esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Special Cases  
(1) In the case of an ideal fluid, i.e., for \(v=0\) and \(b\
eq 0\) from Equations (26) and (27), we obtain the hydrodynamic force and the function \(\Phi\) in the form:  
\[F_{r}=F_{x}+iF_{y}=Am_{L}\Omega_{0}^{2}\exp(i\Omega_{0}t)+Bm_{L}\omega^{2}\Phi \exp(i\omega t) \tag{38}\]  
\[\Phi=\frac{\left(\sigma^{2}-2\Omega_{0}\sigma-\Omega_{0}^{2}+i\gamma b_{m} \sigma\right)}{\left(\gamma\sigma^{2}-2\Omega_{0}\sigma-\Omega_{0}^{2}+ib_{m} \sigma\right)} \tag{39}\]  
or in the dimensionless form:  
\[\frac{F_{r}}{eM\eta_{0}^{2}}=a\mu_{L}s^{2}\exp(i\Omega_{0}t)+b\mu_{L}r^{2}\Phi \exp(i\omega t)\]  
\[\Phi=\frac{\left[\tau^{2}-4\tau s+s^{2}+i\gamma b_{m}(\tau-s)\right]}{\left[ \tau\tau^{2}-2(\tau+1)\tau s+(\tau+1)s^{2}+ib_{m}(\tau-s)\right]}.\]  
where \(\gamma=\frac{q^{2}+1}{q^{2}-1}\), \(b_{n}=\frac{b_{m}}{\eta_{0}}\).  
The case considered here, when the rotor cavity is partially filled with an ideal weakly conductive liquid, was chosen only for reasons of obtaining more or less simple formulas for an engineering assessment of the physical meaning of the process occurring in a nonlinear system [49].  
(2) For synchronous precession of the rotor when \(\omega=\Omega_{0}\), \(\sigma=0\), we have \(\varphi=1\), \(F_{r}=(A+B)m_{L}\Omega_{0}^{2}\exp(i\Omega_{0}t)\), i.e., the foundation and the centrifuge perform circular synchronous precession. The main feature of synchronous precession is that the fluid is motionless with respect to the rotor cavity [13]. There is no wave motion of the fluid. In this case, the rotor and its foundation make forced movements caused by the unbalance of the rotor, and its cavity behaves as if it is completely filled with liquid. In this case, the magnetic field does not affect the motion of the system [50].  
(3) For \(v=0\), \(b_{m}=0\) and \(\tau\to s\Big{(}1+\frac{1}{\gamma}\big{(}1\pm\sqrt{\gamma+1}\big{)}\Big{)}\), the fluid reaction force tends to infinity, which will entail an unlimited increase in the amplitude of forced and natural oscillations of the rotor and its foundation [16].  
(4) For \(v=0\), \(b_{m}\to\infty\) and the reaction force of the liquid will take the form \(F_{r}=Am_{L}\Omega_{0}^{2}exp(i\Omega_{0}t)+Bm_{L}\omega^{2}\tau exp(i\omega t)\). In the case \(\gamma=1\) ((when the rotor cavity is completely filled with liquid), we obtain exactly the same picture as described in subparagraph 1. When \(q\to\infty\) or \(\gamma\to\infty\) (the amount of liquid in the cavity rapidly decreases), the amplitude of natural oscillations tends to infinity.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 2 Methods for Observing and Capturing Geomagnetic Anomalies  
What is geomagnetic anomaly? The Earth's magnetic field is usually regarded as a stochastic process \(f(t)\) with a mean value of a certain value, and the variation range in its normal value is usually not more than tens of \(nT\) up and down in the mean value. In theory, when the difference between the value measured by a magnetic intensity instrument at a certain time and the mean exceeds 3-5 times its mean square deviation, it is considered that its value is abnormal. When this magnetic anomaly is caused by underground geological changes, it is called a geomagnetic anomaly.  
Assume that a discrete time series of geomagnetic observation, \(f(k)\), as an ergodic stationary stochastic process. Its mean can be expressed as follows:  
\[\mu_{f}=E\{f(k)\}, \tag{1}\]  
and its standard deviation is \(S=\sqrt{\frac{1}{N}\sum_{k=1}^{N}\left[f(k)-\mu_{f}\right]^{2}}\). Geomagnetic anomaly \(\Delta F\) is defined as:  
\[\Delta F=\left[f(t)-\mu_{f}\left(t\right)\right]\geq(3\sim 5)S, \tag{2}\]  
A relatively dense magnetic sensing network was deployed in Liangshan Prefecture, Sichuan Province, China, which was prone to earthquakes in 2019. The magnetic intensity sensors are located in an area that is 88 km long and 154 km wide, with a total of about 30 portable three-component magnetometers of model AM2-1001 with a sensitivity of 1 nT, and its performance parameters are shown in Table 1. The stations are distributed in the range of longitude 101.71\({}^{\circ}\)-102.62\({}^{\circ}\) E and latitude 27.29\({}^{\circ}\)-28.92\({}^{\circ}\) N. The distribution of the stations is based on the local terrain and residential areas, and the average distance between adjacent magnetic sensors is about 20 km. Each magnetometer records the three components x, y, and z of the geomagnetic field at its position every 3 s, and transmits the recorded data back to the server through the China Mobile 4G network. Since September 2019, the monitoring has lasted for approximately four years, with approximately 100,000 bytes of data per day. As of now, approximately 1.25 GB of data have been obtained.
# Dosimetric Evaluation in Micro-CT Studies Used in Preclinical Molecular Imaging
## 2 Materials and Methods
### Theoretical Calculations
#### 2.2.2 Imparted Dose at Surface  
A theoretical phantom was included in the calculation to estimate the radiation's Imparted Dose at the Surface (IDS) of the animals during the tomographic study. This phantom consisted of a water cylinder with a diameter similar to a typical experimental mouse; thus, the IDS was defined as the absorbed dose in water (\(D_{w}\)) by the theoreticalphantom. For the calculations, the phantom's geometrical center in the transversal plane coincided with the axis of rotation of the CT unit (the isocenter), as illustrated in Figure 1. In this geometry, \(F_{SID}=(1/(SID-R))^{2}\) with \(R=15\) mm (the cylinder radius).  
For a poly-energetic X-ray beam, \(D_{w}=\tau\ D_{air}\)[17], where \(\tau\) is in turn given by the following expression:  
\[\tau=\frac{\int_{E_{min}}^{E_{max}}\zeta(E)\Big{(}\frac{\mu_{em}(E)}{\rho} \Big{)}_{w}dE}{\int_{E_{min}}^{E_{max}}\zeta(E)\Big{(}\frac{\mu_{em}(E)}{\rho} \Big{)}_{air}dE}. \tag{4}\]  
The \((\mu_{em}(E)/\rho)_{w}\), for each energy at the X-ray spectra, generated at 35 or 45 kV, were calculated by interpolation from the fitting functions obtained via a similar procedure to that performed to obtain the \((\mu_{em}(E)/\rho)_{air}\) as was described previously. The integrals in Equation (4) were calculated by numerical integration and the composite Simpson's rule.  
In this way, IDS (without considering backscattering) was calculated on the phantom's surface as:  
\[IDS=D_{w}=\tau\cdot D_{air} \tag{5}\]  
Uncertainties were calculated by the propagation of the numerical calculations [18; 19; 20].
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 6 Conclusions and Further Work  
The arrival of MR-DC enables great flexibility in the management of network resources, since two nodes are simultaneously connected to a user. Among other advantages, this allows to increase the bandwidth allocated to each user, which can have a positive impact on the QoE. However, a more complex resource management must be tackled. The proposed methodology assigns the user the eNB and gNB that will provide the highest QoE. The results show how this decision varies depending on the characteristics of the area surrounding the user, in addition to the service used. Furthermore, the results demonstrate how resource availability and user-reported RSRQ are more relevant than RSRP in making this selection. In this regard, the performance tests show the improvement of the average QoE compared to the 3 GPP solution in a wide variety of scenarios.  
The methodology has been tested with different services used by indoor and pedestrian users, but other services involving faster mobility can lead to complications in the dynamic management of connections. Hence, an additional subsystem must be added to address these challenges. This study is left for future work.  
Conceptualization, J.B. and L.d.-l.-B.; methodology, J.B.; software, J.B. and L.d.-l.-B.; validation, J.B.; formal analysis, J.B.; investigation, J.B.; resources, J.B. and L.d.-l.-B.; data curation, J.B.; writing--original draft preparation, J.B.; writing--review and editing, L.d.-l.-B. and R.B.; visualization, J.B., L.d.-l.-B. and R.B.; supervision, L.d.-l.-B. and R.B.; project administration, R.B.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript.  
This research was performed in the framework of the Horizon 2020 project LOCUS (grant agreement n. 871249), receiving funds from the European Union.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 7: Percentage of gain achieved by the proposed solution for each service in every scenario. (**a**) Gain in Scenario 1. (**b**) Gain in Scenario 2. (**c**) Gain in Scenario 3. (**d**) Gain in Scenario 4. (**e**) Gain in Scenario 5.
## 3 Experiments and Results  
In the analyses, a graph with \(|V|=225\) and \(|E|=501\) was employed. In this graph, each node was connected to a maximum of eight neighbors, and edge weights were real numbers uniformly chosen from the range \((0,1]\). The simulations involved distributing \(n=200\) ants across \(g\) groups (see Algorithm 1 line 7), with \(g\) taking values from the set \(\{1,2,5,10,20,50\}\) and \(n_{g}\) representing the number of ants in the group. The specific set considered was a subset of all possible divisors of \(n\), that was selected based on practical considerations and to cover a range of scenarios. Smaller group sizes (\(g=10,20,50\)) provided insight into the performance of small groups, while larger group sizes (\(g=1,2,5\)) simulated scenarios where collaboration involved more ants. This approach struck a balance between granularity analysis and computational efficiency, ensuring a thorough exploration of various group sizes while maintaining practical feasibility in terms of computational resources. Exploration by groups started after a fixed time \(T_{e}=|V|\) following the previous group, and an overall time limit \(T_{max}\) was established for the entire colony to reach the exit. This limit was defined as \(T_{max}=c\times|V|\), with \(c\) set to \(150\). Individual ants had specific time windows to reach the exit, determined by \(T_{i}=T_{max}-(T_{e}\times(i-1))\). Pheromonereduction occurred at a fixed degradation interval of \(T_{d}=|V|\), and the global updating rule took place every \(T_{d}\) ticks with evaporation rates of \(\rho=0.01\) and \(\rho=0.001\). The initial pheromone value was \(\tau_{ij}(t=0)=1.0\), as expressed in Algorithm 1 line 2, and the parameter determining the amount of pheromone released by ants after crossing an edge was \(K=0.1\) (as in Algorithm 1 line 32). To assess the impact of group size, 10 independent simulations were conducted for each value of \(g\). Three different kinds of analysis were performed: an _overall analysis_ to evaluate the performance of the colony, a _group analysis_ for every group configuration to evaluate the performance of the single groups, and a _pheromone distribution analysis_ to identify correlations between the temporal diffusion of pheromones and the navigation behavior of ants. All variables and parameters of interest in our model are listed in Table 1 for clarity.
## Results
### Analysis of VOC  
The VOC of different chemical natures and varied sensory descriptors were separated by e-nose, based on head-space gas chromatography with a flame-ionization detector. The twenty VOCs were identified as sensors (markers) with D \(>\) 0.900 (Table 4). The aroma profiles of \"bryndza\" cheese were dominated by esters (7), aldehydes (4), alcohols (4), free fatty acids (2), ketones (2), and one monoterpene. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **B7** & **B8** \\ \hline F (\%) & 24.8 \(\pm\) 2.1 & 24.9 \(\pm\) 1.6 & 19.4 \(\pm\) 2.5 & 24.8 \(\pm\) 1.3 & 24.3 \(\pm\) 3.3 & 26.4 \(\pm\) 0.4 & 26.3 \(\pm\) 3.1 & 22.4 \(\pm\) 0.5 \\ DM (\%) & 47.7 \(\pm\) 1.1 & 48.9 \(\pm\) 1.4 & 42.5 \(\pm\) 2.0 & 54.1 \(\pm\) 2.8 & 49.3 \(\pm\) 2.7 & 52.0 \(\pm\) 1.4 & 53.1 \(\pm\) 2.2 & 45.5 \(\pm\) 1.1 \\ FDM (\%) & 51.8 \(\pm\) 3.4 & 50.9 \(\pm\) 2.2 & 45.6 \(\pm\) 4.5 & 45.8 \(\pm\) 1.4 & 49.1 \(\pm\) 3.9 & 50.8 \(\pm\) 0.9 & 49.5 \(\pm\) 4.0 & 49.4 \(\pm\) 1.3 \\ pH & 5.24 \(\pm\) 0.2 & 5.09 \(\pm\) 0.1 & 5.34 \(\pm\) 0.1 & 5.24 \(\pm\) 0.1 & 5.14 \(\pm\) 0.1 & 5.21 \(\pm\) 0.1 & 5.26 \(\pm\) 0.2 & 5.24 \(\pm\) 0.1 \\ \hline \end{tabular} F—fat, DM—dry matter, FDM—fat in dry matter.  
\end{table} 
Table 2: Physicochemical properties of bryndza cheese (average values, means \(\pm\) SD). \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **B7** & **B8** \\ \hline CB & 3.77 & 3.46 & 6.01 & 6.78 & 3.72 & 6.22 & 5.27 & \(<\)1 \\ E & 5.35 & 7.36 & 7.35 & 7.34 & 4.59 & 7.19 & 6.47 & 2.37 \\ PL & 8.36 & 9.15 & 8.98 & 9.03 & 8.98 & 8.52 & 8.59 & 7.17 \\ PLb & 7.73 & 8.98 & 8.69 & 8.76 & 8.48 & 8.35 & 8.32 & 6.56 \\ Y & 6.62 & 6.21 & 6.26 & 4.65 & 6.52 & 5.96 & 6.19 & 5.80 \\ DG & 5.75 & 5.20 & 5.11 & 4.33 & 5.93 & 5.49 & 6.06 & 5.24 \\ \hline \end{tabular} CB—coliform bacteria, E—enterococci, PL—presumptive lactococci, PLb—presumptive lactobacilli, Y—yeasts, DG—_Dipodascus geotrichum_.  
\end{table} 
Table 3: Microbiological quality of bryndza cheeses (values of geomean) (log CFU/g). The dataset of e-nose was in the two-dimensional (2D) PCA plot. The first two principal components accounted for over 90% of total variance indicating that the first two PCs are sufficient enough to explain the total variance of the dataset. When the samples overlap or close to each other, it means they have a similar aroma as it is shown in Figure 1, 40 \"bryndza\" pieces of cheese from 8 producers divided into 5 groups according to the month of production in PC1 (76.25%). The samples from the May group were far away from the September group, which means that they have significantly different aromas and simultaneously, May and September groups of samples are surrounded by June, July, and August. In general, the PCA result suggested that the e-nose can properly characterize the samples of \"bryndza\" cheese.
## 3 Methodology  
Nowadays, surface defect detection algorithms based on traditional image processing have achieved remarkable results, capable of detecting common surface defects with high precision. However, there is still significant room for improvement in detecting tiny defects. From a data perspective, these methods require much higher image quality than ordinary images, but the quality is often affected by factors such as lighting intensity, acquisition angle, and camera resolution. This leads to the neglect of certain tiny defects during the acquisition process, directly impacting the performance of the algorithms and limiting their applicability in high-precision fields. Furthermore, 2D images lack depth-related information, meaning these methods can only detect defects but are unable to perform any quantitative analysis. In this study, we used a high-precision laser scanner to acquire point cloud data and luminance from the surface of magnetic tiles. By constructing a mapping relationship, we assigned luminance values to the point cloud data, providing color information that enhanced defect detection accuracy. We then introduced an information entropy-based adaptive neighborhood selection method to optimize the cumbersome parameter settings in feature engineering. Following this, we estimated the normal vectors and curvature deviations for rough defect extraction and performed both single-frame and multi-frame point cloud fitting for defect refinement. Finally, we used region growing to complete the defects, thus reducing the miss-detection rate. A brief technical flowchart of the proposed method is shown in Figure 4.
## 1 Introduction
### Mechanism of Antimicrobial Resistance vs. Strategies to Develop Novel Antibiotics  
Drug inactivation can occur either by enzymatic or chemical degradation, while decreased intracellular drug concentration can occur because of increasing drugs efflux and decreasing drugs influx [5]. In this regard, form mutations in resistant strains alter the permeability of bacterial membranes, thus reducing the uptake of antibiotics into the bacterial cell. On the contrary, the hyperexpression of efflux pumps, which pump antibiotics out of the cell, dramatically reduces their concentration inside the cell [6]. Also, by the action of enzymes that chemically modify components of the bacterial outer membrane essential for antibiotic binding, some Gram-negative bacteria such as _P. aeruginosa_, _Acinetobacter baumannii_ and others develop resistance to glycopide and polymorphsin antibiotics. Furthermore, methyltransferases are a class of enzymes capable to modify the target thus promoting the resistance to antibiotics including aminoglycoside, imcosamide, macrolide, streptogramin, and oxazolidinone [7]. Another phenomenon known as \"target protection\" occurs when antibiotic target's resistance proteins, such as the tetracycline ribosomal protection proteins (TRPPs), protect bacteria from the antibiotic-induced inhibition [8]. Additionally, the antibiotic resistance could be caused by the use of antibiotics in feed diet for animal production. The overuse, abuse, and misuse of \(\beta\)-lactams, aminoglycosides, tetracyclines, macrolides, and other antibiotics, with the purpose of promoting the development of animals, can cause the presence of residual antibiotics in the products intended for human consumption obtained from those animals, and can determine antibiotics pollution into the environment [9; 10; 11]. It was reported that some bacterial infections in humans are sustained by animal pathogens, namely zoonotic pathogens, thus proving that antibiotic resistance can be directly or indirectly transmitted from animal to humans [9]. A few practices, including the improvement of animal feed, waste management, and animal natural immunity, as well as the use of antibiotic alternatives such as prebiotics, probiotic vaccines, and bacteriophages can regulate and limit the antibiotic resistance, thus maintaining the potency of  
Figure 1: Mechanisms by which pathogens can become resistant.  
the available drugs [12]. However, more strategies to counteract antibiotic resistance are necessary, and currently they include the use of nanotechnology, computational methods, the use of antibiotic alternatives, drug repurposing, the synthesis of novel antibacterial agents, prodrugs, the development of efficient diagnostic agents also named rapid diagnostic tests (RDTs), the use of combination therapy, as well as the awareness, and knowledge of antibiotic prescribing (Table 1).
## 3 Lens Construction
### Lenes Array  
Researchers propose the array lens to enhance the zoom range and imaging quality of DETL. As a new type of optical element, it comprises multiple small lenses, whose shape, position, or curvature can be adjusted to modify the focal length. [PERSON] et al. [72] developed PDMS lens arrays, where the driver is bonded by sockets providing fluidic coupling, and the compressive deformation is achieved with the help of electrostatic force generated by an applied voltage to change the curvature of the lenses with a zoom ratio of up to 150%, withstanding millions of cycles. [PERSON] et al. [73] developed a tuneable microlens array based on DE actuation, as shown in Figure 11a, where DEA drives the shape deformation of the lens array to realise the adjustment of lens curvature. The focal length of this lens can be adjusted from 950 mm to infinity with good imaging quality, which is suitable for machine vision systems, etc. [PERSON] et al. [74] proposed a bionic compound eye imaging system, as shown in Figure 11b, which achieves a continuous optical zoom from 0.30\(\times\) to 0.90\(\times\) through the focal length linkage mechanism of the [PERSON] lens array and the dual auxiliary lenses with a 50 Ip/mm imaging resolution.  
Figure 11: DEA-driven array DETL. **(a)** Planar array: **(a1)** top view of the lens object; **(a2)** lens zoom schematic, the upper and lower diagrams are the changes in the shape of the lens before and after the application of voltage, respectively [73]. **(b)** Curved array, where the left and right diagrams are the schematic and the object of the lens, respectively [74].  
Figure 10: Composite DETL. **(a)** PDMS lens: **(a1)** physical diagram of the zoom system; **(a2)** top view of the lens in kind; **(a3)** lens zoom schematic diagram; the left and right diagrams are the change in focal length before and after applying the voltage, respectively [70]. **(b)** Curved DEA drive: **(b1)** lens displacement along the optical axis direction schematic diagram; **(b2)** lens zoom schematic diagram, the solid and dotted lines are the change in focal length before and after applying the voltage, respectively; **(b3)** lens zoom effect diagram, the upper and lower diagrams are the imaging pictures of the lens before and after applying the voltage, respectively. voltage; **(b3)** lens zoom effect diagram, the upper and lower diagrams show the imaging pictures of the lens before and after applying voltage, respectively [71].  
Array DETLs offer improved zoom range and imaging quality; however, their preparation process and imaging algorithm are more complex, which may impact the system's response time.
# An Improved Analytical Algorithm on Main Cable System of Suspension Bridge
(2) _Elastic catenary elements_: An elastic catenary curve is defined as the curve formed by a perfectly elastic cable, which obeys Hooke's law and has negligible resistance to bending, when being suspended from its ends and subjected to gravity. It should be noted that the conventional formulations are based on the hypothesis of small deformations, meaning that the forces are integrated with respect to the initial configuration of the catenary. Hence, the weight per unit length does not vary consistently with the elongation of the catenary. This may result in an inaccurate equilibrium of forces in the deformed configuration. The main advantages of the catenary-type cable elements are the reduction of degrees of freedom, the simplicity of finding the dead load geometry of the cable system, the exact treatment of cable sag, the exact treatment of cable weight as it is included in the equations used for element formulation, and the simplicity of including the effect of pre-tension of the cable by simply giving the unstressed cable length. However, the cable segment equation is unsolvable when the initial three components are not set properly because of the so-called initial value sensitivity. The purpose of this paper is to develop a catenary cable element for the nonlinear analysis of cable structures that are subjected to static loadings. Firstly, the tangent stiffness matrix and internal force vector of the element are derived explicitly based on the exact analytical expressions of elastic catenary. Self-weight of the cables can be directly considered without any approximations. The effect of pre-tension of cable is also included in the element formulation. Then, a search algorithm with the penalty factor is introduced to satisfy the convergence requirement with high precision and fast speed. Finally, numerical examples are presented and discussed to illustrate the accuracy and efficiency of the proposed analytical algorithm.