esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 2 Metal Nanoparticle Synthesis
### Green Methods  
Synthesis can be performed in one step by using biological organisms such as bacteria, molds, algae and plants. The nanoparticle synthesis is performed by reduction using molecules in plants and microorganisms such as proteins and enzymes. The green synthesis of metal nanoparticles has the advantage of using more eco-friendly materials, and tends to be cheaper than chemical methods. The above mentioned plants are used as a reducing agent to transform the metallic silver precursor into metallic nanoparticles. They also represent a renewable source of bioactive molecules in obtaining metallic nanoparticles. The method has the ability to minimize the resulting toxicity and it has a small impact on the environment [7].  
A recent study [8] used find extract of Garcinia _mangostana_ was used to obtain silver nano particles. The authors separated the rind, washed to remove impurities and then dried for 10 days at room temperature. The dried rind was then blended and sieved to obtain particles of uniform size. 1.5 g of the obtained particles were suspended in 50 mL of distilled water at 50-70 \({}^{\circ}\)C for 15 min.  
One of the challenges that arise with using the green method is to control the nanoparticles' shape and size. Temperature is thought of having the biggest impact on the material synthesis [8]. The authors discovered that at lower temperatures, nanoparticles were not produced. However, by increasing the temperature up to 80 \({}^{\circ}\)C, the authors observed the formation of metallic nanoparticles. Moreover, when the temperature is around 100 \({}^{\circ}\)C, the reaction rate is very high and prevents the particles from growing. The shape of nanoparticles is influenced by temperature, starting with small spherical particles at lower temperature and nanorods and platelet nanoparticles at higher temperatures. Reaction time is another variable that should be considered when performing the synthesis. [PERSON] et al [8] noted that silver and platinum nanoparticles have formed within 10 min while gold nanoparticles appeared seconds after adding the rind extract. Another study investigated the effect of pH on nanoparticles formation. The authors discovered that at a pH of 5, the synthesis of silver nanoparticles is low-rate process, while at a pH of 9, the production is intense [9].
# Review of Photogrammetric and Lidar Applications of UAV
## 2 History of UAV  
According to the literature [16], UAV remote sensing applications and techniques have come a long way and are based on almost 150 years of unmanned remote sensing with different types of sensors and platforms. Kites, balloons, airships, paragliders, model airplanes, and helicopters were the most common platforms used before modern aircraft. The first experiments with cameras attached to kites and balloons were carried out in the mid-19 th century by the French engineer Colonel [PERSON], who was later considered the father of photogrammetry. More successful attempts at unmanned aerial photography using balloons were made in the 1860s and 1980s in America, Germany, and France. Until the beginning of the 21 st century, the kite was the most widespread unmanned platform for obtaining images from low altitudes. The successful use of the kite as a platform for scientific measurements is documented in the publications of British and American meteorologists [17,18]. After the invention of powered airplanes by the [PERSON] brothers at the turn of the 19 th and 20 th centuries, the role of kites and balloons declined as more aerial photographs were taken from planes. Technical developments in the field of cameras, film, and photo analysis were rapidly developed during the First World War and again during the Second World War due to the need for military surveying and accurate cartographic measurement and mapping. After World War II, photographic equipment and aerial photographs became available in non-military fields and scientific applications. The development of satellite remote sensing during the Cold War pushed Earth observation into new areas again, with unmanned platforms at orbital heights outside airspace. Later, digital scanning sensors provide a new type of imagery. However, the processing of satellite images was very demanding and was carried out by trained specialists with access to specialized and expensive hardware and software. Therefore, at the same time, in the 1970s and 1980s, in addition to satellite remote sensing, low-altitude aerial photography using a platform and sensors with cheap and accessible technology made significant progress, which formed the basis for the discipline of UAV remote sensing that we know today.
## 7 Pure Conditions
### Bracket Ring  
To present the algebra of special conditions we will use a subset of the Grassmann-Cayley algebra--the _bracket ring_ developed explicitly by Neil White [85,86]. This is the classical language of projective geometric invariants, which is the most suitable for efficient expression and manipulation of the determinants of the rigidity matrices. This language has been employed in the projective theory of frameworks [28,29,58,85] and will be embedded in much of our geometric analysis throughout this paper.  
Informally, the key insight is that the pattern of the bracket of \(d+1\) points in projective \(d\)-space \(\mathbb{P}^{d}\), \([a_{0},a_{1},\dots,a_{d}]=[a_{0}a_{1}\ \dots\ a_{d}]\) represents the pattern of the determinant of a \((d+1)\times(d+1)\) matrix of the projective coordinates of \(a_{0},a_{1},\dots,a_{d}\) in \(\mathbb{P}^{d}\), and their products. Geometrically, the bracket \([a_{0},a_{1},\dots,a_{d}]\) represents the normalised volume of the \(d\)-simplex with \(d+1\) vertices \(a_{0},a_{1},\dots,a_{d}\), a volume which is equivalent to a \((d+1)\times(d+1)\) determinant using the affine coordinates of the points as rows of a square matrix.  
Formally, working with variable points, \(a_{0},a_{1},\dots,a_{d}\), an element of the bracket ring \(B\) is a _bracket_\([a_{0},a_{1},\dots,a_{d}]\) with entries as variables. The bracket ring is formed by all such brackets, their (commutative) products and finite sums. All sums and products are homogenous in the degree of the brackets, with real coefficients. The brackets satisfy the following very well-known relations of determinants, called _syzygies_.  
1. _Antisymmetry:_\([x_{0},x_{1},\dots,x_{j},\dots,x_{i},\dots,x_{d}]=-[x_{0},x_{1},\dots,x_{j}, \dots,x_{i},\dots,x_{d}]\) for \(j>i\). Applied repeatedly, we have \[[x_{0},x_{1},\dots,x_{d}]=sign(\sigma)[x_{\sigma(0)},x_{\sigma(1)},\dots,x_{ \sigma(d)}]\] for any permutation \(\sigma\) of \(\{0,1,\dots,d\}\). When we add the requirement that the brackets are linear in the entries, then \([x_{0},x_{1},\dots,x_{d}]=0\) if the vectors are projectively dependent.  
2 Basis Exchange:  
\[[x_{0},x_{1},\ldots,x_{d}][y_{0},y_{1},\ldots,y_{d}]=\sum_{i=0}^{d}[y_{i},x_{1}, \ldots,x_{d}][y_{0},y_{1},\ldots,y_{i-1},x_{0},y_{i+1},\ldots,y_{d}].\]  
The flavour of basis exchange is that if \(\{y_{0},y_{1},\ldots,y_{d}\}\) is a standard basis, then this is the Laplace decomposition of the determinant with \([y_{i},x_{1},\ldots,x_{d}]\) as the \(i\)-th minor and \([y_{0},y_{1},\ldots,y_{i-1},x_{0},y_{i+1},\ldots,y_{d}]\) as the \(i\)-th coordinate of \(x_{0}\). (Note that for \(i=0\), the first term of the sum on the right hand side is \([y_{0},x_{1},\ldots,x_{d}][x_{0},y_{1},\ldots,y_{d}]\).)  
The commutative ring \(B\), with these syzygies imposed, is clearly an integral domain. We observe that the generic bracket ring \(B\) is a unique factorization domain [85]. We can _evaluate_ a bracket polynomial at a realization \(p\in\mathbb{P}\) by substituting the coordinates for the variable points and computing the bracket as a determinant.
## 1 Introduction
### Multifunctional Metasurfaces Exhibiting Similar Functionalities  
Historically, multifunctional metasurfaces designed by single-structure meta-atoms were first realized in reflection geometry, because high-efficiency reflective meta-atoms are much easier to find than their transmissive counterparts. A commonly-used reflective meta-atom is the so-called metal-insulator-metal (MIM) structure, which consists of a metallic planar resonator and a continuous metal sheet separated by a dielectric spacer (see Figure 3a). Such an MIM structure was widely used to design high-efficiency reflective metasurfaces at frequencies ranging from microwave to visible [22; 23; 44; 72; 79; 91], simply because its metallic ground plane can help reflect all incoming waves back (thus, Equation (1) can be easily satisfied) while the phase of reflected wave can undergo a continuous \(-180^{\circ}\) to \(180^{\circ}\) variation as frequency passes through a resonance inside the \"meta-atom\". Such a resonance is usually called \"magnetic resonance\" [14] or \"gap-plasmon resonance\" [22; 72], and its properties are carefully analyzed in [91; 92]. Therefore, tuning the geometrical parameters of such a meta-atom can efficiently modify its two reflection phases (\(\varphi_{xx}\) and \(\varphi_{yy}\)) (see Figure 3b), which then offers enough freedoms to design multifunctional meta-devices.  
Figure 3d depicts a bifunctional meta-device working at telecom wavelengths, which can excite surface plasmon polaritons (SPPs) to flow along two orthogonal directions when the device is shined by external light with polarizations along \(x\) or \(y\) direction [72]. The fabricated device consists of 3 \(\times\) 3 MIM meta-atoms, with geometrical parameters carefully adjusted such that both \(\varphi_{xx}(x,y)\) and \(\varphi_{yy}(x,y)\) satisfy Equation (6) with \(\xi>k_{0}\). Experimental characterizations reveal that the fabricated device can indeed exhibit the desired bi-functionalities (see Figure 3a). Based the same concept, other bi-functional meta-devices were realized based on MIM meta-atoms with patches replaced by other type of planar resonators, to achieve the polarization-dependent anomalous reflection (beam splitting) (Figure 3c) [79] and polarization-controlled dual-image hologram (Figure 3e) [59]. Two common features of these meta-devices are worth mentioning: the realized functionalities are identical  
Figure 2: Schematics of a bi-functional metasurface (blue) with metal ground plane (yellow) which can achieve (**a**) focusing functionality for \(y\)-polarized incident light and (**b**) anomalous-reflection functionality for \(x\)-polarized incident light. The phase distributions of the metasurface are depicted in (**c**) for \(y\)-polarization excitation and in (**d**) for \(x\)-polarization excitation. Reproduced from [90] with permission.  
or similar, and the working efficiencies are not very high, partially caused by the metallic losses at optical frequencies.
# Optimal F-Number of Ritchey-Chretien Telescope Based on Tolerance Analysis of Mirror Components
###### Abstract  
The Ritchey-Chretien telescope has been the key optical module for remote sensing instruments (RSI), in which the root mean square (RMS) random surface wavefront error and the alignment error of the primary and the secondary mirror takes the highest weighting in the tolerance analysis for the fabrication and assembly of the telescope. Therefore, the higher tolerance of those items becomes preferable for higher efficiency of RSI manufacturing. In this paper, the correlation between those tolerance items and the f-number of the telescope has been investigated. Although the f-number is normally a system parameter well specified in the beginning of the design process, it is not very rigid in practice and has a certain amount of allowable range. The optimal f-number can then be chosen based on the consideration of those key tolerance items. The proposed concept can be generalized as a novel methodology of design for tolerance.  
remote sensing instrument; Ritchey-Chretien telescope; tolerance allowance; RMS random surface wavefront error; surface de-center error; surface tilt error; error budget; MTF degradation Article
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
###### Abstract  
Node position information is critical in wireless sensor networks (WSN). However, existing positioning algorithms commonly have the issue of low positioning accuracy due to noise interferences in communication. Hence, proposed in this paper is an iterative positioning algorithm based on distance correction to improve the positioning accuracy of target nodes in WSNs, with contributions including (1) a log-distance distribution model of received signal strength indication (RSSI) ranging which is built and from which is derived a noise impact factor based on the model, (2) the initial position coordinates of the target node obtained using a triangle centroid localization algorithm, via which the distance deviation coefficient under the influence of noise is calculated, and (3) the ratio of the distance measured by the log-distance distribution model to the median distance deviation coefficient which is taken as the new distance between the target node and the anchor node. Based on the new distance, the triangular centroid positioning algorithm is applied to calculate the coordinates of the target node, after which the iterative positioning model is constructed and the distance deviation coefficient updated repeatedly to update the positioning result until the criteria of iterations are reached. Experiment results show that the proposed iterative positioning algorithm is promising and effectively improves positioning accuracy.  
iterative positioning algorithm; distance correction; RSSI; noise impact factor; distance deviation coefficient +
Footnote †: journal: Sensors
## 3 Short-Reach Systems Classification and Enabling Technologies  
This section discusses the short-reach systems with the main focus on the DCNs. The related short-reach system features such as classifications, potential modulation formats, advanced optoelectronic devices, requirements, and challenges that necessitate novel transceiver architectures and DSP techniques are comprehensively analyzed.
## 5 Experimental Results
Herein, the sensitivity of the MEMS calorimetric sensor is defined as the slope of the linear fit between two calibrated data points. Table 2 shows the comparison between our MEMS calorimetric sensor and the other reported CMOS thermal flow sensors. Our MEMS calorimetric sensor gains a prominent normalized sensitivity S of 117 mV/(m/s)/mW with respect to the input heating power, which shows a 21 \(\times\) higher sensitivity as compared to others work [9; 36; 37; 38; 39; 40]. The calorimetric sensor in [41] has a high sensitivity of 103 mV/(m/s)/mW. Compared to the sensor of [41], our MEMS calorimetric sensor reaches a higher sensitivity, higher bandwidth, and lower silicon area. Moreover, our meas  
Figure 10: Experimental setup for the micro-electro-mechanical systems (MEMS) calorimetric sensor testing in the wind tunnel with the reference flow velocity read by Pitot tube. Figure 9: Square-wave time response of the CTA circuit. sensor has a higher bandwidth of 60 kHz compared to all other reported thermal flow sensors. Therefore, our MEMS calorimetric sensor, with the merits of a robust design, high sensitivity, low silicon area, and wide dynamic range, can be deployed as a promising sensing node for direct wall shear stress measurement.
# Review of Ka-Band Power Amplifier
## 3 Topologies
### Power Combining
#### 3.4.2 Balanced Amplifier  
The balanced amplifier (BA) consists of two similar single-ended amplifiers in parallel and two 90\({}^{\circ}\) couplers as shown in Figure 17. The input signal is divided into two signals with the same amplitude and 90\({}^{\circ}\) phase difference through the input 90\({}^{\circ}\) coupler. These two signal components are amplified by PA1 and PA2, respectively. The amplified signal is then recombined through the reverse 90\({}^{\circ}\) phase of the 90\({}^{\circ}\) output coupler to obtain the final output power.  
Figure 16: (**a**) The 2-stage PA in 100 nm GaAs HEMT [80]. (**b**) Combining PA with IBVW in 100 nm GaAs pHEMT [82].  
Figure 17: Balanced amplifier circuit schematic diagram.  
Compared with single-ended amplifier, the balanced amplifier will consume higher DC power and occupy a larger chip area, but it cannot be ignored that it can provide higher output power and stability. After one branch of the balance amplifier is damaged, the other branch can work without stopping the amplifier.  
In [83], [PERSON] proposed a 2-stage architecture amplifier, in which the power stage and the driver stage were both balanced amplifiers combined in parallel with an eight-way Wilkinson combiner (see Figure 18). Fabricated in 150 nm GaAs HEMT, the PA chip achieves peak PAE and P\({}_{\text{sat}}\) of 28% and 35.4 dBm, respectively.  
The following year, in 1998, [PERSON] presented a two-stage balanced power amplifier using a 150 nm GaAs PHEMT process operating from 27.5 to 29.5 GHz for LMDS [84]. The measured results demonstrate P\({}_{\text{sat}}\) and PAE are 30 dBm and 43%, respectively, at a lower operating voltage.  
In 2005, [PERSON] used three cascaded common-emitter (CE) Class-A balanced amplifier stages and a lumped elements matching network to achieve 18 dB gain and 12 dBm P\({}_{\text{sat}}\) at 24 GHz [85].  
[PERSON] proposed two amplifiers in [86]. First, a single-ended amplifier was designed and named MMIC2, and then MMIC1 was realized by balancing two MMIC2 devices with improved Lange coupler impedance and bond-wire inductance (see Figure 19). At a similar frequency, the MMIC1/MMIC2 attained output power 39.4/37 dBm and 26/30% PAE with gain of 28.5/29 dB. The output power of MMIC1 is nearly double that of MMIC2, but the PAE is reduced. Ref. [87] adopted the same design method and combined two third-order PA to achieve 37.1 dBm P\({}_{\text{sat}}\) and 33% PAE in an area of 9.9 mm\({}^{2}\).  
Figure 19: (**a**) Balanced PA, (**b**) single-ended PA in 150 nm GaN HEMT [86].  
Figure 18: The 2-stage balanced PA in 150 nm GaAs HEMT [83].
# Influence of Fiber Mixing Process on the Cracking Resistance of Cold Recycled Asphal Mixture
## 4 Results and Discussions
### Resistance to Freeze-Thaw Damage  
Given that there was a good correlation of the results between SCB tests and ID tests, only the ID test was carried out to study the influence of fiber mixing process on the freeze-thaw damage performance of CRAM. At the same time, to reduce the experimental amount, only the samples of three processes (process A, process C and process D) were tested. Based on the results of freeze-thaw cycle test, the relationship between splitting strength and freeze-thaw cycles of fiber CRAM is illustrated in Figure 12, and the relationship between fracture work and freeze-thaw cycles of fiber CRAM is shown in Figure 13. It can be seen that with the increase of freeze-thaw cycles, the splitting strength and fracture work of fiber CRAM decrease. The fiber mixing process has a significance effect on the reduction rate of splitting strength and fracture work, especially for the specimens under the water saturation condition. After 20 freeze-thaw cycles under completely dry test conditions, the splitting strength of CRAM prepared by process A decreased by 21.6%, while that of fiber CRAM prepared by process C and process D decreased by 9.47% and 20.45%, respectively. Similarly, after 20 freeze-thaw cycles under water saturation condition, the splitting strength of CRAM prepared by process A decreased by 57.52%, while that of fiber CRAM prepared by process C and process D decreased by 34.91% and 39.39%, respectively. Therefore, from the analysis of splitting strength, the fiber CRAM made by process C has the smallest reduction rate, which indicates that the best fiber mixing process is process C.  
Figure 11: The correlation between fracture energy and fracture work.  
Figure 12: Relationship between splitting strength and freeze–thaw cycles: (**a**) completely dry condition; (**b**) water saturation condition.  
On the other hand, from the analysis of fracture work in Figure 13, after 20 freeze-thaw cycles under completely dry test conditions, the fracture work of CRAM prepared by process A decreased by 24.73%, while that of fiber CRAM prepared by process C and process D decreased by 20.79% and 22.04%, respectively. Similarly, after 20 freeze-thaw cycles under water saturation condition, the fracture work of CRAM prepared by process A decreased by 56.99%, while that of fiber CRAM prepared by process C and process D decreased by 37.55% and 43.87%, respectively. The fiber mixture made by process C also has the smallest reduction rate of fracture work, while the CRAM made by process A has the largest reduction rate of fracture work. This is agreement with the analysis result of splitting strength. The greater the reduction rate of fracture work, the worse the freeze-thaw damage resistance of the fiber CRAM. Therefore, the fiber CRAM made by process C has the best resistance to freezing and thawing damage. This indicates that the fiber indeed improves the performance of CRAM against freeze-thaw damage and the proper fiber mixing process further enhances the effect of fiber modification.
## 2 Materials and Methods
### Data Acquisition and QCL System  
The background spectrum of a KBr substrate was obtained before measuring the QCL spectra of the samples. Providing the lack of MIR signals from the employed solid matrix, this background spectrum provided an excellent and smooth reference trace. The first samples were then placed in the wells of metal holders (1.1 cm in diameter and 3 mm deep). Duplicate spectra were collected at 10 different locations on the sample surfaces, resulting in a total of 20 spectra per sample. Sixteen ranges were used for calibration and internal validation or cross-validation (CV), while the remaining four spectra were used for testing or external validation. This process was repeated for each concentration. The spectra were obtained in the reflectance mode at a distance of approximately 15 cm using a LaserScan(tm) MIR pre-dispersive spectrometer (Block Engineering, Marlborough, MA, USA) equipped with three tunable MIR lasers diodes having a tuning range from 990 to 1111 cm\({}^{-1}\), 1111 to 1178 cm\({}^{-1}\), and 1178 to 1600 cm\({}^{-1}\) The spectral linewidth was \(<2\) cm\({}^{-1}\) and the scan time was approximately 1.5 s for each of the diodes. The average power typically varied between 0.5 and 10 mW across the entire tuning range of \(\approx 600\) cm\({}^{-1}\) with 100:1 Transverse Electromagnetic Mode (TEM\({}_{00}\)) polarization and a beam divergence of \(<2.5\) mrad in the x-axis and \(<5\) mrad in the y-axis. The spectrometer had a 7.6-cm diameter ZnSe lens, which was used to focus the MIR beam to collect the reflected light and focus the light onto a thermoelectrically cooled mercury-cadmium-telluride (MCT) detector. The wavelength accuracy and precision were 0.5 and 0.2 cm\({}^{-1}\), respectively. The spectroscopic system worked best at a distance to the target of 15 \(\pm\) 3 cm, with each laser producing an elliptical spot with diameters of 4 and 2 mm in the same space at a distance of 15 cm due to the difference of beam divergence in the axes ([PERSON] et al. [30]).