface morphology of all samples is illustrated in Figure 2A-D and their cross-sections are shown in Figure 2a-d. The nanofibers were randomly distributed with uniform sizes and little directionality. PANf was prepared using coaxial electrospinning with the same core and shell solutions. However, the resultant nanofibers were merged without demarcations. Although the surface of nanofibers in a lower magnification was smooth, the surface of nanofibers was, in fact, uneven (Figure 2a-d), especially for PANp. This evidently indicates that the evaporation of volatile compounds from PMMA and PAN in the carbonization and activation stages generated pores on the wall or fibers. The diameters of PANf and PANp from Figure 2a,d were approximately 500 and 900 nm, and the outer diameters of PANh15 (Figure 2b) and PANh12 (Figure 2c) were approximately 800 and 870 nm with a shell thickness of 77 and 106 nm, respectively. The results imply that the volatile compounds from the thermal decomposition of PMMA were expected to escape from the longitudinal and lateral directions of the nanotubes. It is believed that the removal of PMMA retarded or hindered the fiber shrinkage such that the hollow fibers and PANp possessed larger fiber sizes. In addition, comparing PANh15 with PANh12, the larger applied voltage and injection rates of polymers produced smaller fiber sizes and shell thickness. Figure 2d indicates that PMMA and PAN in copolymer solution were uniformly distributed during the electrospinning process.  
Figure 1: FESEM images of the carbonized samples of: **(a)** PANh15; **(b)** PANh12; **(c)** PANp.
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
## 2 Materials and Methods
### Design of Choke Ring Antenna  
The design of the choke ring antenna enables preventing the signal reflected from the surface from reaching the antenna (Figure 2). The necessity to use a GNSS antenna cover is caused by the possibility of incorrect operation of the GNSS, resulting from the presence of interference, among others, generated electromagnetic waves also by other wireless technical devices operating near the antenna. Considering this and the analysis of the structure of the GNSS antenna, a design of the antenna cover was developed, consisting of four concentrically arranged steel pipes with different external diameters (Figure 2). The diameter of the pipes was selected based on the carrier wavelength L1 of the GPS signal (approx. 0.19 [m]). It was assumed that the proposed pipes would be welded to the sheet in the shape of a ring with holes for mounting bolts. The elements connected in this way (pipes and ring-shaped sheet metal) are attached to the circular base (Figure 3) through nine screw assemblies (nut-bolt), on which the GNSS system antenna will be installed.  
Figure 1: SwiftNav Duro GNSS receiver (**a**) front view, (**b**) rear view.  
The antenna will be attached to the base with a suitably shaped mounting element (Figure 3). This element will be welded to the base, creating a space between it and the base, into which the screw securing the antenna to the sheet will be inserted. This approach enables mounting the antenna to the designed cover without the need to disassemble the pipe. It reduces the time for the possible assembly and disassembly of the GNSS antenna with a cover. An additional advantage of this design is the isolation of the original antenna from the electric currents inducing the housing (Figure 3).  
The order of mounting the designed antenna cover (Figure 4) is as follows:  
* attaching the antenna to the base (Figure 4a,b),
* bolting the element (pipe with a ring-shaped sheet) to the base (Figure 4c,d).  
The constructed choke ring antenna cover made in accordance with the design presented earlier is shown in Figure 5.  
The weight of the designed GNSS antenna cover is approximately 12 [kg].
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 4 Results and Discussion
### Optical Power vs. Driving Current  
Figure 2 illustrates the measured optical output power as a function of driving current, which was varied from 50 mA to 200 mA while maintaining a fixed modulation frequency of 3 MHz.  
Key observations include:  
* 650 nm (Red Laser): Demonstrated the highest optical output across the entire current range, with a quasi-linear and monotonic increase beyond the threshold current (\(\sim\)85 mA). This reflects efficient current-to-photon conversion and favorable stimulated emission characteristics.
* 405 nm (Violet Laser): Produced moderate optical output with a more variable slope. Threshold behavior was observed at approximately 100 mA, with greater sensitivity to current fluctuations than the red diode.
* 532 nm (Green Laser): Exhibited the lowest output power and a higher threshold current (\(\sim\)135 mA), indicating greater internal losses and reduced conversion efficiency.  
These findings aligned with theoretical expectations related to wavelength-dependent emission efficiency and detector responsivity. The red diode's spectral alignment with the responsivity peak of silicon detectors and its reduced susceptibility to scattering effects  
Figure 2: Optical power output (mW) as a function of driving current (mA) for 650 nm, 532 nm, and 405 nm laser diodes.  
underscore its suitability for short-range FSO applications. Additionally, its smoother and more stable output trend across the 100-180 mA operating range simplifies system calibration and enhances modulation reliability--key attributes for low-power optical links.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 2 Description of the Dataset
### RS Dataset
The MODIS sensor was launched as part of the NASA Earth Observation System series of satellites ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), accessed on 20 July 2022). We used the MYD02 QKM and MOD02 QKM products, including the RS images in bands 1 (_Red_) and 2 (_NIR_), which are corrected earth observation data by calibrated and geolocated and with a spatial resolution of 250 m. The temporal resolution of it is nearly 1 day.
## 2 Materials and Methods
### Extraction of Land Parcels
#### 2.4.3 Image Segmentation
Undersegmentation occurs when image segmentation fails to define individual objects, thus constructing a single object that may contain multiple features. By contrast, oversegmentation error occurs when unnecessary boundaries are delimited such that multiple contiguous objects potentially belonging to the same features are formed. Generally, increasing the scale level results in the retention of objects that exhibit the most distinct edges. In the present case, scale levels of 10%, 20%, 30% (Figure 6), and 40% were separately selected to generate different segmentation results. * Segmentation of the modified gradient image through the Watershed Transform. This method is used to generate separate segments based on the concept of hydrologic watersheds [20]. In this framework, the lower the value of the pixel gradient, the  
Figure 5: Cumulative relative histogram showing a corresponding gradient magnitude for a selected cumulative relative histogram level (scale level) of 45% [18]. Such a scale level value is indicated by the horizontal arrow on the Y axis, while the corresponding threshold on gradient magnitude values on X axis is indicated by the vertical narrow. Figure 6: segmentation image obtained by setting the scale level to 30% and the merge level to 97.5%. The scene is masked for all the irrelevant coverage classes. The red polygons outline the boundaries of the resulting segments. The location of the test area is shown as a red dot in the incept. lower its elevation; this type of pixel is called a minimum. The watershed algorithm sorts pixels by increasing gradient value, then floods the image starting with the lowest gradient values (the uniform part of the objects) and proceeding to the highest gradient values (the edges), partitioning the image into segments (regions with similar pixel intensities) based on the computed watersheds. The result is a segmentation image in which each region is assigned the mean spectral values of all the pixels that belong to that region. * Merging of the segmentation image to further refine and correct tendencies to over or undersegmentation. In this step, a Full Lambda Schedule algorithm was applied. This method involves the computation of the mean spectral values, areas, and lengths of common boundaries of a pair of adjacent segments. Then, it computes the Euclidian distance among those attributes for each possible segment pair and merges the objects if their similarity exceeds a defined threshold value [21]. In the present case, due to the observed broad tendency of the dataset to generate oversegmentation errors, the merge level was set to 97.5% to resolve the issue (four different scale levels (10%, 20%, 30%, 40%), each combined with a merge level of 97.5%, were selected through visual interpretation as the preferable solutions for delineating agricultural parcels as accurately as possible compared to the cadastral dataset). The resulting four segmentation images were converted into segmentation vectors. Figure 7 shows the segmentation differences created by varying the scale level from 10% to 40%.
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
## 3 Analysis of Life Cycle Impact Assessment (LCIA)
### Process of LCIA
#### 3.2.3 Manufacturing Stage  
In this stage, waste concrete is selected and crushed to manufacture recycled aggregate [37]. The manufacturing method can be divided into the dry method and the wet method. To assess the environmental impact of each manufacturing method, the amount of electric power used in the manufacturing facility was examined and the environmental impact according to the usage of the energy source was assessed.  
\[\text{Manufacture}_{\text{E.I}}=\sum\left[\left(\text{E}_{\text{(i)}}\text{ / W}_{\text{a}}\right)\times\text{C}\right]\text{ (i = 1: Electric)} \tag{4}\]  
Here, Manufacture E.I is environmental impact (kg-unit/kg) in the manufacturing stage, W\({}_{\text{a}}\) is the amount of recycled aggregate production (kg), E(i) is the amount of energy source use (kwh), and C is characterization value (kg-unit/kwh) of electric power by environmental impact categories.  
The dry production method uses a technology that eliminates mortar on an aggregate surface by crushing waste concrete and easily separates a fair amount of fine powder generated to produce high-quality recycled aggregate as shown in Table 2. The number of crushing and differentiation work taken during the production process was divided for each equipment and shown in Figure 1. The environmental impact emission amount by electric power usage for each equipment unit was calculated for the process of producing recycled aggregate through the dry production method.  
Figure 1: Dry production method.  
This study assumed that the production efficiency of aggregate was 70% and set that cone and jaw crushers were used twice to produce high-quality recycled aggregate.