esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Critical Technical Approach
### Stray Light Suppression Analysis and Structural Design Based on BRDF Modeling
#### 3.3.2 Simulation Model Construction and Parameter Configuration  
To quantitatively evaluate the propagation paths and energy distribution of internal stray light, a full 3D optical model of the system was established using the ray-tracing platform TracePro. Parameterized surface scattering properties and custom ray-tracing strategies were configured accordingly.  
The solar source was defined as a flat-top collimated beam at a wavelength of 1550 nm with a total power of 130 mW, corresponding to the background irradiance during Sun outage conditions. The signal light source was set as a weak, single-wavelength 1550 nm laser with a power of 1.0 \(\times\) 10\({}^{-10}\) W, representing an ultra-low-power communication signal. The two beams are combined within the system via a beamsplitter and propagate along a common optical path. The system output aperture was configured as the receiving surface to evaluate the power coupling ratio between background light and the signal beam. The key component parameters were set as follows: specular reflective optical surfaces were modeled using a _BRDF_ scattering function, with high-reflectivity metallic coatings (reflectance: 98%). The beamsplitter was set with a transmission of 98%, reflectance of 1.8%, and absorption of 0.2%. To enable surface roughness sensitivity analysis, a scan range of 1-100 nm was defined. The inner cavity surfaces were coated with an absorptive black coating with an absorptance of 98%. The U-shaped light trap structure was modeled using an improved micro-surface _BRDF_ model, and the ray tracing energy threshold was set to 10\({}^{-15}\) W. During simulation, 50 million rays were traced from the solar source, with path analysis enabled to record the sequence of surfaces and types of reflections encountered by each ray. These data supported subsequent energy path clustering analysis and structural suppression optimization.  
With the above modeling and simulation configuration, the system enables identification of dominant interference paths, quantitative analysis of reflection mechanisms, and performance evaluation of suppression structures. This provides theoretical support for downstream stray light energy ratio analysis and system design validation.
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 2 Basic Theory
### Altitude Subsystem
#### 2.2.2 Altitude Loop  
The expression of the height loop is Equation (3). Taking height as output, the expression becomes the following:  
\[\left\{\begin{aligned} \dot{h}&=V\sin\gamma\\ y_{h}&=h\end{aligned}\right. \tag{19}\]  
Here, because \(\gamma\) is small, there is \(\gamma\approx\sin\gamma\), and the altitude loop can be regarded as a first-order system with track angle as input.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(m\) & Quality & 137,800 & kg \\ \(I_{yy}\) & \(Y\)-axis moment of & 9,500,000 & kg/m\({}^{3}\) \\ \(S\) & Reference area & 335.2 & m\({}^{2}\) \\ \(\overline{c}\) & Average aerodynamic & 24.384 & m \\ \(\rho\) & Atmospheric density & 0.0125368 & kg/m\({}^{3}\) \\ \(R_{E}\) & Earth radius & 6,371,393 & m \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the hypersonic vehicle.
## 4 Results and Discussion
### Influence of Senderness  
Similar sensitivity analysis was performed for slenderness ratios \(\rm{SLD_{x}}\) and \(\rm{SLD_{y}}\). Figure 9 presents the degree of sensitivity of \(\rm{SLD_{x}}\) and \(\rm{SLD_{y}}\), whereas summary information is indicated in Table 6. The degree of sensitivity of \(\rm{SLD_{x}}\) varied from \(-70\) to \(32\%\) when applying the highest to lowest values. As regards \(\rm{SLD_{y}}\), the degree of sensitivity varied from \(-32\) to \(49\%\) when applying the lowest to highest values. It can be deduced that both slenderness ratios exhibited important effects on the prediction of the critical buckling load, and the effects were more important than those of the initial imperfections, as calculated in the previous section. This fact is relevant, as shown in [PERSON]'s formula for estimating the critical buckling load [85; 86; 87] under zero imperfections.  
As a conclusion, the developed ANFIS-BBO model in this study exhibited a strong capability for the prediction of the critical buckling load of Y-section columns under compression. Quantitative influence of initial geometric imperfections in both out-of-plane and in-plane directions was also deduced using such an ANFIS-BBO model. The ANFIS-BBO model could thus be a robust and useful numerical tool for analyzing the problem of column instability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **Min** & **Q10** & **Q25** & **Q50** & **Q75** & **Q90** & **Max** \\ \hline \(\rm{I_{1}}\) & \(-0.219\) & \(-0.145\) & \(-0.100\) & \(0\) & \(0.041\) & \(0.102\) & \(0.159\) \\ \(\rm{I_{2}}\) & \(-0.416\) & \(-0.329\) & \(-0.195\) & \(0\) & \(0.116\) & \(0.195\) & \(0.284\) \\ \(\rm{I_{3}}\) & \(0.185\) & \(0.177\) & \(0.146\) & \(0\) & \(-0.032\) & \(-0.053\) & \(1.054\) \\ \(\rm{I_{4}}\) & \(-0.194\) & \(-0.049\) & \(-0.025\) & \(0\) & \(0.043\) & \(0.093\) & \(0.168\) \\ \(\rm{I_{5}}\) & \(0.103\) & \(0.067\) & \(0.021\) & \(0\) & \(-0.036\) & \(-0.064\) & \(-0.133\) \\ \(\rm{I_{6}}\) & \(-0.401\) & \(-0.285\) & \(-0.151\) & \(0\) & \(0.048\) & \(0.172\) & \(0.240\) \\ \(\rm{I_{7}}\) & \(0.029\) & \(0.022\) & \(0.018\) & \(0\) & \(-0.031\) & \(-0.046\) & \(-0.059\) \\ \(\rm{I_{8}}\) & \(1.118\) & \(0.976\) & \(0.368\) & \(0\) & \(-0.655\) & \(-1.093\) & \(-1.348\) \\ \(\rm{I_{9}}\) & \(6.467\) & \(4.160\) & \(2.468\) & \(0\) & \(-1.329\) & \(-2.706\) & \(-3.798\) \\ \(\rm{SLDx}\) & \(32.501\) & \(25.259\) & \(20.112\) & \(0\) & \(-25.692\) & \(-50.995\) & \(-69.868\) \\ \(\rm{SLDy}\) & \(-32.845\) & \(-23.565\) & \(-18.997\) & \(0\) & \(-8.743\) & \(34.658\) & \(49.667\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the degree of sensitivity (in %) of input variables at different intensity values.  
Figure 8: Degree of sensitivity (in %) of nine initial geometric imperfections on the prediction of critical buckling load through the ANFIS-BBO model.
# Practical Guide to Ultrasound Beam Forming: Beam Pattern and Image Reconstruction Analysis
## 2 Linear and Phased Array Beam Forming
In Figure 1, a schematic overview of what has been introduced in this section is presented. Linear and phased array beam forming strategies are represented only for a 1D aperture, but these can of course be also applied to a 2D aperture, which gives more flexibility in the definition of the sub-apertures. Moreover, with a 2D aperture, the beam can be steered through the entire volume, rather than only on a plane perpendicular to the aperture [2; 3]. When comparing linear and phased array beam forming, a list of pros and cons can be made. Both approaches form an image line by line, with one line being generated at every transmission event. Linear arrays can image only the area in front of the aperture, while a larger area can be imaged with phased arrays as the beam can be steered. This also means that the aperture of a linear array has to cover the entire area of interest (along the lateral direction). However, this is not the case for phased arrays. Consequently, phased arrays are particularly suitable in situations where there is a small imaging window, as in transthoracic ultrasound imaging, where the ribs represent an obstacle for imaging [5; 6]. On the other hand, the geometries of phased arrays are constrained by the phenomenon of grating lobes, which is particularly demanding when using high frequencies. As a result, more accurate phase sets, and as many as the amount of steering angles, are required. The transmit phase (or active phase), which is the phase that defines the shaping of the spatial distribution of the pressure field amplitude in the volume of interest, has been our focus thus far. In the receive phase, the very same phase sets and apodization functions that are used in the transmit phase can be applied. However, the received echo signals can be also treated differently. Furthermore, a different group of elements than those used in the transmission phase can also be used, as is, for example, the case for synthetic aperture beam forming [7; 8] and multi-line acquisition beam forming [9]. Figure 2 illustrates the differences in the spatial distribution of the pressure amplitudes. The \(-20\) dB beams obtained with a focused sub-aperture, and with an unfocused, focused, and steered full-aperture, are shown. The typical FOVs achievable with linear and phased array beam forming are also shown. Note that when the linear array sensors are distributed along a curve, a larger field of view can also be obtained. This is the case with convex probes. However, the probe loses its flat surface [10].
# Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications
## 3 Device Applications
### Sensing Devices
TE fibers were fabricated using a colloidal gelation extrusion, which is a simple, controllable, industrial, and scalable manufacturing process [50]. When alternating p/n-type TE fibers were woven into fabrics, the TE textiles demonstrated conformal heat energy generation and a multi-pixel touch display panel for hand-writing and communication [50]. Olfaction-like chemical sensors, in which chemical stimuli in a wearable device or the skin are converted into electrical signals, can collect information from their surroundings [51]. The porous and cylindrical structures of fiber-shaped sensors have high active areas, which play a key role in chemical sensing [52]. However, realistically imitating and understanding human-environment interactions remains challenging, due to the limited chemical sensing capabilities of existing sensor technology. As a general interface between humans and their surroundings, textiles could be used to build a sensing platform. To do this, thread-like chemical sensors fabricated using soft, chemically sensitive materials are required. Real-time health monitoring and the long-term detection of physiological signals are important in disease diagnosis [53; 54]. Therefore, fibrous humidity, pH, gas and ion sensors have been developed [55; 56; 57; 58; 59; 60]. Notably, [PERSON] et al. fabricated a bioinspired conductive silk microfiber for diagnosis and wound healing in diabetes [61]. The PEDOT silk microfibers were formed using a bioinspired extraction-protection process, and polydopamine was used as an adhesion layer (Figure 5e) [61]. The conductive silk fibers were woven and fabricated into a fibroin patch to monitor physiological signals (Figure 5f) [61]. Thanks to its antioxidant activity, the patch showed good performance in healing chronic diabetic wounds by reducing inflammation and regulating oxidative stress [61]. Ultraviolet (UV) monitoring sensors based on fiber substrates have been demonstrated to detect UV radiation that causes serious skin diseases. A self-powered UV photodetector was developed by [PERSON] et al. [62]. A photoactive CuZnS/TiO\({}_{2}\) nanotube array was formed on a Ti microwire by a two-step anodic oxidation process and a dipping method [62]. A CNT electrode fiber was entwined around the CuZnS/TiO\({}_{2}\) composite nanotube [62]. The fibrous UV sensor exhibited optical responsivity of 2.54 mA W\({}^{-1}\) at 0 V, a responsivity of 640 A W\({}^{-1}\), and external quantum efficiency of 2.3 \(\times\) 10\({}^{5}\) % under 300 nm-UV light irradiation [62]. Furthermore, a real-time wearable UV-sensing fiber that read out UV-light power density and transmitted data to smartphones using wifi was demonstrated [62]. Although fiber-shaped sensors have been studied during the past few years, sensing textiles are still at an early stage of development. For wearable sensing applications, a broad sensitivity range, fast response/recovery, and high sensitivity to diverse physical, chemical, and physiological stimuli are desirable, and reliability must also be improved [37; 63].
###### Abstract  
This study presents an experimental investigation on Zr separation from Zr-2.5 Nb by anode potentiostatic electrorefining in LiCl-KCl-ZrCl4 0.5 wt. % at 773 K for irradiated CANDU pressure tube decontamination. By the ORIGEN-2 code calculation, radioactive characteristics were investigated to show that Nb-94 was the most significant radionuclide with an aspect of waste level reduction by electrorefining. Three electrorefining tests were performed by fixing the applied potential as \(-\)0.9 V (vs. Ag/AgCl 1 wt. %) at the anode to dissolve only Zr. A cathode basket was installed to collect detached deposits from the cathode. Electrorefining results showed Zr was deposited on the cathode with a small amount of Nb and other alloying elements. The chemical form of the cathode deposits was shown to be only Zr metal or a mixture of Zr metal and ZrCl, depending on the experimental conditions related to the surface area ratio of the cathode to the anode. It was determined that the Zr metal reduction at the cathode was attributed to the two-step reduction reaction of Zr\({}^{4+}\)/ZrCl and ZrCl/Zr.  
Zr recovery; CANDU pressure tube; Zr-2.5 Nb; electrorefining; molten salt; LiCl-KCl; nuclear power plant decommissioning; radioactive waste +
Footnote †: journal: Applied Sciences
## 3 Numerical Simulations and Results
### Methods Used for Numerical Simulations
To determine the complex amplitude of the beam in a certain plane after the focusing element, in the point indicated by vector \(\mathbf{R}\), we simply take the complex amplitude in the input plane, in the point \(\mathbf{r}(\mathbf{R})\) that gives the intersection between the input plane with the ray which passes through \(\mathbf{R}\), and we multiply this value with a factor which accounts for the fact that the density of rays changes along the ray indexed by \(\mathbf{R}\). Ray density is computed using a Voronoi mesh [29] as the inverse of the area of a Voronoi cell; Voronoi mesh is a built-in function of Wolfram Mathematica. The complex amplitude of the beam in the output plane (after the lens) is given in Equation (10), where the phase factor \(\exp[\mathrm{i}\,\,k_{0}\,\,s_{1}(\mathbf{R})]\) was dropped but is later included when the Debye-Wolf integral is performed, in \(\Phi(\mathbf{t})\). \[\mathbf{V}_{lens}(\mathbf{R})\,=\,\,\mathbf{V}(\mathbf{r}\,\,(\mathbf{R}))\, \sqrt{\frac{A(\mathbf{r}\,\,(\mathbf{R}))}{A_{lens}(\mathbf{R})}}\,. \tag{10}\]  
For implementation of the Debye-Wolf integral, we used Equations (11)-(14) from Reference [30], with wavefront aberration determined according to Equation (9) and the complex amplitude of the beam, denoted by \(\mathbf{V}_{lens}\), determined using Equations (8) and (10). The complex amplitude of the beam in the output plane, in the point \(\mathbf{R}\), is proportional to the complex amplitude of the plane-wave with direction given by the ray vector of the ray that passes through \(\mathbf{R}\). Since there is a one-to-one correspondence between the position in the transversal plane, after the focusing element, and the transversal projection of ray vector \(\mathbf{t}\), we can index the complex amplitude depending on \(\mathbf{t}\) as shown in Equation (11). \[\mathbf{V}_{focus}(\mathbf{r}) = -\frac{\mathrm{i}}{\lambda}\int\mathcal{L}(\mathbf{t})\frac{ \mathbf{V}_{lens}(-f\,\mathbf{t})}{t_{z}(\mathbf{t})}\exp[\mathrm{i}\,\Phi\;\;( \mathbf{t})+\mathrm{i}\,k_{0}\,\mathbf{t}\cdot\mathbf{r}]\,\mathrm{d}\mathbf{t}. \tag{11}\] \[\mathcal{L}(\mathbf{t}) = \left[ \begin{array}{cc}1+t_{z}-(1-t_{z})\cos 2\theta&-(1-t_{z}) \sin 2\theta\\ -\,(1-t_{z})\sin 2\theta&1+t_{z}+(1-t_{z})\cos 2\theta\\ -2\,t_{z}\,\cos\theta&-2\,t_{z}\,\sin\theta\end{array} \right]. \tag{12}\]  
Matrix \(\mathcal{L}\), whose expression is in Equation (12), gives the contribution of each plane-wave to the electric field components \(E_{x}\), \(E_{y}\), and \(E_{z}\) depending on its polarization, while \(\theta(\mathbf{t})\) gives the orientation of the ray/plane-wave indexed by \(\mathbf{t}\), around the optical axis.
## 4 Calibration and Analysis of Rotation Axis Position Error
### Spatial Error Field Analysis of Five-Axis CNC Machine Tools  
Using the geometric error model expressed in Equations (11) and (12), the geometric error fields of the five-axis machine tool workspace under three A-axis rotating angles A = 0\({}^{\circ}\), A = 45\({}^{\circ}\), A = 90\({}^{\circ}\) were established to obtain the variation law of the spatial error field during the swing of the A-axis, as shown in Figure 8.  
As shown in Figure 8a, when A = 0\({}^{\circ}\), the spatial comprehensive error \(\Delta E\) of five-axis CNC machine tools tended to increase from inside out. When the workspace was around the origin of the ideal coordinate system of A and C axes established in this paper, \(\Delta E\) was less than 0.06 mm. When the outward expansion of workspace, \(\Delta E\) was continuously expanded. The maximum value of \(\Delta E\) was obtained at the edge of the workspace to be 0.26 mm. As shown in Figure 8b,c, by increasing the rotation angle of A-axis, the maximum value of comprehensive error was also increased, which indicated that the error caused by large-angle rotation was also larger. Therefore, the prevention of large-angle rotation of the A-axis in the cutting process could effectively reduce the influence of geometric error of the machine tool on machining accuracy.  
Figure 7: Calibration of C-axis average position errors.  
Figure 8: Spatial error field distribution of five-axis machine tool. (**a**) Comprehensive error field distribution of machine tool when A = 0\({}^{\circ}\); (**b**) Comprehensive error field distribution of machine tool when A = 45\({}^{\circ}\); (**c**) Comprehensive error field distribution of machine tool when A = 90\({}^{\circ}\).
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Analysis of Results
#### 4.1.1 Ratio of Horizontal Lift to Drag Ratio to Vertical Lift to Drag Ratio (\(W\))  
We attempt to obtain the change in the number of drones required for covering the area with varying values of ratio of horizontal lift to drag ratio to vertical lift to drag ratio (\(W\)). Our experiments show the graph as in Figure 18a. We obtain another plot of \(W\) vs. average leftover energy, shown in Figure 18b. We observe that the number of drones required decreases with an increase in the value of \(W\). However, the average leftover energy decreases with increasing \(W\). Hence, choosing a value of \(W\) is a double optimization problem, and the result can be obtained using the _elbow method_. A value of \(W=2.4\) is the most optimal in our experiments.
## 2 Materials and Methods
### Dataset Collection from the IRIS
#### 2.1.1 Analysis of Seismic Event Recording Quality  
As part of this study, the performance of the seismic stations was evaluated to identify the characteristics of earthquake recordings in Kazakhstan and Kyrgyzstan. The analysis revealed that most of the epicenters recorded are located in Kyrgyzstan, where mountainous regions dominate the landscape. Consequently, seismic stations in remote areas of Kazakhstan, such as north of Talykorgan or near Lake Balkhash, recorded low-intensity seismic events or, in some cases, failed to detect them.
## 2 Materials and Methods
### Size Exclusion Chromatography (SEC)  
Size Exclusion Chromatography (SEC) was carried out on cellulose powders with an Agilent 1200 Series LC Equipment (SEC pump G1310A, RI detector G1362A, columns: PSS Gram30 and PSS Gram1000 in series, flow rate: 1 mL min\({}^{-1}\); eluent for cellulose samples: DMAc/LiCI). The calibration was done with pullulan standards. Cellulose dissolution in DMAc/LiCI is reported to remain stable over a long time and avoids the degradation of cellulose during the dissolution process; therefore, it is a reasonable solvent for SEC measurement [39].
## 2 Proposed Method
### Review of the Fusion Method  
Figure 1 shows that the proposed fusion method contains three steps: inversion, fusion, and selector. Firstly, a morphology image is inverted into a series of inverted images using different averages. The max rule is then used to fuse the inverted images and acoustic image into the pre-fused images sequences. Finally, a selector is used to choose the expected image from the pre-fused images sequence as the fusion images' output.
## 4 Results and Discussion
### Experiment III: The Availability of Transfer Learning Method Based on HAs
Therefore, for RQ3 \"Can HA-based transfer learning improve the training and testing performance of classifier trees?\", we can conclude that the training results of the model with TL in the classifier tree perform better than those without TL.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests
#### 5.3.5 Underground Garage  
In this experiment, the sender node (node \(S\)) was put in an underground car park (2 floors below ground level). Another node was positioned atop the underground node,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 100\% \\ RSSI (db) & [–81; -74] \\ SNR & [5; 8] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results obtained from the park test.  
Figure 13: Positioning of nodes in park test.  
the two receivers were deployed, and the sender node would alternate between these destinations. The setup can be seen in Figure 14. Since, as explained earlier, LoRa performed rather well with nodes at \(100/150\) m apart, in this section we will focus on the connection between the node underground and the one above it.  
The connection with the underground node was rather poor, and with the starting spreading factor of 7, there was a significant loss of packets. Due to this, the automatic spreading factor increase moved the nodes to 10, but even this was not very good, so another increase was performed, arriving at a spreading factor of 12. With the highest possible setting, the PDR was somewhat decent, but there was still a non-negligible packet loss. Tables 7-9 contain the data gathered from the test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 50\% \\ RSSI (db) & [\(-124;-109\)] \\ SNR & [\(-6;-4\)] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results obtained from the underground garage test with spreading factor 7.  
Figure 14: Positioning of nodes in the underground garage test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 62\% \\ RSSI (db) & [\(-112;-99\)] \\ SNR & [\(-2;1\)] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results obtained from the underground garage test with spreading factor 10.  
Seeing the not-so-great results with the underground node, even with the highest spreading factor, it seems unlikely that LoRa could be used when building a critical system where one (or more) node is underground and others are at ground level. It is still necessary to say that the performances can still vary greatly with the height of terrain the signal has to go through: less terrain in-between nodes will definitely provide a better PDR. Additionally, the type of terrain is also a factor that has to be considered since a reinforced concrete structure with a lot of steel would, for example, worsen the connection between nodes.  
A non-critical system could surely still use this solution, for example, to build a presence detection that could provide real-time data on empty parking spaces in the underground garage with the help of acknowledgments and retransmissions to make sure packets are correctly delivered.