esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Path Loss Model  
To accurately study the signal reliability for the PSC in an urban environment, well-defined air-to-ground and ground-to-ground communication links are considered between the interfacing base station of interest (BOI) and the GUEs available. To this end, we consider Okumura-Hata Path Loss (OHPL) models for accurate analysis of signal reliability for the proposed AG-HetNet, especially in an urban terrestrial environment where the base station height does not vary and OHPL is an optimal path model [5]. When a GUE camps on a base station of interest (MOI or UOI), OHPL for the AG-HetNet is given by  
\[\varphi(d)=74.52+26.16\log(f_{\rm{c}}^{\rm{B14}})-20.37\log(h_{\rm{bs}})-3.2( \log(11.75h_{\rm{gue}}))^{2}+38.35\log(d), \tag{6}\]  
where \(f_{\rm{c}}^{\rm{B14}}\) is the carrier frequency in MHz and \(h_{\rm{bs}}\) is the height of MBS, i.e., \(h_{\rm{bs}}=h_{\rm{mbs}}\), \(h_{\rm{gue}}\) is the height of GUEs in meters, and \(h_{\rm{uabs}}\) is the height of UABSs in meters.  
Figure 3 characterizes the empirical path loss cumulative distribution functions (CDFs) for the OHPL model, calculated for all distances between the base stations (\(\mathbf{L}_{\rm{mbs}}^{\rm{3D}}\) and the \(\mathbf{L}_{\rm{uabs}}^{\rm{3D}}\)) and GUEs (\(\mathbf{L}_{\rm{gue}}\)) that are distributed using MCP and TCP. By inspecting Figure 3, we can intuitively conclude that the maximum allowable path loss is diverse when the GUEs are distributed using MCP and TCP. In Figure 3, we plot the CDFs for the cases when 50% and 97.5% of the MBS are destroyed. A close inspection of Figure 3 reveals that the variation in CDFs is minimum for the different numbers of UABSs deployed and for a different number of the MBSs destroyed. Both MCP and TCP observe a similar distribution in CDF, and the maximum allowable path loss for the system when the GUEs are distributed using both processes is approximately 240 dB.
# Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems
## 3 Modeling of the Milling Machine
### Modeling of the Joint/Interface
#### 3.1.2 Sliding Interface  
The contact properties of the elastic interface layer such as Turcite-B in the sliding guide system can be modeled by the following exponential relationship [11].  
\[\delta_{n}=C_{n}P_{n}^{m} \tag{5}\]  
where \(\delta_{n}\) isnormal deformation (mm), \(P_{n}\) is normal pressure (N/mm\({}^{2}\)), \(C_{n}\) is coefficient of normal contact flexibility, and \(m\) is coefficient of non-linearity of deformations. The contact stiffness in the normal direction can be obtained as  
\[K_{n}=\frac{\text{d}P_{n}}{\text{d}\delta_{n}}=\frac{1}{C_{n}m}p_{n}^{1-m} \tag{6}\]  
For modeling sliding guides in a finite element model, the spring layer model was employed to simulate the contact characteristics of the antifriction liners between the guide rail and sliding block. The antifriction layer was replaced by interface elements distributed over this interface (Figure 2), which was described by the stiffness matrix,  
\[\left[K^{e}\right]=\int_{A}\left[N\right]^{T}\left[T\right]^{T}\left[D\right] \left[N\right]\left[T\right]\text{d}A \tag{7}\]  
Figure 2: Modeling of the rolling and sliding interfaces. (**a**) Schematic of angular contact bearing, (**b**) Modeling of the contact interface between rolling ball and groove, (**c**) Schematic of sliding guideway and the modeling of the antifriction sliding interface.  
where [\(N\)] and [\(T\)] is the interpolation function and coordinate transformation matrix, respectively, and [\(D\)] is the contact stiffness matrix of the interface layer described as [\(D\)] = diag[\(K_{n}\), \(K_{s}\)]. \(K_{n}\) and \(K_{s}\) is the contact stiffness in normal and tangential directions of the contact interface.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Biofilm Maturation
#### 4.3.1 Extracellular Polysaccharide Deposit  
One component that supports EPS structure is mannose-related proteoglycan, termed proteomannans. Nuclear magnetic resonance analysis of biofilm EPS revealed 1-4 mannosyl linkages in developing and mature biofilms [31]. Monosaccharide analysis from the biofilm matrix purification discovered fucose (Fuc), galactose [36], glucose (Glc), glyceromanno-heptose [49], \(N\)-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc) as the main component. Amino acid and lipid, especially tetradenoic acid (C14) and hexadecanoic acid (C16) are also contributed to the biofilm architecture [50].  
\(N\)-acetylglucosamine (GlcNAc) structure in the mature biofilm is also contributed to the microbiome environment. Study reported role _H. pylori_ biofilm to the survival of Enter-ovirus 71 (EV71) in the environment. Results show that EV71 particles can persist for up to 10 days when incubated with _H. pylori_ biofilm, and its viability is dependent on the quantity of biofilm formation. The biofilm contains N-acetyl-glucosamine and glycosaminoglycan, which has binding affinity to EV71 [51].
## 3 Results
### Wind Speed  
Note that in satellite measurements of wind speed, this value is decomposed into orthogonal components--east and north. Using them, the total vector of wind speed is determined. The measurement methods are often similar to scattermeter devices, the principle of which is based on the reception of signals reflected from the sea surface, and further analysis of the intensity of the reflected wave [14].  
Since the wind speed value is not only a climatic parameter, but also a parameter for introducing corrections to other parameters, the MERRA-2 reanalysis uses sensors from both ground-based observatories (UCAR and NCEP) and data from a large number of satellite systems [49]:  
* AVHRR atmospheric motion vector, 1 October 1982-present, CIMSS;
* SSM/I surface wind speed, 9 July 1987-4 November 2009, RSS;  
Figure 14: Average values of total precipitation per day (in) according to MERRA-2 for the period 1980–2021.  
* ERS-1 surface wind vector, 5 August 1991-21 May 1996, ESA;
* ERS-2 surface wind vector, 19 March 1996-29 March 2011, ESA;
* QuikSCAT surface wind vector, 19 July 1999-22 November 2009, JPL;
* MODIS atmospheric motion vector, 2 July 2002-present, CIMSS and NCEP;
* SSMIS surface wind speed, 23 October 2003-29 October 2013, RSS;
* WindSat surface wind vector, 13 August 2007-4 August 2012, NCEP;
* ASCAT surface wind vector, 15 September 2008-present, NCEP.  
The MERRA-2 catalogs have wind velocity values available for two vectors (meridional and zonal wind) at 2, 10, 50, and 72 m altitudes, at the daytime surface, and at \"surfaces\" where pressure is 250, 500, and 850 hPa [48]. There is also information on trends in interannual zonal and meridional wind variability. The atlas presents various wind speed characteristics at 50 m altitude. One of the maps is shown in Figure 15, and displays the average wind speed for the entire time interval (1980-2021).  
Analysis of average wind speed by months showed a strong seasonal dependence--the highest speeds up to 5 m/s in winter (extremes in December) in Figure 16a and the lowest speeds in summer--values up to 0.4 m/s and less in the regions of the White Sea and Arkhangelsk in Figure 16b.
# A Symmetric Matrix-Aided MIMO to Improve Reliability for Maritime Visible Light Communications
## 3 Symmetric Matrix-Aided MIMO-VLC System
### Symmetric Matrix-Aided Modulator  
According to the pulse amplitude modulation (PAM) principle [18], we embed the symmetric matrix after the PAM modulation. As shown in Figure 2, there are three modules: S/P, the PAM mapping, and the symmetric matrix. Here, S/P represents the serial to parallel conversion, and the PAM mapping maps each data stream to a PAM symbol, \(u_{1},u_{2},\ldots,u_{p}\in\mathbb{U}\), where \(\mathbb{U}\) represents the M-PAM constellation. We use the symmetric matrix \(\mathbf{S}\) to encode the vector \(\mathbf{u}\), \(\mathbf{S}\) is as follows:  
Figure 1: Transmitter structure for system.  
\[\mathbf{S}_{2\times 2}(a,b)=\left[\begin{array}{cc}b&a\\ a&-b\end{array}\right], \tag{11}\]  
where the dimension of \(\mathbf{S}\) is \(2\times 2\). The elements \(a\) and \(b\) must be real numbers in the symmetric matrix. In the considered MIMO-VLC system, we assume that the transmit power of the transmitter keeps constant. Thus \(a\) and \(b\) should satisfy \(\sqrt{a^{2}+b^{2}}=1\).  
As shown in Figure 2, the PAM vector \(\mathbf{u}=[u_{1},u_{2},\ldots,u_{p}]^{T}\) will be modulated by the symmetric matrix \(\mathbf{S}\), then the modulated signal \(\mathbf{x}\) will be attained as:  
\[\mathbf{x}=\mathbf{S}\cdot\mathbf{u}, \tag{12}\]  
where the \(\mathbf{x}\) is substituted for the \(\mathbf{x}_{j}\) of the \(j\)th user. After that process, the attained vector will be sent to the precoder.  
Next, we present the symmetric matrix \(\mathbf{S}\) expression. Define a error vector \(\tilde{\mathbf{x}}=\mathbf{x}_{i}-\mathbf{x}_{j}\) with \(\mathbf{x}_{i}\
eq\mathbf{x}_{j}\), then the Euclidean distance is [27]:  
\[d=\|\mathbf{\Lambda}(\mathbf{x}_{i}-\mathbf{x}_{j})\|_{F}^{2}, \tag{13}\]  
where \(\mathbf{\Lambda}\) is a diagonal matrix whose elements are singular, and \(\|\cdot\|_{F}\) represents the norm.  
Since the \(\mathbf{u}_{i},\mathbf{u}_{j}\in\mathbb{U}^{p\times 1}\) are the homologous vectors that have been modulated by the PAM according to Equation (12), we have:  
\[d=\|\mathbf{\Lambda}\mathbf{S}(\mathbf{u}_{i}-\mathbf{u}_{j})\|_{F}^{2}. \tag{14}\]  
According to Equation (14), the minimum Euclidean distance is obtained as:  
\[d_{\min}^{i}=\min_{j=1,\ldots,M^{p},j\
eq i}\|\mathbf{\Lambda}\mathbf{S}( \mathbf{u}_{i}-\mathbf{u}_{j})\|_{F}^{2}, \tag{15}\]  
where the \(M\) means the size of the PAM constellation. Since the \(\mathbb{U}\) have \(p\) symbols, thus the total number of the transmitted vectors \(\mathbf{u}\) is \(M^{p}\).  
To improve the reliability, the minimum distance should be maximized [26]. Then we propose to use the symmetric matrix \(\mathbf{S}\) given by  
\[\begin{split}\text{find}:&\mathbf{S}\\ \text{maximize}:&\min_{j=1,\ldots,M^{p},j\
eq i}\| \mathbf{\Lambda}\mathbf{S}(\mathbf{u}_{i}-\mathbf{u}_{j})\|_{F}^{2},\end{split} \tag{16}\]  
where in Equations (15) and (16), we could use the traversal method to select the optimal \(\mathbf{S}\), which in each transmission period is updated with \(\mathbf{\Lambda}\).
## 3 Results and Discussion
### Influence of the Mixed Eccentricity on the Air-Gap Magnetic Field  
In order to investigate the influence of mixed eccentricity on the spatiotemporal distribution of the air-gap magnetic field, Figure 6 shows the three-dimensional spatiotemporal distribution of air-gap flux density under the conditions of \(\varepsilon_{S}\) = 50% and \(\varepsilon_{D}\) = 50%. Figure 6a shows the three-dimensional spatiotemporal distribution of radial air-gap flux density, and Figure 6b shows the three-dimensional spatiotemporal distribution of tangential air-gap flux density. The blue curves along the x axis (times) are the curves of the variation in radial and tangential flux density with time at a given space angle, while the blue curves along the y axis (space angle) are the curves of the variation of radial and tangential components of air-gap flux density with space angle at a given moment. It can be seen from the figures that, under different space angles, the temporal distributions of the radial and tangential flux density are completely different, while the spatial distributions of the radial and tangential flux density at different times are relatively consistent. Therefore, in the following parts of the paper, only the spatial distribution of the air-gap magnetic field is analyzed.  
According to the above calculation, it can be found that mixed eccentricity leads to significant changes in the distribution of the air-gap magnetic field. In order to charac  
Figure 6: The three-dimensional spatiotemporal characteristics of air-gap magnetic field with mixed eccentricity. (**a**) The spatiotemporal distribution of radial component; (**b**) the spatiotemporal distribution of tangential component.  
terize this change, some features representing the radial and tangential components of the magnetic field were extracted, and their variation between different degrees of mixed eccentricity was analyzed.
## 8 Open Issues
* _Cross-border and roaming operations_--The EU rules of the \"free movement of goods and services\" and \"Single European sky\" (the ability of a UAV to operate in any EU country) have implications for communication services for the AFBW sectors; service availability or continuity abroad has to be provided, including the integration of the exposed mobile network control mechanisms with the vertical industry's ICT environment. Wide implementation of LBO roaming architecture (so far, practically unused due to the problem of needing a home PLMN to verify the charging information reported by the visited PLMN), either standalone or hybrid with Home Routed (HR) for specific transmission channels, will have to be implemented based on NS and differentiation of user plane function (UPF), as well as a way of building the inter-operator trust [158]. This problem will be particularly important in the case of UAV applications but also in the case of commercial highly-specialized robot service providers, wildlife migration monitoring with tracking collars, etc. * _End-to-End (E2E) feature span_--PLMN capabilities extend as far as the N6 interface and its termination in a Data Network (DN). Hence, despite a plethora of 5 GS mechanisms, e.g., for data ciphering, integrity protection, authentication of NFs or UEs, secure integration with other PLMNs, or non-3 GPP access, exploiting their full potential is limited. Although some partial solutions exist [63], a generic mechanism is needed to enable the extension of these mechanisms beyond the scope of the 5G network. The same issue can be observed in the context of NS. The PLMN network slices and hence the QoS and security control mechanisms (non-repudiation of data, data integrity, the confidentiality of identities, etc.) inherently do not go over the UPF termination point in a DN (N6 interface). This problem is fundamental in the case of low latency and critical communication, where the QoS violation impacts the safety of operations. To maintain full E2E control over the performance and security and thus mitigate the risks, a PLMN should span the vertical industry's ISs hosting environment or host their ISs on the PLMN's premises. Finally, the complexity of the AFBW sectors is overlooked by the Standards Developing Organizations (SDOs), which manifests in the non-comprehensive approach, resulting, e.g., in an insufficient network capacity for rural locations. To this end, to provide economic benefits, the AFBW sectors should be further investigated by the SDOs in the domain of telecommunications, and the standardization of the ICT ecosystem should be better aligned with the challenges posed by the AFBWs' conditions.
## 4 Low-input Fixed-Time Trajectory Planning Method
### Singularity and Obstacle Avoidance Strategy  
According to general kinematic formula given by the precise study [16] and momentum conservation, one joint angular velocity trajectory of Arm 1 and 2 can be planned by (6) as:  
\[\left\{\begin{array}{l}\dot{\mathbf{\theta}}^{1}=(\mathbf{I}_{m}^{1})^{-1}(\mathbf{V}_{ e}-\mathbf{I}_{0}\mathbf{V}_{0})\\ \dot{\mathbf{\theta}}^{2}=(\mathbf{J}_{c}^{2})^{-1}(\mathbf{C}-\mathbf{J}_{c}^{1}\dot{\mathbf{ \theta}}^{1}-\mathbf{H}_{0}\mathbf{V}_{0})\end{array}\right. \tag{8}\]  
If \(\mathbf{I}_{m}^{1}\) or \(\mathbf{J}_{c}^{2}\) is not full rank, then, the joint angular velocity may be infinite. The form of Jacobian matrix is given by Appendix A. Hence, a fast damped-least-squares (DLS) nonsingular joint angular velocity trajectory planning method [22] can be improved by (8) as  
\[\left\{\begin{array}{l}\dot{\mathbf{\theta}}^{1}=diag\bigg{(}\sum\limits_{i=1}^ {6}\frac{\sigma_{i}^{1}}{\sigma_{i}^{1}+(\lambda_{i}^{1})^{2}}\mathbf{v}_{i}^{1} \big{(}\mathbf{u}_{i}^{1}\big{)}^{T}\bigg{)}(\mathbf{V}_{e}-\mathbf{I}_{0}\mathbf{V}_{0})\\ \dot{\mathbf{\theta}}^{2}=diag\bigg{(}\sum\limits_{i=1}^{6}\frac{\sigma_{i}^{2}}{ \sigma_{i}^{2}+(\lambda_{i}^{2})^{2}}\mathbf{v}_{i}^{2}\big{(}\mathbf{u}_{i}^{2}\big{)} ^{T}\bigg{)}(\mathbf{C}-\mathbf{J}_{c}^{1}\dot{\mathbf{\theta}}^{1}-\mathbf{H}_{0}\mathbf{V}_{0}), \end{array}\right. \tag{9}\]  
with a simpler damping parameter function  
\[\lambda_{i}^{j}=\left\{\begin{array}{l}\lambda_{m}\Bigg{(}1+\frac{\left| \sigma_{i}^{j}\right|}{\varepsilon^{j}}\Bigg{)},\ \left|\sigma_{i}^{j}\right|\leq \varepsilon^{j}\\ 0\hskip 56.905512 pt,\ \left|\sigma_{i}^{j}\right|>\varepsilon^{j}\end{array}\right. \tag{10}\]  
\[\text{where diag}\Big{(}A_{i}^{j}\Big{)}=\left[\begin{array}{ccc}A_{1}^{j}& \cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&A_{6}^{j}\end{array}\right]\text{in which }A_{i}^{j}=\sum\limits_{i=1}^{6}\frac{ \sigma_{i}^{j}}{\sigma_{i}^{j}+(\lambda_{i}^{j})^{2}}\mathbf{v}_{i}^{j}\Big{(}\mathbf{u} _{i}^{j}\Big{)}^{T},\lambda_{m}\text{ is a damping}\]  
factor, \(\varepsilon^{j}\) is the singular threshold value.  
Furthermore, in space, the floating satellite debris and micrometeorites may pose a collision risk to the robotic arm. Hence, a CDF-based trajectory planning method for robotic arm in [20] can be improved by (9) to enable the space robot to avoid the collision (The pseudo inverse based SVD singular decomposition strategy is used here. i.e., \((J)^{-1}\to(J)^{\dagger}=(J)^{T}(J(J)^{T})^{-1}\))  
\[\dot{\mathbf{\theta}}^{1} =\mu\cdot(J_{m}^{1})^{T}\cdot diag\Big{(}\sum_{i=1}^{6}\frac{c_{i} ^{\dagger}}{c_{i}^{\dagger}+(\lambda_{i}^{2})^{2}}v_{i}^{1}\big{(}u_{i}^{1} \big{)}^{T}\Big{)}(\mathbf{V}_{e}-\mathbf{J}_{0}\mathbf{V}_{0})\] (11) \[\quad+\bigg{[}E-\mu(J_{m}^{1})^{T}(J_{m}^{1}(J_{m}^{1})^{T})^{-1}J _{m}^{1}\bigg{]}\dot{\mathbf{\theta}}_{c}^{1}\] \[\dot{\mathbf{\theta}}^{2} =\mu\cdot(J_{c}^{2})^{T}\cdot diag\bigg{(}\sum_{i=1}^{6}\frac{ \sigma_{i}^{2}}{\sigma_{i}^{2}+(\lambda_{i}^{2})^{2}}v_{i}^{2}\big{(}u_{i}^{
# Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications
###### Abstract  
This paper presents a novel design for and an experimental study of a dual-polarized quad-port MIMO antenna. The design achieves resonance at five distinct frequency bands with reduced mutual coupling. The design includes a single annular ring slot, four truncated rectangular corners, and a truncated aperture to improve resonance behavior. The design is then extended to a four-port MIMO antenna by including a ground-plane slit to enhance isolation between antenna elements at the center resonance band. The antenna achieves resonances at 5 distinct bands, ranging from 1.5 to 8.4 GHz, with significant mutual coupling reductions. The resonances of the quad-port pentaband MIMO antenna are achieved at 1.55 GHz (1.5-1.65 GHz), 2.5 GHz (2.4-27 GHz), 5.2 GHz (5-5.85), 7.3 GHz (7.1-7.4), and 8.15 GHz (7.9-8.4), with respective mutual coupling reductions of 27 dB, 37 dB, 21 dB, 29 dB, and 21 dB. Additionally, the 3 dB axial ratio bandwidth (ARBW) is observed at 6.5% (1.5-1.6 GHz) and 15% (2.4-2.7 GHz) in 2 distinct bands, and the envelope correlation coefficient and diversity gain are calculated within the specified band range. Experimental measurements of the prototype for the quad-port antenna are conducted, with excellent agreement found between the results and the simulations.  
circular polarization (CP); dual polarization; multiband/pentaband; MIMO; wireless applications +
Footnote †: journal:  
Acticle  
Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications  
Applications. _Companion_**2023**, _11_, 76. [[https://doi.org/10.3390/computation1040076](https://doi.org/10.3390/computation1040076)]([https://doi.org/10.3390/computation1040076](https://doi.org/10.3390/computation1040076))
## 1 Introduction  
Soil hydraulic modeling aims to estimate key parameters relating to the retention of water in the soil matrix. These parameters are used in a wide range of modeling applications including catchment hydrology, soil management, crop production, nutrient losses, and soil ecosystem functions. It is very challenging to obtain these parameters directly from detailed field measurement at the landscape-to-national scale, since they are costly and time-consuming to gather, so a pedotransferer function (PTF) is usually employed to estimate these important parameters.  
In previous work, a PTF for the soil water content was developed [1] for tensions of 0, \(-\)5, \(-\)10, \(-\)20, \(-\)40, \(-\)100, and \(-\)1500 kPa, as well as derived quantities (e.g., total available water content (TAW)--the difference between \(-\)10 and \(-\)1500 kPa), using attributes from a soil mapping system for New Zealand (S-map [2]) as a source of explanatory variables. Since that model was developed, new training data have become available, and additional explanatory variables have been identified, which suggest that revision of the model is advisable. Further, some shortcomings of the existing PTF model have become apparent, such as poor estimation of the response for some soil types of high importance (e.g., Allophanic and Pumice soils), poor estimates of the upper range of estimates of derived products such as TAW for some soil orders, and poor characterization of the uncertainty associated with predictions.  
While the current PTF [1] has used New Zealand-derived sample data for training, there are several potential alternatives available, such as the globally applicable Rosetta and European hydraulic PTFs models [3; 4], as well as many regional-scale physical and empirical models (e.g., [5; 6]). It is therefore relevant to compare any New Zealand PTF with results from other models.  
The aim of this paper is to describe an updated soil water PTF model for soils in New Zealand. It describes changes in the assumptions concerning the response variable, alterations in the explanatory variables that can be used, a better functional structure for the modeling (i.e., generalized additive model or GAM versus the original logistic regression), and a considerable change in the spatial extent and number of training samples available. The paper also offers a comparison between prediction results from the new model and other relevant global models for the soil-water response that it is feasible to compute for New Zealand soils. Typically, these models are focused on estimates of TAW, which is the derived parameter of most practical use in applications.