esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 ME-SSA Receiver Design
### Receiver Block Scheme Overview  
Figure 4 shows an overview of the physical layer of the MASSIVE receiver architecture. The received input radio frequency (RF) signal consists of the sum of the signals transmitted by several terminals in an uncoordinated manner. Such a signal is downconverted to baseband and low-pass filtered. The samples are stored in a local memory as a sliding window to perform iterative detection and demodulation processing.  
The receiver processing starts with a PS to detect the presence of bursts in the stored memory window. Then, the identified bursts are demodulated. If a burst is successfully demodulated (checked via CRC), it is locally regenerated and subtracted from the memory window, according to the SIC processing of the E-SSA protocol [4], in order to detect and/or demodulate other bursts not detected and/or demodulated at the previous iterations, due to the higher level of MAI as already discussed in Section 1. Hence, at a high level, the ME-SSA receiver performs the same operations as the E-SSA-based receiver. The difference lies in the specific implementation of the preamble searcher and the burst demodulator algorithms, described in the following sections.  
\begin{table}
\begin{tabular}{c c c} \hline
**WF ID** & **ModCod** & **SF** \\ \hline TF\#25 & QPSK1/3 & 16 \\ TF\#9 & QPSK1/3 & 32 \\ TF\#4 & BPSK1/3 & 64 \\ TF\#5 & QPSK1/3 & 64 \\ TF\#7 & QPSK2/3 & 64 \\ TF\#6 & QPSK1/2 & 64 \\ TF\#17 & QPSK1/3 & 128 \\ TF\#33 & QPSK1/3 & 256 \\ \hline \end{tabular}
\end{table}
Table 1: Return Link Waveforms.
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
## 3 Results
### Most Common Diagnosis Methods to Prepare an Inspection
#### 3.1.1 Diagnosis Methods in Roof Claddings
D-A3 has the main purpose of checking whether the minimum slope values for roofs are being respected, taking into account the type of cladding, exposure, slope length, location, and construction layout. Furthermore, through D-A3, surveyors can also assess the planimetry of claddings, taking specifications into account. The results of this diagnosis method can point out the causes of ponding, for instance. A spirit level may be used, as well as an inclinometer. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Building Element** & \begin{tabular}{c} **Defects That Can Be** \\ **Diagnosed by the** \\ **Same Methods** \\ \end{tabular} & \begin{tabular}{c} **Diagnosis Methods** \\ **Used in ECPR** \\ \end{tabular} & \
\begin{tabular}{c} **Diagnosis Methods** \\ **Used in Both** \\ \end{tabular} \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Roof \\ claddings—external \\ claddings of pitched \\ roofs (ECPR); flat \\ roofs (FR) \\ \end{tabular} } & A-A2 and A-A1 & D-C1, D-C3 & D-B2, D-B3, D-C3, D-E1, D-F1 & D-C3 \\  & A-A3 and A-D12 & D-A3 & D-A3 \\  & A-C3 and A-A1 & D-C3 & D-B2, D-B3, D-C3, D-E1, D-F1 & D-C3 \\  & A-C3 and A-A2 & D-C3 & D-B2, D-B3, D-C3, D-E1, D-F1 & D-C3 \\ \cline{1-1}  & A-D1 and A-A1 & D-C3 & D-B2, D-C3, D-F1 & D-C3 \\ \hline \hline \end{tabular} Note: refer to Table 3 and Table 1 for the designation of each defect and diagnosis method, respectively.  
\end{table} 
Table 11: Analysis of the results for claddings of roofs based on the new inter-defects correlation matrix considering diagnosis methods. As for D-C3, it is a diagnosis method with several applications, although it requires experienced professionals to analyse the results. Whether in pitched or flat roofs, thermography is more useful in cases where no thermal insulation is used. Nevertheless, infrared thermography may be used indoors or outdoors to assess watertightness and the occurrence of humidity issues and to pinpoint discontinuities in thermal insulation. An infrared camera is required, as well as a regular camera, to compare thermograms with the visible area of claddings. The technique consists of taking advantage of the heating caused by solar radiation, observing the thermographic images (thermograms) obtained through the infrared camera, identifying the areas where voids, cracks, infiltrations, and other discontinuities that are not visible to the naked eye can be observed; video record the images taken for later interpretation and diagnosis; when interpreting the results, it should be borne in mind that the flaws are warmer than the rest of the coating during the day, revealing a more intense red colour, and are cooler at night, corresponding to a light blue colour.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 5 Concluding Remarks  
In this paper, we have investigated the application of FSO, UAVs, and EH as an adaptable and efficient solution to provide backhaul/fronthaul connectivity to 5G+ networks. There are many benefits supporting this approach. First, FSO technology provides high bandwidth for the 5G RAN. Second, FSO communication links do not interfere with the RF based 5G RAN. Third, the UAVs, which act as flying nodes of a backhaul/fronthaul network, can adapt to changes in weather and traffic conditions to provide reliable links. Nevertheless, the limited battery of the UAVs causes service interruptions when the UAVs need to recharge them. For this reason, we propose the use of EH to collect energy from the transmission of information signals to combine with the EH from the terrestrial optical and RF WPT charging stations. All these techniques are thought to enhance the on-board battery lifetime of UAVs. To assess the benefits of the proposed approach we have considered a realistic yet tractable channel model that includes the effect of turbulence fading, pointing errors and atmospheric attenuation. Using this model, analytical closed-form expressions of the average harvested energy and the bit error rate of an OOK scheme optimized for information transmission and power transfer are derived. The derived expressions allow to evaluate the performance of vertical FSO links between ground-based BSs and UAVs and to properly select the link parameter to optimize the harvested energy while guaranteeing a reliable connection.  
Results show that there exists an interesting trade-off between reliability and harvested energy.  
**Author Contributions:** [PERSON] and [PERSON] defined the scope of the review manuscript. All the authors compiled the necessary information to elaborate this review manuscript. All the authors derived the expressions and obtained the results. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the article. All the authors reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been funded by the University of Malaga.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.
## 4 Results  
### Coseismic Displacements Derived from the LT-1 SAR  
In this paper, traditional D-InSAR is used to process strip-mode LT-1 SAR images to obtain the InSAR coseismic LOS surface deformation field of the 2022 Luding earthquake (as shown in Figure 3). To improve the signal-to-noise ratio, 5:5 (range: azimuth) multi-look and Goldstein filter processing are adopted for the LT-1 SAR image data [30; 31]. The minimum cost flow (MCF) algorithm is then used to unwrap the phase. The spatial distribution and magnitude of the deformation caused by the Luding earthquake can be clearly described based on the coseismic deformation field [32; 33; 34]. The results show that the earthquake was located in Moxi town at the tail end of the Xianshuihe fault. Its motion direction gradually spread southward from northwest to southeast, and the motion mode also gradually shifted from left-slip motion to mainly slip motion with a recoil component. The horizontal motion of the surface on the west side of the fault was projected in the LOS direction as a lifting signal. In general, the deformation distribution characteristics of the InSAR were consistent with the nature of left-slip motion. On the west side of the seismogenic fault (Xianshuihe fault), the positive deformation signals in the satellite LOS direction reached 20 cm, and on the east side of the fault, the deformation in the LOS direction reached \(-\)25 cm. The range of surface deformation caused by the earthquake was approximately 900 km\({}^{2}\). At the same time, multiple local deformation signals were found around the epicenter, due to the landslide and glacier movement triggered by the earthquake, with the L-band sensor of LT-1, which can capture signals of larger deformation gradients.
# Facile Preparation of Graphene Oxide-MIL-101(Fe) Composite for the Efficient Capture of Uranium
## 2 Materials and Methods
### Fabrication of the GO-MIL-101(Fe) Composite with Different GO Contents  
GO-MIL-101(Fe) was synthesized with an in situ growth method. In a typical procedure, 10 mg, 15 mg, 18 mg, 20 mg of GO and 54.1 mg of FeCl\({}_{3}\cdot\)6H\({}_{2}\)O (0.2 mmol) were dispersed in 6 mL DMF and formed solution A, 18.2 mg NH\({}_{2}\)-H\({}_{2}\)BDC (0.1 mmol) and 4 mg PVP were dissolved in 6 mL DMF to form solution B. Subsequently, solutions A and B were placed in a microwave vessel and sealed. After reaction at 160 \({}^{\circ}\)C for 10 min, the solid products were collected by centrifugation at 5000 rpm for 10 min. The samples were washed twice with DMF followed by washing with ethanol two times to remove residual DMF. Finally, the product was dried in vacuum overnight for further use. The achieved composite was denoted as GO10(15, 18, 20)-MIL-101(Fe). MIL-101(Fe) materials were fabricated according to the above method without the addition of GO. The mass ratio of GO and MIL-101(Fe) was calculated to be 1\(\aas@@fstack{\prime}\)7.42 by measuring the mass of both GO15-MIL(101) and the original added GO.
# Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based Visual-Inertial Odometry
## 2 Model of Visual-Inertial Odometry
### Measurement Model  
A direct VO was used as the measurement model where the intensity was used as the measurement directly without feature extraction and matching processes. The position of each point \(j\) was transformed from the SICS into the CCS by the motion state as follows:  
\[\prescript{C}{\mathbf{p}}{}{}_{\text{P}_{j}}=\prescript{C}{\text{I}}{}{}_{\text{SI}} \mathbf{R}_{\text{SI}}^{\text{I}}\mathbf{R}\left(\text{\textsl{I}}{}_{\text{SI}}\mathbf{ \rho}\right)\left(\text{\textsl{SI}}{}_{\text{P}_{j}}-\text{\textsl{SI}}{}_{ \text{P}}\right)+\prescript{C}{\mathbf{p}}{} \tag{11}\]  
The pixel coordinate of point \(j\) in the current view was evaluated according to the pin-hole camera model.  
\[\left[\begin{array}{c}u_{\text{P}_{j}}\\ v_{\text{P}_{j}}\end{array}\right]=\mathbf{\pi}\left(\prescript{C}{\mathbf{p}}{}{}_{ \text{P}_{j}}\right)=\left[\begin{array}{c}c_{x}\prescript{P}{}{}{}_{\text{P}_ {j}}\frac{f_{x}}{z_{\text{P}_{j}}}+c_{x}\\ c_{y}\prescript{f}{}{}{}_{\text{P}_{j}}\frac{f_{y}}{z_{\text{P}_{j}}}+c_{y} \end{array}\right] \tag{12}\]  
The intensity \(I_{k}\left(u_{\text{P}_{j}},v_{\text{P}_{j}}\right)\) of point \(j\) in the RGB image at time \(k\) was used as the measurement  
\[z_{j,k}=I_{k}\left(u_{\text{P}_{j}},v_{\text{P}_{j}}\right)+n_{j,k} \tag{13}\]  
where \(n_{j,k}\) is the measurement noise and assumed to obey a Gaussian distribution with covariance \(R_{j,k}\). Combining Equations (11)-(13), one can obtain the following measurement equation.  
\[\mathbf{z}_{k}=\mathbf{h}\left(\text{\textsl{I}}{}_{\text{SI}}\mathbf{\rho}_{k},\text{ \textsl{SI}}{}_{\text{P}_{k}}\right) \tag{14}\]
## 2 Materials and Methods
### Characterization of Zinc Oxide Nanoparticles  
The physical properties and morphological structures of the prepared ZnO NPs were investigated using different techniques. X-ray diffraction (XRD) analysis was carried out using XRD-7000 (Shimadzu, Kyoto, Japan) diffractometer with Cu K\(\alpha\) radiation beam (\(\lambda\) = 0.154060 nm), operated at 30 KV and 30 mA and collected data were between 10\({}^{\circ}\) and 80\({}^{\circ}\) in 2\(\theta\). Fourier transform infrared (FTIR) spectroscopy (FTIR-8400 S, Shimadzu, Tokyo, Japan) analysis was performed to investigate different functional groups of the prepared extract. The morphological structures and size of ZnO NPs were characterized by scanning electron microscopy (SEM) at 20 kV (JSM-6360 LA, JEOL, Tokyo, Japan). The particle size distribution of biosynthesized ZnO NPs was determined by dynamic light scattering (DLS) with a Zetasizer Nano Z 590 (Malvern, Granta Lodge, UK) instrument at 25 \({}^{\circ}\)C.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 3 Theory and Methods
### Sea Ice Classification Method
#### 3.2.2 Support Vector Machine (SVM)  
SVM is a powerful machine learning algorithm based on statistical learning theory and has the purpose of determining the location of decision boundaries that produce the optimal separation of classes [57]. It is a supervised classification approach, which can deal with linear, nonlinear, high-dimensional samples and result in good generalization. SVM is primarily used for binary classification problems, in which the sample data can be expressed as,  
\[(x_{1},y_{1}),(x_{2},y_{1}),\ldots,(x_{n},y_{n})\in R^{n}\times Y,Y=(-1,1), \tag{10}\]  
where \(x_{i}\) represents the input samples that consist of features for classification, \(y_{i}\) is the class label of \(x_{i}\) For the linear classification, the SVM classifier satisfies the following rule,  
\[y_{i}\Big{(}w^{T}x_{i}+b\Big{)}\geq 1,1\leq i\leq n, \tag{11}\]  
where \(w^{T}x+b=0\) represents a hyperplane, with parameters \(w\) and \(b\) being the coefficient and bias, respectively. The maximum classification interval algorithm can be formulated as,  
\[\left\{\begin{array}{c}min\Big{(}\frac{1}{2}\Big{|}\Big{|}w\big{|}^{2}\Big{)} \\ y_{i}\Big{(}w^{T}x_{i}+b\Big{)}\geq 1\end{array}\right.. \tag{12}\]  
In order to obtain good predictions in nonlinear classification, the slack variables are introduced to express the soft margin optimal problem,  
\[\left\{\begin{array}{c}min\Big{(}\frac{1}{2}\Big{|}\Big{|}w\big{|}^{2}+C \sum_{i=1}^{n}\xi_{i}\Big{)}\\ \xi_{i}\geq 0,y_{i}\big{(}w^{T}\phi(x_{i})+b\big{)}\geq 1-\xi_{i},1\leq i \leq n\end{array}\right., \tag{13}\]  
where \(\xi_{i}\) represents the \(i\)th slack variable, \(C\) is a penalty parameter. \(\phi\) is a high-dimensional feature projection function related to the kernel function [58].
# Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
## 3 Results and Discussion
### AR Technique Overview
#### 3.1.2 AR Technique for Lower Limb Function  
Four of the studies reviewed the use of AR for gait, postural, and/or walking training. All studies for lower limb rehabilitation were developed in South Korea. Two randomized controlled trial studies used the AR systems based on postural control training in the AR environment for guiding the stroke patients to perform ideal postural control motions such as lying, sitting, and standing [34; 35]. Two remaining studies combined AR system with Functional Electric Stimulation during treadmill gait training [36; 37]. Most AR systems utilized a server computer mounted with a camera and a super video HMD connected to an ultra-mobile personal computer. The wireless method applies to exchange the signal between two computers. The patients could watch the modeled movement on one side and the actual movement on another side through the HMD device. Thereby, the patients could compare the patients' movement with the normal movement by watching the modeled movement and listening to a recorded sound.  
These AR rehabilitation systems can give a high quality of present and embodiment which helps patients to have more realism feeling by using the HMD device that allows an immersive 3D experience to subjects. Moreover, a combination of visual and audio feedback in these systems motivates and encourages the users during long-term training sessions. Table 2 shows a summary of the AR-based system for upper limb and lower limb rehabilitation. Tables 3 and 4 summarize the characteristics and intervention outcomes of the clinical trial in the selected articles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
_Appl. Sci._**2022**, _12_, 1848  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Head-mounted \\ display \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 4 Discussion
### Influence of Burial Depth on the Development of Pore Structure
#### 4.3.2 Gas-in-Place Estimation  
Combining the results of methane adsorption and saturated methane NMR experiments on sample L214 with the adsorbed gas content prediction model [35] and the free gas content prediction model (Equation (8)) will yield the in situ shale reservoir's adsorbed and free gas content. The average reservoir pressure gradient, surface temperature, and temperature gradient in the Luzhou area are set at 15 MPa/km, 20 \({}^{\circ}\)C, and 25 \({}^{\circ}\)C/km, respectively. In addition, the reservoir porosity, rock density, and water saturation are set at 6.00%, 2.55 g/cm\({}^{3}\), and 0%, respectively; these parameters do not change with burial depth.  
The absolute adsorbed gas content rapidly increases with the increase in burial depth, and then slowly decreases due to the main influence of shallow burial depth pressure on shale methane adsorption. The increase in pressure leads to a sharp increase in adsorption, as shown in Figure 13a. However, in places with deeper burial depths, the effect of pressure is weaker, and the adsorption capacity is significantly affected by temperature. The adsorbed gas content will slowly decrease as the temperature increases. Free gas content and total gas content both increase with increasing burial depth, increasing slowly at shallow depths (<3500 m) and rapidly at deeper depths (>3500 m). At 4500 m, the free gas and total gas contents increased to 15.05 m\({}^{3}\)/t and 18.89 m\({}^{3}\)/t, respectively. The adsorbed gas dominates in the shallow layer, accounting for over 70% of the total gas content. Then, as the burial depth increases, the proportion of adsorbed gas and free gas is equal (at about 2000 m), while free gas dominates at deeper burial depths, accounting for about 80% at 4500 m, as shown in Figure 13b.  
If we use traditional free gas prediction models (Equation (7)), a shale reservoir with a porosity of 6% has a free gas and total gas content of 8.14 m\({}^{3}\)/t and 9.21 m\({}^{3}\)/t at a depth of 4500 m, respectively [35]. This result is significantly inconsistent with the results (15.31 m\({}^{3}\)/t) of pressure-holding coring in a deep shale gas reservoir with 15% water saturation, as in the L214 well.  
Figure 13: (a) In situ shale reservoir gas contents according to burial depth; (b) proportion of gas contents with burial depth.  
For our predicted free gas results, there is a differentiation from traditional methods in shale reservoirs at depths of approximately 3500 m. The reason why shale reservoirs with greater burial depths have such high gas content may be due to the confinement effect of shale reservoir porosity and permeability [75; 76], especially under high-temperature and high-pressure conditions, the details of which are still unclear. Secondly, under deep-burial conditions, the organic matter of shale reservoirs can continuously generate shale gas and will have good self-sealing properties [71], enabling better storage of shale gas if there are no faults.
## 3 Proposed Method EETO-GA
### Selection of Clusterhead (CH)  
Suppose in cluster C, IoT devices are denoted as \(dev(1),dev(2),...,dev(num(C))\) where \(num(C)\) is the number of devices in cluster C. Assuming that \((h_{i},k_{i})\) be location of \(dev(i)\), where \(i\leq i\leq num(c)\) then centroid of the cluster will be denoted as \((X\_{cen},Y\_{cen})\) as formulated in (1) and (2).  
\[X\_{cen}=\{\sum_{i=1}^{num(c)}h_{i}\}/num(c) \tag{1}\]  
\[Y\_{cen}=\{\sum_{i=1}^{num(c)}k_{i}\}/num(c) \tag{2}\]  
Eligibility of a device \(dev(i)\) as a CH is governed by the function \(cls\_{e}lg(i)\) as in (3), where \((R-E_{i})\) is residual energy of the ith device.  
\[cls\_{e}lg(i)=\frac{R-E_{i}}{\sqrt{\{(h_{i}-X\_{cen})^{2}+(k_{i}-Y\_{cen})^{2 }\}}} \tag{3}\]  
Figure 1: Model Architecture of an UAV–IoT Collaborative System.  
The above mathematical expression is based on the fact that if a node has high residual energy and geographical closeness to the centroid of the cluster then its eligibility increases to become a CH.
## 3 Preliminaries  
This section gives the details of the techniques used in the propounded scheme. The propounded scheme consists of two modules: embedding and extraction. The depiction of these two processes is shown in sub sections. As the transform domain is more robust than the spatial domain, the DWT algorithm is utilized in the embedding process as it has better reconstruction without losing information. SVD is utilized in combination with DWT to overcome noise and compression attacks. Moreover, a cryptographic algorithm (RSA) is used to verify the algorithm's robustness against the channel's vulnerability.
# Environmentally Stable Chiral-Nematic Liquid-Crystal Elastomers with Mechano-Optical Properties
## 3 Results and Discussion
### Optical Stability in Solvents  
For practical mechano-optical sensors, it is desirable that the optical properties show little dependence on the solvent conditions. When a typical N* LC elastomer network is immersed in a solvent, swelling causes network deformation, which drastically affects the optical properties arising from the helically twisted molecular orientation. Although swellable LC polymers have utility as humidity-responsive materials [53; 54], this swelling effect interferes with and degrades the resolution of mechano-optical sensing. Therefore, resistance to solvents, especially to common polar solvents such as water and ethanol, is crucial for the broad application of N* LC elastomers as mechano-optical sensors. As shown in Figure S10, the N* LC (2.0) elastomer exhibited hydrophobicity, as water placed on the elastomer surface formed a droplet and was not absorbed into the elastomer. We also evaluated the environmental stability of the N* LC (2.0) elastomer in polar solvents by immersing the elastomer in water or ethanol (Figure 5a). After immersion for 1 week, the N* LC (2.0) elastomer showed no color change in either water or ethanol. To further investigate the solvent resistance, the N* LC (2.0) elastomer was elongated in water and ethanol environments. As shown in Figure 5b, the N* LC (2.0) elastomer exhibited excellent mechano-optical behavior, even in water and ethanol, without any optical degradation. This excellent environmental stability under harsh conditions can be attributed to the wide elastic temperature range and hydrophobicity of the elastomer. These results suggest that our proposed N* LC (_x_) elastomers provide a pathway for realizing mechano-optical sensors or devices that will function in a wide range of extreme environments.  
Figure 5: (**a**) N* LC (2.0) elastomer immersed in water and ethanol over 1 week, and (**b**) color change under an applied tensile strain in water and ethanol baths.