esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing for Land Administration
###### Abstract  
Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.  
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# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 2 Beam-Steering for Optical Wireless Communications Transmitter
### State of the Art for Narrow Beam-Steering
Unlike the discrete steering approaches of Metasurfaces, Optical Phased Arrays, and other alternatives, this approach offers continuous movement capability, enabling more precise and fluid beam positioning. And wavelength independence ensuring compatibility with any optical telecommunications infrastructure, while the simple implementation using commercial components like benders and tapered fibers reduces system complexity and potential manufacturing costs. The continuous movement and wide Field of View (51.3\({}^{\circ}\)\(\times\) 22.4\({}^{\circ}\)) make this approach particularly promising for dynamic indoor optical wireless communication environments. While current multi-beam functionality is limited, the fundamental design suggests a potential for future scalability and performance improvements. The use of silicon micro \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **This Paper** & **Metasurfaces [5]** & **OPA [10]** & **SLM [7]** & **AWGR [2]** \\ \hline Field of View & 51.3\({}^{\circ}\)\(\times\) 22.4\({}^{\circ}\) & 54.2\({}^{\circ}\) & 160\({}^{\circ}\) & 60\({}^{\circ}\) & 17\({}^{\circ}\)\(\times\) 17\({}^{\circ}\) \\ Throughput & 10 Gbps & NA & NA & 224 Gbps & 112 Gbps \\ Steering Type & Continuous & Discrete & Discrete & Discrete & Discrete \\ Multi-Beams & No & No & No & Yes & Yes \\ Wavelength & 1550 nm & 950 nm & 1550 nm & 1550 nm–1600 nm & 1550 nm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the state of the art on IR beam-steering. Figure 2: Indoor optical wireless system using piezoelectric actuators. (**a**) 2D MEMs cantilever with piezoelectric actuators for beam steering. Actuators in black, control pads in yellow and red for the isolator. (**b**) System topology from residential gateway to the ceiling transmitter, showing fiber-to-the-room connectivity and optical beam transmission to users. lenses with high refractive properties and transparency at IR wavelengths further enhances the system's optical performance, positioning it as an innovative solution for beam steering technologies.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 4 Simulation Results
### Computational Complexity  
Finally, we evaluate the computational complexity of the proposed CSI-free DNN detector and compare it with other benchmark detectors. For both the CSI-free DNN detector and the ZF-DNN detector, once the detector has been successfully trained, it can be used for MIMO detection for a long period of time without further retraining, unless the system parameters such as receiver location have been changed [27]. Hence, only the computational complexity of the online detection process is considered for the CSI-free DNN detector and the ZF-DNN detector, whereas the complexity of the offline training process is not taken into account. Moreover, the computational complexity of the proposed DNN detectors and the other three benchmark detectors is evaluated and compared in terms of computation time, which is a common way for computational complexity evaluation in the literature [34]. Figure 8a,b compare the computation time of the proposed DNN detectors and the other three benchmark detectors for GOSM and GOSMP, respectively. As we can see, for GOSM, the CSI-free DNN detector, the ZF-DNN detector and the ZF-ML detector require nearly the same computation time which is less than 3 s. However, the joint ML detector requires totally 48.42 s to finish the computation, which is significantly longer than that of the other three detectors. It is the same for GOSMP that the CSI-free DNN detector, the ZF-DNN detector, and the ZF-ML detector require comparable computation time, which is much shorter than that required by the joint ML detector. Therefore, the proposed CSI-free DNN detector achieves near-optimal BER performance as the joint ML detector, but with substantially lower computational complexity.  
Figure 7: BER vs. \(\log_{10}\alpha\) of the proposed CSI-free DNN detector for (**a**) GOSM and (**b**) GOSMP.
# Forward/Backward Decomposition for Dispersive Wave Propagation Measurements
## 1 Introduction
The technique presented here is based on the Fourier decomposition of the signals into the frequency domain, where different wave modes' Fourier coefficients superimpose according to simple vector addition. With a sufficient number of measurements, the various vectors corresponding to different individual wave modes can be decomposed at each frequency. After presenting the theory in Section 2, an algorithmic implementation of the technique is presented in Section 3, along with practical considerations to account for random noise in experiments. We then present experimental validation of the proposed approach on a clamped-clamped rectangular beam in Section 4 before providing concluding remarks in Section 5.
## 2 Basic Principles and Methods
### Stress Solution of Lining and Surrounding Rock  
Given the (\(R_{1}\), \(R_{2}\), \(\varphi_{x}\), \(p_{0}\), \(p_{1}\), \(\lambda\), \(\mu_{1}\), \(\mu_{2}\), \(E_{1}\), \(E_{2}\), \(\eta\)) parameter values, Equations (14)-(17) can be combined to solve \(e_{1}\)-\(e_{3}\),\(f_{1}\)-\(f_{3}\), \(g_{1}\), \(h_{1}\), and \(f_{3}\), and the specific expression of analytical function corresponding to the surrounding rock-lining can be obtained. Then, the stress at any position in the lining can be solved via Equation (5); just replace \(\varphi_{j}\)(\(z\)) and \(\psi_{j}\)(\(z\)) in the equation with the corresponding \(\varphi_{L}\)(\(z\)) and \(\psi_{L}\)(\(z\)), respectively.  
\[\left\{\begin{array}{l}\varphi_{L}(z)=\varphi_{1}(z)+\varphi_{3}(z)\\ \psi_{L}(z)=\psi_{1}(z)+\psi_{3}(z)\end{array}\right. \tag{18}\]  
The stress in the surrounding rock is composed of the original stress of the surrounding rock before excavation, the stress response caused by excavation, and the stress response after the lining is applied. Therefore, the two final analytical functions \(\varphi_{R}\)(\(z\)) and \(\psi_{R}\)(\(z\)) are equal to the analytical function before excavation plus the analytical functions corresponding to Equations (7) and (9), i.e.,:  
\[\left\{\begin{array}{l}\varphi_{R}(z)=-\frac{\sigma_{x}(1+\lambda)}{4}z+ \varphi_{2}(z)+\varphi_{4}(z)\\ \psi_{R}(z)=-\frac{\sigma_{y}(1-\lambda)}{2}z+\psi_{2}(z)+\psi_{4}(z)\end{array}\right. \tag{19}\]  
The first term in Equation (19) represents the analytical function corresponding to the surrounding rock before tunnel excavation. Substituting Equation (19) into Equation (5), the stress result at any position in the surrounding rock can be obtained.
## 1 Introduction  
Biocompatible magnetic nanoparticles have drawn attention owing to their potential applications in biomedical therapists [1, 2, 3, 4]. Among various biomedical applications, magnetic resonance imaging (MRI) contrast agents have been an issue because MR image sensitivity and resolution can be improved using contrast agents [5, 6, 7, 8]. Thus, nanoparticles with advanced magnetics properties have been investigated to develop advanced MRI contrast agents [9, 10, 11].  
The clinically available Gd-chelates act as positive (or T\({}_{1}\)) MRI contrast agents due to their efficient induction of longitudinal (or T\({}_{1}\)) water-proton spin relaxation with respect to transverse (or T\({}_{2}\)) water-proton spin relaxation, providing r\({}_{2}\)/r\({}_{1}\) ratios (r\({}_{1}\) and r\({}_{2}\) are longitudinal and transverse water-proton spin relaxivities, respectively), which are close to one [5, 6]. This is due to a high pure spin magnetic moment (S = 7/2) of Gd\({}^{3+}\)[5]. The r\({}_{1}\) values of Gd-chelates are low [5, 6, 7, 8]. However, the r\({}_{1}\) values and as a result, imagingperformance can be improved using ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles because of their high Gd-densities per nanoparticle and appreciable nanoparticle magnetic moments [11]. The ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles have shown r\({}_{1}\) values higher than those of commercial Gd-chelates [11; 12; 13; 14].  
To use ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles as T\({}_{1}\) MRI contrast agents, surface coating is necessary to endow them with hydrophilicity and biocompatibility [15]. In addition, surface-coating ligands can affect their r\({}_{1}\) and r\({}_{2}\) values [16; 17] because these values depend on the water-attracting power of surface-coating ligands around Gd\({}_{2}\)O\({}_{3}\) nanoparticles. According to the inner-sphere model [5], water molecules close to Gd\({}_{2}\)O\({}_{3}\) nanoparticles can affect the r\({}_{1}\) value, whereas the remaining water molecules can contribute to the r\({}_{2}\) value according to the outer-sphere model [5; 18]. Therefore, surface-coating ligands may allow us to vary the r\({}_{2}\)/r\({}_{1}\) ratio and as a result, T\({}_{1}\) and T\({}_{2}\) imaging modes of ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles. In this study, polysapartic acid (PASA), which is a biodegradable and hydrophilic amino acid polymer [19], was used as a surface-coating ligand. PASA-coated ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles exhibited an appreciable r\({}_{2}\)/r\({}_{1}\) ratio, thus allowing us to obtain both T\({}_{1}\) and T\({}_{2}\) MR images.  
In this study, PASA-coated ultrasmall Gd\({}_{2}\)O\({}_{3}\) nanoparticles were synthesized using the one-pot polyol method. The synthesized nanoparticles were subject to various analyses to characterize their particle size, colloidal stability, surface coating, in vitro cellular toxicity, and magnetic and relaxometric properties. They were applied to in vivo MRI in mice to obtain both T\({}_{1}\) and T\({}_{2}\) MR images.
## 3 Methodology
### Machine Learning Approaches
#### 3.1.3 U-Net  
U-Net is a Fully Convolution Network (FCN)-based model for semantic segmentation introduced in [43]. Despite being intended for biomedical image segmentation, it has been used in many studies in the remote sensing field, such as land cover classification and object detection, because it performs well, even with a small amount of training data [44]. The study sites in South Korea also have limitations in extracting training data because of the small scale of the fire damaged region. For this reason, the U-Net was selected as a deep learning model for performance comparison with ensemble learning-based machine learning models in this study while considering the advantage that high performance can be derived even in the limited training data.  
The structure of U-Net is shown in Figure A1 at Appendix A. In U-Net, accuracy was improved by minimizing information loss in the convolution layer using Skip-connection. Furthermore, it is useful since it allows for the usage of previously inaccessible location data [45; 46]. In this study, the optimal hyperparameters were determined based on the grid search method. Ten times tests were performed, and different hyperparameter values were applied for each test. Through this process, optimal hyperparameters were determined in consideration of training accuracy, validation accuracy, and loss. Model parameters optimized were Batch Size = 30, Epoch = 100, Learning Rate = 1 \(\times\) 10\({}^{4}\), Optimizer = 'Adam', and Loss function = 'Categorical cross entropy'. The 32 \(\times\) 32 size patches of input images were extracted and fed into U-Net.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{2-4}  & **Scheme 1** & **Scheme 2** & **Scheme 3** \\ \hline Boosting type & \multicolumn{2}{c}{GBDT (traditional Gradient Boosting Decision Tree)} \\ Colsample bytree & 0.6 & 0.8 & 0.8 \\ Maxdepth & 7 & 1 & 4 \\ Min. child weight & 10 & 20 & 10 \\ Num. of estimators & 900 & 300 & 900 \\ Num. leaves & 15 & 15 & 15 \\ Subsample & 0.6 & 0.6 & 0.6 \\ Learning rate & 0.005 & 0.005 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of LightGBM model.
## 3 Results and Discussion  
### TEM Results  
Figure 1 shows HRTEM and EFTEM images of SN1 and SN2 films annealed at T\({}_{\rm ann}\) = 1150 \({}^{\circ}\)C. Silicon nanocrystals can be seen as sets of lattice fringes (circled) in HRTEM images for both samples (see Figure 1a,d). Bright regions in EFTEM images (see Figure 1b,e) correspond to Si, while the dark ones correspond to Si\({}_{3}\)N\({}_{4}\), thus multilayered structure is also seen. At the same time, it is obvious, that size of Si NCs is not precisely restricted to SiN\({}_{\rm x}\) layer thickness. An effect of coalescence of neighboring nanocrystals throughout Si\({}_{3}\)N\({}_{4}\) barrier layers can be observed, thus their shape starts to be non-spherical (see Figure 1a,d). The mean diameter of Si NCs, which is defined as half sum of ellipse major axes in case of non-spherical particles, was estimated from HRTEM images. According to the statistical observations for more than 100 nanocrystals, their most frequently detected mean diameter was 2 nm, as shown in Figure 1c,f, which correlates well with SiN\({}_{\rm x}\) layer thickness (2 and 1.6 nm). At the same time, coalescent ellipsoidal-shaped nanocrystals with the mean diameter up to 10 and 9 nm were formed in case of SN1 and SN2 samples, respectively. Hence, the overall mean size of Si NCs was calculated to be 3.8 \(\pm\) 2.3 nm for SN1 and 3.4 \(\pm\) 1.7 nm for SN2 sample, which reflects the reduction of SiN\({}_{\rm x}\) layer thickness for the latter sample. Formation of nanocrystals with a non-spherical shape was also observed for annealed SiN\({}_{\rm x}\) monolayered films [21], while similar effect of uncontrollable growth of Si NCs has been demonstrated recently for annealed SiN\({}_{\rm x}\)/SiO\({}_{2}\) hetero-superlattices [12].
## 3 Results
### Analysis of the Overall Consumer Liking  
#### 3.4.1 Overall Consumer Liking  
This study collected the liking data of 76 qualified consumers. A larger sample size could make the results more accurate and instructive, although our sample size (76) was appropriate for consumer liking, as many studies show. [PERSON] et al. [52], [PERSON] et al. [53], and [PERSON] et al. [54] studied tomatoes, Chinese bog bilberry wines, and coffee with a sample size of 54, 93, and 96 respectively. On average, B6 was preferred, receiving a score of 3.60 on a scale of 1 to 7, followed by B2, indicating that the most preferred vinegar was still not liked much (Figure 2). A previous study showed that the satisfaction level of consumers of vinegar products was low at this stage [55]. B1 scored the lowest in liking with a value of 3.18. The liking score range of all the tested products was 0.43, suggesting that respondents provided scores in a relatively limited range. The Kruskal-Wallis test calculated that there was no significant difference between the six products at a significance level of 0.05. This could be attributed to the significant segmentation in the liking results of the respondents, as discussed subsequently. [PERSON] and [PERSON] (2004) [56] mentioned that experts gave a more consistent description of attributes than the trained panelists for different wine product brands.  
4.2 The Association between the Geographical Location of Consumers for the Past Ten Years and Their Likings  
Figure 3 shows a heatmap representing the geographical origins of the respondents and their likings. The results yielded two distinct clusters, one containing B1, B3, and B5, while the other comprising B2, B4, and B6. Respondents from Chongqing, Hunan, and Sichuan generally showed a marked liking for B1, B3, and B5, while participants from Heilongjiang, Henan, Shaanxi, and Inner Mongolia preferred B2, B4, and B6. Respondents from Beijing, Hebei, and Shanxi exhibited lower liking differences regarding the tested products. Moreover, respondents from Hubei, Anhui, and Zhejiang displayed a less positive attitude toward most of the tested samples. Obviously, consumers from different geographical locations had different likings [57].  
Figure 2: Average liking scores of 76 qualified consumers for six kinds of vinegar. The vinegar code corresponds with Table 1.  
It should be noted that the respondents were not evenly distributed in this study, denoting an area that could be improved in further research. Here, 48% of the respondents were from Beijing or had lived in Beijing in recent years (Figure S2). This observation may not remain the same when the sample size increases, representing an interesting phenomenon derived from this dataset.
## 3 Concepts, Models and Technologies
### Network Functions Virtualization  
Network Functions Virtualization (NFV) originated from discussions among telecommunications service providers about improving network operations in response to the significant growth in multimedia traffic volume. As a result of these initial efforts, in 2012, the European Telecommunication Standards Institute (ETSI) proposed the NFV framework. In the document produced, the ETSI working group outlined the overarching goal of NFV: leveraging IT virtualization standards to consolidate various types of equipment into a new industry standard for high-volume servers, storage systems, switches, routers and other network elements. These elements can be deployed across network nodes, data centers and routers or switches referred to by operators as Customer Premises Equipment (CPE), which are installed on the client's or end user's premises (at the network Edge) [11].  
This initiative was driven by the extensive variety of proprietary hardware and software included in CPEs, which posed several challenges for operators. These challenges included the constant reinvestment in technological upgrades (_CapEx - Capital Expenditure_) that could not always be passed on to enterprise and public sector clients, as well as ongoing expenditures in training and recertification of specialized human resources. These factors highlight the fundamental motivation behind the development of NFV.  
Currently, the main driver of NFV is the 5G/6G cellular network. NFV is a fundamental part of the 5G/6G network and, in fact, one of the requirements of its standards.  
NFV is built on the standards of machine virtualization (VM--Virtual Machines) technology as presented in Figure 3, extending its use into the network domain, such as firewalls, caching, DNS, points of presence with web content (or Content Delivery Networks, CDN), etc., decoupling them from hardware or proprietary equipment to software solutions running on VMs [16].  
A comprehensive understanding of NFV architecture needs additional research. Researchers seeking to expand their knowledge are directed to [16; 17; 18], which provide essential discussions and architectural insights to inform further studies.
# Modeling the Directional Clumping Index of Crop and Forest
## 1 Introduction
Section 4 analyzes the influence of various parameters on directional clumping index using the proposed methods. Section 5 summarizes the main conclusion and proposes the limitations in the model and analysis. The symbols used in this paper are listed in Table 1. \begin{table}
\begin{tabular}{c l c} \hline \hline
**Symbol** & \multicolumn{1}{c}{**Description**} & **Unit** \\ \hline \(\theta\) & Zenith angle, \(\mu=\cos\theta\) & degree \\ \(\varphi\) & Azimuth angle & degree \\ \(p\) & Gap fraction & dimensionless \\ \(\Omega\) & Clumping index (\(\Omega\)) & dimensionless \\ \(LAI\) & Leaf area index & dimensionless \\ \(u\) & Leaf volume density & m\({}^{-1}\) \\ \(G_{\theta}\) & Mean projection of unit foliage area along the direction of zenith angle \(\theta\) & dimensionless \\ \(A/A_{1}/A_{2}/H\) & Row period width, row width, gap width, and crop height, respectively. & m \\ \(a_{1}/a_{2}\) & Row width and gap width, respectively, in the direction perpendicular to the row & m \\ \(R\) & Radius of the tree crown & m \\ \(H\) & Height (or depth) of the tree crown & m \\ \(d_{t}\) & Trunk diameter at breast height, DBH & m \\ \(h_{t}\) & Trunk height & m \\ \(C_{CR}\) & Crown closure, counting overlapped crown projection multiple times & dimensionless \\ \(C_{CA}\) & Crown closure, counts overlapped crown projection once & dimensionless \\ \(P\) & Probability & dimensionless \\ \(N\) & Average number of trees in unit area & dimensionless \\ \(M\) & Average number of trees in a quadrat defined by the Poisson distribution & dimensionless \\ \(m_{1}\) & Average group number of trees in a sample area defined by the Neyman & dimensionless \\ \(m_{2}\) & Average number of trees in each group & dimensionless \\ \(\rho\) & Leaf reflectance & dimensionless \\ \(T\) & Leaf transmittance & dimensionless \\ \(r_{S}^{h}\) & Bi-directional reflectance of soil & dimensionless \\ \(r_{S}^{d}\) & Directional-hemispherical reflectance of soil & dimensionless \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of symbols used in the clumping index (\(\Omega\)) models.