esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiport Single Element Mimo Antenna Systems: A Review
## 4 Multiport Single Element System
### Single Element Four-Ports MIMO Antenna System
The uniformity of the circular shaped radiator reduces the surface current, and the fringing field is formed by choosing the modified circular geometry concept. The surface current direction is further disturbed by the octagonal notches loaded in the circular radiator and thus the mutual coupling is reduced. The parasitic element placed diagonally between the ground plane enhances the inter-port isolation and ECC less than 0.5 is achieved. Figure 16: (**a**) Four ports multi-cut shared radiator and stepped ground [78], (**b**) Four ports multi-cut shared radiator and stepped ground [79]. Figure 17: (**a**) Stepped line common radiator prototype [80], (**b**) Notch-loaded circular radiator [81]. The differentially-fed MIMO antenna with dual-band is discussed in [82]. It consists of four ports sharing a common radiator and a square ring ground, suitable for WiMAX application, as shown in Figure 18a. Four differential ports arranged perpendicular to each other connected to virtual ground provides dual polarization and good isolation is achieved for dual frequency bands from 3.2-3.87 GHz and 5.35-5.83 GHz. Since the designed antenna is CPW differential fed, both the radiating patch and the virtual ground is printed on the same layer of the substrate. This makes the fabrication process easy and suitable for integration with RF front-end circuits. In [83], a monopole antenna with four ports sharing a common radiator and a ground plane with slots on all four edges was proposed covering the frequency ranging from 2 GHz to 3 GHz. The steps-shape common radiator patch placed on the top layer and frame-shape ground plane minimizes the mutual coupling effect among the ports which provides better isolation of less than \(-12\) dB as shown in Figure 18b. With the partial removal of ground plane on the bottom plate, the bandwidth is enhanced and ECC below 0.5 is achieved. The common radiator with four port MIMO antenna, discussed in [84], consists of a common radiating element and a square ring ground plane. Good isolation is achieved through space diversity and by etching the stubs in the ground plane, to achieve isolation of less than \(-15\) dB and measured return loss of \(-10\) dB is achieved for frequency range from 4.5 GHz to 7 GHz.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 3 Systematic Errors
### Scatterer Movement (Ground Surface)  
#### 3.3.1 Scatterer Phase Shift  
There are two kinds of changes in point scatterers within the time interval of two acquisitions: one is random motion in which the scatterers move independently from each other and are not spatiotemporally correlated; and the other is when the scatterers move in the same direction together [27; 32]. The random movements create phase noise as a result of volume decorrelation or temporal decorrelation, as stated in Section 4. On the other hand, the latter circumstance introduces a systematic phase offset and is therefore classified as a systematic error.  
A change in a point scatterer can relate to a change in its dielectric constant. This can be influenced by factors such as density, wetness, particle size, shape, and roughness [124]. These alterations cause variations in backscattering and, as a result, phase shifts. For example, if a soil swells by a few millimeters and the effect is visible from a radar sensor, the systematic change of the point scatterers would contribute extra phase shifts to the interferogram. Scatterer phase shift is rarely addressed as a systematic error source because of its minor contribution, but also because scatterer movement is frequently more random than being spatially correlated. For example, a field of vegetation acquired at two separate dates is likely to have larger volume decorrelation and temporal decorrelation, even if the plant or soil moisture change produced a slight phase shift.
## 2 EIT Imaging Basic Principles
### EIT Image Reconstruction  
EIT image reconstruction research is focused on the inverse problem. Based on the established simplified EIT model, the mathematical expression for the EIT inverse problem can be derived as follows:  
\[g=S^{-1}F \tag{10}\]  
In the above equation, \(S^{-1}\) represents the generalized inverse matrix of the \(m\times n\) normalized sensitivity matrix. In most EIT systems, since \(m>>n\), it is not possible to directly compute \(S^{-1}\). This fact highlights the ill-posed nature of the EIT image reconstruction process from another perspective.  
The Tikhonov regularization algorithm, which is discussed in this paper, aims to mitigate the discontinuity in solving inverse problems through the \(L_{2}\) norm regularization and introduce prior information of the image for improving the stability of the solution. This optimization process helps to enhance the solution of ill-posed inverse problems. The mathematical expression of the Tikhonov regularization algorithm is given as follows:  
\[\min_{\Delta\sigma}||Sg-F||_{2}^{2}+\alpha||Lg||_{2}^{2} \tag{11}\]  
In the above equation, \(\alpha\) represents the regularization parameter, which plays a role in adjusting the weight of regularization. If \(\alpha\) is too small, the regularization constraint cannot effectively control the solution. However, if \(\alpha\) is too large, it may cause significant deviation between the reconstructed image and the true image. In this paper, the L-curve method [26] is employed to determine the optimal value of \(\alpha\). Therefore, for the original Tikhonov algorithm mentioned below, the regularization parameter is 0.00004. Meanwhile, the literature [9] suggests that a smaller regularization parameter should be used for the target area of imaging to enhance the detail reconstruction capability of the target area. Consequently, for the reduced-order Tikhonov algorithm, the regularization parameter used for the target area is 0.000025. In the equation, \(L\) represents the regularization operator, typically taken as the identity matrix \(E\). Consequently, the regularized solution \(g\) can be obtained as follows:  
\[g=\Big{(}S^{T}S+\alpha E\Big{)}^{-1}S^{T}F \tag{12}\]
## 4 Results
### Walkability Scores of Senior Care Facilities Within 5-min, 10-min, and 15-min Living Circles  
This study quantitatively evaluated the spatial service efficiency of senior care facilities under 5-min, 10-min, and 15-min walking thresholds by constructing a multi-scale living circle walkability evaluation system. The spatial distribution map of comprehensive walkability scores, generated using the Inverse Distance Weighting (IDW) interpolation method in ArcGIS (Figure 5), reveals substantial gradient differences and multi-core clustering characteristics in the walkability of senior care facility living circles in Changsha City. The core high-value areas are concentrated in mature urban districts on the east bank of the Xiangjiang River, such as Yuhua District and Tianxin District, with secondary cores forming in Liuyang and Ningxiang. This spatial pattern aligns with the distribution of accessible service types discussed in the previous section.  
Figure 5: Interpolation of walkability rating score.  
Notably, quantitative analysis shows that although the highest score for the 15-min living circle reaches 71 points, 56% of senior care facilities score below 15 points. Facilities with low scores (30 points) are primarily distributed in urban areas on the west bank of the Xiangjiang River and rural townships, reflecting spatial imbalances in the allocation of service resources. Furthermore, as the walking time threshold expands, both the mean walkability scores (5 min: 4.28 \(\rightarrow\) 10 min: 10.51 \(\rightarrow\) 15 min: 17.07) and the range of scores (5 min: 31 \(\rightarrow\) 10 min: 58 \(\rightarrow\) 15 min: 73) show an increasing trend. This indicates that spatial inequality is more pronounced in the 15-min and 10-min living circles compared to the 5-min living circle.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 4 Cryptographic Background
### Hash Functions  
A hash function [25] is a deterministic procedure, denoted by \(\mathcal{H}\), which receives an arbitrary bitstring as input (the message) and maps it into a fixed-length, usually between 160 and 256 bits, output (the digest). Let \(M\) be a message. We denote its digest as \(\mathcal{H}(M)\). To be suitable for cryptographic purposes, a hash function must be both one-way and collision-free.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 5 Future Directions
### Deep Space Missions  
The unbounded, limitless area of space that extends beyond the Moon to Mars and throughout our solar system is known as deep space. Comprehending the planets, moons, and minor bodies within our solar system facilitates our exploration of the past, the diversity of our own and other planetary systems, and our understanding of our origins. Thus, deep space missions are expected to show remarkable progress in the near future. Spacecraft need to be resilient to the harsh environmental conditions in space, and strong propulsion and communication systems are required for spacecraft to be able to accomplish this. For such missions, electric propulsion is thought to be a crucial and innovative technology. Furthermore, interest in using small satellites for exploring deep space is growing. The BepiColombo, GOCE, and SMART-1 missions by ESA are a few examples. NASA is also exploring electric propulsion for deep space missions. For instance, the Lunar Gateway's power and propulsion component is expected to showcase cutting-edge, high-power solar electric propulsion. Figure 21 shows a few present and future deep space missions along with their power levels [142; 143]. It is evident that the power requirements for deep space missions show an increasing trend. This increase in power demands intensive research on novel DC-DC converters that can work reliably and efficiently in such radiation-hardened environments.
## 3 Methodology
### Study Selection  
Figure 2 shows the search and screening stages for study selection. After initial filtering through the application of the search equation, a total of 2322 records were found. In the next stage, all papers were screened by reading their titles and abstracts. A total of 1320 records were selected after applying the limits for language (English only) and research topic. The number of publications was further reduced to 464 by excluding those that were not relevant to our interest and those that were in the form of seminar summaries, books, reviews, and editorials. In the final stage, a full-text screening was performed for these articles. Of the 464 papers, only 10 were found to be closely related to the subject of this review and were consequently selected for further analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Review questions** & RQ1: What are the factors affecting farmers’ decision to adopt digital agricultural technologies? \\  & RQ2: What are the analytical methods used for evaluating factors? \\ \hline \multirow{5}{*}{**Search strategy**} & Sources: Web of Science and Google Scholar \\  & Search string: ((“China”) AND (“agriculture”) AND (“digital technology” OR “Agriculture 4.0” or “Industry 4.0” OR “smart farming” OR “precision agriculture” OR “Internet of Things” OR “cloud computing” OR “artificial intelligence” OR “big data analytics” OR “robot” OR “remote sensing” OR “drone technology”) AND (“farm*”) AND (“adoption” OR “use” OR “application” OR “willingness” OR “intention”) \\ \hline \multirow{5}{*}{**Selection criteria**} & Inclusion criteria: \\  & Peer-reviewed journal articles and grey literature. \\ \cline{1-1}  & Studies should provide answers to the research question. \\ \cline{1-1}  & Exclusion criteria: \\ \cline{1-1}  & \(\bullet\) & Summary of seminars and workshops, books, reviews, and editorials. \\ \cline{1-1}  & \(\bullet\) & The publication is not in English. \\ \cline{1-1}  & \(\bullet\) & The publication is not available in full text. \\ \cline{1-1}  & \(\bullet\) & The technologies are not on-farm digital technologies. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Review protocol for systematic literature review.
## 2 Study Sites and Methods
### Scenario Simulations  
The model simulations addressed only sinking litter (plastics). The most relevant sinking plastic polymers are rigid polyvinyl chloride (PVC, density 1.3-1.45 g/cm\({}^{3}\)) and polyethylene terephthalate (PET, density 1.38 g/cm\({}^{3}\)).  
In the shallow city harbor, with a water depth of about 2.5-3.5 m near the harbor wall and up to 6 m in the shipping channel, common sinking plastic particles such as PVC or PET settle to the harbor bottom within minutes. At the bottom, the particles were assumed to be transported with the 50 cm layer of bottom water. Particles deposited on the sediment require a higher bottom shear stress to be remobilized and transported. We assumed that only the highest 25% of the bottom shear stresses, calculated during the simulation period, would cause a transport. These optimistic assumptions generally favor a near-bottom transport.  
The model and Lagrangian plastic-transport simulations covered the Hanse Sail years of 2009 and 2010. The background is that Hanse Sails are likely causing pollution and that several events were accompanied by intensive data collection and field activities on litter emissions. The years 2009 and 2010 were chosen because they had contrasting weather conditions and were among the years with the highest number of visitors. The particle-tracking simulations always started at the beginning of the Hanse Sail and ended after 10 days. Additionally, one simulation assumed that the strongest storm of the year 2009 (strong gale; maximum wind speed of 21.9 m/s) occurred immediately after the Hanse Sail. The aim was to gain insight into the potential reallocation of bottom litter under extreme conditions.  
The field data of several Hanse Sail years allowed an estimation of floating-litter emissions, but were too weak to provide data on sinking-litter emissions into the estuary. For the model simulations, we assumed (expert guess) an emission of litter (plastic) with a density above 1 g/cm\({}^{3}\) of 0.3 emitted macro-litter particles per 1000 visitors per day.  
Figure 3: Exemplary underwater photos from a Tunisian (**a**) (Mediterranean Sea) and a German (**b**) (Baltic Sea) marina as well as the technical setup (**c**) and the method for field application (**d**).  
The scenario hypothetically assumed the emission of one sinking plastic particle per 300 Hanse Sail harbor coastline (about 2.1 km), emitted hourly during the Hanse Sail opening hours (Thu. to Sun. between 12:00-24:00 o'clock). That means, 91 particles were emitted per day and 364 particles were emitted altogether during the whole event.
## 1 Introduction
### Contribution  
Motivated by the explained issues, in this paper, we consider the design and implementation of a co-operative 5G network with an in-band type 2 relay node for the downlink signal using a universal software radio peripheral (USRP) platform and Matlab(tm) tool. Therefore, the main contributions in this work, are summarized as follows:  
1. Design and implementation of a fully functional testbench, based on a MIMO software defined radio platform and Matlab(tm) software, which supports the following 5G downlink signal features [10; 11]:  
* Configuration and parametrization of the synchronization signal block (SSB), which supports the primary synchronization signal (PSS), secondary synchronization signal (SSS), and physical broadcast channel (PBCH) with its associated demodulation reference signal (DM-RS).  
* Configuration of the variable signal bandwidth, CORESET, and search spacing functionalities.
* Physical downlink control channel (PDCCH) and its associated DM-RS.
* Physical downlink shared channel (PDSCH) and its associated DM-RS. In addition, enables the DL-SCH transport channel coding.  
These functionalities allow the generated 5G downlink signal from the emulated gnodeB to be demodulated by the MS2090A-Anritsu LTE/5G spectrum analyzer.
* Use of the developed architecture as an integrated platform supporting the radio access link in the 5G FR1 band, connecting a gnodeB and a relay node.
* An L2 relay node or decode-and-forward (D&F) relay node has been proposed and implemented. D&F node performs the functions of frequency synchronization, error correction, and search cell-ID stages, which allows practical adjustment of the received 5G downlink signal parameters. In addition, practical channel estimation by means of least square (LS) and minimum mean square error (MMSE) equalization techniques are introduced.
* A system model for the D&F node from the proposed hardware interface relay node co-operative network with real assumption is derived.
* Several measurement cases are carried out in real outdoor-to-indoor scenarios, using 64-QAM and 256-QAM modulation schemes for the 5G NR PDSCH.
## 2 Proposed Integrated Mobile VLCP Scheme
### Proposed Scheme for MIMO Configuration
With equal weight coefficients, \(\hat{\mathbf{H}}_{j}^{n+1}(k)\) can be expressed as follows:  
\[\hat{\mathbf{H}}_{j}^{n+1}(k)=\frac{1}{L}\sum_{p=n-L+1}^{n}\mathbf{Y}_{j}^{p}(k )\big{[}\hat{\mathbf{X}}^{p}(k)\big{]}^{-1}. \tag{13}\]  
With unequal weight coefficients, \(\hat{\mathbf{H}}_{j}^{n+1}(k)\) can be expressed as follows:  
\[\hat{\mathbf{H}}_{j}^{n+1}(k)=\sum_{p=n-L+1}^{n}\frac{\alpha^{n-p}(1-\alpha)}{ 1-\alpha^{L}}\mathbf{Y}_{j}^{p}(k)\big{[}\hat{\mathbf{X}}^{p}(k)\big{]}^{-1}. \tag{14}\]  
Based on the encoding matrix of STBC, \(\hat{\mathbf{X}}^{n}(k)\) is always invertible, ensuring the implementation of Equations (13) and (14). The acquired CSI can be further employed to determine the location of the mobile receiver. By setting \(k=1\) in the estimated FDTF \(\hat{P}_{ji}^{n}(k)\) and referring to Equation (7), the distance between the \(i\)th LED and the \(j\)th PD can be estimated as follows:  
\[\hat{D}_{ji}^{n}=\sqrt[n+3]{\frac{(m+1)AT_{s}(\varphi)g(\varphi)h^{m+1}}{2\pi \big{|}\hat{P}_{ji}^{n}(1)\big{|}}}. \tag{15}\]  
With trilateration, we select different groups of the estimated distances to obtain multiple solutions for PD1, denoted as \((\hat{x}_{r1}^{q},\hat{y}_{r1}^{q})\) (\(q=1\), \(2\), \(3\), \(4\)), and for PD2, denoted as \((\hat{x}_{r2}^{q},\hat{y}_{r2}^{q})\), similar to the MISO system. Then, the multiple solutions of the horizontal coordinate of the receiver are calculated as the midpoint between PD1 and PD2, i.e., \(((\hat{x}_{r1}^{q}+\hat{x}_{r2}^{q})/2)\), \((\hat{y}_{r1}^{q}+\hat{y}_{r2}^{q})/2\)). Taking the average among them can finally lead to the final positioning result.
# An Enhanced Auxiliary Information-Based EWMA-t Chart for Monitoring the Process Mean
## 3 Proposed Control Charts
### AIB-GWMA-t Control Charts  
This study also extends the AIB-EWMA-t chart by adding design and adjustment parameters to enhance detection capability. Now, we use the statistic \(T_{i}^{*}\) in Equation (7) to replace the statistic \(X_{i}\) in Equation (10); hence, the statistic for the AIB-GWMA-t chart is \(G_{i}^{*}\), which we define as follows:  
\[G_{i}^{*}=\sum\limits_{j=1}^{i}{(q^{(j-1)^{\alpha}}-q^{ji})T_{i-j+1}^{*}}+q^{ ji}G_{0}^{*} \tag{15}\]  
where the initial value of \(G_{i}^{*}\) is equal to zero (i.e., \(G_{i}^{*}=0\)). For the in-control case, the mean and variance of \(G_{i}^{*}\) are the same as those of \(G_{i}\) in Equations (12) and (13). The chart based on \(G_{i}^{*}\) is called the AIB-GWMA-t chart. The constant control limits of the AIB-GWMA-t chart are:  
\[\left\{\begin{array}{c}\mathit{UCL}=0+L_{ag}\sqrt{Q\cdot\frac{n-1}{n-3}}\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad CL=0\\ \mathit{LCL}=0-L_{ag}\sqrt{Q\cdot\frac{n-1}{n-3}}\end{array}\right. \tag{16}\]  
where \(Q=\underset{i\rightarrow\infty}{\lim}\left(\sum\limits_{j=1}^{i}{(q^{(j-1)^{ \alpha}}-q^{ji})}^{2}\right)\) is an asymptotic value. \(L_{ag}\) is the control limit constant, as in \(L_{ae}\) in Equation (9) or \(L_{g}\) in Equation (14), which is used to determine the desired in-control \(\mathit{ARL}\). When the statistic \(G_{i}^{*}\) moves beyond the control limit, it indicates a process mean shift.  
Figure 1 depicts the relationship among the AIB-GWMA-t chart, AIB-EWMA-t chart, GWMA-t chart, and EWMA-t chart.  
In Figure 1, the AIB-EWMA-t and EWMA-t charts are special cases of the AIB-GWMA-t and GWMA-t charts, respectively when adjustment parameter \(\alpha=1\) and design parameter \(q=1-\lambda\). Moreover, the AIB-GWMA-t and AIB-EWMA-t charts reduce to the GWMA-t and EWMA-t charts, respectively when correlation coefficient \(\rho=0\).  
Figure 1: The structure of the AIB-GWMA-t chart and its special cases.
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
###### Abstract  
In this study, we designed a mobile free space optic receiver that uses several photodetectors to provide omnidirectional receiving capability. Assuming only one transmitter, it is a receiver which builds a single input multiple output optical channel. The photodetectors are fixed to truncated pyramid walls. Electrical signals from the photodetectors are processed using an equal gain combining technique. This architecture allows simple circuits and enables additive noise suppression. The minimum angle between the pyramid base and the direction of falling rays was calculated to determine the threshold for additive noise suppression. Two areas of interest presented themselves: the processing of very weak electrical signals often drowned in noise, and optimization of the number of photodetectors whose fields of view overlapped strongly. We outline the design of the optical receiver circuitry and provide some practical hints concerning its assembly. The receiver was evaluated using bit error rate measurements and comparing signal-to-noise ratio parameters for various photodetector numbers. The measured data confirm the theoretical assumptions.  
free space optic; optical receiver; omnidirectional; truncated pyramid; equal gain combining; parallel amplifier; low noise; noise suppression Article
## 2 Materials and Methods
### Positive Matrix Factorization (PMF) Model  
The PMF model, a receptor-based multivariate factor analysis method for pollution source apportionment [44], mathematically decomposes the target sample concentration matrix (X) into three constituent matrices through factorization: the source contribution matrix (G), source profile matrix (F), and residual matrix (E). The mathematical representation is expressed as follows:  
\[X_{ij}=\sum_{k=1}^{p}\left(G_{ik}\times F_{kj}\right)+E_{ij} \tag{7}\]  
where \(X_{ij}\) denotes the concentration of element \(i\) in sample \(j\); \(G_{ik}\) represents the contribution of element \(i\) to source factor \(k\); \(F_{jk}\) corresponds to the content of element \(i\) in source factor \(k\); \(E_{ij}\) indicates the residual error for element \(i\) in sample \(j\); and \(p\) is defined as the number of pollution factors.  
The optimal solution for matrix optimization through iterative computation via the multilinear engine (ME) model is identified when the objective function \(Q\) reaches its minimum value [44]. The mathematical formulations are expressed as follows:  
\[Q=\sum_{i=1}^{n}\sum_{j=1}^{m}\left(\frac{E_{ij}}{U_{ij}}\right) \tag{8}\]  
\[U_{ij}=\begin{cases}\frac{5}{6}\ \times\ \text{MDL}&\text{ where }\text{c}\leq\text{MDL}\\ \sqrt{(8\times\text{c})^{2}+(\text{MDL})^{2}}&\text{ where }\text{c}>\text{MDL} \end{cases} \tag{9}\]  
where \(Q\) denotes the objective function; n indicates the number of samples; m represents the number of element species; \(U_{ij}\) is defined as the uncertainty value; \(\delta\) corresponds to the relative standard deviation; \(c\) signifies the measured concentration of HMs; and MDL refers to the method detection limit.