esa-sceva/satcom-chunk-collection huggingface dataset:
# Volumetric Analysis of Aortic Changes after TEVAR Using Three-Dimensional Virtual Modeling
## 2 Materials and Methods
### Diameter Measurement from 2D CTA Imaging  
Descending thoracic aortic maximum diameters were taken by manual measurements with MPR reconstructions orthogonal to the aortic flow axis at 5 different regions relative to the stent graft into which the descending aorta was divided (Figure 1):  
1. Proximal neck (native aorta between the origin of the left subclavian artery and the proximal landing zone of the stent graft);
2. Proximal landing zone of the stent graft;
3. Aneurysm;
4. Distal landing zone of the stent graft;
5. Distal neck (native aorta between the distal landing zone of the stent graft and the origin of the celiac trunk).  
Figure 1: (**A**) CTA parasagittal MPR with maximum intensity projection (MIP) reconstruction of the thoracic aorta in a patient with DTAA (*) treated with TEVAR; (**B**) an example of the manual diameter evaluation of the aorta at five different levels relative to the implanted stent graft: (1) proximal neck (native aorta between the origin of the left subclavian artery and the proximal landing zone of the stent graft); (2) proximal landing zone of the stent graft; (3) aneurysm; (4) distal landing zone of the stent graft; (5) distal neck (native aorta between the distal landing zone of the stent graft and the origin of the celiac trunk).  
Maximum aortic diameters were estimated from the CTA images of the retrospectively reviewed patients, both at discharge and 12 months after TEVAR, and the presence of TEVAR-related complications (e.g., endoleak or stent graft migration) were reported, blinded to the outcomes of the patient.  
Two variables were considered for this standard 2D CTA-based analysis: the net difference between the estimated maximum diameter at the 12-month follow-up and the one at discharge (Diameterchange NET, see Equation (1)), and the corresponding percentual difference, referred to as the maximum diameter at discharge (Diameterchange REL, see Equation (2)),  
\[Diameter_{change_{NEL}}(\text{mm})=Diameter_{12M}-Diameter_{DIS} \tag{1}\]  
\[Diameter_{change_{NEL}}(\%)=\left(Diameter_{12M}-Diameter_{DIS}\right)/Diameter_{DIS} \tag{2}\]
# A Review on Reconfigurable Liquid Dielectric Antennas
## 2 Liquid-Based Antennas
### Feeding Technique Variations
Another feed variation was introduced in Reference [52], where a circular conducting ring was coupled around the water holder and used as a feed. A vertical strip was used to connect the feed to a microstrip line. An illustration of this feed type is shown in Figure 3d. A water-based antenna can be excited by slot antenna, such as for example, an archimedean slot antenna illustrated in Figure 3e. In Reference [53], an archimedean spiral slot was etched into the ground plane to excite a circularly polarized radiation of a square shape water holder (TE\({}_{111}^{x}\) and TE\({}_{111}^{y}\) modes). Table 2 summarises the performance of the liquid-based antennas with various feeding designs as reviewed in this subsection. In general, by varying the feeding technique is is possible to excite other modes of the DRA as well as to increase the tuning range. All of the designs provide continuous frequency tuning. Figure 3: Monopole antenna with different feeding probes. (**a**) [46], (**b**) [47; 48; 49], (**c**) [50], (**d**) [51; 52], (**e**) [53]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Ref.** & **Feed** & **Pla-Nar** & **Dimensions** & **Reconfig.** & **Liquid Type** & **Tuning Type** & **Frequency Range** & **TR** & **TX** & **Radiation** \\  & **Type** & & **mm\({}^{2}(\lambda_{0})\)** & **Type** & **Liquid Type** & **Type** & **Range** & **Equation (1)** & **Equation (3)** & **Efficiency** \\ \hline
[46] & disk & no & \(\begin{array}{c}1050\ \times\ 100\ \times\ 100\\ (0.63\lambda_{0})\end{array}\) & frequency & sea water & contin. & 0.063–0.180 & 96\% & 2.86 & 50.2–72.3\% \\ \hline
[47] & cone & no & \(\begin{array}{c}300\ \times\ 300\ \times\ 200\\ (0.70\lambda_{0})\end{array}\) & frequency & pure water & contin. & 0.165–0.700 & 124\% & 4.24 & 75–82\% \\ \hline
[49] & cone & no & \(\begin{array}{c}100\ \times\ 100\ \times\ 41\\ (2\lambda_{0})\end{array}\) & frequency & ionic liquid & contin. \({}^{a}\) & 1.6–5.0 & 103\% & 3.125 & N/A \\ \hline
[50] & spiral \({}^{b}\) & no & \(\begin{array}{c}100\ \times\ 100\ \times\ 41\\ (0.93\lambda_{0})\end{array}\) & frequency & ionic liquid & contin. \({}^{a}\) & a few bands & - & - & N/A \\  & & & & & & between 1.37 & & & \\  & & & & & and 2.8 & & & & \\ \hline
[51] & ring & no & \(\begin{array}{c}1000\ \times\ 330\times\ 110\\ (0.33\lambda_{0})\end{array}\) & frequency & sea water & contin. & 0.065–0.099 & 42\% & 1.52 & 47.0–78.1\% \\ \hline
[52] & ring & no & \(\begin{array}{c}30\times 30\times\ (0.46\lambda_{0})\end{array}\) & frequency & saline water & contin. & 3.5–4.6 & 27\% & 1.31 & 10–80\% \\ \hline
[53] & slot & no & \(\begin{array}{c}350\ \times\ 350\ \times\ 150\\ (0.47\lambda_{0})\end{array}\) & frequency & pure water & contin. & 0.155–0.400 & 88\% & 2.58 & 90\% \\ \hline \hline \end{tabular} \({}^{a}\) not explicitly demonstrated in the paper. \({}^{b}\) without the metasurface lid.  
\end{table} 
Table 2: Feeding Technique Variations.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 1 Introduction  
In recent years, with the increasing demand for space exploration, future space science missions will use larger sensors, higher sampling frequencies, and more accurate instruments compared to those in existing space science missions. Tasks such as autonomous exploration of robots and active debris removal require highly autonomous operations, which require more powerful on-board processing capabilities [1; 2; 3; 4]. Traditional processors used for space missions cannot meet these high-performance requirements, and the process of redeveloping, producing, and certifying space-grade processors is lengthy, complex, and costly. Existing commercial off-the-shelf (COTS) devices have powerful parallel computing capabilities, and the application of artificial intelligence algorithms in space missions is becoming increasingly widespread. Commercial devices have high floating-point computational performance and excellent neural network accelerators, which can improve the efficiency and accuracy of space mission processing. Therefore, the aerospace field is currently searching for and evaluating processors that can meet high-performance requirements from existing commercial devices to alleviate these challenges. Some government aerospace agencies [5; 6; 7; 8] and private enterprises [9; 10] have conducted extensive research on performance evaluation of COTS devices. Some of these works focus on radiation studies of certain products, while others focus on balancing the performance of various architecture processors and determining which ones are suitable for space missions.
# Study of Annealing Temperature Effect on the Photovoltaic Performance of BiOI-Based Materials
## 3 Results and Discussion
### Morphology Analysis  
The SEM images of some annealed BiOI are depicted in Figure 3. From Figure 3A,B, it is obvious that there was no significant difference in the shape of annealed BiOI at 100 \({}^{\circ}\)C and 300 \({}^{\circ}\)C. Both samples  
Figure 2: Raman spectra of annealed BiOI at different temperatures: 100 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 450 \({}^{\circ}\)C, and 550 \({}^{\circ}\)C.  
had irregular nanosheet forms with smooth surfaces. However, we observed that there were some smaller sheets in the annealed BiOI at 100 \({}^{\circ}\)C and 300 \({}^{\circ}\)C. The growth direction of the irregular BiOI sheet in this report was dominated by the (001) facet growth. This was in line with its XRD patterns. This circumstance might be affected by the amount of water added in the hydrolysis reaction during BiOI synthesis. If the more water was introduced to Bi(NO\({}_{3}\))\({}_{3}\) in the early step of the reaction, the hydrolysis and nucleation rate were faster due to the less concentration of Bi(NO\({}_{3}\))\({}_{3}\) and the weaker acidic solution. As a result, the BiOI layer growth along the (110) was dominant. In contrast, due to the less amount of water put into the precursor solution, it induced the BiOI (001) facet growth; this is in line with reported research [2,42]. The measured layer thickness of BiOI nanosheets was around 37.5 nm and 25 nm (Figure 3E,F) for annealed BiOI at 100 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, respectively.  
Figure 3: SEM images of annealed BiOI at different temperatures: 100 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 450 \({}^{\circ}\)C, 550 \({}^{\circ}\)C (**A–D**); and the higher magnification image of the annealed sample at 100 \({}^{\circ}\)C and 300 \({}^{\circ}\)C (**E,F**).  
For the annealed sample at 450 \({}^{\circ}\)C in Figure 3C, it can be seen that this material was also composed by nanosheets which were irregular, similar to the displayed images in Figure 3A,B. However, some pores appeared in the sample surface, and the width of sheets in Figure 3C tended to be smaller, with a sheet thickness of around 60 nm. This image is comparable with the synthesized Bi\({}_{5}\)O\(\gamma\)I by [PERSON] and co-workers [30]. Apart from Bi\({}_{5}\)O\(\gamma\)I, the annealed sample at a higher temperature (550 \({}^{\circ}\)C) resulted in the biggest bismuth-based material like a dumbbell, with a thickness around 75 nm. The same morphology of this dumbbell-like material was also confirmed in the previous report for \(\beta\)-Bi\({}_{2}\)O\({}_{3}\)[36]. Worm-like structures which are similar to our \(\beta\)-Bi\({}_{2}\)O\({}_{3}\) morphology were also reported [31]. Due to the annealing treatment, the material transformation can relate to the shrinkage process, which results in more ordered phase as the effect of the less crystal defects. The shrinkage in our materials might have been caused by iodine deficiency and oxygen incorporation during the annealing process, like in the copper nitride material [43].
## 2 Materials and Methods  
a-SnS\({}_{2}\) films were prepared by the vapor deposition method using ALD-type recipe at the deposition temperature of 100 \({}^{\circ}\)C. We used TDMASn as a tin precursor and hydrogen sulfide (H2S, 99.9%) as a sulfur reactant. The TDMASn was maintained at 50 \({}^{\circ}\)C, and argon gas was supplied at a flow rate of 30 sccm as a bubbler gas. The substrates were thermally oxidized 300 nm thick SiO2/Si wafer and glass. The process pressure was maintained at 1.0 Torr by purging with argon gas, and the following recipe was used: (T 0.5s-P 10s-T 0.5s-P 10s)-(S 0.5s-P 10s-S 0.5s-P 10s), where T is TDMASn, P is purge, and S is H2S. When this process recipe and pressure were used at the deposition temperature of amorphous phase SnS\({}_{2}\), higher growth rates were observed than typical ALD methods. We deposited 935 cycles to obtain a 500 nm thick a-SnS\({}_{2}\) film. Subsequently, the annealing processes were performed in the ALD chamber with a vacuum atmosphere of 10\({}^{-3}\) Torr at various temperatures and different annealing times (300, 400, and 500 \({}^{\circ}\)C for 10 min; 400 \({}^{\circ}\)C for 5, 10, 20, and 30 min). For all annealing processes, the target temperature was reached in 10 min from deposition temperature of a-SnS\({}_{2}\), i.e., 100 \({}^{\circ}\)C. Then, 450 nm thick a-SnS\({}_{2}\) films were deposited on each of the 50 nm crystalline SnS and SnS\({}_{2}\) seed layers. When we deposited the seed layers, the temperatures of 150 and 170 \({}^{\circ}\)C were used as deposition temperatures of crystalline SnS\({}_{2}\) and SnS films, respectively. We performed 1461 cycles for 50 nm crystalline SnS and 1740 cycles for 50 nm crystalline SnS\({}_{2}\), other deposition conditions were the same as the deposition process of a-SnS\({}_{2}\). Unlike the case of a-SnS\({}_{2}\), the deposition of crystalline phase SnS and SnS\({}_{2}\) films showed low growth rates consistent with the characteristics of ALD. These samples were also subsequently annealed at 400 \({}^{\circ}\)C for 10 min. The reactor was a showerhead-type chamber for four-inch wafer deposition manufactured by ASM Genitech Korea, Hwaseong, Korea. Reactant gases flowing from the left lines were sent to the upper part and injected into the chamber through the showerhead. The exhaust was located at the right bottom part of the chamber. The changes of the crystalline phase, crystallinity, and crystalline growth orientations were examined by an X-ray diffraction (XRD) analyzer with Cu K\(\alpha\) radiation (\(\lambda\) = 1.54 A) using \(\theta\)-2\(\theta\) scan, and XPS was used for the identification of chemical bonding states. Film morphology and uniformity were investigated using scanning electron microscopy (SEM). Ultraviolet-visible spectroscopy (UV-vis), a Hall effect measurement system, and a contact angle tester were used for identification of optical properties, electrical characteristics, and surface wettability, respectively.
## 4 Service and Communication Technology Adaptation Method
### Determination of Index Weights Based on the FAHP-Improved CRITIC Combined Weighting Model  
There are primarily two methods for determining index weights: subjective weighting and objective weighting. Subjective weighting relies on the subjective experience of decision makers or experts to determine the weights of evaluation indexes. However, this method can be influenced by various factors such as the knowledge structure, work experience, and preferences of decision makers, which may not fully reflect the importance of the evaluation indexes. Objective weighting determines the weight of evaluation indexes based on the amount of discriminative information and the interrelationships provided by the indexes. However, objective weighting might overlook the rich experience of experts and scholars and have a strong dependence on samples. The results may not reflect the actual conditions.  
Compared to subjective and objective weighting, combined weighting can integrate the subjective and objective weights of various evaluation indexes, reflecting both the objective information of the indexes and the subjective judgment of the evaluators. Therefore, it can accurately reflect the actual weights of each index.  
Based on the adaptability evaluation system, this paper comprehensively utilizes the FAHP method and the improved CRITIC method to weight communication indexes, taking into account both subjective and objective factors. The subjective and objective combination weighting model for communication adaptability of the data center management system is shown in the Figure 4.
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 4 Image Stitching
### 2D Point Cloud Matching  
#### 4.4.1 Method  
There are many objects in the medical device industry where high-resolution images are required for quality assurance. Some of these objects have little or no features that can be used as interest point. At the same time, they have areas that are identical, which eliminates the template matching method. So both the described methods are not applicable. For such objects interest points can be added by use of a random point laser.  
For this, a 2D point cloud matching application (Figure 8) was developed using synthetic point data.  
Figure 6: The yellow area is matched to the overall image in experiment 2.  
Figure 7: Loss in HSV over the image used for experiment 2. Blue indicates no loss and dark red the maximum loss: (**a**) hue; (**b**) saturation; (**c**) lightness; (**d**) HSV.  
In total, 100 points were generated with the X and Y coordinates uniformly randomly generated between \(l_{i}=0\) and \(u_{i}=300\) (Point Cloud 1). A second set of points were generated by mutating the first set with programmable random dropout where the point is replaced with a new random point and programmable point mutation where a random noise is added to the point position (Point Cloud 2). In addition to the mutations each point can be translated \(\pm 20\) positions in X and Y and rotated \(+/-20\) degrees. An additional 50 points are added to point cloud 2 outside the 0 to 300 area.  
A PSO algorithm with the settings \(NP=50\), \(c_{1}=2\), \(c_{2}=0.3\); \(w=0.9\), velocity components clamping within 30% of the search space with \(u_{i}-l_{i}=300\) and \(I_{\text{max}}=1000\) was developed. Each particle has a predicted \(X,Y\) offset and rotation angle. The loss function computes the predicted dataset by offseting each point in point cloud 1 by its \(X,Y\) and rotating it by the rotation angle. For each point in the predicted dataset the distance to its nearest point in point cloud 2 is calculated. The sum of these distances squared is used as the loss function to be minimised.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 3 The Proposed Channel Synthesizer
### Incoherent Scattered Field  
As the MST incoherent scattered field is obtained for the whole canopy as a power only, we need to adapt it for the purposes of the synthesizer. Thus, we break down the integration in Equation (7) into a summation of the fractional incoherent power scattered by individual voxels. For each voxel we, thus, need to use the distance through the canopy to and from it, as Equation (7) includes the term \(s_{1}(\mathbf{r}^{\prime})\) in \(\mathbf{E}_{\text{coh}}^{\text{in}}(\mathbf{r}^{\prime})\) and the term \(s_{2}(r^{\prime})\) in \(V(\mathbf{r},\mathbf{r}^{\prime})\). We should note here that \(s_{1}(\mathbf{r}^{\prime})\) changes due to the UAV movement and \(s_{2}(r^{\prime})\) changes when the terminal reaches a different position.  
Having the individual contributions to the total scattered power, we may turn them into the corresponding field intensities by applying the square root and adding phase information. As the incoherent scattered field is supposed to show a random nature, we assign a random phase \(\varphi_{r}(\mathbf{r}^{\prime})\) to each of the voxels following the uniformly-distributed values between \(0\) and \(2\pi\). Further, based on the distances \(d_{1}(\mathbf{r}^{\prime})\) between the UAV and the individual voxels, we add another phase term: \(-jk_{0}d_{1}(\mathbf{r}^{\prime})\). Similarly, following the distances \(d_{2}(\mathbf{r}^{\prime})\) between the individual voxels and the ground terminal, we add another phase term as \(-jk_{0}d_{2}(\mathbf{r}^{\prime})\). In this way, the overall incoherent scattered power of a particular tree canopy is preserved and its randomness is expressed properly.  
Following [5], Equation (7) can be rearranged as:  
\[E_{dV}=\sqrt{\frac{\left|E_{0}\right|^{2}}{4\pi d_{2}^{2}}\sigma^{eq}(\hat{ \mathbf{k}}_{\text{out}},\hat{\mathbf{k}}_{\text{inc}})e^{-2K^{\prime\prime} (s_{1}(\mathbf{r}^{\prime})+s_{2}(r^{\prime}))}dV^{\prime}} \tag{12}\]  
and the phase information can be added as:  
\[E_{incoh,dV}=E_{dV}e^{-jk_{0}d_{1}(r^{\prime})}e^{-jk_{0}d_{2}(r^{\prime})}e^ {j\varphi_{r}(\mathbf{r}^{\prime})} \tag{13}\]  
which can then be summed over all voxels \(dV^{\prime}\).  
As the individual incoherent scattered field contributions contain the phase information, they can be directly summed with the coherent scattered field obtained by the combined PO/MST model. It should be noted that to reconstruct the _xyz_ field components from Equation (13), the projection following \(\hat{\mathbf{k}}_{\text{out}}\) and the field polarization must be applied, as in [11].
## 3 Routing Protocols for UWSN
### Protocols Featuring Energy Efficiency
#### 3.4.2 Distributed Underwater Clustering Scheme (DUCS)  
Batteries are the power source for the sensor-nodes which are difficult to recharge or replace. So limited energy is a major constrain for UWSN. [PERSON] and prior presented DUCS which is--(1) energy-aware (2) non-time-critical (3) self-organized and (4) provides random node-mobility [60]. A distributed algorithm is used to form the clusters. It is assumed that sensor-nodes constantly generate data which needs to be transmitted. Moreover, these nodes have the capability to change the transmission power level whenever necessary. It uses a self-organizing technique to form local clusters each having one cluster head. All the cluster members transmit their data to their respective heads by  
Figure 10: Data Forwarding Techniques in Focused Beam Routing (FBR) [59].  
single hop transmission. After accumulation of all the data, cluster heads sent them to the sink. But this transmission from cluster-head to the sink is completed by multi-hop communication. Cluster heads control the communication within its own cluster and also the inter-cluster communication. These cluster-heads are chosen randomly from the cluster members on the basis of regular rotation. So, it makes this protocol an energy efficient one. Besides, to avoid fast battery-drainage of the cluster-heads, DUCS allows randomize change of the cluster heads withing the cluster members. The function of operation is divided into two rounds. First is called setup in which clusters are formed and the second round called network operation which completes the data transmission. A series of data are sent from the member-nodes to the heads which maintain a particular routine. This series of data are called frames. Member-nodes sent several such frames to their own heads of the clusters during second round. Simulation of DUCS has shown that--(1) its packet delivery ratio is high (2) network overhead is reduced (3) its throughput increases consequently. Though it has some performance issues, DUCS is energy efficient. The cluster structure can be affected by the movement of the nodes due to water current which may lead to decrease cluster life. It is worth mentioning that, only a cluster head can communicate with another head and this is possible only in the second round. So if by any means these heads are distant-apart such far that they can not communicate directly, even then they can not communicate through the non cluster heads present in between. This can interrupt the network drastically and hamper the life-time as well as performances.
# SpaFLEX: Field Campaign for Calibration and Validation of FLEX-S3 Mission Products +
###### Abstract  
The FLEX-S3 mission by ESA will deliver key Level 2 products such as sun-induced chlorophyll fluorescence (SIF) and vegetation-reflected radiance. To validate these, the SpaFLEX project, funded by the Spanish Ministry of Science and Innovation, is developing a robust calibration and validation strategy in Spain. This includes test site setup, instrument characterization, and sampling protocols. A field campaign was conducted in two Holm Oak forests in Teruel, analyzing Sentinel-2 spatial heterogeneity and collecting ground, UAV, and airborne data. The results support scaling procedures to match the 300 m pixel resolution of FLEX-S3, ensuring product accuracy and compliance with ESA standards.  
FLEX-S3; sun-induced fluorescence; Cal/Val sites +
Footnote †: Based on observations made with the NASA/ESA _Hubble Space Telescope_, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.