esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview on the Evolution of Laser Welding of Vascular and Nervous Tissues
## 2 Laser Welding of Vascular and Nervous Tissues
### Welding Procedure
Research has also been conducted to predict tissue damage and the corresponding effect by a finite element method, which may provide useful pre-operative information for the laser-welding procedure [74]. In laser welding of vascular and nervous tissues, it is also very important to consider the tissue opposition, i.e., the position of the adjacent tissues in which the welding procedure occurs, since it determines the quality of the bonding of the collagen fibrins [45; 71; 73]. In order to improve the welding conditions, traction sutures can be used to approximate the tissues, enabling higher bonding strengths to be achieved [42; 47; 57; 71; 73]. In turn, [PERSON] et al. [48], in laser-assisted vessel repair (LAVR), reported the beneficial effect of preloaded longitudinal compression on the weld strength. These authors reported a success rate of 83% when compared to welds in which preloaded compression was absent. Figure 2 shows the instrumentation used by them, in which the vessel clamp device (Figure 2a) used for the application of the longitudinal compression in the pre-operative welding repair (Figure 2b) can be observed. Additionally, Table 2 summarizes results from previous works on vascular laser welding in which the use of complementary sutures was studied. [PERSON] et al. [40] and [PERSON] [58] recently stated that many surgeons still use additional sutures as a complement to the laser-welding techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Total No.** & **No. of Welds with** & **No. of Additional** \\  & **of Welds** & **Complementary Sures** & **Sures Per Weld** \\ \hline [PERSON] et al. [44] & 5 \({}^{\text{a}}\) & 1 & 1 \\ [PERSON] et al. [57] & 12 & 12 & 1 \\ [PERSON] et al. [71] & 24 & 12 & 1 to 2 \\ [PERSON] et al. [41] & 61 & 55 & 1 \\ [PERSON] et al. [42] & 12 & 8 & 1 to 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Laser welding with complementary sutures in vascular and nerve surgeries. Figure 2: Instrumentation for preloaded longitudinal compression: (**a**) Vessel clamp device (prototype); (**b**) Procedure of preloaded longitudinal compression [48]. Another crucial aspect in vascular and nervous surgeries consists of the analysis of the end point of laser welding. The most conventional method is visual feedback. [PERSON] et al. [56] and [PERSON] et al. [45] created visual identification scores to correlate the different argon-laser parameters, and the resulting end points, with the response of the tissue. Figure 3 shows the stages of welded end points, with the \"whitening\" and the \"beginning of caramelization\" being the most adequate appearances [45; 47; 56; 73]. In addition to visual feedback, other methods have been developed to analyze the end point of laser welding. The most reported method consists of measuring the temperature using thermal feedback systems [42; 45]. As reported above, these systems are also associated with the prevention of tissue weakening by thermal damage [51].
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## Materials and Methods
### Circuit Fabrication  
Monolithically integrated balun circuits containing four graphene field effect transistors each were fabricated on a \(15\times 15\) mm\({}^{2}\) SiC wafer. First, we define the alignment marks. Then we etch the graphene channel and holes in the contacts region for improving contact resistance, as reported previously [21; 22]. The source and drain contacts are patterned using the e-beam lithography (EBL) followed by deposition of 1.5 nm of nickel and 30 nm of gold and lift-off process. Here the 1.5 nm thin layer of nickel is used to improve the metal adhesion on the surface. Then, dual T-gate with gate length (Lg) were defined by EBL using a three layers poly-meta-methacrylate resist. After the development of the multilayers' resist, the gate oxide was obtained by depositing four times 2 nm of aluminum, following by oxidation in ambient air during 24 h. Finally, the coplanar access and interconnections between transistors were fabricated (Ni/Au 50 nm/300 nm).
## Materials and Methods
### Performance Assessment of the Time-Series Smoothing Methods  
#### 2.3.1 Ground Measured NDVI Time-Series vs. Smoothed Satellite NDVI  
The root mean squared error (RMSE) between smoothed satellite NDVI time-series and ground-based NDVI time-series was used to evaluate the accuracy of each smoothing method. In most cases, the method generating the minimum RMSE can be considered the most accurate, and it thereby indicates the optimal capability of the method. However, for unknown areas where ground reference data are lacking, which is commonly the case in operational remote sensing applications, the optimal parameter settings for each method is unknown. Therefore, we also computed the median RMSE and the range of RMSE from all possible settings for a single method. Together, the minimum, median and range provide a general description of the performance of each method. The minimum indicates the best performance of each method given that calibration data are available. When no calibration data are available, the median and range are better indicators, providing information on the average performance and the associate range of each method. A large range indicates a flexible method, meaning that both low and high accuracy could be attained.
# Prussian Blue: A Safe Pigment with Zeolitic-Like Activity
## 2 Synthesis of Insoluble Prussian Blue  
The insoluble PB can be prepared by precipitation from aqueous solution at moderate temperatures. Two methods can be used: the direct method and the indirect one [11]. In the direct method, PB is prepared by either combining at room temperature and a rate of 1-10 mL\(\cdot\)h\({}^{-1}\) an aqueous solution of iron (III) salt, such as FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, with an aqueous solution of a ferrocyanide salt, such as Na\({}_{4}\)[Fe(CN)\({}_{6}\)]\(\cdot\)10H\({}_{2}\)O or K\({}_{4}\)[Fe(CN)\({}_{6}\)]\(\cdot\)3H\({}_{2}\)O, or combining an aqueous solution of iron(II) salt, such as FeCl\({}_{2}\cdot\)4H\({}_{2}\)O, with an aqueous solution of a ferricyanide salt, such as Na\({}_{3}\)[Fe(CN)\({}_{6}\)] or K\({}_{3}\)[Fe(CN)\({}_{6}\)]. In the indirect method, Berlin white, Fe\({}_{2}\)[Fe(CN)\({}_{6}\)], is first prepared by combining an aqueous solution of an iron (II) salt such as FeCl\({}_{2}\cdot\)4H\({}_{2}\)O, with an aqueous solution of a ferrocyanide salt, such as Na\({}_{4}\)[Fe(CN)\({}_{6}\)]\(\cdot\)10H\({}_{2}\)O or K\({}_{4}\)[Fe(CN)\({}_{6}\)]\(\cdot\)3H\({}_{2}\)O. Berlin white is subsequently oxidized to form the final insoluble PB. In both methods, after addition, the mixture was stirred 1 h before being centrifuged for 15 min. The supernatant was removed and the nanoparticles of PB were washed successively with water and ethanol and dried under vacuum to obtain a dark blue powder.  
The direct method is the most conventional. A modification of the direct method, so-called single-precursor method, is based on the use of [Fe(CN)\({}_{6}\)]\({}^{4-}\) or [Fe(CN)\({}_{6}\)]\({}^{3-}\), which can slowly release ferric or ferrous ions that are reduced or oxidized into ferrous or ferric ions, respectively, in acidic conditions, and that then react with the unreacted ions. When the coprecipitation is carried out at high temperature and pressure, the variant constitutes a hydrothermal method.  
A detailed description of other ways for preparing PB nanoparticles is found in the review of [PERSON] et al. [12].
## 3 Measurement Results
Figure 9: (a) 4-port MIMO prototype, and (b) Measured and simulated reflection coefficients for each MIMO port, S\({}_{11}\), S\({}_{22}\), S\({}_{33}\), and S\({}_{44}\). Table 2 presents a comparative analysis between the proposed antenna design and the state-of-the-art implementations reported in the recent literature. The benchmarking results demonstrate that our design achieves a competitive performance across key metrics including operational bandwidth, radiation gain, and port-to-port isolation. This evaluation reveals several notable advantages of the proposed configuration: (1) improved bandwidth characteristics across operational frequencies, (2) consistent gain performance, (3) enhanced isolation compared to conventional designs. These results position the proposed design as a viable solution for next-generation wireless systems requiring high-performance antenna arrays.
The final results of the performed experiment were statistically assessed and showed a better Galileo planimetric performance while, from an altimetric point of view, the GPS and GLONASS systems performed better.
## 5 Discussion  
Except for the increase in forestland, the vegetation coverage in the region evidently improved during the period due to restricted grazing on grassland, improved cropland management, and strict protection of natural vegetation. We analyzed the change trend of the annual maximum EVI during 2000-2012 and found that in 90.4% of the region, the EVI or vegetation coverage experienced an increasing trend; for the whole region, this value increased by 1.47% per year. This finding is consistent with the results of several previous studies: [PERSON] et al. [42] found that the vegetation coverage in the same region significantly increased by 1.2% per year since the implementation of the GFG program. [PERSON] et al. [43] indicated that from 1999 to 2013, vegetation coverage in the Loess Plateau almost doubled. In the Shaanxi-Gansu-Ningxia region, [PERSON] et al. [44] found that 97.4% of the region experienced an upward trend in vegetation coverage during 2000-2014 and concluded that the vegetation gradually stabilized. By analyzing the NPPs of different types of vegetation, [PERSON] and [PERSON] [45] observed that a change of vegetation was the most important factor affecting biomass change in the Loess Plateau.  
It should be noted that the GPP was estimated using a simple light use efficiency model, and the results were not tested due to lack of available observational data. Compared to data from the limited literature in the similar areas, the estimated GPP seemed reasonable. For instance, the estimated grass GPP in 2008 was 606.6 g C m\({}^{-2}\) yr\({}^{-1}\), which is close to the value of 616.8 g C m\({}^{-2}\) yr\({}^{-1}\) in a similar area reported by [PERSON] et al. [38]. The mean GPP was 571.2 g C m\({}^{-2}\) yr\({}^{-1}\), which is 16.2% lower than the value of 617.5 g C m\({}^{-2}\) yr\({}^{-1}\)[46]. The estimated forest GPP was 1594.8 g C m\({}^{-2}\) yr\({}^{-1}\) in 2003 and 1623.0 g C m\({}^{-2}\) yr\({}^{-1}\) in 2005--11.29% and 8.2% higher than the relevant records of 1433.0 and 1490.0 g C m\({}^{-2}\) yr\({}^{-1}\)[47], respectively.  
Ultimately, this study analyzed the effect of the GFG program on primary plant production, thereby contributing to the understanding of the program's role in promoting restoration of the destroyed vegetation. The results suggest that the program has efficiently improved plant productivity and should continue to be supported by the government.
## 2 A Brief Introduction to B-Spline Theory  
The first step in the design process is curve modeling, which essentially employs the Bezier and B-spline curve methods. The B-spline curve theory is appropriately derived from the Bezier curve theory, thus allowing the extension of Bezier curves to B-spline curves. B-spline curves possess excellent geometric properties, including endpoint interpolation, affine invariance, and a convex hull. Additionally, such approaches are essentially characterized by fast algorithmic execution speed and numerical stability and can be extended to non-rational B-splines (NURBS) and extensively utilized in various computer geometric standards, such as initial graphics exchange specification (IGES) and standard for the exchange of product model data (STEP).
# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
## 2 Materials and Methods
### Data Collection and Chemical Analysis  
For this study, we excavated 15 full-length sections on the site. Soil samples were collected from each established genetic horizon over the entire width of the soil profile [33]. Additionally, 40 soils were sampled from the upper horizon (0-10 cm) to assess the spatial distribution of soil properties. The selected samples were air-dried, ground and carefully passed through a 1 mm sieve. Soil descriptions in the field and further agrochemical analyses were carried out using conventional methods [34; 35; 36]. Under laboratory conditions, SOC content, soil acidity (pH), soluble salts, alkaline hydrolyzed nitrogen (N), available phosphorus (P\({}_{2}\)O\({}_{5}\)) and potassium (K\({}_{2}\)O), exchangeable calcium (Ca\({}^{2+}\)) and magnesium (Mg\({}^{2+}\)) cations and exchangeable sodium (Na\({}^{+}\)) were determined.  
The water-physical properties of soils were measured according to [PERSON] and Karpachevskii guidelines [37]. The heavy metal content was determined in conformity with the methodological guidelines [38]. Soil availability criteria for organic matter, nutrients and micronutrients were assessed by [PERSON] [39]. The index of soil quality and yield was defined by [PERSON] et al. [40].  
Soil categorization and the characteristics of the ground profile and layers were accomplished considering substantive genetic classification [41]. The World Reference Base duplicated the consequent soil type name for Soil Resources [42].
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 2 Study Area and Data Sources
### Data Sources  
This research obtained data on 657 historical landslide points (Figure 1) in the study region up to 2023. The data were acquired from pre-flood investigation reports by relevant departments. Figure 1 illustrates the distribution of landslide points, with the increased prevalence of soil landslides being approximately twice as numerous as rock landslides. Rock landslides are primarily distributed in areas with higher elevations and steeper terrain. The source and distribution criteria of non-landslide points can be found in Section 3.2. Landslide materials comprise gravelly soil and clastic rock, characterised by weak and soft lithology. The primary trigger for landslides in the region is heavy rainfall. Furthermore, considerable topographic relief, fragile geological conditions, and frequent slope cutting activities substantially contribute to landslide predisposition in the area. Accordingly, this study integrates regional characteristics and selects 11 geographic factors closely related to landslide occurrence, such as topography, meteorological conditions, human engineering activities, geological conditions, and land cover, as feature factors. Table 1 presents the feature data sources. Precipitation and NDVI were processed by averaging annual data from 2000 to 2020, ensuring data stability. Moreover, precipitation data were resampled to a 30-metre resolution using the bilinear interpolation method to ensure uniform data processing and mapping and confirming that all factors were analysed at the same scale. The remaining data included single-period datasets. The DEM was processed to derive various secondary feature factors, such as basic topographic features (elevation), the response of topographic orientation to precipitation and solar radiation (aspect), different terrain forms (plan curvature and profile curvature), and the effects of surface water flow and accumulation (terrain wetness index). Geological conditions, such as the stability differences in rock types (lithology) and fault activity (distance to fault), are used to assess their impact on landslides. The cut-slope height data from the 2019 Anhui Geological Environmental Monitoring Station's statistics on road and construction slope cutting heights reflect the influence of road and housing slope cutting on landslides. Figure 2 displays the spatial distribution of each feature variable. Figure 1: Geographical location of the study area and distribution of historical landslide points.
# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
## 4 Implementation
### Future Work  
Future research and development in the field of anomaly detection and localization could be conducted in the following areas.  
Researchers can continue to improve the accuracy and robustness of existing anomaly-detection algorithms by developing more effective feature extraction techniques, incorporating additional context and background knowledge, and addressing class imbalance issues.  
Developing new approaches for anomaly detection: There is a need for the development of new anomaly-detection methods that can handle a variety of data types, such as high-dimensional data or streaming data, and that can be applied to a wide variety of use cases.  
Including domain-specific expertise: Anomalies are frequently easier to detect and localize when domain-specific knowledge is considered. For instance, in a manufacturing environment, understanding the normal operation of a specific machine can help to more precisely identify and locate anomalies.  
Exploiting temporal dependencies: numerous anomalies exhibit temporal dependencies, which means they tend to occur in a particular pattern over time. The accuracy and effectiveness of anomaly-detection algorithms can be improved by incorporating this information.  
Creating robust and effective localization techniques: Anomaly localization is frequently a difficult task, especially when the data are of high dimension or when the anomalies are small or subtle. The development of robust and efficient localization methods that can precisely identify the location and extent of anomalies can be a significant area of future research.  
In many applications, the characteristics of the data and the types of anomalies that can occur can change over time. Important research topics include the development of anomaly-detection algorithms that can adapt to these changes and continue to detect and localize anomalies effectively.
# Overview of an Experimental Program for Development of Yield Surfaces Tracing Method
## 4 Discussion  
General overview of the yield surfaces presented in Figures 5 and 6 confirms the existence of yield surfaces and their evolution under the effect of strain hardening, as well as good functionality of the developed yield surfaces tracing method.  
As can be seen from Figure 6, the initial yield surface of a cold-drawn S235 JR steel is relatively well predicted by the von [PERSON] yield criterion possessing circular shape in (\(\sigma\), \(\surd 3\tau\)) stress space. The initial YS of P250 GH grade shows some initial elongation in the shear direction that could be possibly explained in terms of uncertainties of the actual production history of the semiproduct. As the material microstructure can have crucial impact on ductility of materials [33], a detailed metallographic analysis will be performed to obtain the real grain structure and texture that could be a source of initial anisotropies.  
Subsequent YSs depicted in Figures 5B and 6B,C provide evidence of pronounced kinematic hardening. Pure axial prestrain induced the commonly reported slight sharpening of the forward YS apex, as well as flattening of the rear part [8; 9; 26]. In the case of P250 GH grade, decrease of shear yield points magnitudes indicated a negative cross effect.  
Probably the most important problem encountered throughout the YST method development is the design of an optimal procedure for automated evaluation of elastic moduli, \(E\) and \(G\). For instance, a standard Instron tensile testing software offers almost 10 different methods for moduli calculation. At the same time, precise evaluation of a YP is extremely sensitive to the precision of the fit of the elastic line. An inappropriate inset of the elastic line leads to significant variation of the point where the stress-strain curve starts to deviate from linearity, and, consequently, results in either premature or delayed fulfilment of the YP definition, (22). This may explain the non-convexities recorded within subsequent YSs depicted in Figures 5 and 6. Therefore, the next task for the YST method development is the optimization of elastic moduli calculation procedure, i.e., a design for calculation of \(E\) and \(G\) that is sufficiently high quality and repeatable. This is particularly important for probes characterized by significant superiority of one of the stress components (either the axial stress, or the shear stress) over the other. Fulfilment of this task could eventually lead to an opportunity to improve precision, to decrease the yield point threshold under the current value of 200 \(\mu\)e equivalent plastic strain, and to mitigate the \"probing effect\".
## 2 Complete System Formation
### PV Generator  
In this research work, a two-diode PV cell model is considered (Figure 2) because it is a simple and accurate model compared to the single-diode PV cell. By means of photoelectric effect, the conversion of solar energy to electricity takes place and output power can be enhanced by connecting numerous solar cells in shunt or series as required. Both diodes are employed to represent polarization occurrence with current source exhibiting sun insolation, followed by power loss delivered by resistances (series/Parallel) used. The prognosis of the overall system is calculated based on accurate equivalent modeling. The output of the PV current is expressed mathematically as [26]:  
\[I_{PVo}=I_{PVG}-I_{RSC}\big{(}I^{\prime}+2\big{)}-\bigg{(}\frac{V_{PVo}+I_{PVo} \times R_{series}}{R_{parallel}}\bigg{)} \tag{1}\]  
where,  
\[I^{\prime}=exp\bigg{(}\frac{V_{PVo}+I_{PVo}\times R_{series}}{V_{Thermal}} \bigg{)}+exp\bigg{(}\frac{V_{PVo}+I_{PVo}\times R_{series}}{A\times V_{Thermal}} \bigg{)} \tag{2}\]  
\(I_{PVG}\) = Photo Current  
\(I_{RSC}\) = Diode reverse saturation current  
\(I_{PVo}\) = Output PV current  
\(V_{PVo}\) = PV output voltage  
\(R_{series}\) = Resistance in series  
Figure 1: BLDC-driven Photovoltaic Complete System Formation.  
\(R_{Parallel}\) = Resistance in parallel  
\(V_{Thermal}\) = PV module thermal voltage  
\(A\) = Ideality constant of diode