esa-sceva/satcom-chunk-collection huggingface dataset:
# From Far-Field to Near-Field Micro- and Nanoparticle Optical Trapping
## 1 Introduction  
Imagine reducing the size of our fingers by one million times and being able to use them to probe the nanoworld. Now, it is easy to imagine that we could easily capture things of a similar size, such as dielectric nanoparticles, quantum dots with a DNA strand attached to them, proteins and viruses. More than that, we could have the ability to move them in space. In reality, although we cannot modify our fingers in this way, we have found a way to manipulate objects of that size using light!  
For more than four centuries, it has been known that light can exert forces on objects [1]. Much later, in 1873, using [PERSON]'s electromagnetic theory [2], the transfer of momentum from light to illuminated objects was described, resulting in the so-called radiation pressure that leads to objects moving along the direction of light propagation [3]. There were many experiments to follow that confirmed [PERSON]'s calculations, but all of them concluded with the fact that these optical forces were so small that it was difficult even to measure them, let alone utilise them in some meaningful application. In 1936, [PERSON] experimentally demonstrated the transfer of angular momentum from light to a crystal plate and studied the change of polarisation of the beam due to the interaction with matter [4]. However, the birth of lasers in 1960 [5, 6] was really what opened new possibilities and topics for research in the field of light-matter interactions.  
As the use of lasers became more and more popular in science exploration, [PERSON], in 1970, experimentally demonstrated how optical radiation forces exerted by lasers can be used to change the motion of dielectric microparticles. He succeeded in trapping them by creating a stable optical potential well [7], thus establishing the new research topic that is known today as optical tweezers.  
As always, nature follows its own rules, and soon the primary challenge for optical tweezers became apparent, i.e., the diffraction limit. It seemed to be impossible to focus light beyond the constraints imposed by this limit and, consequently, this created a restriction on the smallest size of particle that could be trapped. Subsequently, the next step in the field's progress was to use surface plasmons excited on metallic nanostructures to confine light into highly intense optical fields, thus enabling superior trapping performance [8]. The first experimental demonstration of trapping using plasmonic structures was reported by [PERSON] et al. [9] in 2007 and, since then, the field of plasmonic optical tweezers started developing rapidly and opened further scientific avenues for investigation. Numerous implementations arose from the research on optical forces and plasmonics and are discussed in further detail elsewhere [10; 11]
# Low Profile Monopole Meander Line Antenna for WLAN Applications
## 4 Parametric Study
### Middle Meander Line Length  
Figure 5 indicates the impact of the middle meander line (ML) length, exhibiting the changing length outcomes in various reflection coefficients and a slight shift in frequency. It is clearly illustrated that when the meander line is at its optimal length, L1 olv = 15.95 mm; the reflection coefficient is \(-22.16\) dB at the center frequency of 2.43 GHz.  
On the other hand, the reflection coefficient is \(-23.72\) dB at the center frequency of 2.44 GHz when the length of the meander line is cut in half and becomes L1 dlv = 7.98 mm, while increasing the ML length to L1 ilv = 20 mm results in a reflection coefficient of \(-17.48\) dB at the center frequency of 2.41 GHz. Finally, after removing ML, L1\({}_{\text{no line}}\) = 0 mm, the reflection coefficient is \(-15.62\) dB at the 2.38 GHz center frequency. We can clearly see that the change in the middle ML length does not affect the bandwid  
Figure 4: Upper meander line length effect, the sketches on the most\(-\)right side in Figure 4 show the geometries of the meander line changes.  
Figure 5: Middle meander line length effect: the sketches on the most-right side in Figure 5 show the geometries of the meander line changes.  
frequency range, as we can see in the figure below, where the frequency range has been shifted. The figure below shows the geometry of the middle ML length.
## 3 Results
### Optical Properties  
The optical properties of Co-Si LDHs before and after the inclusion of zirconium and titanium were investigated by diffuse reflectance spectroscopy in the range of 200-700 nm. In case of Co-Si LDHs, it is clear that the spectrum is dominated by absorption in the UV region with its maximum at 230 nm, as shown in Figure 11. With the inclusion of zirconium in Co-Si LDHs, there is a shift of absorption toward the visible region. It showed a triplet of bands at 250 nm, 350 nm, and 550 nm. In the same trend, the insertion of titanium and zirconium in the Co-Si LDH structures caused a little shift of the maximum bands from 250 nm and 350 nm to 275 nm and 375 nm, respectively.  
Figure 10: Schematic representation for the plates of Co–Si LDHs after zirconium and titanium inclusion.  
Figure 9: Schematic representation for the nanofibers and nanoneedles of Co–Si LDHs.  
The process of light reflectance by the prepared materials is related to their electronic structures. Therefore, the diffuse reflectance results have been used for calculating the band gap energy. The optical band gap energy was determined by a Tauc equation [34]. The Kubelka-Munk relation is used to convert the diffuse reflectance into an absorption coefficient [35]. The absorption coefficient (Abs) = \((1-\mathrm{R})^{2}/2\mathrm{R}\), where R is the reflectance. Accordingly, the Tauc relation becomes (Abs.E) = A(E = \(\mathrm{E}-\mathrm{E}_{\mathrm{g}})^{1/2}\). It expresses the direct transition, thus showing direct band gap energy (E\({}_{\mathrm{g}}\)). The relation of both (Abs.E)\({}^{2}\) and (E) for the prepared materials is displayed in Figure 12. The direct band gap values were determined by extrapolating the linear regions of these plots to (Abs.E)\({}^{2}\) = 0. The band gap energy of Co-Si LDHs was 4.4 eV. A sharp reduction of the band gap was observed with the inclusion of zirconium, where the band gap of Co-Si LDHs became 2.95 eV. The reduction of the band gap continued with the insertion of titanium in the LDH structure giving 2.8 eV.  
It is clear that the optical properties of Co-Si LDHs were improved by introducing zirconium and titanium into its nanolayers. This improvement was observed as a significant red shift in the absorption edge of Co-Si LDHs toward the visible light region. This shift was confirmed by a reduction in the band gap energy of Co-Si LDHs. This behavior could be explained by inducing a new energy level into the main band gap because of the new bands of Co(II)-O-Zr(IV) and Co(II)-O-Ti(IV) that were introduced in the LDHs structure. These new bands could change the band gap energy and improve the visible light absorption properties of the LDHs [36]. These results indicate the effective electronic coupling between Co(II)-O-Si(IV) and Co(II)-O-Zr(IV) in addition to Co(II)-O-Si(IV) and Co(II)-O-Ti(IV) in the LDH structures.  
Figure 11: Absorbance of Co-Si LDHs: (**a**) pure, (**b**) after zirconium inclusion, and (**c**) after zirconium and titanium inclusion.  
Figure 12: Band gap energy of Co–Si LDHs: (**a**) pure, (**b**) after zirconium inclusion, and (**c**) after zirconium and titanium inclusion.
# Comparative Analysis of Kazakhstan and European Approaches for the Design of Shallow Foundations
###### Abstract  
The design of shallow foundations is performed in accordance with different building regulations depending on geotechnical and geological conditions. This paper involves the design calculations applying Kazakhstan and European approaches. The design of shallow foundations in Nur-Sultan city in Kazakhstan was implemented by the calculation of bearing capacity and elastic settlement in accordance with the design procedures provided in SP RK 5.01-102-2013: Foundations of buildings and structures, and Eurocode 7: Geotechnical design. The calculated results of bearing capacity and elastic settlement for two types of shallow foundations, such as pad foundation and strip foundation, adhering to Kazakhstan and European approaches are relatively comparable. However, the European approach provided higher values of bearing capacity and elastic settlement for the designed shallow foundation compared to the Kazakhstan approach. The difference in the results is explained by the application of different values of partial factors of safety for the determination of bearing capacity and different methods for the calculation of the elastic settlement of shallow foundations (i.e., elasticity theory and layer summation method).  
Keywords:comparative analysis; shallow foundation design; bearing capacity; elastic settlement; Eurocode 7; SP RK; SNiP Article
## 2 Design Procedure
Another important parameter to be chosen is the size of the element (\(D1\times D2\)), which will have to be defined depending on if the element will be used in an array or  
Figure 1: Geometry of the single dual-polarized patch antenna: (**a**) side view; (**b**) exploded view; (**c**) top view of the ground plane with slots and terminal part of the feeding lines; (**d**) Enlargement relative to H polarization. as a single antenna, or if beam steering is required. An important role for a wideband high-isolation ACMA antenna is fulfilled by the slots. Requiring them to be resonant without a large footprint, we have chosen them with the shape of dog-bones and with a reciprocal position between the two polarizations (\(V_{offset}\) and \(H_{offset}\)), as shown in the Figure 1c. Evidently, the X-pol of the V polarization may suffer from a slight deterioration due to the loss of symmetry of the field with respect to the axis of the patch. The optimization procedure can be schematized as shown in the flow chart of Figure 2 and described with the following steps:  
1. Choice of substrates (e.g., Rogers), heights (\(h_{1}\), \(h_{4}\) and \(h_{5}\)), and the ground-plane dimensions \(D1\) and \(D2\): Fix the value of the slot widths (\(W_{5}^{H,V}\)) and the size of the head (\(TL_{5}^{H,V}\)) and foot (\(T_{S}^{H,V}\)) of the dog-bone. Fix the width \(W_{1}^{H,V}\) of the first feeding line to 50 ohms. Fix the initial lengths of the slot (\(L_{5}^{H,V}\)) and patch sizes (\(WA_{1}\), \(WA_{2}\), \(LA_{1}\) and \(LA_{2}\)) all close to a quarter wavelength at center frequency;
2. Optimization of the reflection coefficient of H polarization for a 40% bandwidth: The optimized parameters are the lengths of the slot (\(L_{5}^{H}\)) and the dimension (width and length) of the second (\(W_{2}^{H}\) and \(L_{2}^{H}\)) and third feed lines (\(W_{3}^{H}\) and \(L_{3}^{H}\)) for H polarization. The size of the two patches (\(LA_{1}\) and \(LA_{2}\)) transverse to the H-polarization slot and the two honeycomb heights (\(h_{2}\) and \(h_{3}\)) are also optimized:
3. Once the optimization of H-polarization is finished, we deal with the optimization for V-polarization. The values obtained during the previous optimization are used for the heights (\(h_{1}\) and \(h_{2}\)) of the two honeycomb layers. The new optimized parameters are: the V polarization length slot (\(L_{5}^{V}\)), the feed line (\(W_{2}^{V}\), \(W_{3}^{V}\), \(L_{2}^{V}\) and \(L_{3}^{V}\)), and patch dimensions (\(WA_{1}\) and \(WA_{2}\)) (transverse to the V-polarization slot).
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
###### Abstract  
Dry-wet cycle conditions have significant effects on the corrosion of concrete under sulfate attack. However, previous studies have only applied them as a method for accelerating sulfate attack and not systematically studied them as an object. In order to explore the impact of sulfate attack with different dry-wet cycle periods on concrete, in this study, four dry-wet cycle periods (3, 7, 14, and 21 days) were selected. The flexure strength, relative dynamic modulus, and mass were tested, and the microstructures of the eroded specimens were also analyzed. The intensity and depth of sulfate erosion were influenced by the wet-dry cycle period. The results show that the deterioration of concrete first increased and then decreased with an extension of the dry-wet cycle period. Microstructural analysis indicated that, with an increase in the dry-wet cycle period, the corrosion depth of sulfate attack increased. Moreover, the erosion products such as ettrinigite and gypsum were greatly increased, in agreement with the macroscopic variations. However, excessively prolonging the dry-wet periods does not significantly further the deterioration of concrete's performance. Therefore, considering the strength and depth of corrosion caused by sulfate attack, it would be appropriate to employ dry-wet cycle periods of 7-14 days under natural dry conditions in studies on concrete.  
sulfate attack; dry-wet cycle period; concrete; erosion intensity +
Footnote †: journal: Applied Sciences
## 4 Limitations and Practical Applications  
### Limitations  
This research, while innovative, has several limitations that future studies need to address. First, the data variability and completeness issues stem primarily from the datasets gathered through controlled experiments, which may not capture the full variability and complexities of real-world agricultural settings. Future research should incorporate more diverse, field-based datasets across varying climatic zones, soil types, and farming practices to improve generalizability. This would help validate the models under real-world conditions and enhance their robustness. Additionally, the high rate of missing data and the exclusion of certain variables could impact the robustness and generalizability of the findings. Second, the ML models, despite showing promising results, carry a potential risk of overfitting, particularly noted in models like XGB and Gradient Boosting when applied to unseen data. Enhancing the models' generalization capabilities to various agricultural environments remains critical for future research. Third, the experiments were conducted on a relatively small scale, and scaling these findings to more extensive commercial agricultural operations could introduce new challenges, such as logistical, economic, and climatic variations that were not accounted for in this study. Lastly, the effectiveness of FW-derived fertilizers is heavily dependent on the specific pre-treatment processes used, and different treatments may yield varying effectiveness across different soil types and crop species, which this study did not fully explore.
### Methods for Accuracy Assessment  
To assess the accuracy of the proposed algorithm, confusion matrices were derived using the synthetic validation datasets. Each tree pair \((s,d)\) (assigned by the algorithm) was classified as either _Matcling_ or _Not Matching_ (predicted class). The actual class was set to _Matching_, if \(s\) actually corresponds to \(d\), but _Not Matching_ otherwise. A co-registration result was classified by the algorithm as either _Co-Registration successful_ or _Co-Registration failed_ (predicted class). The actual class was set to _Co-Registration successful_, if at least 50% of the tree assigned tree pairs actually match, and _Co-Registration failed_ otherwise.  
If the probabilities predicted by the algorithm (Sections 3.6.3 and 3.6.4) are in-fact probabilities, we would expect a one-to-one correlation to observed probabilities. To test this, the predicted probabilities for tree matching and co-registration were derived by applying the algorithm using all synthetic validation datasets. Then, equidistant intervals were defined to calculate the average predicted probability and the average observed probability (correct classifications per number of classifications) within each interval. Finally, a regression line was fitted for both tree matching and co-registration.
# Experimental Study on the Surface Properties of Nanoalumina-Filled Epoxy Resin Nanocomposites
## 2 Materials and Experimental Procedures
### Experimental Procedures  
CAs on EPNCs samples (without saturation) were measured with a contact angle tester using the sessile drop technique (DSA100 drop shape analyzer, Kruss GmbH [36]). Figure 1 illustrates the CA, \(\theta\), of an ideal (smooth) surface [37]. In this study, the surface of the EPNCs samples where not polished and the apparent (measured) CA, \(\theta\)', on a rough surface was obtained. The measurements were performed at room temperature and on dry samples. The probe liquid consisted of ultrapure water in a dosing syringe of 500 \(\mu\)L with a needle 1.81 mm diameter. The drop volume ranged from 1 to 50 \(\mu\)L on the solid surfaces of EPNC samples. The results were based on the average values of at least five replicates.  
Topographic images of the EPNCs surfaces were obtained from AFM by using a Nanosurf FlexAFM System (Flex-Axiom). EPNC samples were cleaned beforehand by wiping the surface with 99% ethanol. Next, they were dried with a paper towel to remove the excess ethanol and subjected to a  
Figure 2: Alumina nanoparticles (NPs): (**a**) scanning electron microscopy (SEM) micrograph (\(\kappa\)60957); (**b**) NP size distribution.  
jet of compressed air until complete dryness. The measurements were performed with tapping mode (phase contrast) with a scanning rate between 1 and 2.5 Hz using a non-contact long reflex (NCLR) probe (\(k=48\) N/m, \(f=190\) kHz). For each EPNC sample, multiple images from areas of \(10\times 10\) um\({}^{2}\) were acquired at different locations.  
Based the obtained topographic images of the EPNCs surfaces and using Gwyddion software to analyze AFM data, amplitude parameters were evaluated. These parameters characterize the surface based on the vertical deviations of the roughness map from the mean surface. Basically, they reduce all of the information from the topographic images to a single number. In this study, two amplitude parameters were evaluated [37]: the arithmetic average roughness \(R_{a}\) (also called AA or CLA; Equation (1)) and the root mean square roughness \(R_{q}\) (also called \(R_{rms}\); Equation (2)). These amplitude parameters are widely used in the literature.  
\[R_{a}=\frac{1}{n_{x}n_{y}}\sum_{i=1}^{n_{x}}\sum_{j=1}^{n_{y}}[Z(i,j)-Z_{ave}] \tag{1}\]  
\[R_{q}=\sqrt{\frac{\sum_{i=1}^{n_{x}}\sum_{j=1}^{n_{y}}[Z(i,j)-Z_{ave}]^{2}}{n_ {x}n_{y}}} \tag{2}\]  
In the previous equations, the meaning of the parameters are the following ones: \(i\) and \(j\) corresponds to pixels in \(x\) and \(y\) direction, respectively, \(n_{x}\) and \(n_{y}\) are the maximum number of pixels in the two directions, \(Z(i,j)\) represents the topography data for the surface (after specimen tilt-correction), and \(Z_{ave}\) represents the height of the average surface.
# NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation
## 2 Background
### Montgomery Reduction  
The Montgomery reduction is a technique used in modular arithmetic to efficiently compute the modulo operation (remainder) of large integers [27]. The Montgomery reduction is used to enhance the multiplication speed within NTT/InvNTT computations. Utilizing the Montgomery reduction in modular multiplication can replace division operations with shift operations. This multiplication converts integers \(a\) and \(b\) into the Montgomery form to calculate \(a\cdot b=\text{mod}\ N\).
# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 3 Background
### Object Detection and Feature Extraction Using Deep Learning
#### 3.3.2 Network Models for Training and Classification  
This is the model architecture that improves both training and detecting speed proposed by [PERSON] et al. at Microsoft Research in 2016 [38]. Faster R-CNN classifies objects and specifies object locations in an image, in which the output is the coordinates of a rectangle and the object inside that rectangle. It has gone through many versions such as R-CNN [39] and Fast R-CNN [40]. Faster R-CNN architecture (Figure 3) is at the pinnacle of the R-CNN family models and achieves the near-best results in object recognition problems. Faster R-CNN uses Region Proposal Network (RPN) instead of selective search algorithms in order to solve the defect of execution time of R-CNN and Fast R-CNN. Faster R-CNN is considered to achieve higher speed and accuracy than it's predecessors. It may not be the simplest or fastest method for object detection, but it is still one of the methods that provides high accuracy. In this study, we propose to use this network model for object detection and classification on video frames to evaluate the experimental results.  
Single Shot Detector (SSD)  
Recently, a new group of object detection networks has been proposed, in which the region proposal network (RPN) is completely eliminated. It significantly improves processing speed compared to Faster R-CNN but in a completely different way. Faster R-CNN performs two separate phases, one for defining region proposals, the other for objects detection on each region proposal. SSD conducts both tasks in a single phase, predicts bounding boxes and labels while processing images. SSD is a typical example for this group designed to detect objects in real time [41]. This model uses boxes with different scales to identify areas of objects and classify objects. The SSD is conceptually simpler than the other methods because it eliminates the creation of region proposals thus increasing the processing speed without sacrificing performance.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 2 Materials and Methods
### Measurements Method  
The measurements with both receivers--Leica GS18T and u-blox ZED-F9P--were performed in RTK Network mode. In this mode Leica declares a slightly higher measurement accuracy of its receiver due to the distance to the base [29]. To obtain consistent results, in both cases, RTCM corrections to the observations were taken from the same network (Leica Smart Net Poland), source NAVGEO_VRS_3_2 (GPS + GLO + GAL + BDS).  
The measurement at the station consisted of at least five measurement series (the exception was station 7) for each hardware configuration. Measurements in successive configurations (Leica GS18T (Figure 3), u-blox ZED-F9P + Leica AS10 (Figure 4), u-blox ZED-F9P + ANN-MB-00 (Figures 1a and 4)) were performed alternately to ensure that each receiver had the most similar measurement conditions (access to the same satellites).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point** & **X [m]** & **Y [m]** & **H [m]** & \(\sigma_{\text{2D}}\) **[m]** & \(\sigma_{\text{H}}\) **[m  
The scheme of one measurement series on the station was as follows:  
* receiver initialization--maximum 30 s,
* measurement (collection of observations) --30 s,
* change of antenna/receiver.  
The collected observations in one 30-s measurement were averaged. After completing the measurement at the station, at least five separate (averaged) measurements of each antenna were obtained and taken for further analysis.