esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamental Concepts
In the uniform sampling case \(t_{n}=nT\) and \(\omega_{n}=n\Omega\), and we must have \(2W<\Omega\), stating the well known Nyquist rule: the sampling interval must be less than one half on the inverse of the higher frequency of the signal. The relation (7) shows that reducing the average distance between the impulses in the time domain (4) corresponds to increasing the distances between the replicas in the frequency domain (8). **Theorem 2**. : _Let \(x(t)\) be a continuous-time bandlimited signal with Fourier transform, \(X(\omega)\), that is null outside the band \(|\omega|<W\in\mathbb{R}^{+}.\) We can recover \(x(t)\) from its samples \(x(t_{n})\) with a low-pass filtering provided that the filter frequency response is constant in the band of the signal. In the case of an ideal low-pass filter with bandwidth \(W\), the output reads [24]_  
\[0\leq|\tau_{n}|<\frac{T}{4}. \tag{2}\]0  
This theorem constitutes a generalization of the classical sampling theorem. This results deserves an interpretation in terms of linear systems. Let us consider that  
\[0\leq|\tau_{n}|<\frac{T}{4}. \tag{2}\]1  
is the impulse response of a low-pass linear system and that the input to such system is  
\[0\leq|\tau_{n}|<\frac{T}{4}. \tag{2}\]2  
Then the output will be given by the expression in (9). The signal \(g(t)\) is called the interpolating function and its FT is a rectangle which gives a justification to its name of 'ideal low-pass filter'. If the \(t_{n}\) sequence is uniform, that is, if \(\tau_{n}\equiv 0\), then  
\[0\leq|\tau_{n}|<\frac{T}{4}. \tag{2}\]3  
that states the usual sampling theorem, where \(WT<\pi\).
## 3 Results and Discussion
### DIC Analysis  
The DIC analysis revealed more insight into the fracture behaviour of the tested sliced lamellae. Figure 5a-d illustrate a common fracture mechanism depicting stress concentration around the most extensive slicing checks. A fracture occurred in the radial-longitudinal plane. The strain maps in Figure 5(Ia,IIa) show the detection of significant strain corresponding to several slicing checks at the beginning of loading. By the end of the loading, as observed in Figure 5(Ib,IIb), the failure would occur following extensive slicing checks.  
Figure 5: Principal maximal strain for (**a**) 1.5 mm, (**b**) 2.5 mm, (**c**) 3.5 mm, and (**d**) 4.5 mm thick sliced lamellae (arrows indicate fracture area); (**Ia,IIa**) strain concentration around areas of slicing checks at the beginning of loading and (**Ib,IIb**) at the breaking point.  
Generally, principal maximal strain tends to increase with increasing sliced lamellae thickness (Figure 5), similar to the strain rate from experimental data. The method should be further explored to enable robust and interchangeable results between the two methods.
## 4 Materials and Methods
### INAA Measurements and Quality Control  
Before irradiation at the IBR-2 reactor of the JINR FLNP in Dubna, each aliquot was packed in polyethylene bags for the determination of short-living isotopes (three aliquots) and in aluminum foil (three aliquots) for long-living ones. It is worth mentioning that the mass fractions of short-living isotopes were determined by thermal neutron activation while in the case of long-living isotopes we have used epithermal neutrons. More details concerning irradiation time and neutron flux densities in each irradiation channel can be found in [37].  
After irradiation, the gamma spectrum of each sample was recorded using a HPGe detector with a resolution of 1.9 keV for the \({}^{60}\)Co 1332 keV line, analyzed by means of a Genie 2000 Canberra software and processed by a proprietary software [37]. This permitted determination of the mass fraction of each of the 38 considered elements together with the associated CSU [21]. In all cases, the CSU was estimated by taking into account the statistical error, influence of measurement geometry, detector efficiency, the content, as well as the error of each element for all Certified Standard Material (CSM) utilized for calibration. To avoid any environmental contamination, all resulting radioactive materials were deposited in a permanent low activity repository.  
The quality control was assured in the case of sediments and soils by a simultaneous analysis of Standard Reference Materials (SRM) provided by the National Institute for Standard and Technologies (NIST), i.e., 1633c Coal fly ash, 667 Estuarine sediment, 2710 Montana Soil and 1547 Peach leaves for short half-life time isotopes. In the case of long life-time isotopes, the best results were obtained using-2709 Trace elements in soil, 1632c-Trace elements in coal, 690 CC-Calcareous soil, 2709a-San Joaquin soil, 1632c-Trace elements in coal and SRM-AGV2-Andesite.  
Further, by using all of the above-mentioned SRM a Group of Standard Sample (GSS) [37; 38] was realized which permitted the choice of the most appropriate SRM lines to minimize errors in determining the mass fractions of all investigated elements. It is worth menting that GSS is a proprietary software which, besides helping to determine the mass fractions of chosen elements, it allowed the calculation of the extent to which the mass fractions of SRM corresponded to certified ones [37; 38]. Following this procedure, the accuracy quantified by means of standard deviation corresponding to each group of three aliquots was lower than 10%. In the case of SRM, the CSU varied between 3 and 15%, higher in the case of REE, but never greater than 20%. It is worth mentioning that for all 38 investigated elements, the detection limit was of 1 mg/kg and lower [37; 38].  
To illustrate the sensitivity and accuracy of INAA measurements following the CSS software, in Table 11 we have reproduced, as an example, the experimentally determined mass fractions of SRM 2709. Similar determinations were completed in the case of all utilized SRM.
# Mathematical Background and Algorithms of a Collection of Android Apps for a Google Play Store Page
## 4 Discussion
### Vocal SMS Writing and Reading App for Visually Impaired People  
As presented, a simple app was created, which redefines the meaning of SMS messaging with the use of voice interaction. The app can record a voice message, transform it into text, and send it as an SMS message. The app can also read an SMS message that is received aloud with the implemented text-to-speech software. In this way, the app can be used for vocal conversation, but with the reduced price of SMS messaging.  
The adaptation is that it added voice functionality to an SMS client. The mathematics and algorithms presented here make the text-to-speech and speech-to-text functionalities. This app is better than the ones that exist, due to the fact that it is kept as a simple SMS client, but it adds only a voice option in order to be used by visually impaired people, drivers, or busy people. The idea is to use the app almost as we would make a phone call, but with the price of an SMS.
## 2 Principle of DETL
### Tuning Principle
#### 2.2.1 Deformation-Based
The degree of bending of the lens membrane is adjusted indirectly by applying a voltage to change the volume of liquid in the annular region to achieve the zoom function; the experimental results show that the zoom ratio of this lens can be as high as 414%, and it has a better imaging quality. [PERSON] et al. conducted a detailed theoretical analysis and experimental verification of the configuration and explored the main factors affecting the focal length of the liquid lens through theoretical analysis [36]. [PERSON] et al. added a detailed theoretical anal  
Figure 2: Directly driven deformable DETL. (**a**) Direct deformation liquid lens: (**a1**) lens physical top view; (**a2**) lens zoom principle diagram, the upper picture is the shape of the lens when no voltage is applied, and the lower picture is the DEA squeezing the lens after applying a voltage to make the shape change; (**a3**) lens zoom imaging effect diagram [30]. (**b**) Direct deformation soft solid lens: (**b1**) lens zoom principle diagram, where the left side shows the shape of the lens when no voltage is applied, and the right side shows the soft solid lens squeezed by DEA after applying voltage, which makes the shape change; (**b2**) top view of the lens in kind [31]. ysis of the variation range of the focal length of the configuration based on pre-stretching, which provided a thorough theoretical basis for the optimal design of the lens [37]. Indirectly driven DETLs transmit deformation forces through a liquid, enabling a wider zoom range and making them more suitable for application scenarios that require significant focal length adjustments. However, such lenses typically rely on a liquid or gas cavity connected to the outside, often several times the size of the lens body, increasing the overall system's size and complexity. Additionally, the preparation process is more complicated, potentially affecting the reliability and lifespan of the system.
## 2 Materials and Methods
### Classifier Comparison
#### 2.4.3 Maximum Likelihood Classifier  
The maximum likelihood classifier (MLC) is based on the probability that a pixel belongs to or is within macro-classes or a particular class [59; 60].  
The MLC algorithm calculates the weighted distance or probability \(D\) that an unknown value in the vector \(M_{p}\) belongs to one of the macro-classes or classes \(M_{c}\). This likelihood is based on the Bayesian equation [61]:  
\[D=\ln{(a_{c})}-\frac{\ln(|Cov_{c}|)}{2}-\frac{(M_{p}-M_{c})T(Cov_{c}-1)(M_{p}-M _{c})}{2} \tag{8}\]  
where \(D\) is the weighted distance or probability, \(c\) is a particular macro-class or class, \(M_{p}\) is the measurement vector of the candidate pixel, \(M_{c}\) is the mean vector of the sample of macro-class or class \(c\), \(a_{c}\) is the percentage probability of any pixel belonging to the macro-class or class \(c\), \(Cov_{c}\) is the covariance matrix of the pixels in the sampled macro-class or class \(c\), \(|Cov_{c}|\) is the determinant of the matrix \(Cov_{c}\), \(Cov_{c-1}\) is the inverse of \(Cov_{c}\) and \(T\) is the transposition function.
# Surface Erosion Analysis for Thermal Insulation Materials of Graphite and Carbon-Carbon Composite
## 3 Experimental Results and Discussion
\[\dot{\mathrm{r}}_{\mathrm{m}}=8.8491\times 10^{-9}\mathrm{c}^{*}\left(\log\mathrm{P }\right)^{2} \tag{12}\]  
The measured and calculated total erosion rates are shown in Table 5 and Figure 10 where the errors between the experimental and predicted values was 7.53% for R4340, 6.29% for G6520, 2.34% for EX60, 4.2% for EX70 and 2.53% for the 3D C-C composite. The overall errors were less than 10%, confirming that the accurate erosion rate can be predicted by considering mechanical erosion. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Graphite**} & \multicolumn{2}{c}{**C–C**} \\ \cline{2-6}  & **R4340** & **G6520** & **EX60** & **EX70** & **3D** \\ \hline \(\dot{\mathrm{r}}_{\mathrm{c,}}\) ch (mm/s) & 0.0305 & 0.0308 & 0.0218 & 0.0251 & 0.0339 \\ \(\dot{\mathrm{r}}_{\mathrm{c,d}}\) (mm/s) & 0.0130 & 0.0131 & 0.0092 & 0.0106 & 0.0145 \\ \(\dot{\mathrm{r}}_{\mathrm{th}}\) (mm/s) & 0.0183 & 0.0184 & 0.0130 & 0.0149 & 0.0203 \\ Error (\%) & 25.34 & 5.14 & 1.56 & 27.35 & 28.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated thermochemical erosion rate of graphite and the C–C composite. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Graphite**} & \multicolumn{2}{c}{**C–C**} \\ \cline{2-6}  & **R4340** & **G6520** & **EX60** & **EX70** & **3D** \\ \hline Test time (s) & 120 & 120 & 120 & 120 & 120 \\ Total erosion (mm) & 1.75 & 2.10 & 1.54 & 1.40 & 1.90 \\ Erosion rate (mm/s) & 0.0146 & 0.0175 & 0.0128 & 0.0117 & 0.0158 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The measured erosion of graphite and the C–C composite and their erosion rates. Figure 9: Erosion cross section view of (**a**) R4340 (**b**) G6520 (**c**) EX60 (**d**) EX70 (**e**) the 3D C–C composite. The main causes of erosion are thermochemical and mechanical factors. The surface temperature, surface pressure, species concentration and density are the main influences on thermochemical erosion, and porosity [3; 4; 5]. Characteristic exhaust velocity and friction are also factors related to mechanical erosion. Figures 11 and 12 show the comparison of erosion rates in terms of density and porosity of graphite. The higher the density, the smaller the erosion rate, and the density of the material has a great effect on the erosion. Additionally, the low porosity slows the erosion rate. When the graphite and C-C composites had a density similar to that of G6520 (1750 kg/m\({}^{3}\), 17%) and 3D (1700 kg/m\({}^{3}\), 12%), the lower porosity and lighter C-C composites exhibited stronger resistance to erosion. As the surface temperature increases, and as the concentration of H\({}_
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### IRSs for Cellular Networks
#### 2.2.2 Interference Mitigation  
Cellular networks are often plagued by interference, particularly in densely populated areas where multiple users compete for a limited spectrum. IRSs can mitigate interference in two ways: first, a passive IRS can be strategically placed to reflect signals away from interference sources; second, an active IRS can dynamically adjust their configurations to either reflect interfering signals away from the receiver or cancel out the interference altogether through destructive interference techniques [100]. The surface achieves this by applying calculated phase shifts to incoming signals, ensuring that interfering signals are either redirected or canceled at the receiver's location [101].
# A New Data Processing Approach for the SHPB Test Based on PSO-TWER
## 4 Hopkinson Stress Balance Effect Index
### The Stress Equilibrium Index of SHPB  
In the stress equilibrium state described in the study of [PERSON] and [PERSON], the loading process can be divided into three stages: the stress superposition stage, the stress equilibrium stage, and the stress deterioration stage. The stress difference between the two ends of the specimen will fluctuate in a range during the impact loading, and it is challenging to reach absolute stress equilibrium in rock specimens. Because the specimen experiences a brief state of stress disequilibrium-stress equilibrium-stress disequilibrium, the number of calculated stress equilibrium factors close to one point can be used to evaluate the stress equilibrium effect, and a new evaluation index can be established by Equation (12).  
\[R_{f}=\frac{n_{c}}{n_{s}} \tag{12}\]  
where \(R_{f}\) is the evaluation index of the test results, \(n_{s}\) is the number of points in the segment, and \(n_{c}\) is the number of points close to the stress equilibrium position.  
The calculation of \(n_{c}\) is shown in Equation (13).  
\[c(t)=\sigma_{\text{eq}}(t)-a \tag{13}\]  
where \(c(t)\) represents the function related to the loading time (t) and \(a\) is the correction coefficient, its physical meaning being the allowable error. In this study, the median is taken as 1.5.
# Relation of EDL Forces between Clay Particles Calculated by Different Methods
## 1 Introduction
A reasonable calculation of the EDL force between clay particles considering the characteristics of finite length and nonparallel arrangement of particles in an actual clay-electrolyte system is necessary for DEM simulations of clay. [PERSON] and [PERSON] proposed a numerical model to calculate the repulsive force between two inclined charged plate particles with finite length, which are symmetric about a mid-plane in a dielectric medium [28]. This model was frequently used in later DEM simulations of clays [20; 21; 22; 24; 27]. However, [PERSON]'s stress was not considered explicitly in their model. Recently, the EDL force between two parallel three-dimensional colloidal plates with finite size was calculated numerically [34]. Usually, the calculations of the forces corresponding to osmotic pressure and Maxwell's stress, which are denoted by osmotic force and electrical force, respectively, are performed by integrations over an arbitrary enclosing boundary [23; 32; 33] around a charged particle without discussing the possible influence of the location of the enclosing boundary. It has also been shown that the force calculated by the integration of the electrostatic force density over the proper domain [28] is the same as the integration of the potential along the boundary enclosing a simply connected domain, and is in fact the osmotic force. However, to the authors' knowledge, the subtle relation of the forces calculated by the different existing methods has not yet been investigated. The aim of this paper is to investigate such a relation systematically, which is necessary for correct calculation of the EDL force and thus important for understanding the macroscopic behavior of natural materials involving considerable clay particles. The remainder of this paper is organized as follows. In Section 2, the calculation methods of EDL forces between charged clay particles in the literature are summarized briefly, and then the relation between the EDL forces calculated by different methods is analyzed. In Section 3, the revealed relations are validated and illustrated by numerical simulations. In Section 4, the conclusions of the present study are drawn.
## 2 State-of-the-Art Smart Transport Infrastructure
### Machine Learning and Deep Learning  
THE ML-based VANET system needs sensitive information to choose diverse aspects, such as selection of training and communication. Nowadays, with the help of privacy mechanisms, most sensitive information is disguised. Furthermore, On-Board Units (OBUs) require too many resources in the VANET environment, and consequently, ML-based and misuse-based learning approaches are not suited for vehicles network. For the VANET environment, a lightweight, sensitive information transfer protocol is necessary for VANET [60].  
From the resource requirement standpoint, high-performance computational resources must train DL-based models [61]. DL has been extensively involved and gained important attention in several domains of VANET to enhance the performance of various tasks, such as anomaly detection in vehicular communication, network traffic classification, and attack detection [3; 62]. DL models automatically evaluate and extract useful features from the supplied data. In a VANET context, DL-based models achieve high performance based on the raw traffic inputs and with minimum resources, and this is a suitable technique that can be applied in a vehicular network.  
DL has been widely involved and gained significant consideration in different fields of VANET to improve the performance of various tasks, such as anomaly detection in vehicular communication, network traffic classification, and attack detection [3; 62].  
In the VANET environment, integrating intervehicle communications with ML and DL can achieve different applications and safety procedures such as secure communication, intrusion detection, prediction of wide traffic congestion, predicting the collision, object detection, early anomaly detection and prevention, smart unmanned vehicle management, smart fleet monitoring, identification, and cross border traffic management [3; 7; 62; 63; 64; 65; 66; 67]. Explicitly, with the support of DL, the environmental perception is accomplished using existing vehicle sensor sensitive data. With the help of exchanging traffic-associated information, the extended perception range is achieved via communication between vehicles. Intelligent Vehicles (IV) increase their self-observation range via the information distribution techniques and, with the help of self-collected data, percept the neighboring environment in a VANET. However, numerous privacy and security challenges exist in the VANET environment because of the dynamic changes, such as resource management, data transmission, packet loss in real-time scenarios, and computation power.
# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 3 Results
### Effect of Land Cover on Bee Foraging
As demonstrated in Table 4, in the lower part of the matrix, several significant correlations were observed between various land cover types. As anticipated, the proportion of anthropogenic areas exhibited a strong negative correlation with the proportion of agricultural land (\(\tau=-0.76\), \(p<0.05\)), reflecting the inherent trade-off between highly \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Topographical Landscape** & **Nectect Collection (kg)** & **Anthro-ogenic** & **Agricultural** & **Broad-Leaveg Forest** & **Coniferous Forest** & **Grassland** & **Shrubs** & **Wetlands** \\ \hline Anthropogenic & \(0.24\) & & & & & & & \\ Agricultural & \(-0.22\) & \(-0.76\)* & & & & & & \\ Broad-leaved & \(-0.06\) & \(-0.20\) & \(0.13\) & & & & & \\ Forest & \(-0.02\) & \(-0.07\) & \(-0.18\) & \(0.29\) & & & & \\ Confierous forest & \(-0.11\) & \(-0.07\) & \(-0.13\) & \(-0.38\) & \(-0.16\) & & & \\ Shrubs & \(0.46\) & \(0.18\) & \(0.25\) & \(0.09\) & \(0.23\) & \(-0.04\) & & \\ Wetlands & \(-0.07\) & \(0.12\) & \(-0.15\) & \(0.27\) & \(0.12\) & \(-0.35\) & \(-0.20\) & \\ Water bodies & \(-0.14\) & \(0.09\) & \(-0.17\) & \(0.53\)* & \(0.54\)* & \(-0.44\) & \(-0.15\) & \(0.21\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Kendall’s tau (\(\tau\)) correlation matrix between annual nectar yield (live weight gain) and the proportion of individual land cover types, as well as among land cover variables. Statistically significant values (\(p<0.05\)) are indicated in bold and asterisk. Figure 4: Land cover of landscape types within the 3 km bee foraging area (urban and rural sites). Maps is based on Sentinel-2 satellite imagery at 10 m resolution. urbanised zones and cultivated areas. Of particular interest are the positive correlations observed between the share of broad-leaved forests and surface water bodies (\(\tau\) = 0.53, \(p\) < 0.05), as well as between coniferous forests and surface waters (\(\tau\) = 0.54, \(p\) < 0.05). This finding suggests that, in certain locations, the presence of substantial forested areas is often accompanied by the presence of nearby water bodies. A list of the most common and nectar-rich plant species across different land cover types is illustrated in Figure S6.