esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Spatiotemporal Variations
#### 3.1.2 Characteristics of Spatial Variation  
Figure 3 depicted the regional variation features of WUE in different periods, the WUE showed a spatially heterogeneous distribution characterized by a gradually decreasing trend from the southwest to the northeast. The WUE during both 1982-2000 and 2000-2018 exhibited similar spatial distribution patterns, progressively decreasing from southwest to northeast during 1982-2018.  
The annual average WUE of the YRB from 1982 to 2018 was 0.93 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, and the distribution value range was between 0.04 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O and 2.61 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O. The regions with high values of annual average WUE (>1.60 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) are mainly located in Qinghai, northern Sichuan, southwestern Gansu, southern Shaanxi, eastern Shanxi and parts of Henan. The region of low values (<0.52 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) is predominantly found in the upstream and midstream areas of the YRB, mainly covering northwestern Qing  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Upstream**} & \multicolumn{3}{c}{**Midstream**} & \multicolumn{3}{c}{**Downstream**} \\ \hline  & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** \\ \hline Annual & 1.04 & 1.24 & 0.75 & 0.81 & 0.97 & 0.59 & 0.95 & 1.18 & 0.78 \\ Grow & 0.90 & 1.06 & 0.67 & 0.70 & 0.83 & 0.52 & 0.84 & 1.04 & 0.69 \\ Spring & 1.08 & 1.63 & 0.66 & 1.48 & 1.87 & 1.20 & 1.46 & 1.82 & 0.90 \\ Summer & 1.36 & 1.57 & 1.02 & 1.15 & 1.35 & 0.82 & 1.48 & 1.87 & 1.20 \\ Autumn & 1.93 & 2.38 & 1.38 & 2.38 & 2.84 & 1.99 & 2.42 & 2.91 & 2.04 \\ Winter & 0.29 & 0.58 & 0.03 & 0.15 & 0.32 & 0.02 & 0.41 & 0.84 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics on WUE in the YRB from 1982 to 2018. (Unit gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O).  
Figure 3: Spatial variation characteristics of WUE in the YRB. (**a**) Annual WUE in 1982–2018; (**b**) Annual WUE in 1982–2000; (**c**) Annual WUE in 2000–2018; (**d**) Grow WUE in 1982–2018; (**e**) Annual WUE in 1982–2000; (**f**) Annual WUE in 2000–2018.  
hai, northeastern Gansu, Ningxia, northern Shaanxi, and much of Inner Mongolia. The WUE from 1982 to 2000 was 0.88 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O, with a range of values between 0 and 2.66 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O, while the average annual WUE from 2000 to 2018 was 0.99 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O, with a range of values between 0.07 and 2.66 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O, WUE was increased by 0.11 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O from 2000 to 2018 relative to 1982-2000. The WUE exhibited a pattern of progressive decrease in spatial distribution from the southwest to the northeast across the periods 1982-2000 and 2000-2018. The results indicated that there were differential characteristics in the WUE of the YRB before and after 2000, but there were similarities in the characteristics of its spatial distribution.
# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 2 Modeling of a Carbon Nanotube
### Equivalent Transmission Line (TL) Modeling
The work reports that \(L_{E}\) is always negligible with respect to \(L_{k}\), whereas \(C_{E}\) dominates in the capacitive branch impedance when the number of walls exceeds 20 and the distance from the ground is greater than half the external radius, because it is one order of magnitude smaller than \(C_{Q}\). In CNTs with a small number of walls located close to the ground, \(C_{E}\) and \(C_{Q}\) are in the same order of magnitude, and thus none of them can be neglected.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 3 Proposed Framework
### Quantum Machine Learning Task
#### 3.1.1 Host Management and Unified Software  
Aside from the aforementioned functions, the host also manages the interaction between the QPU and the FPGA accelerator. The host memory management system plays a crucial role, handling the allocation of physically contiguous memory buffers and ensuring proper synchronization between host and FPGA device memory spaces. Additionally, the host carefully manages cache coherency and provides efficient mechanisms for high-bandwidth data transfer to/from the FPGA. Memory management on the host is implemented using a unified buffer allocation system that allocates buffers on the host and device sides, establishing a robust framework for data transfer between the host and FPGA. Several key buffers are allocated: prediction buffers for immediate predictions, extended prediction buffers for batch processing, weight buffers for parameter storage, gradient buffers for optimization computations, and loss buffers for performance tracking. All buffers utilize numpy's float64 data type to maintain high numerical precision.  
Data flow in the host system follows a structured pipeline, beginning with quantum circuit parameter preparation followed by data pre-processing and normalization. The system implements batch processing for training optimization, where each batch of data undergoes quantum circuit simulation before being processed by the FPGA accelerator. Memory synchronization is maintained through \(sync\_to\_device()\) and \(sync\_from\_device()\) high-level methods which enable ease-of-use, ensuring data coherency between host and FPGA memory spaces.  
The host implementation carefully manages the training loop, implementing mini-batch gradient descent optimization. Within each training iteration, the system coordinates data movement between quantum circuit execution on QPU and FPGA acceleration, handles weight updates based on computed gradients, and monitors training progress through loss computation and accuracy metrics. This architecture enables efficient parallel processing of quantum circuit parameters while maintaining the flexibility of quantum circuit execution on the QPU.
# Angioplasty Using 4-Hexylresorcinol-Incorporated Silk Vascular Patch in Rat Carotid Defect Model
## 4 Discussion
In particular, the Wingless-type Murine mammary tumor virus (MMTV) integration site family, member 3A (Wnt3a) protein is associated with macrophage-mediated angiogenesis, and is overexpressed by the protein solution from the inner silk layer [58]. Moreover, the platelet-derived growth factor gene, which plays a significant role in angiogenesis, is also overexpressed in the protein solution from the inner silk layer [57]. These factors are believed to have contributed to the increased expression level of VEGF-A, which plays a critical role in angiogenesis, that was observed in the 4-HR silk patch group (Figure 1) [59]. In in vitro studies, 4-HR demonstrated anti-thrombotic effects in microvascular anastomosis by delaying blood coagulation and inhibiting thrombus formation in vascular anastomosis [32]. Additionally, venous thrombosis is critical to the success of microvascular free-flap transfer and angioplasty [60; 61]. In our study, incorporating 4-HR was found to induce anti-thrombotic effects in the blood coagulation test (Figure 2). This result confirmed that incorporating 4-HR as a material component of the silk patch contributes to the prevention of thrombosis in the vessel after patch angioplasty. Moreover, new regeneration of the arterial wall was observed in the artificial defect, and the diameter of artery was well maintained in the absence of any inflammatory reaction (Figures 8 and 9). In contrast, although the arterial diameter was generally well maintained, some endothelial overgrowth was observed in the PTFE group (Figure 7). Furthermore, in the control group, the artery tended to be narrowed and severely shrunken in the direct closure area; this phenomenon caused the non-angioplasty control group to have the highest blood flow velocity. These results indicated that patch angioplasty can effectively repair small-diameter artery defects, as it was demonstrated to provide good arterial patency and blood flow as compared to results of the direct closure method. Thus, we demonstrated that the 4-HR silk patch can be effectively used as a patch material, and that it has higher biocompatibility and hemocompatibility than the PTFE patch.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 3 Preliminaries
### Homomorphic Encryption
#### 3.1.3 Fully Homomorphic Encryption  
FHE permits performing an unlimited number of operations from the available ones. Different research areas have received FHE because of its applicability in real-life applications. Also, because of its mathematical concepts, implementing FHE schemes is complex. The major schemes are Ideal Lattice-based, Over Integers, RLWE-based, and NTRU-like [32].  
Section 4.3 will mention the type of Homomorphic Encryption used in our privacy-preserving scheme and the justification for selecting it.
# Mechanical Performance of Cement Bound Granular Mixtures Using Recycled Aggregate and Coconut Fiber
## 3 Results and Discussion
### Indirect Tensile Strength  
The ITS test results at 7 and 28 days of curing are presented in Figure 4, where the bars represent the average results, and the error marks represent the standard deviation. For each CBGM and curing age, three specimens were tested. The lowest ITS values were obtained with the RA_2_0 mixture, with 0.374 and 0.542 MPa at 7 and 28 days of curing, respectively. Thus, all CBGM exceed the minimum ITS indicated by several references such as a former Portuguese specification by Junta Autonoma de Estradas (JAE) [59] and SAPEM [58]. SAPEM indicates a minimum ITS at 7 days of 0.20 and 0.25 MPa for the CSM-C4 and CSM-C3 classes, respectively (for CSM-C2 and CSM-C1, the minimum ITS is not set). JAE indicates a minimum ITS of 0.20 and 0.30 MPa at 7 and 28 days, respectively.  
Figure 3: Unconfined compressive strength of the CBGM at 7 days of curing.  
The use of RA, as a substitution of the NA, did not compromise the ITS. In the case of the CBGM with 2% cement, if compared with the CBGM with NA, the CBGM with RA presented a variation in ITS of \(-38\%\) and \(-24\%\) without CF and \(-6\%\) and \(+17\%\) with CF at 7 and 28 days, respectively. For the CBGM with RA, at 7 days, the increase in the cement dosage from 2% to 3% increased ITS by 56% and 26% for the CBGM without CF and with CF, respectively. For the CBGM with RA, at 28 days of curing, the increase in the cement dosage from 2% to 3% increased ITS by 58% and 21% for the CBGM without CF and with CF, respectively.  
Regarding the use of CF, the effects in the ITS of the CBGM were not obvious. In the CBGM with NA, the use of CF led to a reduction in ITS of 23% at both the 7 th and 28 th days. The use of CF in the CBGM with RA caused variations in ITS of +16% and +18% in the mixture with 2% cement and \(-6\%\) and \(-9\%\) in the mixture with 3% cement at 7 and 28 days, respectively.  
However, the effects on strength were not clear. The use of CF highly enhanced the integrity of the CBGM specimens post-test. Figure 5 presents an example of CBGM with RA at post-test, without CF (left) and with CF (right). While the specimens without CF tended to split into two halves and presented some disaggregation, the specimens with CF maintained their integrity, and the loss of particles was minimal. Such an effect is visually more impactful on the ITS and FLS tests than on the others analyzed. However, a similar effect occurred with the UCS post-test.
## 2 Methods
### Point Transformer with Extreme Loss Function  
Point Transformer is a transformer-based deep network. The introduction of a self-attention mechanism also gives it a strong feature extraction ability for forest point clouds with complex structures. It consists of three core modules: a down-sampling module, an up-sampling module, and a transformer module. The Point Transformer network structure is shown in Figure 3.  
Figure 2: The main steps of data preprocessing. (**a**) Point cloud filtering; (**b**) Elevation normalization; (**c**) Divide subplot; (**d**) Point cloud denoising. The points surrounded by the red circle are the noise points at the boundary of the subplot; (**e**) Down-sampling; (**f**) Coordinate normalization.  
The down-sampling module reduces the number of data points and extracts deeper feature information at the same time. Firstly, the FPS algorithm is used to sample the input point cloud to obtain the sampled point cloud. Secondly, in order to extract deeper feature information, each sampled point is taken as the center point, and the kNN graph is used to find the k nearest neighbor points around the center point. Finally, each center point and its neighbors form a set, which is input into the deep network for deep feature extraction.  
The up-sampling module assigns deep features to non-sampled points. Firstly, the features of the point cloud composed of center points are extracted by MLP, BN, and ReLU. Secondly, the inverse distance weight interpolation algorithm is used to interpolate the features of each center point to its neighboring point. Finally, the features of the corresponding down-sampling block are aggregated to the corresponding up-sampling block by skip connection.  
The Transformer module strengthens the local semantic awareness of each point to the points around it. It consists of two MLPs and a Transformer layer. The Transformer layer is shown in Equation (1):  
\[y_{i}=\sum\
olimits_{x_{j}\in X(i)}\varrho\big{(}\gamma\big{(}\beta\big{(} \varphi(x_{i}),\ \psi\big{(}x_{j}\big{)}\big{)}+\delta\big{)}\big{)}\odot\alpha\big{(}x_{j}+ \delta\big{)}\, \tag{1}\]  
where \(\beta\) is a relation function; \(\varrho\) is a normalization function such as softmax; \(\gamma\) is an MLP with two linear layers and a ReLU nonlinearity; \(\varphi\), \  
the true offset of the point to the extreme point. \(\Delta t_{i}^{pred}\) is the offset predicted by the network for each point; X is a vector; \(\omega_{i}(\text{X})\) is a function to calculate the dynamic weight according to the length of vector X. Points near the extreme of the tree are less dependent on extreme offset and \(\left\|\text{X}\right\|_{2}\) denotes the length of the vector X.
## 2 Materials and Methods
### Evaluation and Assessment of Parameters Using AHP
#### 2.4.2 Derivation of the Overall Relative Weights  
The relative significance or weight of the factor after a pairwise comparison matrix was computed based on systematic AHP assessment and expert's inputs. This step was conducted by calculating the normalized values for each criterion and alternative, and choosing the normalized main priority vectors. Normalized values for each criterion and alternative in their respective matrices were derived by dividing each cell into its column and producing a total column of 1 for each criterion and alternative. Weights were calculated by averaging the rows of the matrix. The resulting value will give relative weight to every criterion concerning the best goal, and provide relative weight for the alternatives with respect to the criteria. The final relative weights of the alternatives were defined by computing the product's linear combination (LC) between the relative weight of each criterion and the alternative for the specific criterion. The decision-makers choose the best according to the alternatives' overall weights if the experts' judgments are proven consistent. This is mathematically expressed using Equation (1).  
\[C=\left\{C_{j}|j=1,2,\ldots,n\right\} \tag{1}\]  
The pairwise comparison on the criteria can be generalized using an evaluation matrix \(A\), as shown as Equation (2), in which every element is the quotient of weights of the criteria given in Equation (3) [32].  
\[A=\left[\begin{array}{cccc}a_{11}&a_{12}&.&a_{1n}\\ a_{21}&a_{22}&.&a_{2n}\\.&.&.&.\\ a_{n1}&a_{n2}&.&a_{nn}\end{array}\right],\ a_{ii}=1,a_{ji}=\frac{1}{a_{ji}},\ a_{ij}\
eq 0 \tag{2}\]
## 2 Design and Optimization
### Optimization of the Single Element AVA Design  
The single element AVA antenna is evolved in three stages, as shown in Figure 1. The type-A covers three frequency bands 19.81-20.71 GHz, 24.7-26.35 GHz, 36.79-38.17 GHz and has the limitation of not covering the lower frequency spectrum of the Ku-band. In type-B, the width of the radiating leaf is nearly doubled (to 5.2 mm) in order to cover the four frequency bands of 15.04-16.39 GHz, 19.42-20.44 GHz, 25.27-27.22 GHz, and 37.03-38.56 GHz. To enhance the bandwidth, the leaf structure of type-B is split into two thinner leaves (each 2.2 mm) which results in the proposed element geometry of type-C. The type-C antenna covers significant portion of the Ku (12-18 GHz), K (18-27 GHz), and Ka (27-40 GHz) frequency bands. The three bands covered by type-C are, 14.44-20.98 GHz, 24.34-29 GHz, and 33-40 GHz. These bands are designated by the IEEE and ITU for radar applications, however, the 28 GHz and 38 GHz are recently proposed for 5G mobile communications. Figure 2 compares the reflection coefficients (S\({}_{11}\)) of the three types of antennas.  
Figure 1: Optimization of the single element antipodal Vivaldi antenna.  
Figure 2: Comparison of reflection coefficient (S\({}_{11}\)) by optimizing the structure of the single element AVA.
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 2 The Campaign Sites and Instrumentations
### Instruments
#### 2.2.3 CALIPSO Satellite  
In the last decades, several efforts have been made to achieve global Earth monitoring using remote sensing techniques. As part of this effort, in April of 2006, the CALIPSO satellite was launched in order to provide the vertical profile of the Earth's atmosphere on a global scale [47]. The satellite flies as part of the A-Train satellite constellation, in a 705 km sun-synchronous polar orbit with an equator-crossing time of about 1330 local solar time. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is the primary instrument aboard CALIPSO and works based on a two-wavelength laser, 532 nm and 1064 nm, with a pulse repetition rate of 20.16 Hz [48]. The CALIOP measures attenuated backscatter coefficients using both mentioned wavelengths, with a horizontal resolution of 333 m and a variable vertical resolution according to the values: 30 m from the ground up to 8.2 km, 60 m from 8.2 km up to 20.2 km, and 180 m from 20.2 km up to 30.1 km. Several CALIOP data products are gathered from the attenuated backscatter signals and separated in two categories to monitor the atmospheric optical properties: Level 1 profiles, composed by the attenuated profiles; and level 2, which are derived from level 1 products and are composed of layer, profiles and vertical feature mask (VFM) products [49]. The CALIOP algorithms use a set of information about the aerosol layers such as altitude, location, surface type, depolarization ratio and integrated attenuated backscatter, in order to classify the aerosol subtype in the troposphere region applying a selection scheme [50; 51]. The CALIOP aerosol subtype classification scheme for version 4 level 2 data enables the derivation of a total of 11 different types of aerosol: Clean marine, dust, polluted continental/smoke, clean continental, polluted dust, elevated smoke, dusty marine for the tropospheric region, and polar stratospheric aerosol, volcanic ash, sulfate/other, smoke, for the stratospheric region. For each aerosol subtype, it is assigned an extinction-to-backscatter ratio (i.e., lidar ratio) and its uncertainty [50; 51]. The lidar ratio is applied to another set of algorithms and plays an important role in the aerosol extinction profile and aerosol optical depth retrieval [52]. In this manuscript, Level 2 version 4.10 was employed to derive important aerosol optical properties such as AOD, lidar ratio and aerosol subtype in order to describe the aerosol mean profile, variability and trends in the MAPV.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 4 Results and Discussions  
### PCA's Results  
Table 2 indicates the results of PCA, including: (i) the contribution of 11 inputs to 11 principal components (denoted by PC\({}_{\text{k}}\) with k = 1:11); (ii) the explained variance (EV) of each PC\({}_{\text{k}}\) and (iii) the cumulative sum (CS) of explained variance, respectively. It can be seen that PC\({}_{1}\) exhibits 29.71% and the first 9 PC\({}_{\
## 3 Results
### Discriminant Performance of VIs  
The calculated NDVI, RE/R, and PRI values on days 4 and 8 are shown in Table 3. MCA values were used to distinguish each temperature level. The average, standard deviation, and MCA results of the NDVI acquired from Chinese cabbage were obtained at the different temperature levels (20 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, and 36 \({}^{\circ}\)C). On day 4, the NDVI values in Group 1 differed from those in Groups 2 and 3, and Group 2 showed larger deviation values than those of the other groups. In the MCA results, Group 2 was clearly distinguished from the other two groups.  
In the RE/R values on day 4, based on the MCA results, Groups 1 and 2 were distinguished from Group 3, and the standard deviation values of Group 1 were much larger than those of the others. This indicates that the absorption efficiency of the chlorophyll content of Groups 1 and 2 was better than that of Group 3. Group 3 had the highest RE/R value on day 4. However, on day 8, there was no difference in the RE/R values, and none of the groups could be classified by the MCA outcome.  
The PRI values on days 4 and 8 were almost the same, and their deviations were also almost the same (approximately 0.1). This is different from the NDVI and RE/R outcomes. In addition, the temperature levels could not be classified on days 4 and 8.  
Figure 3: Average reflectance spectra of heat-stressed samples from 460–860 nm; reflectance spectra under different temperatures (20 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, and 36 \({}^{\circ}\)C on days 4 and 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & **Day** &
\begin{tabular}{c} **Temp.** \\ (\({}^{\circ}\)**C**) \\ \end{tabular} & **NDVI** & **RE/R** & **PRI** & **STD \({}^{\bf 1}\) of NDVI** & **STD \({}^{\bf 1}\) of RE/R** & **STD \({}^{\bf 1}\) of PRI** \\ \hline
1 \({}^{\rm d}\) & & 20 & 0.333 \({}^{\rm a}\) & 0.382 \({}^{\rm a}\) & 0.901 \({}^{\rm a}\) & 0.133 & 0.042 & 0.043 \\
2 \({}^{\rm e}\) & 4 & 28 & 0.306 \({}^{\rm b}\) & 0.378 \({}^{\rm a}\) & 0.916 \({}^{\rm a}\) & 0.037 & 0.027 & 0.037 \\
3 \({}^{\rm f}\) & & 36 & 0.306 \({}^{\rm b}\) & 0.359 \({}^{\rm b}\) & 0.899 \({}^{\rm a}\) & 0.034 & 0.019 & 0.032 \\
4 \({}^{\rm g}\) & & 20 & 0.244 \({}^{\rm a}\) & 0.499 \({}^{\rm a}\) & 0.906 \({}^{\rm a}\) & 0.064 & 0.076 & 0.081 \\
5 \({}^{\rm h}\) & 8 & 28 & 0.250 \({}^{\rm a}\) & 0.498 \({}^{\rm a}\) & 0.909 \({}^{\rm a}\) & 0.058 & 0.055 & 0.075 \\
6 \({}^{\rm i}\) & & 36 & 0.304 \({}^{\rm b}\) & 0.501 \({}^{\rm a}\) & 0.897 \({}^{\rm a}\) & 0.077 & 0.060 & 0.087 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated normalized difference vegetation index (NDVI), red-edge ratio (RE/R), and photochemical reflectance index (PRI) and the corresponding multiple comparison analysis values of Chinese cabbage grown at 20 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, and 36 \({}^{\circ}\)C for 4 and 8 days.